From using AI to shaping it: Building AI fluency after a career in Risk, Trust & Safety

For a long time, I saw AI primarily through the lens of efficiency. Then I realised I was looking at it too narrowly. As AI moved from basic task automation to systems capable of real-time adaptation and agentic reasoning, I began to see that simply working alongside AI-driven systems was no longer enough. If I wanted to help build tools that genuinely made a difference, I needed to understand what was happening under the hood.

That meant going beyond high-level familiarity and developing a deeper understanding of machine learning, Generative AI, AI agents, architecture, edge cases and governance. That realisation became the starting point of my journey with ISB’s AI in Business: Fundamentals to Applications programme.

Bringing AI Into Risk, Policy and L&D

Today, as a Service Delivery Manager in L&D and Policy Strategist in Risk & Fraud at Cognizant, I work at the intersection of learning, policy, risk and technology. By combining L&D principles and policy strategy with my experience in risk, fraud and abuse prevention, I help global platforms stay safe, compliant and scalable. I also lead a team of instructors and policy specialists who translate high-level product policies into actionable practices.

Whether I am navigating complex change management or architecting a knowledge management system from scratch, my goal has always been to build high-performing teams that can stay ahead of the curve. Over the years, my team and I have also played an active role in ideating, implementing and designing workflows for AI and machine learning-powered solutions and automation. This requires close collaboration with Product, Engineering, Programme Management, Business Excellence and Operations.

But the pace at which AI and machine learning have evolved over the past four years has been extraordinary. The technology has moved rapidly, and in many ways, traditional frameworks have struggled to keep pace. I realised that if I wanted to drive meaningful conversations around AI and help advance effective business applications, I needed a more grounded understanding of the technology itself.

Moving Beyond AI as an Efficiency Tool

The real turning point for me was recognising that treating AI simply as a quick efficiency tool was missing the bigger picture. I was seeing the technology move from basic, static task automation towards complex systems capable of real-time adaptation and agentic reasoning. It became clear that to build tools that truly make a difference, I could not just sit on the sidelines as an end-user. I needed to understand what was happening under the hood, including the architectural boundaries, edge cases and governance frameworks that make AI systems reliable.

That changed how I thought about AI. The real value of AI isn’t just speed. It is how effectively it can augment human capability when properly integrated. Building formal AI fluency was therefore not about becoming a technical specialist. It was about understanding enough of the technology to participate meaningfully in decisions around how AI is designed, implemented and governed.

Why I chose ISB’s AI in Business Programme

AI and machine learning are becoming increasingly central to risk, fraud, trust and safety. I realised that effectively guiding AI initiatives requires more than high-level familiarity. It requires a grounded understanding of the underlying technology.

What drew me to the AI in Business: Fundamentals to Applications programme was its seamless progression from foundational AI and machine learning principles and Python to Generative AI, AI agents, business applications and ethics. I was looking for a curriculum that could help me bridge the gap between technical capability and business execution. Understanding the mechanics of Generative AI and how AI agents orchestrate workflows allowed me to better evaluate realistic use cases, identify technical bottlenecks and think more clearly about implementation frameworks.

Two of my favourite areas of the programme were AI agents and automation, and AI ethics. They helped me better understand how risk identification models work and, in turn, helped my team and me think about designing stronger end-to-end workflows.

My biggest takeaway was that a technically sophisticated AI solution is only as good as the governance and ethical guardrails around it. It also depends on how effectively cross-functional teams across Technology, Operations and L&D work together. Without trust, alignment and robust risk frameworks, even the most advanced model can fail to deliver value at scale.

From Strategy to Execution

This learning experience has changed how I evaluate AI projects. I now look beyond the hype and focus more deliberately on realistic use cases, governance frameworks and practical limitations. Put simply, it has changed how we move from strategy to execution.

Under our senior leadership’s guidance, my team and I recently co-deployed an AI agent alongside our cross-functional partners. The solution is helping us bridge quality limitations while also addressing leakage. The experience has reinforced the importance of looking at AI as more than a technology implementation. The real challenge is understanding where it can create meaningful value, where its limitations lie and what controls need to be built around it. The ISB experience complemented this perspective particularly well.

In addition to a well-structured curriculum, what elevated the experience for me was the unvarnished realism of the live lectures and hands-on demonstrations. The faculty and industry leaders did not shy away from the difficult parts of AI implementation. We analysed failures, edge cases and deployment bottlenecks with the same rigour as successful applications.

Learning alongside professionals from diverse functions and industries also pushed me to challenge my assumptions and look at problems through multiple lenses. That commitment to candid, real-world insight was invaluable in developing a more mature and practical understanding of AI execution.

From Reactive Risk Management to Proactive Design

Perhaps the biggest transformation has been in how I see my own role in enterprise transformation. The programme helped me move from a reactive mindset to a proactive design mindset. Coming from L&D and Policy Strategy, it can be easy to view technology as something designed by others, which we then need to deploy, safeguard or train people to use.

Understanding the mechanics behind AI, from machine learning fundamentals to agentic workflows, helped bridge that gap for me. I can now step into cross-functional discussions not just as a functional domain leader, but as someone who can actively translate policy and learning goals into intelligent, automated systems that deliver real organisational value.

That shift is particularly important as AI becomes more central to risk management, trust, and safety. As AI moves from being a passive tool towards orchestrating increasingly complex workflows, I see it becoming increasingly important in managing risk and combating abuse across digital platforms. We are also seeing a similar shift in capability development and workforce enablement. We are moving away from one-size-fits-all training towards AI orchestration that operates in the flow of work.

But greater autonomy also brings greater responsibility. When AI systems deliver real-time feedback or execute tasks autonomously, the quality and reliability of the output become paramount. My learning at ISB has given me greater technical fluency to design controls, evaluate agent limitations and balance autonomy with human oversight. By bridging technical mechanics with governance frameworks, I feel better positioned to lead this transition proactively and help architect systems that are not only innovative, but also trusted, scalable and compliant.

AI Fluency is Not Just for Technical Teams

One of the biggest myths is that AI fluency belongs solely to technical teams. As AI takes on larger roles in decision-making and workflow execution, I believe some of the biggest gaps will not necessarily be coding gaps. There will be gaps in governance, alignment and capability.

To any policy strategist, risk professional or L&D leader who feels that AI programmes are meant only for technical people, my advice is simple: don’t hold back. This learning is not about becoming a programmer. It is about gaining a seat at the table where decisions about AI architecture, application and governance are made. Understanding machine learning fundamentals, agent workflows and ethical constraints gives you the confidence to lead cross-functional teams, challenge assumptions and ensure that technology genuinely elevates human performance safely.

For me, this journey has been about moving from being a passive consumer of AI tools to becoming an active participant in shaping how AI is applied to complex business problems. As AI continues to evolve, I believe the professionals who can connect technical understanding with deep domain expertise, governance and human judgment will be best positioned to lead that transformation. Ultimately, AI is not about replacing the judgment that professionals bring to their roles. It is about extending what that judgement can achieve.

Synopsis

Nandini Chakravarty, Service Delivery Manager in L&D and Policy Strategist in Risk & Fraud at Cognizant, pursued ISB’s AI in Business: Fundamentals to Applications programme to build deeper AI fluency. She shares how the programme strengthened her understanding of Generative AI, AI agents, governance and applying AI to risk, policy and workforce challenges.