How AI is redefining delivery in the digital engineering era

As enterprises race to reimagine their digital foundations, Shyam Enjeti, Chief Delivery Officer at Encora, is focused on how delivery itself must adapt in an AI-driven world. With over two decades of global experience across HCL Tech and several US-based technology firms, he brings a rare, hands-on perspective on building distributed, AI-enabled delivery ecosystems that bridge scale, speed, and human creativity.

In this conversation with Dataquest, Enjeti reflects on how generative AI is reshaping software engineering and redefining the role of human expertise in an increasingly automated landscape. He discusses why platform-led delivery models are emerging as a critical lever for transformation, how global engineering teams are evolving in the age of intelligent orchestration, and what it takes to balance innovation with trust, governance, and accountability. Excerpts.

How are traditional delivery models evolving in an AI-first world?

Delivery is no longer about managing linear projects with fixed handoffs. In an AI-first world, the model itself must be adaptive. We are moving from effort-based delivery to outcome-based delivery, powered by automation, predictive analytics, and self-healing systems.

For example, in software engineering, AI is accelerating requirement gathering, generating code, testing autonomously, and preventing incidents before they occur. That means the delivery model is shifting from “people writing every line of code” to “people orchestrating intelligent agents” that can generate, test, and scale solutions with far greater velocity, while exercising human judgement to ensure quality, compliance, innovation, and client satisfaction.

In what ways has generative AI changed the way engineering teams actually work at Encora?

At Encora, we see AI not as a tool bolted onto the process, but as a peer embedded in the workflow. Engineers are increasingly working hand in hand with in-house, proprietary agents which help with backlog grooming, code generation, and defect detection. Today, domain-specific agents can help automate tasks like compliance reporting or log analysis, freeing up engineers to focus on higher-order design, architecture, and problem-solving.

The roles are evolving—less manual coding, more orchestration. Skill sets are expanding to include prompt design, AI observability, and human-in-the-loop governance. Structurally, we are moving toward smaller, more empowered, AI-enabled teams that combine human judgement with the limitless compute power of AI to deliver value at an unprecedented scale and speed.

Building a globally distributed engineering ecosystem is no small task. What challenges do you face in areas like regulation, latency, or talent markets, and how are you addressing them?

Global distribution is in our DNA. We have over 9,000 engineers across India, Latin America, Eastern Europe, and Southeast Asia. The challenge is not just geography; it’s regulation, compliance, and consistency of outcomes across borders. To solve this, we’ve invested in platform-led delivery models that embed compliance guardrails, observability, and AI-enabled collaboration tools into the workflow itself.

Latency and collaboration challenges are addressed by placing nearshore teams closer to clients for high-velocity work, while leveraging India for expertise at scale. And talent? We’ve shifted the conversation from cost arbitrage to capability advantage. Encorians are constantly upskilled in AI fluency, orchestration, and the latest technologies so they can thrive in this new model.

You often talk about platform-led service delivery. What does that really mean in practice?

Think of delivery not as a collection of projects, but as a living system. Our proprietary orchestration platform, AIVA™ serves as the “operating system” for delivery. It brings together multi-agent orchestration, compliance guardrails, observability, and reusability.

That means:

• Reduced friction through pre-built agents and templates.
• Increased reuse by composing solutions from modular building blocks.
• Improved speed and quality by embedding AI in every stage of the SDLC—requirements, code, test, deploy, and run.

Platform-led delivery makes engineering composable, so enterprises can launch in weeks instead of years and adapt to change on the go while scaling outcomes.

As AI becomes central to delivery, how do you balance speed of innovation with the need for trust, security, ethical responsibility, and regulatory compliance?

That balance is non-negotiable. We use what we call confidence-bounded automation: AI agents execute when confidence and compliance thresholds are met but escalate to humans when judgement is required.

We embed policy-as-code, audit trails, and observability into workflows so that every decision is traceable. And we are deeply industry-aware—what is permissible in utilities can be different from healthcare or banking. Trust is earned not by slowing down innovation, but by making safety and compliance intrinsic to the delivery platform.

Looking ahead, what should enterprises be doing right now to prepare for the next wave of transformation in AI, edge computing, and cloud architectures?

First, modernise the foundations—data platforms, cloud-native architectures, and observability. Without this, AI adoption remains surface-level. Second, embrace composability: re-architect business and technology as modular building blocks that can be rapidly reassembled. Third, invest in people. The next decade will be about “digital workers”—humans augmented by AI agents. Enterprises must upskill teams, define governance, and build cultures that are comfortable with AI as a collaborator.

The most common mistake I see is chasing shiny tools without changing workflows. A dozen AI pilots won’t move the P&L if the delivery model itself doesn’t evolve. My advice: pick two critical workflows, reimagine them with human-led, AI-executed orchestration, and scale from there. That’s how transformation becomes real.