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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.

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