How Nagarro bridges the gap between AI ambition and enterprise reality

Enterprise AI is no longer short on ambition. What it often lacks is realism. As organisations race to deploy personalised, AI-led systems, the real challenge lies not in algorithms, but in data quality, human adoption, and trust. In this conversation with Dataquest,Viyom Jain, MD and Global Business Unit Head for Enterprise Products at Nagarro, reflects on moments where expectations ran ahead of reality, how a flat culture enables ethical course correction, and why future-ready technology leaders must think beyond stacks and systems. His insights offer a grounded view of what it truly takes to make AI work at enterprise scale.

AI personalisation is touted as a Nagarro strength, but the industry often over-promises. Can you share a recent project where ambition ran ahead of reality—and how you recalibrated client expectations without slowing innovation?

The industry often speaks of instant transformation, whereas AI maturity evolves over time. We have seen this in several projects ourselves. For example, we partnered with a global specialty chemicals company to use AI for generating structured meeting summaries, extracting action items, and speeding up decision-making. The challenge was not the AI itself but the fragmented inputs such as emails, handwritten notes, and whiteboard images, which led to early outputs that were promising but inconsistent.

To align expectations, we created a phased roadmap with business and sales leaders. We began with summaries for structured meetings, then trained the models to understand context, technical language, and customer intent. Teams saved hours each week, decision cycles improved, and AI became a trusted partner instead of a black box.

A similar journey played out with a global electronics leader, where we built an AI-powered Agentforce architecture for Salesforce Service Cloud. The initial expectation was that AI would handle a wide range of service queries immediately. We helped the client see that conversational AI improves as it learns from real interactions. By expanding data sources, tuning responses, and using live dashboards, we built confidence gradually.

Across both programmes, the lesson was clear: balance ambition with transparency. By celebrating early wins, showing measurable progress, and sharing ownership of the learning process, we kept innovation moving while making it more human, scalable, and effective.

Nagarro often claims a flat, non-bureaucratic culture. In practice, how does that structure help you move faster or challenge orthodoxy, especially when tough calls on tech direction, ethics, or product-market fit arise?

At Nagarro, our flat and non-bureaucratic structure shapes how we work every day. Decisions are guided by ideas rather than titles, and the lack of rigid hierarchy allows every Nagarrian, regardless of role or background, to voice opinions, challenge assumptions, and influence the company’s direction.

This openness becomes especially important when we face critical choices around technology, ethics, or product strategy. With diverse and inclusive teams contributing different perspectives, we align faster and make more thoughtful, ethical decisions instead of relying on top-down directives. The core belief is simple: what matters is your opinion, not your designation.

A good example is an AI-enabled customer engagement project where a young data scientist raised concerns about potential bias in a training dataset that could have skewed recommendations towards a particular customer segment. Leadership took the concern seriously, paused the rollout, and rebuilt parts of the model with stronger ethical safeguards. Moments like these reflect our culture in action.

You are often required to think beyond technology to people, context, and consequences. Can you recall a moment when Nagarro’s fail-fast attitude actually saved a client from a costly mistake? How did you create permission for that candour in a high-stakes environment?

Experimentation is core to how we work. We encourage teams to fail fast so they can quickly identify ideas that are not worth pursuing. This mindset fuels innovation, supports continuous learning, and helps us respond quickly to change.

A clear example comes from a global retail client exploring an AI-driven personalisation engine. Early pilots looked promising, but our data engineers spotted inconsistencies in how regional data was being processed. Instead of pushing ahead to meet an ambitious launch timeline, we paused the rollout and ran targeted simulations. Within a week, we identified the issue: incomplete data normalisation in one region.

Catching the problem early helped the client avoid a costly marketing misstep and potential brand damage. More importantly, the transparency deepened trust. By creating a culture where teams can question assumptions without hesitation, even in high-stakes situations, we turn early failures into long-term success.

Nagarro often talks about future-proofing as technology keeps shifting faster than ever. With AI, cloud, and quantum changing the game, how do you help clients stay adaptable while understanding where scalability might hit its limits?

When we talk about future-proofing, it is important to remember that nothing in technology stays permanent; it stays adaptable. With the pace of change across AI, cloud, and now quantum computing, the real challenge is not how far systems can scale, but how quickly they can evolve when assumptions shift.

One of Nagarro’s core strengths is how closely we work with our customers. Because we understand their processes and workflows deeply, we can bring an AI-first mindset that keeps them flexible as their needs change. For example, we work with both open-source and proprietary models, large and small, through APIs or on-prem deployments. In many cases, we fine-tune models to deliver precise outcomes, ensuring clients can respond quickly to new business demands.

With Nagarro’s focus on breakthroughs, how do you set yourselves apart from pure-play outsourcers? Can you share an example where mindset, not just tech stack, turned an idea into measurable business impact?

We pride ourselves on being one of the most agile companies, combining rapid time to market with a deeply collaborative working style. Outsourcing has moved well beyond cost savings. Organisations now look for partners who can co-innovate, drive transformation, and deliver measurable business outcomes.

To meet this shift, we introduced Fluidic Intelligence, a framework designed to help clients achieve over 20 percent productivity gains across projects. It rests on three pillars. Advisory and Consulting helps clients extract more value from existing PaaS and SaaS investments. Fluidic Forge leverages our engineering depth to build data and AI accelerators, both horizontal and industry-specific. Fluidic Teams focuses on people, building an AI-ready culture through upskilling, modern tools, and new ways of working.

Together, these pillars help clients stay adaptable, innovate continuously, and translate ideas into real business impact.

Implementing AI-led transformation can be complex, especially when it comes to real-world adoption. What have you learned about turning innovation into practical solutions that teams can trust and use effectively?

From our experience, the first requirement is strategic clarity. AI must solve a real problem, not exist as a proof of concept. Every initiative begins with defining measurable business impact and ensuring strong data foundations.

Human adoption and trust are equally critical. Even the most advanced systems struggle without buy-in from the teams who use them. Nagarro’s human-in-the-loop design philosophy and focus on data and AI democratisation help people understand how AI works and influence its evolution. This bridges the gap between automation and human judgement.

The technology leadership role itself is changing. From your vantage point, how will the tech leader of the next decade look different, and what do you see as your personal responsibility in that shift?

The tech leader of the next decade will not be defined by mastery of every stack, but by the ability to act as a Chief Ecosystem and Trust Officer. The role will shift from managing systems to orchestrating how humans and autonomous AI agents work together, with strong governance and reliability.

This evolution spans stewardship, translation, and empowerment. Stewardship ensures fairness and ethical guardrails. Translation turns complex technology into outcomes people can understand and trust. Empowerment creates environments where teams challenge assumptions and innovate confidently.

My own responsibility is to lead with this mindset, champion responsible AI, question impact before implementation, and ensure every decision aligns with what is right for people and businesses.