NelsonHall: Digital Transformation Technologies & Services blog feed

Capgemini Launches ’One Operations’ to Support CPG Enterprises in Driving Revenue Growth

Capgemini has launched a new digital transformation service, One Operations, with the specific goal of driving client revenue growth.

One Operations: Key Principles

Some of One Operations’ principles, such as introducing benchmark-driven best practice operations models, taking an end-to-end approach to operations across silos, and using co-invested innovation funds, are relatively well established in the industry. However, what is new is building on these principles to incorporate an overriding focus on delivering revenue growth. The business case for a One Operations assignment focuses on facilitating the client’s revenue growth and taking a B2B2C approach focused on the end customer, emphasizing the delivery of insights that enable client personnel to make earlier decisions focused on the enterprise’s customers.

Capgemini’s One Operations account teams involve consulting and operations working together, with Capgemini Invent contributing design and consulting and the operational RUN organization provided by Capgemini’s Business Services global business line.

Implementing a One Operations philosophy across the client organization and Capgemini is achieved through shared targets to reduce vendor/client friction and co-invested innovation funds. One Operations assignments involve setting joint targets with a continuously replenished co-invested innovation fund of ~10–15% of Capgemini revenues used to fund digital transformation.

One Operations is very industry-focused, and Capgemini is initially targeting selected clients within the CPG sector, looking to assist them in growing within an individual country or small group of countries by localizing within their global initiatives. The key to this approach is demonstrating to clients that it understands and can support both the ’grow’ and ’run’ elements of their businesses and having an outcome-based conversation. Capgemini is typically looking to enable enterprises to achieve 4X growth by connecting the sales organization to the supply chain.

Assignments commence with working sessions brainstorming the possibilities with key decision-makers. The One Operations client team is jointly led by a full-time executive from Capgemini Invent and an executive from Capgemini’s Business Services. The Capgemini Invent executive remains part of the One Operations client team until go-live. The appropriate business sector expertise is drawn more widely from across the Capgemini group.

One Operations assignments typically have three phases:

Deployment planning (3–6 months) to understand the processes and associated costs and create the business case
Deployment (6–15 months) to create the ’day one’ operating model
Sustain, involving go-live and continuous improvement.

At this stage, Capgemini has two live One Operations assignments with further discussions taking place with clients.

Using End-to-End Process Integration to Speed Up Growth-Oriented Insights

Capgemini’s One Operations has three key design principles:

Re-inventing the organization by embedding a growth mindset by reducing business operations complexity and enabling an AI-augmented workforce to focus on their customers and higher-value services
Increasing the level of end-to-end integration by improving data accuracy and incorporating AI to achieve ’touchless forecasting & planning’ and enable better decisions and speed of innovation. ’Frictionless’ end-to-end integration is used to support more connected decisions and planning across the value chain
Transforming at speed and scale.

These transformations involve:

Shaping the strategic transformation agenda through defining the target operating model based on peer benchmarks and using standardized operating model design, assets, and accelerators
Using a digital-first framework incorporating One Operations pre-configured digital process evaluation and digital twins
Deployment of D-GEM technology accelerators, including AI-augmented workforce solutions and Capgemini IP such as Tran$4orm and ranging from platforms to microtools
Augmented operations using Capgemini Business Services.

Changing the mindset within the enterprise involves freeing personnel from tactical transactional activities and providing relevant information supporting their new goals.

Capgemini aims to achieve the growth mindset in client enterprises by enabling an integrated end-to-end view from sales to delivery, facilitating teams with digital tools for process execution and growth-oriented data insights. Within this growth focus, Capgemini offers an omnichannel model to drive sales, augmented teams to enable better customer interactions, predictive technology to identify the next best customer actions, and data orchestration to reduce customer friction.

One Operations also enables touchless planning to improve forecast accuracy, increase the order fill rate, reduce time spent planning promotions, and accelerate cash collections to reduce DSO, while improving promotions accuracy and product availability are also key to revenue growth within CPG and retail environments.

Shortening Forecasting Process & Enhancing Quality of Promotional Decisions: Keys to Growth in CPG

The overriding aim within One Operations is to free enterprise employees to focus on their customers and business growth. In one example, Capgemini is looking to assist an enterprise in increasing its sales within one geography from ~$1bn to $4bn.

The organization needed to free up its operational energies to focus on growth and create an insight-driven consumer-first mindset. However, the organization faced the following issues:

70% of its planning effort was spent analyzing past performance, and ~100 touches were required to deliver a monthly forecast
Order processing efficiency was below the industry average
Approx. 30% of its trucks were leaving the warehouse half-empty
Launching products was taking longer than expected.

Capgemini took a multidisciplinary approach end-to-end across plan-to-cash. One key to growth is the provision of timely information. Capgemini is aiming to improve the transparency of business decisions. For example, the company has rationalized the coding of PoS data so that it can be directly interfaced with forecasting, shortening the forecasting process from weeks to days and enhancing the quality of promotional decisions.

Capgemini also implemented One Operations, leveraging D-GEM to develop a best-in-class operating model resulting in a €150m increase in revenue, 15% increase in forecasting accuracy, 50% decrease in time spent on setting up marketing promotions, and a 20% increase in order fulfillment rate.

WNS Repositions its Data & Analytics Practice to Assist Organizations in Becoming 'Insights-Led Enterprises'

Analytics has often been run as a series of periodic and siloed exercises. However, to respond to their customers in the smartest, fastest, most efficient manner, WNS perceives that organizations increasingly need to run their analytics always-on, in almost real-time, and on an enterprise rather than siloed basis. To do this and become ‘insights-led enterprises’, organizations’ analytics need to be supported by a suitable underlying enterprise data ecosystem, typically cloud-based.

WNS has had a strong Data & Analytics practice for many years. In the past, the scope of WNS’ analytics-led engagements was somewhat limited and frequently priced on an FTE basis. WNS now seeks to significantly broaden the scope, powered by data management, Artificial Intelligence (AI), and cloud, and aggressively incorporate alternative and outcome-based pricing models. WNS has now repositioned to work more upstream on client engagements and participate in larger data lake transformations, rebranding its Data & Analytics practice as WNS Triange.

Repositioning and Expanding Horizons as an ‘End-to-End Industry Analytics’ Player

This repositioning aims to establish WNS with a clear identity as ‘an end-to-end industry analytics player’ delivering outcomes and not just personnel and assisting the practice in targeting transformational activity for functional business heads, CDOs, CTOs, and CDOs outside of WNS BPS engagements. It also assists the Data & Analytics practice in establishing a stronger identity within WNS and attracting talent in a challenging talent market.

WNS is also aiming to change the scope of engagements from running individual use case analytics in silos to assisting organizations with the broader management of their underlying data ecosystems and rolling out analytics on an enterprise basis at scale.

Accordingly, WNS Triange is building data & analytics capability on the cloud, together with data/AI Ops capability to run large-scale data operations and governance at scale. These capabilities are supported by an Analytics CoE that brings together “best practices” on cloud, data, and AI, together with associated governance mechanisms and domain expertise.

Investing in High-End consulting and Hyperscaler-Certified IP

WNS Triange currently has ~4,500 personnel and is being restructured into three components:

Triange Consult. WNS Triange is placing much greater emphasis on up-front consulting than previously and is increasingly recruiting and locating senior consultants onshore operating from its design labs. WNS has also built framework assets in support of Triange Consult in the past two years, covering areas such as analytics and AI strategy, data strategy, and data quality & governance strategy, together with domain-specific consulting
Triange NxT. WNS continues to focus on the creation of accelerators. These include SKENSE, Unified Analytics Platform, Insurance Analytics in a BOX, Emerging Brands and Trends, InsighTRAC, and Datazone.ai
Triange CoE, for analytics project and service implementation.

WNS has invested in platforms to address intelligent cloud data ops as well as in analytics AI models. These Triange NxT platforms assist WNS in delivering speed-to-execution and speed-to-value since these elements are pre-built models with tested connectors to third-party data and are being cloud-certified with the necessary governance and built-in security protocols.

For example, the Triange NXT Insurance Analytics Platform provides pre-trained AI and non-AI based analytics models in support of insurance analytics related to claims, pricing, underwriting, fraud, customer marketing, and service & retention. These models are underpinned by APIs to leading insurance platforms, connectors with workflow systems, ML Ops, and what-if analyses. WNS also incorporates platforms from partnerships with start-ups and specialized data providers as part of its prepackaged solutions.

Key WNS platforms within Triange NxT include Skense for data extraction and contextualization, Insurance Analytics Platform, InsighTRAC for procurement insights, and SocioSEER, a social media analytics platform. WNS is currently finalizing the certification of each platform on AWS and Azure and making them available in cloud marketplaces.

SKENSE platform based solutions have been built to address a range of use cases across finance & accounting, customer interaction services, legal services and procurement, as well as banking & financial services, shipping & logistics, healthcare, and insurance.

Increasing Use of Co-Innovation and Non-FTE Pricing Models

WNS Triange revenues have grown ~25% over the past year, and WNS is increasing its use of co-innovation and non-FTE pricing models.

For example, WNS has deployed its AI/ML platform to capture the quality control data from the various plants of an FMCG company, create summaries, change the data into a suitable format for generating insights, and return the summary notes and insights to the FMCG company’s data lake.

This resulted in an 82% reduction in processing cost per document compared to what had previously been a very manual process.

WNS undertook the development of this IP largely at its own expense and now owns it, with the client paying some elements of the development fee and a licensing fee. In addition, WNS will pay the initial client a percentage of the revenue if this IP is sold to other CPG companies.

WNS helped an Insurance client automate the process of identifying subrogation opportunities in the Claims processing workflow. WNS used MLOps frameworks to identify recovery opportunities based on historical data and predict opportunities in the current transactional data with higher chances of recovery. This helped the client in improving the recovery rates by multiple percentage points.

Elsewhere, WNS is working with a media client to transform the enterprise into a digital media agency and reinvent its traditional approach to processes such as media planning and customer segmentation. Here, WNS is assisting the company with multiple data & analytics initiatives. In some cases, this involves the Triange Consult practice, in others provision of platforms, and in others, the application of the Triange CoE approach.

For example, WNS Triange Consult is helping the company establish an appropriate cloud architecture and organize its data appropriately, establish how to run machine learning ops, and identify the appropriate design for a complete reporting center.

The company’s data has traditionally been paper-based, so WNS NxT is using platforms to digitize its data and provide insights for real-time decision-making. WNS is also helping the company set up its training infrastructure for data & analytics.

This repositioning is underlined by systemic structural changes that will enable WNS to adopt a more consultative and enterprise-scale approach to analytics. While many organizations will still address analytics on a siloed case-by-case basis, and these use cases remain important, WNS now has the structure to go beyond individual use cases, further augmenting its traditional strengths in domain-based analytics and assisting organizations in adopting more systematic approaches to establishing and scaling their enterprise analytics infrastructures end-to-end with enterprise-level data, analytics, and AI.

The Role of Organizational Change Management in Digital Transformation

Digital transformation and the associated adoption of Intelligent Process Automation (IPA) remains at an all-time high. This is to be encouraged, and enterprises are now reinventing their services and delivery at a record pace. Consequently, enterprise operations and service delivery are increasingly becoming hybrid, with delivery handled by tightly integrated combinations of personnel and automations.

However, the danger with these types of transformation is the omnipresent risk in intelligent process automation projects of putting the technology first, regarding people as secondary considerations, and alienating the workforce through reactive communication and training programs. As many major IT projects have discovered over the decades, the failure to adopt professional organizational change management procedures can lead to staff demotivation, poor system adoption, and significantly impaired ROI.

The greater the organizational transformation, the greater the need for professional organizational change management. This requires high workforce-centricity and taking a structured approach to employee change management.

In the light of this trend, NelsonHall's John Willmott interviewed Capgemini's Marek Sowa on the company’s approach to organizational change management.

JW: Marek, what do you see as the difference between organizational change management and employee communication?

MS: Employee communication tends to be seen as communicating a top-down "solution" to employees, whereas organizational change management is all about empowering employees and making them part of the solution at an individual level.

JW: What are the best practices for successful organizational change management?

MS: Capgemini has identified three best practices for successful organizational change management, namely integrated OCM, active and visible sponsorship, and developing a tailored case for change:

Integrated OCM – OCM will be most effective when integrated with project management and involved in the project right from the planning/defining phase. It is critical that OCM is regarded as an integral component of organizational transformation and not as a communications vehicle to be bolted on to the end of the roll-out.

Active and visible sponsorship – C-level executives should become program sponsors and provide leadership in creating a new but safe environment for employees to become familiar with new tools and learn different practices. Throughout the project, leaders should make it a top priority to prove their commitment to the transformation process, reward risk-taking, and incorporate new behaviors into the organization's day-to-day operations.

Tailored case for change – The new solution should be made desirable and relevant for employees by presenting the change vision, outlining the organization's goals, and illustrating how the solution will help employees achieve them. It is critical that the case for change is aspirational, using evidence based on real data and a compelling vision, and that employees are made to feel part of the solution rather than threatened by technological change.

JW: So how should organizations make this approach relevant at the workgroup and individual level?

MS: A key step in achieving the goals of organizational change management is identifying and understanding all the units and personnel in the organization that will be impacted both directly and indirectly by the transformation. Each stakeholder or stakeholder group will likely find itself in a different place when it comes to perspective, concerns, and willingness to accept new ways of working. It is critical to involve each group in the transformation and get them involved in shaping and driving the transformation. One useful concept in OCM for achieving this is WIIFM (What's In It For Me), with WIIFM identified at a granular level for each stakeholder group.

Much of the benefit and expected ROI is tied to people accepting and taking ownership for the new approach and changing their existing ways of working. Successfully deployed OCM motivates personnel by empowering employees across the organization to improve and refine the new solution continually, stimulating revenue growth, and securing ROI. People need to be both aware of how the new solution is changing their work and that they are active in driving it – and thanks to that, they are actively making the organization a "powerhouse" for continuous innovation.

How an enterprise embeds change across its various siloes is very important. In fact, in the context of AI, automatization is not only about adopting new tools and software but mostly about changing the way the enterprise's personnel think, operate and do business.

JW: How do you overcome employees' natural fear of new technology?

MS: To generate enthusiasm within the organization while avoiding making the vision seem unattainable or scary, enterprises need to frame and sell transformations incorporating, for example, AI as evolutions of something the employees are doing already, not merely as "just the next logical step" but reinventions of the whole process – from both the business and experience perspective. They need to retain the familiarity which gives people comfort and confidence but, on the other hand, reassure them that the new tool/solution adds to their existing capability, allowing them to fulfill their true potential – something that is not automatable.

Capgemini Looks to Accelerate Process Transformation with the "Frictionless Enterprise"

The value of automation using tools such as RPA, and more recently intelligent automation, has been accepted for years. However, there is still a danger in many automation projects that while each project is valuable in its own right, they become disconnected islands of automation with limited connectivity and lifespans. Accordingly, while elements of process friction have been removed, the overall end-to-end process can remain anything but friction-free.

Capgemini has developed the "Frictionless Enterprise" approach in response to this challenge, an approach the company is now applying across all Capgemini’s Business Services accounts.

What is the Frictionless Enterprise?

The Frictionless Enterprise is essentially a framework and set of principles for achieving end-to-end digital transformation of processes. The aim is to minimize friction in processes for all participants, including customers, suppliers, and employees across the entire value chain of a process.

However, most organizations today are far from frictionless. In most organizations, the processes were designed years ago, before AI achieved its current maturity level. Similarly, teams were traditionally designed to break people up into manageable groups organized by silo rather than by focusing on the horizontal operation they are there to deliver. Consequently, automation is often currently being used to address pain points in small process elements rather than transform the end-to-end process.

The Frictionless Enterprise approach requires organizations to be more radical in their process reengineering mindsets by addressing whole process transformation and by designing processes optimized for current and emerging technology.

Capgemini’s Business Services uses this approach to assist enterprises in end-to-end transformation from conception and design through to implementation and operation, with the engine room of Capgemini Business Services now focused on technology, rather than people, for transaction processing.

A change in mindset is critical for this to succeed. Capgemini is increasingly encouraging its clients to move from customer-supplier relationships to partnerships around shared KPIs and adopt dedicated innovation offices.

The five fundamentals of the Frictionless Enterprise

Capgemini views the Frictionless Enterprise as depending on five fundamentals: hyperscale automation, cloud agility, data fluidity, sustainable planet, and secure business.

Hyperscale automation

This ultimately means the ability to reach full touchless automation. Hyperscale automation depends on exploiting artificial intelligence and building a scalable and flexible architecture based on microservices and APIs.

Cloud agility

While the frictionless transformation approach is designed to work at the sub-process level and the overall process level, it is important that any sub-process changes are a compatible part of the overall journey.

Cloud agility emphasizes improving the process in ways that can be reused in conjunction with future process changes as part of an overall transformation. So any changes made to sub-processes addressing immediate pain points should be steps on the journey towards the final target end-to-end operating model rather than temporary throwaway fixes.

Accordingly, Capgemini aims to bring the client the tools, solutions, and skills that are compatible with the final target transformation. For example, tools must be ready to scale, and at present, API-based architectures are regarded as the best way to implement cloud-native integration. This has meant a change in emphasis in the selection and nature of relationships with partners. Capgemini now spends much more time than it used to with vendors, and Capgemini’s Business Services has a global sales officer with a mandate to work with partners. In addition, this effort is now much more focused, with Capgemini concentrating its efforts on a limited set of strategic partners. All the solutions chosen are API native, fully able to scale, AI at the core, and cloud-based. One example of a Capgemini partner is Kryon in RPA, since it can record processes as well as automate them.

Data fluidity

It's important within process transformations to use both internal and external data, such as IoT and edge data, efficiently and have a single version of the truth that is widely accessible. Accordingly, data lakes are a key foundational component in frictionless transformations.

However, while most enterprises have lots of data to leverage, they also have lots of data points that need to be fixed. Master data management is critical to successful transformation and remains an important part of transformed operations.

Digital twins are key to removing process friction and are used as the interface between how the business currently operates and how it needs to operate in the future. As well as providing an accurate view of the reality of current process execution, process mining also speeds up process transformation, enabling transformation consultants to focus on evaluation and prioritization of opportunities for change rather than collecting process data. Process mining can also help with maintaining best practice compliance post-transformation by monitoring how individual agents are using their systems, with the potential to guide them through proactive online training and removing the need to compensate for agent inefficiencies with automation.

Sustainable planet

It's also becoming extremely important when reviewing end-to-end processes to consider their impact on the planet across the whole value chain, including suppliers. For example, this covers both carbon impact and social aspects such as diversity, including ensuring a lack of bias in AI models. Sustainability is becoming increasingly important in financial reporting, and in response, Capgemini has added sustainability into its integrated architecture framework.

Secure business

Enterprises cannot undertake massive transformations unless they are guaranteed to be secure, and so the Frictionless Enterprise approach encompasses account security operations and cybersecurity compliance. Similarly, change management is of overwhelming importance within any transformation project, and the Frictionless Enterprise approach focuses on building trust and transparency with customers and partners to facilitate the transformation of the value chain.

A client example of Frictionless Enterprise adoption

Capgemini is helping a CPG company to apply the Frictionless Enterprise approach to its sales & distribution planning. The company was already upper quartile at each of the individual process elements such as supply planning and distribution planning in isolation, but the overall performance of its end-to-end planning process was inadequate. Accordingly, the company looked to improve its overall inventory and sales KPIs dramatically by reengineering its end-to-end order forecasting process. For example, improved prediction would help achieve more filled trucks, and improved inventory management has a direct impact on sustainability and levels of CO₂ production.

The CPG company undertook planning quarterly, centrally forecasting orders. However, half of these central forecasts were subsequently changed by the company's local planners, firstly because the local planners had more detailed account information and did not believe the centrally generated forecasts, and secondly because quarterly forecasts were unable to keep up with day-to-day account developments.

So there was a big disconnect between the plan and the reality. To address this, Capgemini undertook a process redesign and proposed daily planning, entailing:

Planning overnight daily with machine learning used to forecast orders based on the levels of actual orders up until that point
Removing local planners' ability to change order forecasts but making them responsible for improving the quality of the master data underpinning the automated forecasts, such as identifying the correct warehouse used to deliver to a particular customer.

This process redesign involved comprehensive automation of the value chain and the use of a data lake built on Azure as the source of data for all predictions.

Capgemini has now been awarded a 5-year contract with a contractual goal of completing the transformation in three years.

Capgemini’s Intelligent Process Automation, Part 2: Intelligent Transaction Routing & Digital Twins Maximize RoI Delivery

Part 1 of this blog focused on Capgemini’s structured approach to workforce motivation and upskilling when transitioning to a Frictionless Enterprise that leverages a digitally augmented workforce. This second part looks at how, when adopting a digitally augmented workforce, it is critical to ensure optimized routing of incoming queries and transactions between humans and machines, and to ensure that the expected RoI is delivered from automation projects.

Intelligent Routing of Transactions between Workforce and Machines

Intelligent query access and routing is essential to successfully deploy a hybrid human/machine workforce to achieve the optimal allocation of transactions between personnel and machines. For example:

For a North American manufacturer, Capgemini combined RPA with multiple microservices from AWS and Google and Capgemini code to classify 41 categories of incoming accounts payable queries. If a classification is possible, the query is allocated either to a human or machine. Queries that go the machine route have their text analyzed using NLP, and actions are then triggered to collect the information necessary to answer the query. If the confidence level in the response exceeds 95%, the answer is sent automatically. If not, then the query and response are sent to a human for review and confirmation. This is an example of a digitally augmented workforce
For another client, Capgemini reduced the cost per query of procure-to-pay queries from 180 cents to 17 cents by using a digitally augmented workforce. The company’s AI Query Classifier uses NLP and ICR to extract the relevant information from the unstructured text, validate the query and automate ticket creation. Its AI Workload Distribution then orchestrates the process and decides whether each case goes through automated or human resolution
Elsewhere, a client had a large team serving billable transactions in 24 languages, but 30%-40% of the transactions they received were not relevant to this team. Capgemini implemented 90% automated identification & indexing for 21 of these 24 languages. The data is validated, further data retrieved where necessary, and then the data revalidated. Business rules are then applied to identify whether the transaction is handled manually or automated. Savings of ~75% of the total effort were achieved.

The use of machine translation is becoming increasingly important in these situations, and Capgemini is now working on machine language translation to reduce its dependency on nearshore centers employing large numbers of native speakers in multiple languages.

Preformed automation assets are also important in combining best practices and intelligent automation. Here, Capgemini has introduced 890 by Capgemini. This catalog of analytics services enables organizations to access analytical and AI solutions and datasets from within their own organization, from multiple curated third-party providers, and from Capgemini. Capgemini has focused on the provision of sector-specific solutions and currently offers ~110 sector solutions.

Introduction of Digital Twins Ensure Delivery of RoI from Technology Deployment

Capgemini’s approach to data-driven process discovery and excellence is based on combining process mining using process logs, task capture and task mining using desktop recorders, productivity analytics for each individual, and use of digital twins.

Tools used include Fortress IQ, Celonis, and Capgemini’s proprietary Prompt tool. These tools are combined with Capgemini’s Digital Global Enterprise Model (D-GEM) platform to incorporate best-in-class processes and frictionless processing.

Digital twins are used to progress process discovery beyond digital snapshots and provide ongoing process watching, assessment, and definition of opportunities. It also allows Capgemini to simulate the real returns that will be achieved by the introduction of technology by highlighting any other process constraints that will be exposed and limit the expected RoI from automation initiatives.

Capgemini’s approach to process digital twin introduction is:

To start with business mining, a combination of process mining, task mining, and Capgemini’s D-GEM platform
This is followed by benchmarking the processes against D-GEM
Then simulating the impact of introducing technology, calculating the business case, and ensuring that the result achieved is close to what was anticipated by identifying any potential process bottlenecks that might reduce the technology deployment’s savings. These simulations also help in accelerating the approval of intelligent automation projects and the scaling of digital transformation within the enterprise, since they increase management confidence in the certainty of project outcomes
This is followed by continuous improvement and identifying ongoing areas for improvement.

Also, during the pandemic, it is increasingly difficult to run onsite workshops for automation opportunity identification. It is becoming increasingly necessary to use digital twin process mining of individuals’ machines to remotely build business cases. This development may become standard practice post-pandemic if it proves to be a faster and more reliable basis for opportunity identification than interviewing SMEs.

Conclusion

In conclusion, the deployment of technology is arguably the easy part of intelligent process automation projects. Two more challenging elements have always been interpreting and routing unstructured transactions and queries and identifying and delivering RoI. Capgemini’s Frictionless Enterprise approach – that leverages a digitally augmented workforce – addresses both these challenges by combining technologies for classification and routing unstructured transactions and queries, and introducing process digital twins to ensure RoI delivery.

You can read Part 1 of this blog here.

Capgemini's Intelligent Process Automation, Part 1: Significant Growth From Frictionless Enterprise Approach

This is Part 1 of a two-part blog looking at Capgemini’s Intelligent Process Automation practice. Here I examine Frictionless Enterprise, Capgemini’s framework for intelligent process automation that focuses on the adoption of a digitally augmented workforce.

Digital transformation has been high on enterprise agendas for some years. However, COVID-19 has given the drive to digital transformation even greater impetus as organizations have increasingly looked to reduce cost, implement frictionless processing, and decouple their increasingly unpredictable business volumes from the number of servicing personnel required.

For Capgemini, this has resulted in unprecedented increases in Intelligent Process Automation bookings and revenue in 2020.

Frictionless Enterprise & the hybrid workforce

There is always a danger in intelligent automation projects of regarding people as secondary considerations and addressing the workforce through reactive change management. As part of its Frictionless Enterprise approach, Capgemini's framework for intelligent process automation stresses the adoption of a digitally augmented workforce, and aims to avoid this pitfall by maintaining high workforce-centricity, stressing the need to involve employees in the automation journey by taking a structured approach to workforce communication and upskilling.

Capgemini's Intelligent Automation Practice emphasizes the workforce communication and reskilling needed to achieve a digitally augmented or hybrid workforce. This involves putting humans at the center of the hybrid workforce and motivating and reskilling them.

The personnel-related stages in the journey towards a Frictionless Enterprise that leverages a digitally augmented workforce used by Capgemini are:

Design of the augmented workforce. On the design side, it is important to ask, "what is the impact of technology on the workforce and how should the organization's competency model change?" How is the workforce of the future defined?
Building the augmented workforce
Creating the right context.

Client cases

In one client example, Capgemini assisted a major capital markets firm in designing and building its digitally augmented workforce, using a four-step process:

Resource profiling
Dedicated curriculum creation
Pilot on 15% of resources
Augmented workforce scaling.

Step 1: Involved identifying personnel with a statistics or mathematics background who could be potential candidates for, say, ML data analysis. These potential candidates were then interviewed and tested to ensure their ability, for example, to run a Monte Carlo simulation.

Having established the desired job profiles, these personnel were allocated to various job families, such as automation business analysts, data analysts, power users, and developers, with developers split into low code/no-code developers and advanced developers.

Step 2: A dedicated curriculum was created in support of each of the job families. However, to ensure the training was focused and to increase employee engagement and retention, each employee was tasked up-front with clearly defined projects to be undertaken following training. This kept the training relevant and avoided a demotivating disconnect between training and deployment

Step3: 15% of the entire team were then trained and deployed in their new roles. This figure ranges between 5% and 15% depending on the client, but it is important to deploy on a sub-set of the workforce before rolling out more widely across the organization. This has the dual advantages of testing the deployment and creating an aspirational group that other employees wish to join

Step 4: Roll-out to the wider labor force. The speed of roll-out typically depends on the sector and company culture.

Capgemini has also helped a wealth management company enhance its ability to supply information from various sources to its traders by enhancing its capabilities in data management and automation. In particular, this required upskilling its workforce to address shortages of data, automation, and AI skillsets.

This involved a 3-year MDM Ops modernization program with dedicated workforce augmentation and upskilling for digitally displaced personnel, starting with three personnel groups.

This resulted in an average processing speed increase of 64% and an estimated data quality increase of 50%, and the approach was subsequently adopted more widely within the company's in-house operations.

AI Academy Practitioner's Program

Capgemini has created its AI Academy Practitioner's Program, an "industrialized approach" to AI training to support workforce upskilling. This program is mentor-led and customizable by sector and function to ensure that it supports the organization's current challenges.

The program's technical elements include:

"Qualifying" (6 hours over 3 days) for personnel who only need to be aware of the potential of AI
"Professional" (10-hours per week for 4 weeks), where personnel are provided with low code tools to start developing something
"Expert" (10-hours per week for 4 weeks), incorporating custom AI & ML model building.

The program's functional courses include:

Data literacy (4 hours over 4 days)
Business functional (10-hours per week for 4 weeks)
Business influencer (CXO) (15 hours over 3 days)
Intelligent process automation (15 hours over 3 days), highlighting combining automation stack with AI.

Conclusion

In conclusion, the deployment of technology is arguably the easy part of intelligent process automation projects. A more challenging element has always been to motivate the workforce to come forward with ideas and enthusiastically adopt change. Capgemini's Frictionless Enterprise approach – that leverages a digitally augmented workforce – addresses this challenge by adopting an aspirational approach to upskilling the workforce and removing the disconnect between training and deployment.

2020 Lessons & Future Success Factors: Q&A with Wipro’s Nagendra P Bandaru

The following is a discussion between Nagendra (Nag) P Bandaru, President, Wipro Limited – Global Business Lines-iCore, and John Willmott, NelsonHall CEO, covering lessons learned in 2020 and success factors for 2021. Nag is responsible for Infrastructure and Cloud Services, Digital Operations and Platforms, and Risk Services and Enterprise Cybersecurity – three key strategic business lines of Wipro Limited that help global clients accelerate their digital journeys. Together, these business lines generate revenues of USD 4.3 billion with an overall operation of approximately 100,000+ employees across 50 global delivery locations. He is a member of Wipro’s Executive Board, the apex leadership forum of the company. Nag is based in Plano, Texas.

JW: Nagendra, what was the most important lesson that you learned in 2020 in the face of COVID-19 and its impact on business?

Nag: What 2020 taught us was that no plans, great systems, or great processes would work in an unprecedented environment. It was a year of being resilient – of creating hope when you have no hope. For us, that was the starting point.

In a world where everything is constrained, how do you keep your operations live? Such situations often lend to the danger of overemphasizing technology, frameworks, systems, and processes while underestimating people. But, I believe that it is most important to have a resilient team. During BCP implementation in response to government-imposed lockdowns across the globe, the team’s leadership and decisive action have been the cornerstone of our success. Some of our employees were stuck at home without access to their secure office desktops. Our teams worked closely with local administrations to obtain the appropriate government permissions and ship laptops to employees’ homes – often crossing inter-state borders – through ground transport. Then, we had to set up the software and security remotely and change all the dongles to direct-to-home broadband. At that time, it was about keeping things simple and working out the basics. We saw simplicity being redefined by the pandemic. In getting the basics right, the bigger stuff began to fall in place. The experience taught us to focus on the basics.

Our global teams ensured that we moved from BCP to Business as usual within a few weeks of the lockdown, with 93% of our colleagues working securely from their homes. This proved that business resilience is about having the right talent. My leaders also emerged strong and worthy in those extremely difficult times. It is important to build future leaders whose core capability is the ability to anticipate and prevent risk. This ability to manage risk is the biggest leadership trait a company needs to be successful.

JW: The IT and BPS industry was remarkably resilient in 2020 and has a very promising outlook for 2021. What do you see as the main growth opportunities?

Nag: A good client experience comes from executing well, where the client is able to rely on the supplier’s strong delivery organization. The pandemic proved to be a challenging environment for several suppliers, and, as a result, clients were quick to change their vendors. The speed of decision-making has become much faster now, with little patience for lengthy procurement cycles. We sensed this as soon as the crisis struck. That is why, when one of our customers, a large bank in the U.S., wanted to launch a full-fledged digital solution to support thousands of small businesses and their employees under the fiscal stimulus program initiated by the U.S. government, we worked round-the-clock to deliver the application in just 48 hours. Similarly, we helped our client, a Postal Services company in the Middle East, launch a special medicine delivery service as a part of the government’s COVID response for citizens. For another client, we processed over 2.7 Mn claims in the first 3 weeks of the lockdown with nearly 100% accuracy. We were obsessed about delivery with minimal business disruption and supporting clients during their toughest time.

We now see that much of the growth is coming from vendor consolidation resulting from good execution. It rests primarily on the ability to understand the client’s critical needs when they require a tremendous amount of help. And many companies require that help now, which is why contracting decisions are being executed very, very fast. In some cases, accounts that used to take a decade to get into are now doing business in a week. Most often, the questions being asked are, “Can you do it correctly, and can you ramp up several hundred people?” If the answers are affirmative, you will get the business. Having said that, if you are unable to deliver within 15 days, you stand to lose the contract. Thus, the industry growth is also stemming from good execution.

Here, I would also note that the IT and ITES industry is recession-proof since volumes grow when client business is growing, and when there is a recession, clients undertake widespread initiatives to cut their operational costs but these initiatives are transformative in nature

There are also short-term growth engines such as the Cares Act in the US, together with regulatory changes and consolidation opportunities. Additionally, operational excellence opportunities, where customers want cost savings to readjust their cost bases to maintain their bottom lines, also offer growth.

However, the larger growth lies in business transformation, which has been a global trend even prior to the pandemic. The pandemic has forced industries and companies to accelerate tech investments. There is no denying that technology is at the core of this digital transformation. It powers both the front-end—to gain better access to markets, as well as the backend—to improve efficiency and optimize costs. Those customers who have invested in higher levels of RPA and AI or Intelligent Automation are finding out that they are in a better position to provide elasticity to their operations. Going forward, much of the growth for the industry will be led by next-generation technologies and services, including digital, cloud, data, engineering, and cybersecurity. Wipro has a massive role to play in helping businesses get onto the cloud as we have been investing in these areas for the last few years, and they are integral to our strategy.

JW: How are your delivery structures and processes changing as a result of COVID-19?

Nag: Security of data and processes has kept everyone awake. Add to that the several ransomware attacks on companies, and it’s a nightmare. The single biggest threat to companies right now is risk, which is why risk tolerance is very low. You will no longer be excused or pardoned if you do anything remotely risky. That is why we are constantly investing from a controls perspective, from a process perspective, and from a systems perspective. I’ve never experienced the importance of risk and security at this magnitude. Today, I have four teams ensuring four-eyes checks on everything we do. Yet, there is vulnerability, and we are constantly on the alert. That is the biggest thing that has changed in our lives.

Security is another reason why, I believe, the IT industry may not be able to embrace work-from-home permanently in the long term. Security will continue to remain vulnerable despite heavy investment in software and tightening of controls and processes. Therefore, I expect the future of delivery to be a distributed network from well-secured office environments of the supplier. However, this could definitely involve a shift to small offices, rather than operating from large delivery centers, thus leading to a high increase in the number of delivery locations. The future will see a “work-from-anywhere” model instead of a pure “work-from-home.”

JW: COVID-19 is also credited with a major increase in uptake of digital transformation. To what extent is this impacting your staff development programs?

Nag: The past nine months have seen unprecedented changes in the industry. There has been a big shift in the adoption of technology and its key role in making businesses resilient in the post-Covid world. While several changes were related to technology, some have been structural and are here to stay. Rapid digital transformation has created demand for new skills and flexibility. While employee wellness and safety has been our prime focus during the pandemic, we have also empowered employees to develop new skills through our robust internal upskilling initiatives. This helps build fungibility and keeps us agile. For example, there has been a major spike in loan administration volumes across both consumer and institutional loans, while other sectors such as auto claims and healthcare claims have seen considerably reduced volumes. In such situations, the fungibility of people becomes an important factor for quick ramp-up and fast delivery.

However, it is equally important to have a culture of obsession with employee experience. How we manage talent is going to be the single biggest determining factor for future success. I believe that skills and talent are at the center of that success, and the nature of education will have to evolve as we go into a very integrated disciplinary world. Earlier computing infrastructure used to be dominated by a small number of players. Today, the fragmentation in the cloud world is so immense that the large legacy fixed cost has been spread across many companies. This means that individuals will now need experience in a much wider range of technologies and software companies.

The gap between employable talent and educated talent is massive. Training is one area where budgets need to be increased across both skill-based and competency-based training. Skills requirements may change over time, but it is very important to give people experiences that enable them to develop competencies. That is why training has been the core focus of employee initiatives at Wipro.

Automation as a theme has been there for years. We have to understand that automation is a continuous journey of converting manual processes to straight-through processing. This, too, creates opportunities for employees to move up to roles that leverage their skills.

Our customers have always valued our passion for innovation, work ethics, and culture. They expect us to be the best at execution while being a proactive force of change. In order to be passionately committed to delivering lasting value and be the trusted partner to our clients in their transformation journey, we must continuously evolve. And for that, we will continue to focus on attracting, developing, and retaining the best talent in our industry.

Capgemini's CIAP 2.0 Assists Enterprises in Rapid & Cost-Effective Scaling of Automation Initiatives

Capgemini has just launched version 2 of the Capgemini Intelligent Automation Platform (CIAP) to assist organizations in offering an enterprise-wide and AI-enabled approach to their automation initiatives across IT and business operations. In particular, CIAP offers:

Reduced TCO and increased resilience through use of shared third-party components
Support for AIOps and DevSecOps
A strong focus on problem elimination and functional health checks.

Reduced TCO & increased ability to scale through use of a common automation platform

A common problem with automation initiatives is their distributed nature across the enterprise, with multiple purchasing points and a diverse set of tools and governance, reducing overall RoI and the enterprise's ability to scale automation at speed.

Capgemini aims to address these issues through CIAP, a multi-tenanted cloud-based automation solution that can be used to deliver "automation on tap." It consists of an orchestration and governance platform and the UiPath intelligent automation platform. Each enterprise has a multi-tenanted orchestrator providing a framework for invoking APIs and client scripts together with dedicated bot libraries and a segregated instance of UiPath Studio. A central source of dashboards and analytics is built into the front-end command tower.

While UiPath is provided as an integral part of CIAP, CIAP also provides APIs to integrate other Intelligent Automation platforms with the CIAP orchestration platform, enabling enterprises to continue to optimize the value of their existing use cases.

The central orchestration feature within CIAP removes the need for a series of point solutions, allowing automations to be more end-to-end in scope and removing the need for integration by the client organization. For example, within CIAP, event monitoring can trigger ticket creation, which in turn can automatically trigger a remediation solution.

Another benefit of this shared component approach is reducing TCO by improved sharing of licenses. The client no longer has to duplicate tool purchasing and dedicate components to individual automations; the platform and its toolset can be shared across each of infrastructure, applications, and business services departments within the enterprise.

CIAP is offered on a fixed-price subscription-based model based on "typical" usage levels, with additional charges only applicable where client volumes necessitate additional third-party execution licenses or storage beyond those already incorporated in the package.

Support for AIOps & DevSecOps

CIAP began life focused on application services, and the platform provides support for AIOps and DevSecOps, not just business services.

In particular, CIAP incorporates AIOps using the client's application infrastructure logs for reactive and predictive resolutions. In terms of reactive resolutions, the AIOps can identify the dependent infrastructure components and applications, identify the root cause, and apply any automation available.

CIAP also ingests logs and alerts and uses algorithms to correlate them, so that the resolver group only needs to address a smaller number of independent scenarios rather than each alert individually. The platform can also incorporate the enterprise's known error databases so that if an automated resolution does not exist, the platform can still recommend the most appropriate knowledge objects for use in resolution.

Future enhancements include increased emphasis on proactive capacity planning, including proactive simulation of the impact of change in an estate and enhancing the platform's ability to predict a greater range of possible incidents in advance. Capgemini is also enhancing the range of development enablers within the platform to establish CIAP as a DevSecOps platform, supporting the life cycle from design capture through unit and regression testing, all the way to release within the platform, initially starting with the Java and .NET stacks.

A strong focus on problem elimination & functional health checks

Capgemini perceives that repetitive task automation is now well understood by organizations, and the emphasis is increasingly on using AI-based solutions to analyze data patterns and then trigger appropriate actions.

Accordingly, to extend the scope of automation beyond RPA, CIAP provides built-in problem management capability, with the platform using machine learning to analyze historical tickets to identify the causes and recurring problems and, in many cases, initiate remediation automatically. CIAP then aims to reduce the level of manual remediation automation on an ongoing basis by recommending emerging automation opportunities.

In addition to bots addressing incident and problem management, the platform also has a major emphasis within its bot store on sector-specific bots providing functional health checks for sectors including energy & utilities, manufacturing, financial services, telecoms, life sciences, and retail & CPG. One example in retail is where prices are copied from a central system to store PoS systems daily. However, unreported errors during this process, such as network downtime, can result in some items remaining incorrectly priced in a store PoS system. In response to this issue, Capgemini has developed a bot that compares the pricing between upstream and downstream systems at the end of each batch pricing update, alerting business users, and triggering remediation where discrepancies are identified. Finally, the bot checks that remediation was successful and updates the incident management tool to close the ticket.

Similarly, Capgemini has developed a validation script for the utilities sector, which identifies possible discrepancies in meter readings leading to revenue leakage and customer dissatisfaction. For the manufacturing sector, Capgemini has developed a bot that identifies orders that have gone on credit hold, and bots to assist manufacturers in shop floor capacity planning by analyzing equipment maintenance logs and manufacturing cycle times.

CIAP has ~200 bots currently built into the platform library.

A final advantage of using platforms such as CIAP beyond their libraries and cost advantages is that they provide operational resilience by providing orchestrated mechanisms for plugging in the latest technologies in a controlled and cost-effective manner while unplugging or phasing out previous generations of technology, all of which further enhances time to value. This is increasingly important to enterprises as their automation estates grow to take on widespread and strategic operational roles.

Genpact Acquires Rightpoint to Strengthen 'Experience' Capability

Enterprise operations transformation requires three critically important capabilities:

Domain process expertise and the ability to identify new “digital” target operating models
Transformational technology capability, leveraging technologies such as cloud platforms and intelligent automation to elevate straight-through processing and self-service principles ahead of agent-based processing
Experience design and implementation, now highly important to optimize the experience across entire customer, employee, and partner populations.

Genpact has strong domain process expertise and, in recent years, has developed strong transformational technology capability but, despite its acquisition of TandemSeven, has historically possessed lower levels of capability in “experience” design and development.

However, TandemSeven’s experience capability was becoming highly important to Genpact even in core activities such as order management and collections, and Genpact recognized that “experience” was potentially a key differentiating factor for the company. Accordingly, having seen the benefits of integrating TandemSeven, Genpact increasingly looked to go up the value chain in experience capability by both enhancing and scaling its existing capabilities.

Rightpoint Judged to be Highly Complementary to TandemSeven

Rightpoint was then identified as a possible acquisition target by the Genpact M&A team, with Genpact judging that Rightpoint’s assets and capabilities were highly complementary to those of TandemSeven.

Rightpoint currently employs ~450 personnel and positions as a full-service digital agency offering multidisciplinary teams across strategy, design, content, engineering, and insights. The company was formed with the thesis that employee experience is paramount, with the company initially focusing on employee experience, a key area for Genpact, and subsequently developing an increasing emphasis on consumer experience in recent years.

Genpact perceives that Rightpoint can make a significant contribution to helping organizations “define the creative, define the interactive, and hence define a higher experience.” The company’s clients include Aon, Sanofi, M Health, Grant Thornton, Flywheel, and Walgreens. For example, Rightpoint has defined and designed the entire employee experience for Grant Thornton, where the company developed an employee information sharing and knowledge management platform. In addition, Rightpoint has assisted a large pharmaceutical company in creating a patient engagement application to encourage patients to monitor their insulin and sugar levels.

In addition to a complementary skillset, Rightpoint is also complementary to TandemSeven in industry presence. TandemSeven has a strong focus on financial services, with Rightpoint having a significant presence in healthcare and clients in consumer goods, auto, and insurance.

Maximizing the Synergies Between Genpact & Rightpoint

Genpact expects to grow both Rightpoint’s and its own revenues by exploiting the synergies between the two organizations.

One initial synergy being targeted by Genpact is providing end-to-end and “closed loop” services to its clients. Rightpoint employs both creative and technology personnel, with its creative personnel typically having a blend of technology capability allowing them to go from MVP to first product to roll-out. Rightpoint is a Microsoft Customer Engagement Alliance National Solution Provider, a Sitecore Platinum Partner, a certified Google Developer Agency, and also has partnerships with Episerver and Salesforce.

However, the company lacks the process and domain expertise that Genpact can bring to improve process target models and process controls & management. For example, for the medical company example above, Rightpoint could develop the app, while Genpact could run the app and provide the analytics to improve patient engagement, with Rightpoint then modifying the app accordingly.

Secondly, Genpact will support Rightpoint’s growth by bringing financial muscle to Rightpoint, facilitating:

An ability to invest in new technology capability in platforms such as Shopify and Adobe
The financial means to be able to spend a significant amount of time doing discovery work with clients and prospects, and hence targeting larger-scale assignments.

However, Genpact is being careful not to overstretch Rightpoint. The company intends to be highly disciplined in introducing Rightpoint to its accounts, initially targeting just those champion accounts where Rightpoint will enable Genpact to create a significant level of differentiation.

Genpact also perceives that it can learn from Rightpoint delivery methodologies. Rightpoint has a strong methodology in driving agile delivery and makes extensive use of gig workers (with ~10-15% of its workforce being gig workers) and these are both areas where Genpact perceives it can apply Rightpoint practice to its wider business.

Rightpoint Will Retain its Identity, Culture & Management

Rightpoint and TandemSeven are planned to be integrated with a porting of expertise and resources between both companies, and with Ross Freedman heading an expanded Rightpoint capability and reporting into Genpact’s transformation services lead.

In terms of the current organization, RightPoint has an experience practice and a digital operations practice. This includes an offshore delivery center in Jaipur and technology practice groups. However, while the practices are national, most of Rightpoint’s client delivery work is carried out in regional centers to give strong client proximity. The company’s HQ is in Chicago, with regional centers in Atlanta, Boston, Dallas, Denver, Detroit, Los Angeles, New York, and Oakland.

In due course, Genpact will likely further restructure some of the delivery, with a greater proportion of non-client-facing activity being moved into offshore CoEs.

IPsoft Looks to Reduce Time to Value While Increasing Return on AI

NelsonHall recently attended the IPsoft Digital Workforce Summit in New York and its analyst events in NY and London. For organizations unfamiliar with IPsoft, the company has around 2,300 employees, approximately 70% of these based in the U.S. and 20% in Europe. Europe is responsible for aproximately 30% of the IPsoft client base with clients relatively evenly distributed over the six regions: U.K., Spain & Iberia, France, Benelux, Nordics, and Central Europe.

The company began life with the development of autonomics for ITSM in the form of IPcenter, and in 2014 launched the first version of its Amelia conversational agent. In 2018, the company launched 1Desk, effectively combining its cognitive and autonomic capabilities.

The events outlined IPsoft’s positioning and plans for the future, with the company:

Investing strongly in Amelia to enhance its contextual understanding and maintain its differentiation from “chatbots”
Launching “Co-pilot” to remove the currently strong demarcation between automated and agent interactions
Building use cases and a partner program to boost adoption and sales
Positioning 1Desk and its associated industry solutions as end-to-end intelligent automation solutions, and the key to the industry and the future of IPsoft.

Enhancing Contextual Understanding to Maintain Amelia’s Differentiation from Chatbots

Amelia has often suffered from being seen at first glance as "just another chatbot". Nonetheless, IPsoft continues to position Amelia as “your digital companion for a better customer service” and to invest heavily to maintain Amelia’s lead in functionality as a cognitive agent. Here, IPsoft is looking to differentiate by stressing Amelia’s contextual awareness and ability to switch contexts within a conversation, thereby “offering the capability to have a natural conversation with an AI platform that really understands you.”

Amelia goes through six pathways in sequence within a conversation to understand each utterance and the pathway with highest probability wins. The pathways are:

Intent model
Semantic FAQ
AIML
Social talk
Acknowledge
Don’t know.

The platform also separates “entities” from “intents”, capturing both of these using Natural Language Understanding. Both intent and entity recognition is specific to the language used, though IPsoft is now simplifying implementation further by making processes language-independent and removing the need for the client to implement channel-specific syntax.

A key element in supporting more natural conversations is the use of stochastic business process networks, which means that Amelia can identify the required information as it is provided by the user, rather than having to ask for and accept items of information in a particular sequence as would be the case in a traditional chatbot implementation.

Context switching is also supported within a single conversation, with users able to switch between domains, e.g. from IT support to HR support and back again in a single conversation, subject to the rules on context switching defined by the organization.

Indeed, IPsoft has always had a strong academic and R&D focus and is currently further enhancing and differentiating Amelia through:

Leveraging ELMo with the aim of achieving intent accuracy of >95% while using only half of the data required in other Deep Neural Net models
Using NLG to support Elaborate Question Asking (EQA) and Clarifying Question & Answer (CQA) to enable Amelia to follow-up dynamically without the need to build business rules.

The company is also looking to incorporate sentiment analysis within voice. While IPsoft regards basic speech-to-text and text-to-speech as commodity technologies, the company is looking to capture sentiment analysis from voice, differentiate through use of SLM/SRGS technology, and improve Amelia’s emotional intelligence by capturing aspects of mood and personality.

Launching Co-pilot to Remove the Demarcation Between Automated Handling and Agent Handling

Traditionally, interactions have either been handled by Amelia or by an agent if Amelia failed to identify the intent or detected issues in the conversation. However, IPsoft is now looking to remove this strong demarcation between chats handled solely by Amelia and chats handled solely by (or handed off in their entirety) to agents. The company has just launched “Co-pilot”, positioned as a platform to allow hybrid levels of automation and collaboration between Amelia, agents, supervisors, and coaches. The platform is currently in beta mode with a major telco and a bank.

The idea is to train Amelia on everything that an agent does to make hand-offs warmer and to increase Amelia’s ability to automate partially, and ultimately handle, edge cases rather than just pass these through to an agent in their original form. Amelia will learn by observing agent interactions when escalations occur and through reinforcement learning via annotations during chat.

When Amelia escalates to an agent using Co-pilot, it will no longer just pass conversation details but will now also offer suggested responses for the agent to select. These responses are automatically generated by crowdsourcing every utterance that every agent has created and then picking those that apply to the particular context, with digital coaches editing the language and content of the preferred responses as necessary.

In the short term, this assists the agent by providing context and potential responses to queries and, in the longer term as this process repeats over queries of the same type, Amelia then learns the correct answers, and ultimately this becomes a new Amelia skill.

Co-pilot is still at an early stage with lots of developments to come and, during 2019, the Co-pilot functionality will be enhanced to recommend responses based on natural language similarity, enable modification of responses by the agent prior to sending, and enable agents to trigger partial automated conversations.

This increased co-working between humans and digital chat agents is key to the future of Amelia since it starts to position Amelia as an integral part of the future contact center journey rather than as a standalone automation tool.

Building Use Cases & Partner Program to Reduce Time to Value

Traditionally, Amelia has been a great cognitive chat technology but a relatively heavy-duty technology seeking a use case rather than an easily implemented general purpose tool, like the majority of the RPA products.

In response, IPsoft is treading the same path as the majority of automation vendors and is looking to encourage organizations (well at least mid-sized organizations) to hire a “digital worker” rather than build their own. The company estimates that its digital marketplace “1Store” already contains 672 digital workers, which incorporate back-office automation in addition to the Amelia conversational AI interface. For example, for HR, 1Store offers “digital workers” with the following “skills”: absence manager, benefits manager, development manager, onboarding specialist, performance record manager, recruiting specialist, talent management specialist, time & attendance manager, travel & expense manager, and workforce manager.

At the same time, IPsoft is looking to increase the proportion of sales and service through channel partners. Product sales currently make up 56% of IPsoft revenue, with 44% from services. However, the company is looking to steer this ratio further in support of product, by targeting 60% per annum growth in product sales and increasing the proportion of personnel, currently approx. two-thirds, in product-related positions with a contribution from reskilling existing services personnel.

IPsoft has been late to implement its partner strategy relative to other automation software vendors, attributing this early caution in part to the complexity of early implementations of Amelia. Early partners for IPcenter included IBM and NTT DATA, who embedded IPsoft products directly within their own outsourcing services and were supported with “special release overlays” by IPsoft to ensure lack of disruption during product and service upgrades. This type of embedded solution partnership is now increasingly likely to expand to the major CX services vendors as these contact center outsourcers look to assist their clients in their automation strategies.

So, while direct sales still dominate partner sales, IPsoft is now recruiting a partner/channel sales team with a view to reversing this pattern over the next few years. IPsoft has now established a partner program targeting alliance and advisory (where early partners included major consultancies such as Deloitte and PwC), implementation, solution, OEM, and education partners.

1Desk-based End-to-End Automation is the Future for IPsoft

IPsoft has about 600 clients, including approx. 160 standalone Amelia clients, and about a dozen deployments of 1Desk. However, 1Desk is the fastest-growing part of the IPsoft business with 176 enterprises in the pipeline for 1Desk implementations, and IPsoft increasingly regards the various 1Desk solutions as its future.

IPsoft is positioning 1Desk by increasingly talking about ROAI (the return on AI) and suggesting that organizations can achieve 35% ROAI (rather than the current 6%) if they adopt integrated end-to-end automation and bypass intermediary systems such as ticketing systems.

Accordingly, IPsoft is now offering end-to-end intelligent automation capability by combining the Amelia cognitive agent with “an autonomic backbone” courtesy of IPsoft’s IPcenter heritage and with its own RPA technology (1RPA) to form 1Desk.

1Desk, in its initial form, is largely aimed at internal SSC functions including ITSM, HR, and F&A. However, over the next year, it will increasingly be tailored to provide solutions for specific industries. The intent is to enable about 70% of the solution to be implemented “out of the box”, with vanilla implementations taking weeks rather than many months and with completely new skills taking approx.. three 3 months to deploy.

The initial industry solution from IPsoft is 1Bank. As the name implies, 1Bank has been developed as a conversational banking agent for retail banking and contains preformed solutions/skills covering the account representative, e.g. for support with payments & bills; the mortgage processor; the credit card processor; and the personal banker, to answer questions about products, services, and accounts.

1Bank will be followed during 2019 by solutions for healthcare, telecoms, and travel.

Blue Prism Offers A Lever for Culture Change to Mature Enterprises

Blue Prism adopted the theme “Connected RPA – Powering the Connected Entrepreneur Enterprise” at its recent Blue Prism World conferences, the key components of connected-RPA being the Blue Prism connected-RPA platform, Blue Prism Digital Exchange, Blue Prism Skills, and Blue Prism Communities:

Components of Blue Prism's connected-RPA

Blue Prism is positioning by offering mature companies the promise of closing the gap with digital disruptors, both technically and culturally. The cultural aspect is important, with Blue Prism technology positioned as a lever to help organizations attract and inspire their workforce and give digitally-savvy entrepreneurial employees the technology to close the “digital entrepreneur gap” and also close the gap between senior executives and the workforce.

Within this vision, the Blue Prism roadmap is based around helping organizations to:

Automate more – here, Blue Prism is introducing intelligent automation skills, ML-based process discovery, and DX
Automate better – with more expansive and scalable automations
Automate together – by learning from the mistakes and achievements of others.

Introducing intelligent document processing capability

When analyzing the interactions on its Digital Exchange (DX), Blue Prism unsurprisingly found that the single biggest use, with 60% of the items being downloaded from DX, was related to unstructured document processing.

Accordingly, Blue Prism has just announced a beta intelligent document processing program, Decipher. Decipher is positioned as an easy on-ramp to document processing and is a document processing workflow that can be used to ingest & classify unstructured documents. It can be used “out-of-the-box” without the need to purchase additional licenses or products, and organizations can also incorporate their own document capture technologies, such as Abbyy, or document capture services companies within the Decipher framework.

Decipher will clean documents to ensure that they are ready for processing, apply machine learning to classify the documents, and then to extract the data. Finally, it will apply a confidence score to the validity of the data extracted and pass to a business user where necessary, incorporating human-in-the-loop assisted learning.

Accordingly, Decipher is viewed by Blue Prism as a first step in the increasingly important move beyond rule-based RPA to introduce machine learning-based human-in-the-loop capability. Not surprisingly, Blue Prism recognizes that, as machine learning becomes more important, people will need to be brought into the loop much more than at present to validate “low-confidence” decisions and to provide assisted learning to the machine learning.

Decipher is starting with invoice processing and will then expand to handle other document types.

Improving control of assets within Digital Exchange (DX)

The Digital Exchange (DX) is another vital component in Blue Prism’s vision of connected-RPA.

Enhancements planned for DX include making it easier for organizations to collaborate and share knowledge and facilitating greater security and control of assets by enabling an organization to control the assets available to itself. Assets will be able to be marked as private, effectively providing an enterprise-specific version of the Blue Prism digital exchange and within DX, there will be a “skills” drag-and-drop toolbar so that users, and not just partners, will be able to publish skills.

Blue Prism, like Automation Anywhere, is also looking to bring an e-commerce flavor to its DX: developers will be able to create skills and then sell them. Initially, Blue Prism will build some artifacts themselves. Others will be offered free-of-charge by partners in the short-term, with a view in the near term to enabling partners to monetize their assets.

Re-aligning architecture & introducing AI-related skills

Blue Prism has been working closely with cloud vendors to re-align its architecture, and in particular to rework its UI to appeal to a broader range of users and make Blue Prism more accessible to business users.

Blue Prism is also improving its underlying architecture to make it more scalable as well as more cloud-friendly. There will be a new, more native and automated means of controlling bots via a browser interface available on mobiles and tablets that will show the health of the environment in terms of meeting SLAs, and provide notifications showing where interventions are required. Blue Prism views this as a key step in moving towards provision of a fully autonomous digital workforce that manages itself.

Data gateways (available on April 30, 2019 in v6.5) are also being introduced to make Blue Prism more flexible in its use of generated data. Organizations will be able to take data from the Blue Prism platform and send it to ML for reporting, etc.

However, Blue Prism will continue to use commodity AI and is looking to expand the universe of technologies available to organizations and bring them into the Blue Prism platform without the necessity for lots of coding. This is being done via continuing to expand the number of Blue Prism partners and by introducing the concept of Blue Prism skills.

At Blue Prism World, the company announced five new partners:

Bizagi, for process documentation and modeling, connecting with both on-premise and cloud-based RPA
Hitachi ID Systems, for enhanced identity and access management
RPA Supervisor, an added layer of monitoring & control
Systran, providing digital workers with translation into 50 languages
Winshuttle, for facilitating transfer of data with SAP.

At the same time, the company announced six AI-related skills:

Knowledge & insight
Learning
Visual perception: OCR technologies and computer vision
Problem-solving
Collaboration: human interaction and human-in-the-loop
Planning & sequencing.

Going forward

Blue Prism recognizes that while the majority of users presenting at its conferences may still be focused on introducing rule-based processes (and on a show of hands, a surprisingly high proportion of attendees were only just starting their RPA journeys), the company now needs to take major strides in making automation scalable, and in more directly embracing machine learning and analytics.

The company has been slightly slow to move in this direction, but launched Blue Prism labs last year to look at the future of the digital worker, and the labs are working on addressing the need for:

More advanced process analytics and process discovery
More inventive and comprehensive use of machine learning (though the company will principally continue to partner for specialized use cases)
Introduction of real-time analytics directly into business processes.

Automation Anywhere Monetizes Bot Store to Provide ‘Value as a 2-Way Street’

Automation Anywhere’s current Bot Store contains ~500 bots and has received ~40K downloads. In January 2019, these bots were complemented by Digital Workers, with bots being task-centric and Digital Workers being persona- and skill-centric.

So far, downloads from the Bot Store have been free-of-charge, but Automation Anywhere perceives that this approach potentially limits the value achievable from the Bot Store. Accordingly, the company is now introducing monetization to provide value back to developers contributing bots and Digital Workers to the Bot Store, and to increase the value that clients can receive. In effect, Automation Anywhere is looking to provide value as a two-way street.

The timing for introducing monetization to the Bot Store will be as follows:

April 16, 2019: announcement and start of sales process validation with a small number of bots and bot bundles priced within the Bot Store. Examples of “bot bundles” include a number of bots for handling email operation around Outlook or bots for handling common Excel operations
May 2019: Availability of best practice guides for developers containing guidelines on how to write bots that are modular and easy to onboard. Start of developer sign-up
Early summer 2019: customer launch through the direct sales channel. At this stage, bots and Digital Workers will only be available through the formal direct sales quotation process rather than via credit card purchases
Late summer 2019: launch of “consumer model” and Bot Store credit card payments.

Pricing, initially in US$ only, will be per bot or Digital Worker, with a 70:30 revenue split between the developer and Automation Anywhere, with Automation Anywhere handling the billing and paying the developer monthly. Buyers will have a limited free trial period, initially 30 days but under review, but IP protection is being introduced so that buyers will not have access to the source code. The original developer will retain responsibility for building, supporting, maintaining, and updating their bots and Digital Workers. Automation Anywhere is developing some Digital Workers itself in order to seed the Bot Store with some examples, but Automation Anywhere has no desire to develop Digital Workers medium-term itself and may, once the concept is well-proven, hand over/license the Digital Workers it has developed to third-party developers.

Automation Anywhere clearly expects that a number of smaller systems integrators will switch their primary business model from professional services to a product model, building bots for the Bot Store, and is offering developers the promise of a recurring revenue stream and global distribution ultimately through not only the Bot Store but through Automation Anywhere and its partners. Although payment will be monthly, developers will receive real-time transaction reporting to assist them in their financial management. For professional services firms retaining a strong professional services focus, but used to operating on a project basis, Automation Anywhere perceives that licensing and updating Digital Workers within this model could provide both a supplementary revenue stream, and possibly, more importantly, a means to maintain an ongoing relationship with the client organization.

In addition to systems integrators, Automation Anywhere is targeting ISVs who, like Workday, can use the Bot Store and Automation Anywhere to facilitate deployment and operation of their software by introducing Digital Workers that go way beyond simple connectors. Although the primary motivation of these firms is likely to be to reduce the time to value for their own products, Automation Anywhere expects ISVs to be cognizant of the cost of adoption and to price their Digital Workers at levels that will provide both a reduced cost of adoption to the client and a worthwhile revenue stream to the ISV. Pricing of Digital Workers in the range $800 to as high as $12k-$15K per annum has been mentioned.

So far, inter-enterprise bot libraries have largely been about providing basic building blocks that are commonly used across a wide range of processes. The individual bots have typically required little or no maintenance and have been disposable in nature. Automation Anywhere is now looking to transform the concept of bot libraries to that of bot marketplaces to add a much higher, and long-lived, value add and to put bots on a similar footing to temporary staff with updateable skills.

The company is also aiming to steal a lead in the development of such bots and, preferably Digital Workers, by providing third-parties with the financial incentive to develop for its own, rather than a rival, platform.

Automation Anywhere Looking to 'Deliver the Digital Workforce for Everyone'

Automation Anywhere, as with the RPA market in general, continues to grow rapidly. The company estimates that it now has 1,600 enterprise clients, encompassing 3,800 unique business entities across 90 countries with ~10,000 processes deployed. At end 2018, the company had 1,400 employees, and it expects to have 3,000 employees by end 2019.

The company was initially slow to go-to-market in Europe relative to Blue Prism and UiPath, but estimates it has more than tripled its number of customers in Europe in the past 12 months.

NelsonHall attended the recent Automation Anywhere conference in Europe, where the theme of the event was “Delivering Digital Workforce for Everyone” with the following sub-themes:

Automate Everything
Adopted by Everyone
Available Everywhere.

Automate Everything

Automation Anywhere is positioning as “the only multi-product vendor”, though it is debatable whether this is entirely true and also whether it is desirable to position the various components of intelligent automation as separate products.

Nonetheless, Automation Anywhere is clearly correct in stating that, “work begins with data (structured and unstructured) – then comes analysis to get insight – then decisions are made (rule-based or cognitive) – which leads to actions – and then the cycle repeats”.

Accordingly, “an Intelligent RPA platform is a requirement. AI cannot be an afterthought. It has to be part of current processes” and so Automation Anywhere comes to the following conclusion:

Intelligent digital workforce = RPA (attended + unattended) + AI + Analytics

Translated into the Automation Anywhere product range, this becomes:

Adopted by Everyone

Automation Everywhere clearly sees the current RPA market as a land grab and is working hard to scale adoption fast, both within existing clients and to new clients, and for each role within the organization.

The company has traditionally focused on the enterprise market with organizations such as AT&T, ANZ, and Bank of Columbia using 1,000s of bots. For these companies, transformation is just beginning as they now look to move beyond traditional RPA, and Automation Anywhere is working to include AI and analytics to meet their needs. However, Automation Anywhere is now targeting all sizes of organization and sees much of its future growth coming from the mid-market (“automation has to work for all sizes of organization”) and so is looking to facilitate adoption here by introducing a cloud version and a Bot Store.

The company sees reduced “time to value” as key to scaling adoption. In addition to a Bot Store of preconfigured bots, the company has now introduced the concept of downloadable “Digital Workers” designed around personas, e.g. Digital SAP Accounts Payable Clerk. Automation Anywhere had 14 Digital Workers available from its Bot Store as at mid-March 2019. These go beyond traditional preconfigured bots and include pretrained cognitive capability that can process unstructured data relevant to the specific process, e.g. accounts payable.

In addition, Automation Anywhere believes that to automate at the enterprise-wide level you have to onboard your workforce very fast, so that you can involve more of the workforce sooner. Accordingly, the company is providing role-based in-product learning and interfaces.

To enable the various types of user to ramp up quickly, the coming version of Automation Anywhere will provide a customizable user interface to support the differing requirements of the business, IT, and developers, providing unique views for each. For example:

The business user interface can be set up with a customized tutorial on how to build a simple bot using a Visio-like graphical interface. The advanced functionality can be hidden when they start using the tool. Alternatively, the business user can use the recorder to create a visual representation of what needs to be done, including documenting cycle times and savings information, etc., then passing this requirement to a developer
Advanced developers, on the other hand, can be set up with advanced functionality including, for example, the ability to embed their own code in, say, Python
An IT user can learn about and manage user management, including roles and privileges, and license management.

The Automation Anywhere University remains key to adoption for all types of user. Overall, Automation Anywhere estimates that it has trained ~100K personnel. The Automation Anywhere University has:

An association with 200 educational institutions
26 training partners
9 role-based learning tracks
120 certified trainers
Availability in 4 course languages.

An increased emphasis on channel sales is also an important element in increasing adoption, with Automation Anywhere looking to increase the proportion of sales through partners from 50% to 70%. The direct sales organization consists of 13 field operating units broken down into pods, and this sales force will be encouraged to leverage partners with a “customer first/partner preferred” approach.

Partner categories include:

BPOs with embedded use of Automation Anywhere, and Automation Anywhere is now introducing tools that will facilitate support for managed service offerings
Global alliance partners (major consultancies and systems integrators)
The broader integrator community/local SIs
A distributor channel. Automation Anywhere is currently opening up a volume channel and has appointed distributors including TechData and ECS
Private Equity. Automation Anywhere has set up a PE practice to go after the more deterministic PEs who are very prescriptive with their portfolio companies.

In addition, Automation Anywhere is now starting to target ISVs. The company has a significant partnership with Workday to help the ISV automate implementation and reduce implementation times by, for example, assisting in data migration, and the company is hoping that this model can be implemented widely across ISVs.

Automation Anywhere is also working on a partner enablement platform, again seen as a requisite for achieving scale, incorporating training, community+, etc. together with a demand generation platform.

Customer success is also key to scaling. Here, Automation Anywhere claims a current NPS of 67 and a goal to exceed the NPS of 72 achieved by Apple. With that in mind, Automation Anywhere has created a customer success team of 250 personnel, expected to grow to 600+ as the team tries to stay ahead of customer acquisition in its hiring. All functions with Automation Anywhere get their feedback solely through this channel, and all feedback to clients is through this channel. In addition, the sole aim of this organization is to increase the adoptability of the product and the organization’s NPS. The customer success team does not get involved in up-selling, cross-selling, or deal closure.

Available Everywhere

“Available Everywhere” encompasses both a technological and a geographic perspective. From a hosting perspective, Automation Anywhere is now available on cloud or on-premise, with the company clearly favoring cloud where its clients are willing to adopt this technology. In particular, the company sees cloud hosting as key to facilitating its move from the enterprise to increasingly address mid-market organizations.

At the same time, Automation Anywhere has “taken installation away” with the platform, whether on-premise or on cloud, now able to be accessed via a browser. The complete cloud version “Intelligent Automation Cloud” is aimed at allowing organizations to start their RPA journey in ~4 minutes, while considerably reducing TCO.

In terms of languages, the user interface is now available in eight languages (including French, German, Japanese, Spanish, Chinese, and Korean) and will adjust automatically to the location selected by the user. At the same time, the platform can process documents in 190 languages.

Automation Anywhere also provides a mobile application for bot management.

Summary

In summary, Automation Anywhere regards the keys to winning a dominant market share in the growth phase of the RPA market as being about simultaneously facilitating rapid adoption in its traditional large enterprise market and moving to the mid-market and SMEs at speed.

The company is facilitating ongoing RPA scaling in large enterprises by recognizing the differing requirements of business users, IT, and developers, and establishing separate UIs to increase their acceptance of the platform while increasingly supporting their need to incorporate machine learning and analytics as their use cases become more sophisticated. For the smaller organization, Automation Anywhere has facilitated adoption by introducing free trials, a cloud version to minimize any infrastructure hurdles, and a Bot Store to reduce development time and time to value.

D-GEM: Capgemini’s Answer to the Problem of Scaling Automation

Finance & accounting is at the forefront of the application of RPA, with organizations attracted by its high volumes of transactional activity. Consequently, activities such as the movement and matching of data within purchase-to-pay have been a frequent start-point for organizational automation initiatives.

Organizations starting on RPA are initially faced with the challenges of understanding RPA tools and approaches and typically lack the internal skills necessary to undertake automation initiatives. Once these skills have been acquired, RPA is then often applied in a piecemeal fashion, with each use case considered by a governance committee on its own merits. However, once a number of deployments have been achieved, organizations then look to scale their automation initiatives across the finance function and are confronted by the sheer complexity, and impossibility, of managing the scaling of automation while maintaining a ‘piecemeal’ approach. At this point, organizations realize they need to modify their approach to automation and adopt a guiding framework and target operating model if they are to scale automation successfully across their finance & accounting processes.

In response to these needs, Capgemini has introduced its Digital Global Enterprise Model (D-GEM to assist organizations in scaling automation across processes such as finance & accounting more rapidly and effectively.

Introducing D-GEM

The basic premise behind D-GEM is that organizations need both a vision and a detailed roadmap if they are to scale their application of automation successfully. Capgemini is taking an automation-first approach to solutioning, with the client vision initially developed in “Five Senses of Intelligent Automation” workshops. Here, Capgemini runs workshops for clients to demo the various technologies and the possibilities from automation, and to establish their new target operating model taking into account:

The key outcomes sought within finance & accounting under the new target operating model. For example, key outcomes sought could be reduced DSO, increased working capital, and reduced close days
How the existing processes could be configured and connected better using “five senses”:
- Act (RPA)
- Think (analytics)
- Remember (knowledge base)
- Watch (machine vision & machine learning)
- Talk (chatbot technology).

However, while the vision, goals, and technology are important, implementing this target operating model at scale requires an understanding of the underlying blueprint, and here Capgemini has developed D-GEM as the “practitioners’ guidebook, a repository showing (e.g., for finance & accounting) what can be achieved and how to achieve it at a granular level (process level 4).

D-GEM essentially aims to provide the blueprint to support the use of automation and deliver the transformation. It is now being widely used within Capgemini and is being made available not just to the company’s BPO clients but for wider application by non-BPO clients within their SSCs and GBS organizations.

From GEM to D-GEM

Capgemini’s original GEM (Global Enterprise Model) was used for solutioning and driving transformation within BPO clients prior to the advent of intelligent automation technologies. Its transformation focus was on improving the end-to-end process and eliminating exceptions. It aimed to introduce best-in-class processes while optimizing the location mix and improving domain competencies and reflected the need to drive standardization and lean processes to deliver efficiency.

While the focus of D-GEM remains the introduction of “best-in-class” processes, best-in-class has now been updated to take into account Intelligent Automation technologies, and the transformation focus has changed to the application of automation to facilitate best-in-class. For example, industrialization of the inputs needs to be taken into account at an early stage if downstream processes are to be automated at scale. Alongside the efficiency focus on eliminating waste, it also looks to use technology to improve the user experience. For instance, rather than eliminating non-standard reporting as has often been a focus in the past, deployment of reporting tools and services on top of standardized inputs and data can enhance the user experience by allowing them to produce their own one-off reports based on consistent and accurate information.

D-GEM provides a portal for practitioners using the same seven levers as GEM, namely:

Grade Mix
Location Mix
Competencies
Digital Global Process Model
Technology
Pricing and Cost Allocations
Governance.

However, the emphasis within each of these levers has now changed, as explained in the following sections.

Role of the Manager Changes from Managing Throughput to Eliminating Exceptions

Within Grade Mix, Capgemini evaluates the impact of automation on the grade mix, including how to increase the manager’s span of control by adding bots as well as people, how to use knowledge to increase the capability at different grades, and how to optimize the team structure.

Under D-GEM, the role of the manager fundamentally changes. With the emphasis on automation-first, the primary role of the manager is now to assist the team in eliminating exceptions rather than managing the throughput of team members. Essentially, managers now need to focus on changing the way invoices are processed rather than managing the processing of invoices.

The needs of the agents also change as the profile of work changes with increased levels of task automation. Typically, agents now need to have a level of knowledge that will enable them to act as problem-solvers and trainers of bots. Millennials typically have great problem-solving skills, and Capgemini is using Transversal and the process knowledge base within D-GEM to skill people up faster and ensure Process Champions are growing within each delivery team, so knowledge management tools have a key role to play in ensuring that knowledge is effectively dispersed and able junior team members can expand their responsibility more quickly.

The required changes in competency are key considerations within digital transformations, and it is important to understand how the competencies of particular roles or grades change in response to automation and how to ensure that the workforce knows how automation can enrich and automate their capabilities.

The resulting team structure is often portrayed as a diamond. However, Capgemini believes it is important not to end up with a top-heavy organization as a result of process automation. The basic pyramid structure doesn’t necessarily change, but the team now includes an army of robots, so while the span of managers will typically be largely unchanged in terms of personnel, they are now additionally managing bots. In addition, tools such as Capgemini’s “prompt” facilitate the management of teams across multiple locations.

Within Location Mix, as well as evaluating that the right processes are in the right locations and how the increased role of automation impacts the location mix, it is now important to consider how much work can be transitioned to a Virtual Delivery Center.

Process & Technology Roadmaps Remain Important

Within Digital Global Process Model, D-GEM provides a roadmap for best-practice processes powered by automation with integrated control and performance measures. Capgemini firmly believes that if an organization is looking to transform and automate at scale, then it is important to apply ESOAR (eliminate, standardize, optimize, automate, and then apply RPA and other intelligent automation technologies) first, not just RPA.

Finance & accounting processes haven’t massively changed in terms of the key steps, but D-GEM now includes a repository for each process, based on ESOAR, which shows which steps can be eliminated, what can be standardized, how to optimize, how to automate, how to robotize, and how to add value.

Within the Technology lever, D-GEM then provides a framework for identifying suitable technologies and future-proofing technology. It also indicates what technologies could potentially be applied to each process tower, showing a “five senses” perspective. For example, Capgemini is now undertaking some pilots applying blockchain to intercompany accounting to create an internal network. Elsewhere, for one German organization, Capgemini has applied Tradeshift and RPA on top of the organization’s ERP to achieve straight-through processing.

In addition, as would be expected, D-GEM includes an RPA catalog, listing the available artifacts by process, together with the expected benefits from each artifact, which greatly facilitates the integration of RPA into best practices.

Governance is also a critical part of transformation, and the Governance lever within D-GEM suggests appropriate structures to drive transformation, what KPIs should be used to drive performance, and how roles in the governance model change in the new digital environment.

Summary

Overall, D-GEM has taken Capgemini’s Global Enterprise Model and updated it to address the world of digital transformation, applying automation-first principles. While process best practice remains key, best practice is now driven by a “five senses” perspective and how AI can be applied in an interconnected fashion across processes such as finance and accounting.

AntWorks Positioning BOT Productivity and Verticalization as Key to Intelligent Automation 2.0

Last week, AntWorks provided analysts with a first preview of its new product ANTstein SQUARE, to be officially launched on May 3.

AntWorks’strategy is based on developing full stack intelligent automation, built for modular consumption, and the company’s focus in 2019 is on:

BOT productivity, defined as data harvesting plus intelligent RPA
Verticalization.

In particular, AntWorks is trying to dispel the idea that Intelligent Automation needs to consist of three separate products from three separate vendors across machine vision/OCR, RPA, and AI in the form of ML/NLP, and show that AntWorks can offer a single, though modular, “automation” across these areas end-to-end.

Overall, AntWorks positions Intelligent Automation 2.0 as consisting of:

Multi-format data ingestion, incorporating both image and text-based object detection and pattern recognition
Intelligent data association and contextualization, incorporating data reinforcement, natural language modelling using tokenization, and data classification. One advantage claimed for fractal analysis is that it facilitates the development of context from images such as company logos and not just from textual analysis and enables automatic recognition of differing document types within a single batch of input sheets
Smarter RPA, incorporating low code/no code, self-healing, intelligent exception handling, and dynamic digital workforce management.

Cognitive Machine Reading (CMR) Remains Key to Major Deals

AntWorks’ latest release, ANTstein SQUARE is aimed at delivery of BOT productivity through combining intelligent data harvesting with cognitive responsiveness and intelligent real-time digital workforce management.

ANTstein data harvesting covers:

Machine vision, including, to name a modest sub-set, fractal machine learning, fractal image classifier, format converter, knowledge mapper, document classifier, business rules engine, workflow
Pre-processing image inspector, where AntWorks demonstrated the ability of its pre-processor to sharpen text and images, invert white text on a black background, remove grey shapes, and adjust skewed and rotated inputs, typically giving a 8%-12% uplift
Natural language modelling.

Clearly one of the major issues in the industry over the last few years has been the difficulty organizations have experienced in introducing OCR to supplement their initial RPA implementations in support of handling unstructured data.

Here, AntWorks has for some time been positioning its “cognitive machine reading” technology strongly against traditional OCR (and traditional OCR plus neural network-based machine learning) stressing its “superior” capabilities using pattern-based Content-based Object Retrieval (CBOR) to “lift and associate all the content” and achieve high accuracy of captured content, higher processing speeds, and ability to train in production. AntWorks also takes a wide definition of unstructured data covering not just typed text, but also including for example handwritten documents and signatures and notary stamps.

AntWorks' Cognitive Machine Reading encompasses multi-format data ingestion, fractal network driven learning for natural language understanding using combinations of supervised learning, deep learning, and adaptive learning, and accelerators e.g. for input of data into SAP.

Accuracy has so far been found to be typically around 75% for enterprise “back-office” processes, but the level of accuracy depends on the nature of the data, with fractal technology most appropriate where the past data strongly correlates with future data and data variances are relatively modest. Fractal techniques are regarded by AntWorks as being totally inappropriate in use cases where the data has a high variance e.g. crack detection of an aircraft or analysis of mining data. In such cases, where access to neural networks is required, AntWorks plans to open up APIs to, for example, Amazon and AWS.

Several examples of the use of AntWorks’ CMR were provided. In one of these, AntWorks’ CMR is used in support of sanction screening within trade finance for an Australian bank to identify the names of the parties involved and look for banned entities. The bank estimates that 89% of entities could be identified with a high degree of confidence using CMR with 11% having to be handled manually. This activity was previously handled by 50 FTEs.

Fractal analysis also makes its own contribution to one of ANTstein’s USPs: ease of use. The business user uses “document designer”, to train ANTstein on a batch of documents for each document type, but fractal analysis requires lower numbers of cases than neural networks and its datasets also inherently have lower memory requirements since the system uses data localization and does not extract unnecessary material.

RPA 2.0 “QueenBOTs” Offer “Bot Productivity” through Cognitive Responsiveness, Intelligent Digital Automation, and Multi-Tenancy

AntWorks is positioning to compete against the established RPA vendors with a combination of intelligent data harvesting, cognitive bots, and intelligent real-time digital workforce management. In particular, AntWorks is looking to differentiate at each stage of the RPA lifecycle, encompassing:

Design, process listener and discoverer
Development, aiming to move towards low code business user empowerment
Operation, including self-learning and self-healing in terms of exception handling to become more adaptive to the environment
Maintenance, incorporating code standardization into pre-built components
Management, based on “central intelligent digital workforce management.

Beyond CMR, much of this functionality is delivered by QueenBOTs. Once the data has been harvested it is orchestrated by the QueenBOT, with each QueenBOT able to orchestrate up to 50 individual RPA bots referred to as AntBOTs.

The QueenBOT incorporates:

Cognitive responsiveness
Intelligent digital automation
Multi-tenancy.

“Cognitive responsiveness” is the ability of the software to adjust automatically to unknown exceptions in the bot environment, and AntWorks demonstrated the ability of ANTstein SQUARE to adjust in real-time to situations where non-critical data is missing or the portal layout has changed. In addition, where a bot does fail, ANTstein aims to support diagnosis on a more granular basis by logging each intermittent step in a process and providing a screenshot to show where the process failed.

AntWorks’ is aiming to put use case development into the hands of the business user rather than data scientists. For example, ANTstein doesn’t require the data science expertise for model selection typically required when using neural network based technologies and does its own model selection.

AntWorks also stressed ANTstein’s ease of use via use of pre-built components and also by developing its own code via the recorder facility and one client talking at the event is aiming to handle simple use cases in-house and just outsourcing the building of complex use cases.

AntWorks also makes a major play on reducing the cost of infrastructure compared to traditional RPA implementations. In particular, ANTstein addresses the issue of servers or desktops being allocated to, or controlled by, an individual bot by incorporating dynamic scheduling of bots based on SLAs rather than timeslots and enabling multi-tenancy occupancy so that a user can use a desktop while it is simultaneously running an AntBOTs or several AntBOTs can run simultaneously on the same desktop or server.

Building Out Vertical Point Solutions

A number of the AntWorks founders came from a BPO background, which gave them a focus on automating the process middle- and back-office and the recognition that bringing domain and technology together is critical to process transformation and building a significant business case.

Accordingly, verticalization is a major theme for AntWorks in 2019. In addition to support for a number of horizontal solutions, AntWorks will be focusing on building point solutions in nine verticals in 2019, namely:

Banking: trade finance, retail banking account maintenance, and anti-money laundering
Mortgage (likely to be the first area targeted): new application processing, title search, and legal description
Insurance: new account set up, policy maintenance, claims handling, and KYC
Healthcare & life sciences: BOB reader, PRM chat, payment posting, and eligibility
Transportation & logistics: examination evaluation
Retail & CPG: no currently defined point solutions
Telecom: customer account maintenance
Media & entertainment: no currently defined point solutions
Technology & consulting: no currently defined point solutions.

The aim is to build point solutions (initially in conjunction with clients and partners) that will be 80% ready for consumption with a further 20% of effort required to train the bot/point solution on the individual company’s data.

Building a Partner Ecosystem for RPA 2.0

The company claims to have missed the RPA 1.0 bus by design (the company commenced development of “full-stack ANTstein in 2017) and is now trying to get out the message that the next generation of Intelligent Automation requires more than OCR combined with RPA to automate unstructured data-heavy industry-specific processes.

The company is not targeting companies with small numbers of bot implementations but is ideally seeking dozens of clients, each with the potential to build into $10m relationships. Accordingly the bulk of the company’s revenues currently comes from, and is likely to continue to come from, CMR-centric sales with major enterprises either direct or through relationships with major consultancies.

Nonetheless, AntWorks is essentially targeting three market segments:

Major enterprises with CMR-centric deals
RPA 2.0, through channels
Point solutions.

In the case of major enterprises, CMR is typically pulling AntWorks’ RPA products through to support the same use cases.

AntWorks is trying to dissociate itself from RPA 1.0, strongly positioning against the competition on the basis of “full stack”, and is slightly schizophrenic about whether to utilize a partner ecosystem which is already tied to the mainstream RPA products. Nonetheless, the company is in the early stages of building a partner ecosystem for its RPA product based on:

Referral partners
Authorized resellers
Managed Services Program, where partners such as EXL build their own solutions incorporating AntWorks
Technology Alliance partners
Authorized training partners
University partners, to develop up a critical mass of entry-level automation personnel with experience in AntWorks and Intelligent Automation in general.

Great Unstructured Data Accuracy but Needs to Continue to Enhance Ease of Use

A number of AntWorks’ clients presented at the event and it is clear that they perceive ANTstein to deliver superior capture and classification of unstructured data. In particular, clients liked the product’s:

Superior natural language-based classification using limited datasets
Ability to use codeless recorders
Ability to deliver greater than 70% accuracy at PoC stage

However, despite some the product’s advantages in terms of ease of use, clients would like further fine tuning of the product in areas such as:

The CMR UI/UX is not particularly user-friendly. The very long list of options is hard for business users to understand who require shorter more structured UI
Improved ease of workflow management including ability to connect to popular workflows.

So, overall, while users should not yet consider mass replacement of their existing RPAs, particularly where these are being used for simple rule-based process joins and data movement, ANTstein SQUARE is well worth evaluation by major organizations that have high-volume industry-specific or back-office processes involving multiple types of unstructured documents in text or handwritten form and where achieving accuracy of 75%+ will have a major impact on business outcomes. Here, and in the industry solutions being developed by AntWorks, it probably makes sense to use the full-stack of ANTstein utilizing both CMR and RPA functionality. In addition, CMR could be used in standalone form to facilitate extending an existing RPA-enabled process to handle large volumes of unstructured text.

Secondly, major organizations that have an outstanding major RPA roll-out to conduct at scale, are becoming frustrated at their level of bot productivity, and are prepared to introduce a new RPA technology should consider evaluating AntWorks' QueenBOT functionality.

The Challenge of Differentiating from RPA 1.0

If it is to take advantage of its current functionality, AntWorks urgently needs to differentiate its offerings from those of the established RPA software vendors and its founders are clearly unhappy with the company’s past positioning on the majority of analyst quadrants. The company aimed to achieve a turnaround of the analyst mindset by holding a relatively intimate event with a high level of interaction in the setting of the Maldives. No complaints there!

The company is also using “shapes” rather than numbers to designate succeeding versions of its software. Quirky and could be incomprehensible downstream.

However, these marketing actions are probably insufficient in themselves. To complement the merits of its software, the company needs to improve its messaging to its prospects and channel partners in a number of ways:

Firstly, the company’s tagline “reimagining, rethink, recreate” shows the founders’ backgrounds and is arguably more suitable for a services company than for a product company
Secondly, establishing an association with Intelligent Automation 2.0 and RPA 2.0 is probably too incremental to attract serious attention.

Here the company needs to think big and establish a new paradigm to signal a significant move beyond, and differentiation from, traditional RPA.

Application of RPA & AI to Unstructured Data Processing: The Next Big Milestone for Shared Services

Shared Services Centers (SSCs) have made progress in the initial application of RPA, gained some experience in its application, and are typically now looking to scale their use of RPA widely across their operations. However, although organizations have often undertaken some level of standardization and simplification of their processes to facilitate RPA adoption, one stumbling block that still frequently inhibits greater levels of automation and straight-through processing is an inability to process unstructured data. And this is limiting the value organizations are currently able to realize from automation initiatives.

NelsonHall recently interviewed 127 SSC executives across industries in the U.S. and Europe to understand the progress made in adopting RPA & AI, along with their satisfaction levels and future expectations. To quote from one executive interviewed, “I think the main strategy in the past has been to avoid unstructured data or pre-process it to make it structured. Now we are beginning to embrace the challenge of unstructured data and are growing an internal understanding of how to piece together automation.”

Low Satisfaction in Handling Unstructured Data Widespread in SSCs

This is an important next step. Unstructured data remains rife in organizations within customer and supplier emails and documents, with, for example, supplier invoices taking on a myriad of supplier-dependent formats and handwritten material far from extinct within customer applications.

This need to process unstructured data impacts not just mailroom document management, but a wide range of shared services processes. By industry sector, the processes that have a combination of high levels of unstructured data and a significant level of dissatisfaction with its capture and processing are:

Retail & Commercial Banking: new account set-up and customer service
P&C Insurance: fraud detection, claims processing, mailroom document management, policy maintenance, and customer service
Telecoms: customer service.

Within finance & accounting shared services, the same issues are found within supplier & catalog management, purchase invoice processing, and 3-way matching.

So, it is highly important that SSCs get to grips within handling unstructured documents and data within these process areas. However, this is unknown territory for many SSCs; they are typically in the early stages of automating handling of unstructured data and lack expertise in effective identification and implementation of suitable technologies. In addition, SSCs often lack the necessary experience in process change management and speed of process change when handling RPA & AI projects. Indeed, SSCs have often struggled in the early stages of automation with the challenge of realizing the expected cost savings from this technology. Applying automation is one thing, but realizing its benefits through effective process change management and ensuring that unexpected exceptions don’t derail the process and the associated cost realization, has sometimes been a significant issue.

Combining OCR & Machine Learning is Critical to Processing Unstructured Data

Accordingly, it is critical that SSCs now automate data classification and extraction from their unstructured documents. At present, 80% of SSCs across sectors are still manually classifying documents, with OCR only used modestly and not to its full potential. However, there are strong levels of intention to adopt OCR and RPA & AI technologies in support of processing unstructured data within SSCs during 2018 and 2019, as shown below:

SSCs are considering a broad range of technologies for processing unstructured data, with OCR clearly a key technology, but further supported by machine learning in its various forms for effective text classification and extraction. To quote from one executive interviewed, “We want to speed up deployment of automation within the mailroom, we want more OCR and natural language processing in place.”

Need for Improved Turnaround Times Now the Main Driving Force

However, in terms of benefits achievement, there is currently quite a significant difference between organizations’ current automation aspirations and what they have already achieved. While organizations placed a high initial emphasis within their automation initiatives on cost savings, and the achievement of cost savings remains very important to SSCs, the focus of executives within SSCs has now increasingly turned to improving process turnaround times.

Within the telecoms sector, this leads to a high expectation of improved customer satisfaction. However, executives with property & casualty and finance & accounting SSCs tend to attach an equal or higher importance to the impact of these technologies on employee satisfaction - by automating some of the least satisfying types of work within the organization, thus allowing personnel to focus on more added value aspects of the process (i.e. other than finding and entering data from customer documents and invoices).

The principal benefits sought by SSCs from implementing RPA & AI in support of processing of unstructured data are shown below:

70% of SSCs Highly Likely to Purchase Operational Service Covering Unstructured Data Processing

While automation is often depicted as having an adverse impact on the outsourcing industry, the reality is often quite the opposite, and organizations seek help in effectively deploying new digital technologies. Indeed, this is certainly the case with unstructured data processing.

SSCs will tend to implement unstructured data handling in-house where the information being handled is highly sensitive, where security is critically important, and where regulation or the set-up of internal systems inhibits use of a third-party service. However, elsewhere, where these constraints do not apply, SSC executives express a high level of intent to purchase external services in support of document classification and extraction of unstructured data. ~70% of SSCs are highly likely to purchase operational services for document processing, including document classification and extraction of unstructured data, while only a minority express a high intent to implement in-house or via a systems integrator.

NelsonHall conducts continuous research into all aspects of RPA and AI technologies and services as part of its RPA & Cognitive Services research program. A major report on RPA & AI Technology Evaluation by Dave Mayer has just been published, and coming soon is a major report on Business Process Transformation through RPA & AI by John Willmott. To find out more, contact Guy Saunders.

HCL's 3-Lever Approach to Business Process Automation: Risk & Control Analysis; Lean & Six Sigma; Cognitive Automation

HCL has undertaken ~200 use cases spanning finance & accounting, contact, product support and cross-industry customer onboarding, and claims processing, using products including Automation Anywhere, Blue Prism, UiPath, WorkFusion, and HCL’s proprietary AI tool Exacto.

This blog summarizes NelsonHall’s analysis of HCL's approach to Business Process Automation covering HCL’s 3-lever approach, its Integrated Process Discovery Technique, its AI-based information extraction tool Exacto, the company’s offerings for intelligent product support, and its use of its Toscana BPMS to drive retail banking digital transformation.

3-Lever Approach Combining Risk & Control Analysis, Lean & Six Ssigma, and Cognitive Automation

The 3 lever approach forms HCL’s basis for any “strategic automation intervention in business processes”. The automation is done using third-party RPA technologies together with a number of proprietary HCL tools including Exacto, a cutting-edge Computer Vision and Machine Learning based tool, and iAutomate for run book automation
HCL starts by conducting a 3-lever automation study and then creates comprehensive to-be process maps. As part of this 3-lever study, HCL also conducts complexity analysis to create the RPA and AI roadmap for organizations using its process discovery toolkit. For example, HCL has looked at their entire process repository for several major banks and classified their business processes into four quadrants based on scale and level of standardization
When generating the “to be” process map, HCL’s Integrated Process Discovery Technique places a high emphasis on ensuring appropriate levels of compliance for the automated processes and on avoiding the automation of process steps that can be eliminated
The orchestration of business processes is being done using HCL’s proprietary orchestration platform, Toscana©. Toscana© supports collaboration, analytics, case management, and process discovery and incorporates a content manager, a business rules management system, a process simulator, a process modeler, process execution engines, and integrated offering including social media monitoring & management.

Training Exacto AI-based Information Extraction Tool for Document Triage within Trade Processing, Healthcare, Contract Processing, and Invoice Processing

HCL’s proprietary AI enabled, machine learning solution, Exacto, is used to automatically extract and interpret information from a variety of information sources. It also has natural language and image based automated knowledge extraction capabilities
HCL has partnered with a leading U.S. University to develop its own AI algorithms for intelligent data extraction and interpretation for solving industry level problems, including specialist algorithms in support of trade processing, contract management, healthcare document triage, KYC, and invoice processing
Trade processing is one of the major areas of focus for HCL. Within capital markets trade capture, HCL has developed an AI/ML solution Exacto | Trade. This solution is able to capture inputs from incoming fax based transaction instructions for various trade classes such as Derivatives, FX, Margins, etc. with accuracy of over 99%.

Combining Watson-based Cognitive Agent with Run Book Automation to Provide “Intelligent Product Support”

HCL has developed a cognitive solution for Intelligent Product Support based on a cognitive agent LUCY, Intelligent Autonomics using for run book automation, and Smart Analytics with MyXalytics for dashboards and predictive analytics. LUCY is currently being used in support for IT services by major CPG, pharmaceuticals, and high-tech firms and in support of customer service for a major bank and a telecoms operator
HCL’s tool is used for run book automation, and HCL has already automated 1,500+ run books. uses NLP, ML, pattern matching, and text processing to recommend the “best matched” for a given ticket description. HCL estimates that it currently achieves “match rates” of around 87%-88%
HCL estimates that it can automate 20%-25% of L1 and L2 transactions and has begun automating internal IT infrastructure help-desks.

Positioning its Toscana Platform to Drive Digital Transformation in Retail Banking

HCL is embarking on digital transformation through this approach and has created predefined domain-specific templates in areas including retail banking, commercial lending, mortgages, and supply chain management. Within account opening for a bank, HCL has achieved ~ 80% reduction in AHT and a 40% reduction in headcount
In terms of bank automation, HCL has, for one major bank, reduced the absolute number of FTEs associated with card services by 48%, a 63% decrease based on the accompanying increase in the workload. Elsewhere, for another bank, HCL has undertaken a digital transformation including implementation of Toscana©, resulting in a reduction of the number of FTEs by 46%, the implementation of a single view of the customer, a reduction in cycle time of 80%, and a reduction in the “rejection rate” from 12% to 4%.

Amelia Enhances its Emotional, Contextual, and Process Intelligence to Outwit Chatbots

IPSoft's Amelia

NelsonHall recently attended the IPSoft analyst event in New York, with a view to understanding the extent to which the company’s shift into customer service has succeeded. It immediately became clear that the company is accelerating its major shift in focus of recent years from autonomics to cognitive agents. While IPSoft began in autonomics in support of IT infrastructure management, and many Amelia implementations are still in support of IT service activities, IPSoft now clearly has its sights on the major prize in the customer service (and sales) world, positioning its Amelia cognitive agent as “The Most Human AI” with much greater range of emotional, contextual, and process “intelligence” than the perceived competition in the form of chatbots.

Key Role for AI is Human Augmentation Not Human Replacement

IPSoft was at pains to point out that AI was the future and that human augmentation was a major trend that would separate the winners from the losers in the corporate world. In demonstrating the point that AI was the future, Nick Bostrom from the Future of Humanity Institute at Oxford University discussed the result of a survey of ~300 AI experts to identify the point at which high-level machine intelligence, (the point at which unaided machines can accomplish any task better and more cheaply than human workers) would be achieved. This survey concluded that there was a 50% probability that this will be achieved within 50-years and a 25% probability that it will happen within 20-25 years.

On a more conciliatory basis, Dr. Michael Chui suggested that AI was essential to maintaining living standards and that the key role for AI for the foreseeable future was human augmentation rather than human replacement.

According to McKinsey Global Institute (MGI), “about half the activities people are paid almost $15tn in wages to do in the global economy have the potential to be automated by adapting currently demonstrated technology. While less than 5% of all occupations can be automated entirely, about 60% of all occupations have at least 30% of constituent activities that could be automated. More occupations will change than can be automated away.”

McKinsey argues that automation is essential to maintain GDP growth and standards of living, estimating that of the 3.5% per annum GDP growth achieved on average over the past 50 years, half was derived from productivity growth and half from growth in employment. Assuming that growth in employment will largely cease as populations age over the next 50 years, then an increase/approximate doubling in automation-driven productivity growth will be required to maintain the historical levels of GDP growth.

Providing Empathetic Conversations Rather than Transactions

The guiding principles behind Amelia are to provide conversations rather than transactions, to understand customer intent, and to deliver a to-the-point and empathetic response. Overall, IPSoft is looking to position Amelia as a cognitive agent at the intersection of systems of engagement, systems of record, and data platforms, incorporating:

Conversational intelligence, encompassing intelligent understanding, empathetic response, & multi-channel handling. IPSoft has recently added additional machine learning and DEEP learning
Advanced analytics, encompassing performance analytics, decision intelligence, and data visualization
Smart workflow, encompassing dynamic process execution and integration hub, with UI integration (planned)
Experience management, to ensure contextual awareness
Supervised automated learning, encompassing automated training, observational learning, and industry solutions.

For example, it is possible to upload documents and SOPs in support of automated training and Amelia will advise on the best machine learning algorithms to be used. Using supervised learning, Amelia submits what it has learned to the SME for approval but only uses this new knowledge once approved by the SME to ensure high levels of compliance. Amelia also learns from escalations to agents and automated consolidation of these new learnings will be built into the next Amelia release.

IPSoft is continuing to develop an even greater range of algorithms by partnering with universities. These algorithms remain usable across all organizations with the introduction of customer data to these algorithms leading to the development of client-specific customer service models.

Easier to Teach Amelia Banking Processes than a New Language

An excellent example of the use of Amelia was discussed by a Nordic bank. The bank initially applied Amelia to its internal service desk, starting with a pilot in support of 600 employees in 2016 covering activities such as unlocking accounts and password guidance, before rolling out to 15,000 employees in Spring 2017. This was followed by the application of Amelia to customer service with a silent launch taking place in December 2016 and Amelia being rolled out in support of branch office information, booking meetings, banking terms, products and services, mobile bank IDs, and account opening. The bank had considered using offshore personnel but chose Amelia based on its potential ability to roll-out in a new country in a month and its 24x7 availability. Amelia is currently used by ~300 customers per day over chat.

The bank was open about its use of AI with its customers on its website, indicating that its new chat stream was based on the use of “digital employees with artificial intelligence”. The bank found that while customers, in general, seemed pleased to interact via chat, less expectedly, use of AI led to totally new customer behaviors, both good and bad, with some people who hated the idea of use of robots acting much more aggressively. On the other hand, Amelia was highly successful with individuals who were reluctant to phone the bank or visit a bank branch.

Key lessons learnt by the bank included:

The high level of acceptance of Amelia by customer service personnel who regarded Amelia as taking away boring “Monday-morning” tasks allowing them to focus on more meaningful conversations with customers rather than threatening their livelihoods
It was easier than expected to teach Amelia the banking processes, but harder than expected to convert to a new language such as Swedish, with the bank perceiving that each language is essentially a different way of thinking. Amelia was perceived to be optimized for English and converting Amelia to Swedish took three months, while training Amelia on the simple banking processes took a matter of days.

Amelia is now successfully handling ~90% of requests, though ~30% of these are intentionally routed to a live agent for example for deeper mortgage discussions.

Amelia Avatar Remains Key to IPSoft Branding

While the blonde, blue-eyed nature of the Amelia avatar is likely to be highly acceptable in Sweden, this stereotype could potentially be less acceptable elsewhere and the tradition within contact centers is to try to match the nature of the agent with that of the customer. While Amelia is clearly designed to be highly empathetic in terms of language, it may be more discordant in terms of appearance.

However, the appearance of the Amelia avatar remains key to IPSoft’s branding. While IPSoft is redesigning the Amelia avatar to capture greater hand and arm movements for greater empathy, and some adaptation of clothing and hairstyle are permitted to reflect brand value, IPSoft is not currently prepared to allow fundamental changes to gender or skin color, or to allow multiple avatars to be used to develop empathy with individual customers. This might need to change as IPSoft becomes more confident of its brand and the market for cognitive agents matures.

Partnering with Consultancies to Develop Horizontal & Vertical IP

At present, Amelia is largely vanilla in flavor and the bulk of implementations are being conducted by IPSoft itself. IPSoft estimates that Amelia has been used in 50 instances, covering ~60% of customer requests with ~90% accuracy and, overall, IPSoft estimates that it takes 6-months to assist an organization to build an Amelia competence in-house, 9-days to go-live, and 6-9 months to scale up from an initial implementation.

Accordingly, it is key to the future of IPSoft that Amelia can develop a wide range of semi-productized horizontal and vertical use cases and that partners can be trained and leveraged to handle the bulk of implementations.

At present, IPSoft estimates that its revenues are 70:30 services:product, with product revenues growing faster than services revenues. While IPSoft is currently carrying out the majority (~60%) of Amelia implementations itself, it is increasingly looking to partner with the major consultancies such as Accenture, Deloittes, PwC, and KPMG to build baseline Amelia products around horizontals and industry-specific processes, for example, working with Deloittes in HR. In addition, IPSoft has partnered with NTT in Japan, with NTT offering a Japanese-language, cloud-based virtual assistant, COTOHA.

IPSoft’s pricing mechanisms consist of:

A fixed price per PoC development
Production environments: charge for implementation followed by a price per transaction.

While Amelia is available in both cloud and onsite, IPSoft perceives that the major opportunities for its partners lie in highly integrated implementations behind the client firewall.

In conclusion, IPSoft is now making considerable investments in developing Amelia with the aim of becoming the leading cognitive agent for customer service and the high emphasis on “conversations and empathic responses” differentiates the software from more transactionally-focused cognitive software.

Nonetheless, it is early days for Amelia. The company is beginning to increase its emphasis on third-party partnerships which will be key to scaling adoption of the software. However, these are currently focused around the major consultancies. This is fine while cognitive agents are in the first throes of adoption but downstream IPSoft is likely to need the support of, and partnerships with the major contact center outsourcers who currently control around a third of customer service spend and who are influential in assisting organizations in their digital customer service transformations.

RPA Operating Model Guidelines, Part 3: From Pilot to Production & Beyond – The Keys to Successful RPA Deployment

As well as conducting extensive research into RPA and AI, NelsonHall is also chairing international conferences on the subject. In July, we chaired SSON’s second RPA in Shared Services Summit in Chicago, and we will also be chairing SSON’s third RPA in Shared Services Summit in Braselton, Georgia on 1st to 2nd December. In the build-up to the December event we thought we would share some of our insights into rolling out RPA. These topics were the subject of much discussion in Chicago earlier this year and are likely to be the subject of further in-depth discussion in Atlanta (Braselton).

This is the third and final blog in a series presenting key guidelines for organizations embarking on an RPA project, covering project preparation, implementation, support, and management. Here I take a look at the stages of deployment, from pilot development, through design & build, to production, maintenance, and support.

Piloting & deployment – it’s all about the business

When developing pilots, it’s important to recognize that the organization is addressing a business problem and not just applying a technology. Accordingly, organizations should consider how they can make a process better and achieve service delivery innovation, and not just service delivery automation, before they proceed. One framework that can be used in analyzing business processes is the ‘eliminate/simplify/standardize/automate’ approach.

While organizations will probably want to start with some simple and relatively modest RPA pilots to gain quick wins and acceptance of RPA within the organization (and we would recommend that they do so), it is important as the use of RPA matures to consider redesigning and standardizing processes to achieve maximum benefit. So begin with simple manual processes for quick wins, followed by more extensive mapping and reengineering of processes. Indeed, one approach often taken by organizations is to insert robotics and then use the metrics available from robotics to better understand how to reengineer processes downstream.

For early pilots, pick processes where the business unit is willing to take a ‘test & learn’ approach, and live with any need to refine the initial application of RPA. Some level of experimentation and calculated risk taking is OK – it helps the developers to improve their understanding of what can and cannot be achieved from the application of RPA. Also, quality increases over time, so in the medium term, organizations should increasingly consider batch automation rather than in-line automation, and think about tool suites and not just RPA.

Communication remains important throughout, and the organization should be extremely transparent about any pilots taking place. RPA does require a strong emphasis on, and appetite for, management of change. In terms of effectiveness of communication and clarifying the nature of RPA pilots and deployments, proof-of-concept videos generally work a lot better than the written or spoken word.

Bot testing is also important, and organizations have found that bot testing is different from waterfall UAT. Ideally, bots should be tested using a copy of the production environment.

Access to applications is potentially a major hurdle, with organizations needing to establish virtual employees as a new category of employee and give the appropriate virtual user ID access to all applications that require a user ID. The IT function must be extensively involved at this stage to agree access to applications and data. In particular, they may be concerned about the manner of storage of passwords. What’s more, IT personnel are likely to know about the vagaries of the IT landscape that are unknown to operations personnel!

Reporting, contingency & change management key to RPA production

At the production stage, it is important to implement a RPA reporting tool to:

Monitor how the bots are performing
Provide an executive dashboard with one version of the truth
Ensure high license utilization.

There is also a need for contingency planning to cover situations where something goes wrong and work is not allocated to bots. Contingency plans may include co-locating a bot support person or team with operations personnel.

The organization also needs to decide which part of the organization will be responsible for bot scheduling. This can either be overseen by the IT department or, more likely, the operations team can take responsibility for scheduling both personnel and bots. Overall bot monitoring, on the other hand, will probably be carried out centrally.

It remains common practice, though not universal, for RPA software vendors to charge on the basis of the number of bot licenses. Accordingly, since an individual bot license can be used in support of any of the processes automated by the organization, organizations may wish to centralize an element of their bot scheduling to optimize bot license utilization.

At the production stage, liaison with application owners is very important to proactively identify changes in functionality that may impact bot operation, so that these can be addressed in advance. Maintenance is often centralized as part of the automation CoE.

Find out more at the SSON RPA in Shared Services Summit, 1st to 2nd December

NelsonHall will be chairing the third SSON RPA in Shared Services Summit in Braselton, Georgia on 1st to 2nd December, and will share further insights into RPA, including hand-outs of our RPA Operating Model Guidelines. You can register for the summit here.

Also, if you would like to find out more about NelsonHall’s expensive program of RPA & AI research, and get involved, please contact Guy Saunders.

Plus, buy-side organizations can get involved with NelsonHall’s Buyer Intelligence Group (BIG), a buy-side only community which runs regular webinars on RPA, with your buy-side peers sharing their RPA experiences. To find out more, contact Matthaus Davies.

This is the final blog in a three-part series. See also:

Part 1: How to Lay the Foundations for a Successful RPA Project

Part 2: How to Identify High-Impact RPA Opportunities

HCL: Applying RPA to Reduce Customer Touch Points in Closed Book Life Insurance

This is the third in a series of blogs looking at how business process outsourcing vendors are applying RPA in the insurance sector.

HCL provides closed book life insurance outsourcing services, and is currently engaged in RPA initiatives with three insurance clients.

In order to capture customer data in a smarter, more concise way, HCL is using ‘enhancers’ at the front end, providing users with intuitive screens based on the selected administrative task. These input forms aim to request only the minimum, necessary data required with RPA now being used to transfer the data to the insurance system, ALPS, via a set of business rules.

For example, one RPA implementation undertaken can recognize the product type, policy ownership, values, and payment methods, and it can prepare and produce correspondence for the customer. If all rules are met, it is then able to move onto payment on the due date. This has been done with a view to reducing the number of touchpoints and engaging with the customer only when required. Indeed, HCL is working with its clients to devise a more exhaustive set of risk-based rules to further reduce the extent to which information needs to be gathered from customers.

Seeking a 25% cost take-out in high volume activities

On average, 11k customer enquiries are received by one HCL insurance contact center every month, and these were traditionally handed off to the back office to be resolved. However, HCL is now using RPA and business rules to enable more efficient handling of enquires/claims with limited user input, with the aim of creating capacity for an additional 4.4k customer queries per month to be handled within the contact center.

Overall, within its insurance operations, HCL is applying RPA-based business rules to ~10 core process areas that together amount to around 60% of typical day-to-day activity. These process areas include:

Payments out, including maturities, surrenders, and transfers
Client information, including change of address or, account information
Illustrations.

These processes are typically carried out by an offshore team and the aspiration is to reduce the effort taken to complete each of them by ~25%. In addition, HCL expects that capturing customer data in this new way will shorten the end-to-end journey by between 5% and 10%.

One lesson learned has been the need for robust and compatible infrastructure, both internally (ensuring that all systems and platforms are operating on the same network), and with respect to client infrastructure; e.g. ensuring that HCL is using the same version of Microsoft or Internet Explorer as the client environment.

RPA Operating Model Guidelines, Part 2: How to Identify High-Impact RPA Opportunities

This is the second in a series of blogs presenting key guidelines for organizations embarking on an RPA project, covering project preparation, implementation, support, and management. Here I take a look at how to assess and prioritize RPA opportunities prior to project deployment.

Prioritize opportunities for quick wins

An enterprise level governance committee should be involved in the assessment and prioritization of RPA opportunities, and this committee needs to establish a formal framework for project/opportunity selection. For example, a simple but effective framework is to evaluate opportunities based on their:

Potential business impact, including RoI and FTE savings
Level of difficulty (preferably low)
Sponsorship level (preferably high).

The business units should be involved in the generation of ideas for the application of RPA, and these ideas can be compiled in a collaboration system such as SharePoint prior to their review by global process owners and subsequent evaluation by the assessment committee. The aim is to select projects that have a high business impact and high sponsorship level but are relatively easy to implement. As is usual when undertaking new initiatives or using new technologies, aim to get some quick wins and start at the easy end of the project spectrum.

However, organizations also recognize that even those ideas and suggestions that have been rejected for RPA are useful in identifying process pain points, and one suggestion is to pass these ideas to the wider business improvement or reengineering group to investigate alternative approaches to process improvement.

Target stable processes

Other considerations that need to be taken into account include the level of stability of processes and their underlying applications. Clearly, basic RPA does not readily adapt to significant process change, and so, to avoid excessive levels of maintenance, organizations should only choose relatively stable processes based on a stable application infrastructure. Processes that are subject to high levels of change are not appropriate candidates for the application of RPA.

Equally, it is important that the RPA implementers have permission to access the required applications from the application owners, who can initially have major concerns about security, and that the RPA implementers understand any peculiarities of the applications and know about any upgrades or modifications planned.

The importance of IT involvement

It is important that the IT organization is involved, as their knowledge of the application operating infrastructure and any forthcoming changes to applications and infrastructure need to be taken into account at this stage. In particular, it is important to involve identity and access management teams in assessments.

Also, the IT department may well take the lead in establishing RPA security and infrastructure operations. Other key decisions that require strong involvement of the IT organization include:

Identity security
Ownership of bots
Ticketing & support
Selection of RPA reporting tool.

Find out more at the SSON RPA in Shared Services Summit, 1st to 2nd December

Also, if you would like to find out more about NelsonHall’s expensive program of RPA & AI research, and get involved, please contact Guy Saunders.

Plus, buy-side organizations can get involved with NelsonHall’s Buyer Intelligence Group (BIG), a buy-side only community which runs regular webinars on sourcing topics, including the impact of RPA. The next RPA webinar will be held later this month: to find out more, contact Guy Saunders.

In the third blog in the series, I will look at deploying an RPA project, from developing pilots, through design & build, to production, maintenance, and support.

RPA Operating Model Guidelines, Part 1: Laying the Foundations for Successful RPA

This is the first in a series of blogs presenting key guidelines for organizations embarking on RPA, covering establishing the RPA framework, RPA implementation, support, and management. First up, I take a look at how to prepare for an RPA initiative, including establishing the plans and frameworks needed to lay the foundations for a successful project.

Getting started – communication is key

Essential action items for organizations prior to embarking on their first RPA project are:

Preparing a communication plan
Establishing a governance framework
Establishing a RPA center-of-excellence
Establishing a framework for allocation of IDs to bots.

Communication is key to ensuring that use of RPA is accepted by both executives and staff alike, with stakeholder management critical. At the enterprise level, the RPA/automation steering committee may involve:

COOs of the businesses
Enterprise CIO.

Start with awareness training to get support from departments and C-level executives. Senior leader support is key to adoption. Videos demonstrating RPA are potentially much more effective than written papers at this stage. Important considerations to address with executives include:

How much control am I going to lose?
How will use of RPA impact my staff?
How/how much will my department be charged?

When communicating to staff, remember to:

Differentiate between value-added and non value-added activity
Communicate the intention to use RPA as a development opportunity for personnel. Stress that RPA will be used to facilitate growth, to do more with the same number of people, and give people developmental opportunities
Use the same group of people to prepare all communications, to ensure consistency of messaging.

Establish a central governance process

It is important to establish a strong central governance process to ensure standardization across the enterprise, and to ensure that the enterprise is prioritizing the right opportunities. It is also important that IT is informed of, and represented within, the governance process.

An example of a robotics and automation governance framework established by one organization was to form:

An enterprise robotics council, responsible for the scope and direction of the program, together with setting targets for efficiency and outcomes
A business unit governance council, responsible for prioritizing RPA projects across departments and business units

A RPA technical council, responsible for RPA design standards, best practice guidelines, and principles.

Avoid RPA silos – create a centre of excellence

RPA is a key strategic enabler, so use of RPA needs to be embedded in the organization rather than siloed. Accordingly, the organization should consider establishing a RPA center of excellence, encompassing:

A centralized RPA & tool technology evaluation group. It is important not to assume that a single RPA tool will be suitable for all purposes and also to recognize that ultimately a wider toolset will be required, encompassing not only RPA technology but also technologies in areas such as OCR, NLP, machine learning, etc.
A best practice for establishing standards such as naming standards to be applied in RPA across processes and business units
An automation lead for each tower, to manage the RPA project pipeline and priorities for that tower
IT liaison personnel.

Establish a bot ID framework

While establishing a framework for allocation of IDs to bots may seem trivial, it has proven not to be so for many organizations where, for example, including ‘virtual workers’ in the HR system has proved insurmountable. In some instances, organizations have resorted to basing bot IDs on the IDs of the bot developer as a short-term fix, but this approach is far from ideal in the long-term.

Organizations should also make centralized decisions about bot license procurement, and here the IT department which has experience in software selection and purchasing should be involved. In particular, the IT department may be able to play a substantial role in RPA software procurement/negotiation.

Find out more at the SSON RPA in Shared Services Summit, 1st to 2nd December

Also, if you would like to find out more about NelsonHall’s extensive program of RPA & AI research, and get involved, please contact Guy Saunders.

Plus, buy-side organizations can get involved with NelsonHall’s Buyer Intelligence Group (BIG), a buy-side only community which runs regular webinars on sourcing topics, including the impact of RPA. The next RPA webinar will be held in November: to find out more, contact Matthaus Davies.

In the second blog in this series, I will look at RPA need assessment and opportunity identification prior to project deployment.

TCS Leapfrogging RPA & as-a-Service with Neural Automation & Services-as-Software

Much of the current buzz in the industry continues to be centered on RPA, a term currently largely synonymous with automation, and this technology clearly has lots of life left in it, for a few years at least. Outside service providers, where its adoption is rapidly becoming mature, RPA is still at the early growth stage in the wider market: while a number of financial services firms have already achieved large-scale roll-outs of RPA, others have yet to put their first bot into operation.

RPA is a great new technology and one that is yet to be widely deployed by most organizations. Nonetheless, RPA fills one very specific niche and remains essentially a band-aid for legacy processes. It is tremendous for executing on processes where each step is clearly defined, and for implementing continuous improvement in relatively static legacy process environments. However, RPA, as TCS highlights, does have the disadvantages that it fails to incorporate learning and can really only effectively be applied to processes that undergo little change over time. TCS also argues that RPA fails to scale and fails to deliver sustainable value.

These latter criticisms seem unfair in that RPA can be applied on a large scale, though frequently scale is achieved via numerous small implementations rather than one major implementation. Similarly, provided processes remain largely unchanged, the value from RPA is sustained. The real distinction is not scalability but the nature of the process environment in which the technology is being applied.

Accordingly, while RPA is great for continuous improvement within a static legacy process environment where processes are largely rule-based, it is less applicable for new business models within dynamic process environments where processes are extensively judgment-based. New technologies with built-in learning and adaptation are more applicable here. And this is where TCS is positioning Ignio.

TCS refers to Ignio as a “neural automation platform” and as a “Services-as-Software” platform, the latter arguably a much more accurate description of the impact of digital on organizations than the much-copied Accenture “as-a-Service” expression.

TCS summarizes Ignio as having the following capabilities:

“Sense”: ability to assimilate and mine diverse data sources, both internal and external, both structured and unstructured (via text mining techniques)
“Think”: ability to identify trends & patterns and make predictions and estimate risk
“Act”: execute context-aware autonomous actions. Here TCS could potentially have used one of the third-party RPA software products, but instead chose to go with their own software instead
“Learn”: improving its knowledge on a continuous basis and self-learning its context.

TCS Ignio, like IPsoft Amelia, began life as a tool for supporting IT infrastructure management, specifically datacenter operations. TCS Ignio was launched in May 2015 and is currently used by ten organizations, which includes Nationwide Building Society in the U.K. All ten are using Ignio in support of their IT operations, though the scope of its usage remains limited at present, with Ignio being used within Nationwide in support of batch performance and capacity management. Eventually the software is expected to be deployed to learn more widely about the IT environment and predict and resolve IT issues, and Ignio is already being used for patch and upgrade management by one major financial services institution.

Nonetheless, despite its relatively low level of adoption so far within IT operations, TCS is experiencing considerable wider interest in Ignio and feels it should strike while the iron is hot and take Ignio out into the wider business process environment immediately.

The implications are that the Ignio roll-out will be rapid (expect to see the first public example in the next quarter) and will take place domain by domain, as for RPA, with initial targeted areas likely to include purchase-to-pay and order-to-cash within F&A and order management-related processes within supply chain. In order to target each specific domain, TCS is pre-building “skills” which will be downloadable from the “Ignio store”. One of the initial implementations seems likely to be supporting a major retailer in resolving the downstream implications of delivery failures due to causes such as traffic accidents or weather-related incidents. Other potential supply chain-related applications cited for Ignio include:

Customer journey abandonment
The profiling, detection, and correction of check-out errors
Profiling, detecting, and correcting anomalies in supplier behavior
Detection of customer feedback trends and triggering corrective action
Profiling and predicting customer behavior.

Machine learning technologies are receiving considerable interest right now and TCS, like other vendors, recognizes that rapid automation is being driven faster than ever before by the desire for competitive survival and differentiation, and in response is adopting a “if it can be automated, it must be automated” stance. And the timescales for implementation of Ignio, cited at 4-6 weeks, are comparable to that for RPA. So Ignio, like RPA, is a relatively quick and inexpensive route to process improvement. And, unlike many cognitive applications, it is targeted strongly at industry-specific and back office processes and not just customer-facing ones.

Accordingly, while RPA will remain a key technology in the short-term for fixing relatively static legacy rule-based processes, next generation machine learning-based “Services-as-Software” platforms such as Ignio will increasingly be used for judgment-based processes and in support of new business models. And TCS, which a year ago was promoting RPA, is now leading with its Ignio neural automation-based “Services-as-Software” platform.