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Generative AI is going to change the services we provide, how we work and how we compete. With the help of our platform-based approach, we’re currently involved in early deployments of the transformational technology across the world.

Wherever there’s knowledge work, there’s potential for gen AI. By 2026, over 100 million humans will engage with gen AI according to Gartner, using tools like Microsoft 365 Copilot to contribute to enterprise work. From what we see, many companies have now moved beyond the initial phase and begin to address longer-term, enterprise-scale implications. Enterprise deployments typically address:

  • General productivity like code analysis, workflow management and semantic search
  • Business specific areas like call center automation, product prototyping and claims management
  • Domain specific areas like HR, finance, and marketing

We’re engaged in promising gen AI programs all around the globe, launching proofs of concept, deploying automation tools, testing broader hypothesis in data science, and offering a glimpse into the future relationship between technology, employee, and customer. Among the projects, we have seen significant focus across the order-to-cash lifecycle, the software development lifecycle, the procurement-to-pay lifecycle, and the case management lifecycle (e.g. customer support, claims processing, technology operations, corporate shared services, etc.).

Start with software engineering

While we are seeing promising results with initial gen AI prototypes that perform well at small scale, it is much more challenging to deliver the quality, consistency and reliability needed for customer-facing and mission critical processes at scale. According to my experience, succeeding requires fine-tuning over time, and needs the ability to track, tweak and re-deploy models in a continuous cycle. For this reason, we are seeing organizations proceeding with caution in direct, customer-facing use cases and looking towards internal applications to build gen AI experience and capability.

Where should a business start then? Augmenting the delivery of tech-enabled change should be a high priority to any company. This is for four reasons:

  1. Tech-enabled change is strategically important for all large organizations, enabling better business performance and business agility.
  2. Gen AI tooling for tasks like software engineering is relatively mature and can be quickly deployed.
  3. Change delivery professionals like software engineers are expert users, who well understand the limitations of gen AI and can effectively validate AI suggestions.
  4. Resources freed up by productivity gains across delivery management, design, engineering, and quality can be directly re-purposed to explore other AI opportunities.

When business start in the area of software development, they are able to create high impact gen AI programs that progressively build momentum through re-investing gains in further development.

Modern, platform-based approach

At Cognizant, we have a platform-based approach to AI adoption in software engineering:

  • Flowsource is our full-stack engineering platform that integrates, orchestrates, and automates workflows. It accelerates engineering productivity by up to 40 percent and enhances the value of gen AI coding companions such as GitHub Copilot by fully integrating them with developer experience and DevOps automation solutions.
  • Neuro AI is our platform for accelerating understanding, consumption, and customization of state-of-the-art gen AI models to develop enterprise-grade solutions. It provides a complete toolkit ​to adopt, adapt, build, and scale AI-powered applications for business value.

We are seeing success from this approach. Platforms help us to combine disruptive AI capabilities with pre-existing tooling that combines the best of both worlds. For example, in Flowsource we combine software templating with AI copilots to increase consistency and compliance of generated outputs. In Neuro AI we combine large language models (LLM) with evolutionary AI to provide conversational experiences over the top of sophisticated recommendation models.

As a business, taking a platform-centric approach also has many advantages. Platforms provide consistency, transparency and risk mitigation by ensuring that similar problems are solved centrally and consistently. Platforms also enhance return on investment, as capabilities developed for on AI use case can be re-used for many.

A clear pattern emerges

In the race to exploit the potential of AI, organizations are engaged in the biggest wave of technology disruption since the Internet. However, implementing AI is not a simple task, and thoughtful strategies are needed to incrementally build maturity whilst managing cost and risk. We are seeing clear patterns emerging – consider all knowledge work, start with software engineering, and build scale through platforms.

It’s an exciting journey ahead, both to Cognizant and our customers!

To learn more, please visit our Gen AI section of the web.

Mike Turner

VP, Software & Platform Engineering, Cognizant

Mike Turner

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