In an era where “business as usual” is a relic of the past, there is a dire need to understand the essential intersection of product-led operating models and AI-driven automation. The path to an AI-first transformation requires more than high-level ambition as it demands a robust execution backbone built on the principles of Agile, DevSecOps, and Site Reliability Engineering or SRE.
In this conversation, Alok Uniyal, SVP and Head of IT Process Consulting Practice at Infosys, deep-dives beyond the buzzwords to explore how platform ecosystems are no longer just supplementary tools, but the very heartbeat of a modern enterprise. By prioritizing customer-centricity and aligning value streams, Uniyal sets the stage for a fundamental shift in how organizations perceive and deliver value in an increasingly complex digital landscape.
Through the practical lens of Infosys Topaz and the Exponential Engineering initiative, he demonstrates how modular architectures and Agentic AI act as catalysts to accelerate “Speed to Value.” This blueprint provides more than a temporary performance boost—it offers a sustainable foundation for scaling innovation and achieving a genuine quantum leap in productivity for the forward-thinking enterprise. Excerpts from the chat:
How does a product-led operating model accelerate measurable business value and speed to market?
Alok Uniyal: A product-led operating model is rooted in customer centricity, value stream alignment, and business outcome orientation. Product Teams are organized around customer journeys and value streams; focus is on data-driven, real-time tracking of flow of value across the product development lifecycle as well as on speeding value realization through AI led automation and engineering excellence.
The cross-functional and persistent product teams own the end-to-end product roadmap, deliver features in short iterations using the MVP (minimum viable product) based approach, validate with user/market data, and pivot quickly as needed. The top-level business OKRs related to Revenue, NPS, Market Share, Time to value, Bottomline impact etc are broken down and cascaded to team level KPIs – thereby ensuring razor-sharp focus on expected business outcomes.
Capacity-based funding of product value streams avoids stop-start project cycles and reduces delays in decision making. Governance pivots from “scope/time/cost” to Flow of Value using Product OKRs, lean governance, and data-driven decisions—so every investment is explicitly tied to outcomes and can be reviewed and adjusted through shorter funding/priority cycles. Using a product-led operating model a leading European Telco was able to successfully launch multiple growth products, 4X times faster to market leading to top-line impact.
In what ways do platform ecosystems enable layered innovation and enterprise-wide scale?
Alok Uniyal: Platforms turn once-specialized capabilities into shared enterprise services – offered as reusable, self-service “building blocks” for product teams, eliminating the need to recreate common technology and business capabilities – thereby paving the way for product teams to run fast.
The modular and layered platform architecture allows innovation to happen at each layer (Core, Services and Application/Experience layer), with minimal dependency thanks to the well-defined interfaces between the layers. Innovation scales horizontally as more contributors build on the same foundation. Platform architectures follow privacy and security by design principles, ensuring that the required guardrails are baked into the platform and governance is driven centrally.
With the increasing adoption of Agentic AI in enterprises, this aspect assumes much greater significance. Technology platforms that offer core capabilities around Cloud, Infra as a Code, Data, APIs, DevSecOps, Security, Observability etc. as a service, have significantly enhanced ‘Developer Experience’. A Platform can also extend beyond the boundaries of an enterprise – across Partners and Customers, thereby allowing ecosystem innovation. Rather than owning all innovation, the platform owner orchestrates value creation, while external innovation fills gaps and explores new use cases. The enterprise gains innovation scale far beyond what internal teams alone could produce.
How are Agile, DevSecOps and Site Reliability Engineering critical to building reliable, AI-ready organizations?
Alok Uniyal: Agile, DevSecOps, and SRE are the execution backbone for AI-ready organizations because they operationalize “Speed to Value”, “Trust” & “Reliability” simultaneously. Agile enables faster ‘idea to market’ cycles, DevSecOps embeds security and automation in development lifecycle while SRE ensures resilient operations. AI increases both change velocity and operational risk and having a robust foundation of these three practices becomes critical in scaling AI adoption in an enterprise.
In agentic AI, ‘agent observability’ has become an essential capability. AI adoption succeeds only when systems you build are dependable, auditable, and safe at scale. A product & platform led operating model, with agile-devsecops-sre at the heart of it, enables enterprises to identify & prioritize the right uses cases for AI; uses cases that can have highest impact on the top-level business OKRs.
How does combining platformization with AI-powered automation across the SDLC create a sustainable foundation for AI-first transformation?
Alok Uniyal: Platformisation offers a sustainable base for standardizing delivery through shared, self-service capabilities that enhance developer experience and allow the product teams to run fast. Centralizing common functions into platforms simplifies processes, accelerates development, inculcates innovation mind-set, and drives compliance centrally, which is crucial for AI-first transformation.
The responsible AI guardrails are embedded in the underlying tooling/platforms used by the product teams. AI-powered automation in the SDLC is enabled by the underlying integrated AI toolchain. This along with the supporting technology platforms across Cloud, Data, APIs, Observability etc. creates the right environment for product teams to embrace AI responsibly. The AI-first software engineering approach at Infosys is based on these key tenets, leveraging the Infosys Topaz capabilities.
Mature enterprises are now building Agentic AI platforms that enable a product team, comprising of humans & agents, to orchestrate the end-to-end SDLC – from intent to market launch; thereby driving order of magnitude increase in productivity and dramatic reduction in time to value. At Infosys this is being done under the Exponential Engineering initiative.