In August, NelsonHall spoke with the leadership of Movate AI to discuss the market expectations and challenges of AI integration, their in-house holistic framework, target applications, existing deployments, and how the company plans to build commercial relationships around this AI reinvention model.

From platform to framework to dedicated AI unit

Like most BPS players, Movate has been actively integrating AI and, over the last two years, GenAI into its existing technology stack; examples include the intelligent automation platform Movate Contelli, the CX transformation platform Movate Edison, and the business intelligence platform Movate Insights. The company developed Movate Athena, a modular plug-and-play AI + data framework that helps enterprises create an AI strategy, build advanced AI capabilities, and aims to transform them into a data-driven, AI-first business.

The Movate Athena framework aims to optimize enterprise functions such as sales, marketing, HR, IT, and customer service. It looks to support different internal and external stakeholders, such as customers, partners, and employees. Its ultimate goal is to purposefully enable experiences to improve CX and grow customer lifetime value. All these elements are supported by the company’s digital product engineering resources and its network of transformation partnerships (e.g., Salesforce Einstein, Databricks, Uniphore, Conversica, NICE, and others).

In August 2024, Movate launched a dedicated AI-first IT development and BPS technology integration subsidiary, Movate AI Inc. This is the last stage of the evolution for Movate AI. The subsidiary has its own P&L, separate branding, and new leadership, headed by the company CTO, Gourishanker Jha.

Bumps on the road to a promising AI future

While enterprises’ interest in GenAI capabilities has not subsided over the last 18 months, actual deployments at scale face many expected and new challenges. Fragmented corporate structures and functional silos, legacy technology, low data quality and data overload, change management and people upskilling, and even digital fatigue are all commonplace in technology-led enterprise transformation. Other common hurdles, such as cost, ROI, and innovation budgeting, have been exacerbated by the breakneck speed of GenAI iteration. The new potential roadblocks come from inherent risks within LLMs, with AI seed issues such as discrimination and bias, misinformation and disinformation, privacy and trust, and overall governance and accountability to deliver responsible AI. Adding on top security and regulatory risks, it is reasonable to suggest that enterprise adoption of GenAI is not replacing human-led processes – yet.

Movate AI looks to minimize and avoid these challenges through its North Star design thinking tenets. These guiding principles for delivering enterprise-wide use cases cover the end-to-end process:

• At the Discover stage, Movate AI consultants identify personas and journeys, processes and workflows data, platforms, technology, channels, and experiences
• During the Assimilate stage, the solution teams develop customer journeys and use cases, consider the AI and data perspectives and viewpoints, and design the data and AI acceleration levers
• The Define stage is where all the standards, best practices, governance models, roadmaps and prioritizations, tools, employee training, and target state operating models are developed
• Realize is the execution from licensing to prototyping and eventual development, testing, implementation, and ongoing maintenance
• Finally, the Train and Optimize step is for audits, data cleansing and enrichment, RAG, model tuning, and optimization.

Next, Movate AI will organize the use case by vertical and horizontal dimensions. For example, automated code review and optimization and AI-powered code generation and completion for technology AI-guided software engineering; or CX agent assist in telecom customer churn prediction and prevention.

The best AI enablement ecosystem

Movate AI will source from existing company capabilities in digital services, CX services, and insights. In CX, the company’s vision for AI-enablement has quite a few enablers:

• User-centric design and personalization
• Personalized omnichannel CX continuity
• Context, proactive engagement at speed
• Multi-brand governance
• Asset creation
• Translation – internationalization and localization
• Asynchronous messaging
• Universal search and self-service.

While all these levers can be treated as the end targets of the Movate AI framework in CX, the company already integrates its existing GenAI-based tools into live client projects. For example, it accelerated payment terminal business inquiries with a GenAI-based chatbot as part of the larger CX transformation for a payment client, where it consolidated global teams and standardized support processes; implemented Amazon Connect for global voice and chat, deploying five chatbots; launched GenAI for digital assets, automated customer interactions, and integrated Salesforce with Atlassian for improved data flow.

Movate also developed a smart case manager for intelligent routing, quality bots for feedback collection, and standardized CSAT tracking for all interactions. With this unified transformation approach, Movate delivered 20% deflection of issues and 50% automation of installation bookings, boosting back-office productivity by 30%. It enhanced self-service capabilities, unified portal access, and reached a 20% reduction in TCO.

In IT services, for a technology company with ~160k employees and ~140k vendor partners worldwide, Movate implemented a global service desk where it co-created AI solutions, provided operational insights, and recommended AI use cases. It also implemented proactive monitoring and self-healing, enhanced AI accuracy in ticket creation, routing, and knowledge recommendations for effective performance measurement, and tested and rated the developer copilot’s performance on multiple parameters. Among the achieved results were 98% accuracy in ticket routing, 95% in problem ticket creation, 85% success rate in recommending relevant knowledge articles, and improved engineers’ productivity by ~25%.

Success stories dependent on outcome-based flex pricing

While Movate AI looks to solve for current client uncertainties around GenAI with its two-week service assessment to craft an AI/GenAI adoption roadmap and boasts ~400 reusable
accelerator templates, the long-term question is about the sustainable commercial relationship between BPS vendors and clients. There are fundamental questions around what business goals organizations are willing to pay for AI implementations today. And who owns the outcome? In 2024, the common answer to the first is productivity improvements, while the second remains unanswered. For example, who gets paid for the future self-service content created by GenAI?

These convenient pricing models attuned to business needs can balance the client and vendor interests if they have a very well-defined outcome-based structure and executive buy-in. Movate has for several years worked on these flexible pricing models and has case studies such as a U.S. telecom for which it created a hybrid delivery model leveraging automation, FTEs, and on-demand experts to achieve a 25% TCO reduction with pay-per-resolution pricing. Taking into account the GenAI opportunities as well as uncertainties, the complexity of designing and selling to the client stakeholders increases exponentially. A positive development is a market maturing towards acceptance of the required ‘skin in the game’ for both sides.

Movate’s AI roadmaps

Movate has several immediate AI roadmaps, ranging from bringing to production the existing ~100 AI POCs and prototypes to injecting the framework into a prospect’s sales process. From an operational perspective, it plans to create ‘super’ agents and engineers with virtual assistants and copilots (e.g., autonomous quality engineering) and expand to other internal functions such as Finance and HR. Other medium-term AI plans include the CX enablers mentioned above, utilizing the full potential of the underlying client data for continuous insight services in the new economy landscape and reaching Gen 5 operations with predictive maintenance and support.