Akshay Nagpal is an agentic product leader at a UK based GenAI and product innovation studio, where he helps lead technical teams and teams of AI agents on product building, model training, and fine-tuning. His work sits at the intersection of product, technology, and applied AI, with a focus on how operating models change as intelligent systems become more capable.
Before that, Akshay worked across product, technology, and AI at ING and Nokia. That mix of experience gives him a practical perspective on where AI can improve real workflows, and where it should be constrained by process, review, and clear business purpose.
Background
Akshay began his career in product and technology roles, building a foundation that later informed his move into applied AI. His background spans work at Nokia and ING, where he gained experience across product, technology, and AI in large, operational environments.
He now works as an agentic product leader at a UK based GenAI and product innovation studio. In that role, he helps lead tech teams and AI agents for product development, including work on large language models, vision models, and the evolving responsibilities of product managers working alongside AI systems.
A recurring theme in Akshay’s career is translating AI from concept into controlled, useful business workflows. On the podcast, he described deployments in lead qualification, email handling, workflow automation, and product operations, always with a focus on scope, risk, and the right level of human oversight. His current work reflects a broader career arc from traditional product and technology roles into hands-on AI implementation and operating model design.
Core Expertise
Akshay’s core expertise is in agentic AI product development, workflow automation, and the practical application of large language models in business settings. He is especially focused on systems that can reason, make limited decisions, and complete defined tasks on behalf of users, while remaining bounded by human approval and organizational controls.
He works deeply with use cases such as summarization, extraction, decision support, customer communication, and software testing. A consistent part of his method is to start with the business goal, then assess risk, cost, and operational trade-offs before implementation. He is particularly attentive to guardrails, including human in the loop review, access restrictions, and context management, because he sees those as essential to making AI reliable in production.
Academia
The dossier provided for Akshay does not include his undergraduate education.
No graduate, executive, or professional program was specified in the dossier.
No additional academic credentials or professional certifications were provided in the source material.
Key Perspectives that Akshay Nagpal Shares on the Podcast
Akshay’s central view is that AI is already capable of replacing many repeatable, low-stakes tasks, but it still needs careful system design. He argues that the right way to think about AI is not as a universal substitute for human judgment, but as a tool that should take on routine work while humans retain oversight for higher-risk decisions.
He also emphasizes that the most useful way to adopt AI is to begin with business value and risk, not fascination with the technology itself. Across the conversation, he returned to a practical framework: identify a workflow, test whether AI can materially improve it, and then apply controls such as human review, limited access, and clear approval rules. For him, the main skill shift is toward critical thinking, business judgment, and adaptation, rather than simply learning new tools.
A Quote from this Conversation with Akshay Nagpal
“The replacement risk is real and it’s coming first with the productivity and efficiency gain that we are seeing in organizations.”
