Cognizant’s Pramod Bijani on why platforms will define enterprise AI outcomes, India focus & more

Cognizant’s latest quarterly results underscored steady demand for large transformation deals even as enterprises remain cautious on discretionary spending. Against this backdrop, the Nasdaq-listed IT services firm is sharpening its platform-first strategy—embedding AI-infused engineering platforms deeper into delivery to drive speed, resilience and cost efficiency. In an interview with TechCircle, Pramod Bijani, Senior Vice President, Platform Group (Engineering), explains how Cognizant is rethinking service delivery, scaling agentic AI, and using India as the nerve centre of its platform innovation. Edited excerpts.

Cognizant’s platform-first strategy marks a shift from traditional service delivery to engineered solutions. What prompted this transformation, and how is it reshaping client outcomes?

Enterprises that are successfully integrating agentic AI into their software development and operational workflows are gaining a decisive edge through speed and agility. The debate is no longer about whether to adopt AI, but how quickly organisations can rewire themselves to harness its potential. The challenge lies in scaling AI beyond pilots. Many firms face high upfront investments and long lead times to make AI systems production-grade, secure and compliant, which weakens the ROI case.

This gap prompted Cognizant to standardise across talent, methods and, most importantly, platforms. Our platforms help enterprises move from experimentation to predictive, cost-effective AI deployment using proven solutions rather than building everything from scratch. They also simplify complex IT estates by modernising legacy systems and optimising run and operations through AI-led automation. The impact shows up as productivity-led cost efficiencies, stronger resilience through proactive issue reduction, greater agility in responding to market shifts, and the adoption of AI-native systems that improve quality, decision-making and operational effectiveness.

You’ve described this as turning IP into enterprise acceleration. How does embedding AI-infused, reusable IP into delivery differentiate Cognizant from peers?

The key difference is consumption. Our platforms are embedded directly into day-to-day delivery rather than licensed as standalone products that clients must operate independently. They are used consistently across data and AI, custom applications, packaged systems, infrastructure services and business operations. When engineering talent, domain expertise and operating models work in tandem with platforms, intelligence and automation are applied seamlessly across execution.

This approach reduces adoption friction and extends Cognizant’s accountability beyond technology deployment to business outcomes. We have invested heavily in enabling our teams on these platforms, keeping them current with rapid technology shifts, and appointing dedicated product managers who work with clients and ecosystem partners to shape roadmaps. Interoperability is central—our platforms integrate with clients’ preferred tech stacks, allowing us to meet them where they are and accelerate AI initiatives with minimal overhead.

Platforms like Flowsource, Neuro IT Ops and Skygrade are now core to delivery. Where are you seeing the biggest impact?

Demand is strongest around production-grade agentic AI at enterprise scale. With AI agents increasingly capable of decision-making and autonomous action, enterprises are reimagining how they build software and run operations. Our platforms are driving outcomes in faster software development—accelerating builds, automating coding and deployment, modernising legacy systems—and in creating smarter, more resilient business functions through AI-infused applications that learn, adapt and act with minimal human intervention.

These capabilities are delivering results across healthcare and life sciences, financial services, manufacturing, retail, media and technology. Flowsource accelerates governed product delivery, Skygrade addresses legacy constraints, Neuro IT Operations improves reliability while reducing run costs, and Neuro AI Engineering scales compliant AI services. Together, they support initiatives ranging from building and managing small language models across hyperscalers to launching AI-native cloud solutions, modernising complex estates and automating IT operations to free up budgets for innovation.

How do these platforms help clients balance speed, resilience and cost efficiency in complex multi-cloud environments?

In multi-cloud environments, these priorities often conflict. Our platforms address them at the point of execution. Flowsource brings an AI-native engineering approach that accelerates product delivery while abstracting tooling complexity. Skygrade modernises legacy infrastructure, applications and data into cloud-native architectures, creating simpler and more resilient estates. Neuro IT Operations applies AI-led automation to improve reliability and lower operational costs, while Neuro AI Engineering provides the foundation for building, deploying and managing production-grade AI services at scale. Deployed across AWS, Azure, GCP and NVIDIA environments, a single instance of these platforms can orchestrate multiple tools and clouds. This gives enterprises consistent speed, resilience and efficiency across diverse technology landscapes.

India seems to play a pivotal role in Cognizant’s platform R&D. Why is it so central to your strategy?

India plays a pivotal role due to the depth of technical expertise, adaptability and strong innovation culture of our talent here. Our expanding footprint and growing AI Lab presence in India enable close collaboration with teams in the US, particularly the Bay Area, to industrialise innovation through platforms. This combination accelerates our AI initiatives and advances our platform roadmap globally.

How is Cognizant upskilling engineers to sustain this AI-infused delivery model?

We follow a structured approach that blends formal training, hands-on labs and continuous peer learning. Through Cognizant Academy, engineers train on AI-enabled delivery frameworks, earn certifications and gain practical experience in lab environments. Knowledge-sharing sessions and mentoring ensure continuous learning. Large-scale experiential initiatives, including a record-setting global generative AI hackathon last year, help teams experiment, prototype and build confidence in AI-augmented engineering.

As agentic AI scales, how do you see platform engineering evolving?

As AI adoption accelerates, platform engineering becomes essential to scaling best practices, reducing risk and delivering first-time-right solutions. It abstracts complexity across orchestration, context engineering, model operations and trustworthy AI, allowing teams to focus on business features. In an ecosystem evolving as fast as generative and agentic AI, platform engineering will be critical to enabling rapid, secure and enterprise-grade AI adoption.