The Human Plus AI Quotient: Inside Ascendion's strategy to make AI an amplifier of human talent

Radhakrishnan Rajagopalan, a three-decade veteran in technology and analytics, serves as the Chief Delivery and Technology Officer at Ascendion. His career, spanning leadership positions at Cognizant, LTIMindtree, and PwC, has given him a unique perspective on the seismic shifts currently redefining the tech industry. In this exclusive interview, he discusses how Ascendion is leveraging AI not just as a service, but as the core of its operational DNA, and how this shift is reshaping the talent landscape in India and globally. Edited excerpts.

Right from our inception, we made the conscious choice to be an AI-embracing company. We wanted to amplify software engineering—platform engineering, application modernisation, and data insights—with AI. While AI adoption in core business processes is still evolving globally, AI in the engineering side has seen very rapid acceptance. This early pivot has given us a significant advantage.

Not all clients are at the same point in their AI journey. Some are early movers or adopters, especially in high-tech industries—they tend to be ahead of the game. When it comes to engineering, such as modernizing or re-engineering legacy application stacks, some clients have made significant strides, while others are still learning and evolving. The skepticism we saw two or three years ago is fading; back then, many were unsure what would work and were cautious about investing. Now, most organisations realise that AI is here to stay and is advancing rapidly. The main difference among clients is how far along they are in their AI journey—some are very advanced, others are catching up. But broadly, we’re not seeing naysayers anymore; almost everyone is embracing AI in some shape or form.

It’s a global trend—everyone is investing in cross-skilling and upskilling their existing talent. Academic institutions, especially in India, are putting renewed emphasis on AI fundamentals and their practical applications. There’s an increasing focus on integrating AI into daily productivity and engineering. With the abundance of evolving tools and technologies, the workforce is rapidly transforming to have a “human plus AI” quotient. At Ascendion, we’re no exception; we invest heavily in grooming our talent, and AI adoption is becoming a baseline skill. It’s getting hard to find anyone who hasn’t used AI in some way in their daily work.

My thinking is that the job losses and stagnation you mention are likely slightly overstated. Those who embrace AI are not suffering; they are augmenting their capabilities. It's not as if AI is stealing everything a person does. AI is helping tremendously in engineering, operations, and process optimisation.

The overall workforce needs to embrace that the "human plus AI" quotient is taking shape. Roles such as design, architecture, and governance, which require high-level interpretation, require significant human input. The ultimate impact comes down to whether you are picking up the AI skill or not. If you pick up the skill and use AI for productivity, you secure your career.

We’ve always prioritised hiring for the right attitude and adaptability. Technical skills evolve—mainframes lasted forty years, client-server about twenty, and digital waves even less. Skills will come and go, so we focus on candidates with a strong willingness to learn and invest in themselves. That’s foundational. What’s changed now is the importance of being open to AI. We don’t require deep AI expertise at the outset, but we do look for those who are ready to embrace it. This approach explains why our workforce is so quick to adapt to AI—it’s ingrained in how we hire and develop our people.

That’s something that sets us apart. Because we hire for aptitude and a positive attitude toward AI, our employees aren’t worried about their jobs being replaced. Over 60 percent of our workforce in India is trained in AI in some form, and everyone—from new hires to longtime employees—regularly uses Gen AI tools. We’ve built internal platforms like AAVA, our engineering canvas, which integrates AI across agile project management, DevOps, and more. The strong foundation we've built means onboarding to new AI tools happens smoothly. Our people are actually educating clients on AI adoption, not fearing it. For us, working with AI is part of our DNA, so there’s no undue concern about the future.

The war for talent has always existed—it’s just the scale and timing that change. For us, the quality of work and the opportunities we provide are key to retention. Being fundamentally an AI-first company is a big differentiator, and our “AI-first” mindset is wired into our DNA. Our employees see a real difference in how we approach projects, always asking how AI can add value. We’ve created an environment that encourages experimentation and learning, and the IP our teams develop—sometimes even around best practices for AI adoption—becomes part of our organisational knowledge base. While some attrition is natural, the platform and culture we've built are major reasons why people stay at Ascendion.

The biggest challenge for many clients—especially those outside the tech industry, such as life sciences or insurance—is the sheer volume of knowledge locked in their existing workforce. There is immense domain knowledge that needs to be retained.

Our job is to help them simplify the learning construct. The good news is that for a large cross-section of the workforce, "skilling in AI" is not about mastery of mathematics; it's about improving English writing skills to prompt effectively. We often share prompt libraries with clients because the ability to ask the right question and interpret the output is a significant win. We help simplify the process by focusing on better questioning, reasoning, and interpretation of AI-generated results.

I see four clear trends dominating client requests:

Accelerated Modernisation: Clients now demand reduced upgrade cycles, not just cost savings. GenAI enables clients to modernise legacy codebases—such as C++ or COBOL—faster.

User Experience: Clients want ultra-modern, responsive systems that emulate the fast, seamless experience of their phones.

Data-Driven Personalisation: Clients want to deploy AI and data science to enable consumers to make informed decisions and offer highly personalised experiences.

The Agentic World: Service is moving to agents. Clients now use pervasive agents to automate complex end-to-end workflows, such as those in call centres.

The slowness is not hesitation; it's caution. They are correctly prioritising governance. You cannot have uncontrolled access to LLMs in a regulated environment—it’s a disaster waiting to happen.

The sweet spot involves three synchronised initiatives:

Workforce Adoption: They must continuously train their workforce in AI adoption.

Governance Maturity: They need to work with experts to rapidly build guardrails and governance models that define which data leaves their premises and which remains locked in.

Controlled Infrastructure: They must leverage cloud and private infrastructure to create a controlled experimentation environment where they can test, learn, and "fail fast" without exposing sensitive information.

Most CTOs and CDOs have now understood that game, and all three elements—infrastructure, governance, and upskilling—must fire in tandem.

Absolutely. It starts with a simple search box, like Google or Perplexity, but it's used by the business user.

The process is:

Concept Generation: A business user prompts the system: "I want to build a product to onboard customers onto my financial services ecosystem with these business objectives." The LLM generates the initial product story, workflows, and business processes.

Refinement and Architecture: The user refines the story, adding security and compliance restrictions. The system then chunks the story into epics, and the architecture team prompts the LLM to build a specific architectural model aligned with their standards.

Code and Quality Assurance: The code begins to generate automatically. Simultaneously, the Quality Assurance team prompts the system: "For this code base, can you build the test scripts?"

This entire workflow—from ideation, architecture, and code generation to testing—is orchestrated through prompt engineering, turning the LLM into a powerful collaborative partner at every single step.