Interview

The People in the Training Room Know Things the Model Does Not

At Evalueserve, AI fluency isn't the property of one team — it runs through every function, and it looks different depending on where you're standing. We spoke with two people who sit on opposite ends of that experience: Abhishek Kumar, who builds and deploys the systems, and Mansi Yadav, who helps the rest of the organization learn to work with them well. Their answers describe the same principle from two directions: the model moves fast, but the advantage still belongs to the person who knows the work.

Abhishek Kumar

Manager, Agentic AI Trainer — Technical & Production

A graduate of NIT Jamshedpur with a background spanning data science, generative AI, and AI solution architecture, Abhishek now focuses on training programs, framework design, and deployment — helping organizations put agentic AI into production, not just into demos.

When you first started working seriously with AI tools, what was the moment you realized the technology wasn't the hard part?

Abhishek: My first AI project was a simple chatbot built on RAG. The technology itself is fairly straightforward — any competent developer can learn it quickly. What was harder were the expectations surrounding it. Some stakeholders wanted verified, grounded answers delivered in a fraction of a second. Others assumed the whole thing would amount to nothing. Managing those two extremes turned out to be the real project. AI is neither a panacea nor a gimmick. Like any technology, it needs careful engineering and calibration to create real impact — and getting people to a realistic middle ground is harder than writing the code.

At what point does domain knowledge become the actual bottleneck — more than compute, more than data?

Abhishek: Building AI solutions comes down to two things above all: an analytical mindset and deep domain knowledge. The development itself can often be outsourced once you know exactly what you’re building, who it’s for, what experience you want to create, how much manual effort the work takes today, and which parts of it require judgment versus deterministic execution. Without that clarity, even a brilliant AI engineer will struggle — you can’t articulate the problem, and they can’t apply the technology well. Think of AI as a high-performance autonomous vehicle. Domain knowledge is the navigation system. No matter how powerful the vehicle is, without the right destination and route, it isn’t taking you anywhere meaningful.

What's a signal to you that an AI deployment is going to fail, even before it launches?

Abhishek: I don’t think AI projects truly fail — most perceived failures are stepping stones to the next attempt. But using the conventional definition, the strongest warning sign is when decision-makers have only a superficial understanding of what AI can and can’t do. A close second is an overcommitment to ‘fail fast,’ stretched past its useful meaning into ‘move fast and AI will figure out the rest.’ Wanting to accelerate a project without understanding its constraints, on the assumption that AI will work miracles, is a clear red flag. AI rewards informed ambition. It doesn’t reward blind optimism.

Mansi Yadav

AI Adoption & Enablement — Non-Technical Teams

Mansi works with the people on the other side of that equation: the non-technical teams across financial services, insurance, healthcare, and operations who are handed AI copilots and expected to use them well, starting on day one.

What have you learned from training experienced domain professionals?

Mansi: The biggest surprise has been how often the most valuable insight in the room comes from the participants, not the technology. During a generative AI session with a mid-sized insurance firm, a senior underwriter stopped an exercise halfway through. She looked at an AI-generated claims summary and said, ‘This would fail our subrogation review — the model doesn’t know that step exists.’ The room went quiet. Then the rest of the underwriters started jumping in: one flagged an exception the model had missed, another pointed out a compliance issue, someone else described a customer scenario that would change the whole outcome. In under ten minutes, we learned more about that process than weeks of documentation had told us. Nobody in that room was resisting AI. They were doing exactly what experienced professionals do — applying judgment. That’s the moment I look for in every session: people arrive assuming I’m there to teach them about AI, and by the middle of the session, the learning is going both ways.

Do employees sometimes underestimate the value of their existing knowledge?

Mansi: Constantly — and I’d argue it’s one of the biggest barriers to real adoption. AI sounds confident, and when something sounds confident, it’s easy to assume it’s correct. I once worked with a logistics analyst who had spent years managing supplier performance in her region. The AI produced a well-structured, professional-sounding answer about supplier lead times — except some of the underlying information was outdated. She spotted it immediately, but when I asked why she hadn’t pushed back sooner, she said, ‘I thought maybe I was wrong.’ She wasn’t. The model was. Confidence and competence are not the same thing, and one of the most valuable skills in an AI-enabled workplace is simply being willing to say, ‘that doesn’t look right.’

What does effective AI training actually look like?

Mansi: It starts with the work, not the tool. Plenty of sessions focus on prompts and demos — people leave impressed, but unsure how any of it connects to their job. Real confidence comes from applying AI to work you already do: drafting the report you actually need to write, analyzing the data you already review, investigating the case sitting in your queue. Just as important is seeing where it gets things wrong. I use a four-step cycle. Create: use AI to generate an output tied to a real task. Inspect: examine the assumptions, evidence, gaps, and risks inside it. Improve: add missing context, correct the reasoning, strengthen the result. Decide: determine what happens next, who owns the outcome, and what level of review it needs. That last step is the one I now consider non-negotiable. As organizations move toward agentic AI, the question isn’t only whether the model can produce an answer — it’s who stays accountable once that answer triggers an action.

What changes as companies move toward agentic AI?

Mansi: Everything, because the agent acts instead of just generating content. In workshops, processes that look simple on paper — six steps, two handoffs — reveal layers of exceptions, judgment calls, and approval steps the moment the people who actually do the work get involved. That’s where the real complexity lives. Before any agent gets autonomy, an organization needs a straight answer to four questions: What can it do on its own? What data can it access? When should it stop and escalate? And who is accountable for the outcome? If a team can’t answer those four cleanly, it isn’t ready to hand the process to an agent.

Closing — In Their Own Words

Evalueserve's model is built on the idea that human judgment steers the AI, not the other way around. We asked both of them what that actually looks like from where they sit.

Abhishek Kumar:

It’s non-negotiable. AI is a powerful tool, but it’s still a tool — its ability to generate content or automate a task comes from probabilistic prediction and computational power, nothing more. It’s not unlike how the calculator changed arithmetic: the calculator didn’t replace mathematicians, it amplified them. AI is doing the same for knowledge work. What it doesn’t have is ownership. It carries no accountability, no responsibility, no consequences. Humans do. That’s why the decisions, trade-offs, and ethical calls have to stay with people, even when AI is doing the heavy lifting.

Mansi Yadav:

Don’t compete with AI on speed — that’s not a race worth trying to win. The people who thrive aren’t the ones trying to outperform the model. They’re the ones who know how to work with it: use it to accelerate the work, then apply the experience, context, and judgment that AI still can’t replace. The future belongs to people who understand both the technology and the work itself. They’re the ones who will end up shaping how AI gets used, instead of just reacting to it.

The model is fast. Judgment is still the advantage.

— Abhishek Kumar & Mansi Yadav, Evalueserve

Every enterprise now has access to AI. Far fewer have built the discipline to make it trustworthy in daily work — on the production floor and on the front line alike. That discipline is what Abhishek and Mansi are each building, from opposite ends of the same organization: one engineering the systems, one engineering the confidence to use them well. It’s a small window into a larger principle Evalueserve has operated on for 25 years — the technology only creates value once it’s steered by people who understand the work.

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