Preety Thakur — moving from designing Agentic AI solutions to defining AI product strategy. I see AI Governance and Compliance as the next product frontier, where security, trust, explainability, and business value converge. Having worked hands-on with enterprise AI implementations, I want to influence what gets built, why it gets built, and how it scales responsibly.
Product management for agentic AI isn't primarily a UI problem — it's a problem of deciding what an agent is allowed to do, when it must stop, and how you prove that to a regulator afterward. That's the exact job I have already been doing as an architect. The four things a governed agent needs are the same four things I have spent a decade building.
Years spent translating business rules into config tables that non-engineers can own — the same discipline a PRD demands: define the rule once, let the system enforce it everywhere.
Every automation I have shipped needed a pass/fail check before it touched a customer. That's acceptance-criteria thinking — the core skill of scoping what "done and safe" means for a feature.
I've spent years bringing together business, engineering, operations, and leadership to make informed decisions. My role has never been just delivering projects—it's been balancing customer needs, business priorities, risks, and execution to keep everyone moving in the same direction.
I've learned to communicate success in terms of outcomes, not activities. Whether it's efficiency gains, cost savings, risk reduction, or improved customer experience, I focus on the metrics that matter to business and product leaders.
Every metric below came out of shipping something real into a regulated financial-services environment, not a lab demo.
My experience has taught me that AI governance isn't complete without measurement. I've helped build solutions where trust is backed by metrics—monitoring drift, compliance, adoption, and operational outcomes—so decisions are driven by evidence rather than assumptions.
Throughout my career, I've relied on data to prioritize decisions, measure outcomes, and build trustworthy AI solutions. For me, analytics isn't a reporting function—it's the foundation for better products.
My experience spans the complete AI product lifecycle—from orchestration and enterprise integration to governance, analytics, and stakeholder reporting.
Over the last decade, I've worked across every stage of enterprise automation and AI—from development and solution architecture to leading transformation programs. Each role has strengthened my understanding of customers, technology, governance, and business outcomes, naturally shaping my transition into Product Management.
I design and lead enterprise AI and workflow automation initiatives, partnering with business and engineering teams to deliver scalable solutions. My focus extends beyond implementation to governance, stakeholder alignment, measurable outcomes, and long-term product value.
I led end-to-end automation initiatives across multiple business functions, balancing customer needs, technical feasibility, delivery, and governance to create solutions that could scale across the organization.
Working in a highly regulated BFSI environment taught me the importance of reliability, compliance, and risk-aware decision making—principles that continue to shape how I approach AI and product strategy today.
I built and deployed automation solutions firsthand, giving me a practical understanding of how systems behave in production and why designing for maintainability and user adoption matters.
This is where I developed the habit of understanding business problems before proposing technical solutions—a mindset that continues to guide how I think about products today.
Every enterprise shipping an agent that recommends, decides or pays on a customer's behalf needs the same four-layer governance spine. Today most teams build it once, bespoke, per project. The product opportunity is to make that spine configurable — so the same core ships into insurance, banking, healthcare or telecom, and adapts to whichever regulator is in the room.
The four control layers — policy configuration, guardrail checks, human-in-the-loop routing, and audit & traceability — stay constant. What changes per market is the rule set sitting inside them: disclosure thresholds, consent language, escalation SLAs and data-residency rules. A governance product should let a compliance lead configure that difference without a single line of code changing — the same instinct behind her existing policy-configuration work, applied at product scale.
Risk-tiered obligations for high-risk AI systems, transparency duties and human-oversight requirements — maps directly onto the human-in-the-loop layer.
A voluntary risk-management framework plus a growing patchwork of state-level AI and insurance-conduct rules — configuration has to flex state by state.
Data-protection consent rules plus sector regulators for banking and insurance — the exact regulatory pair her BFSI experience already sits inside.
Fairness, ethics, accountability and transparency principles for financial-sector AI — a natural template for the guardrail-check layer.
Outcomes-based conduct regulation for financial services — reinforces why "disclosure until source figure confirmed" has to be a configurable rule, not a hardcoded one.
The management-system standard and risk curriculum she's already mapped her four-layer model against — the constant core every local rule set plugs into.
Both are scripted, click-through prototypes she built to pressure-test the governance thesis above inside a concrete BFSI/insurance journey — the same journeys the deck below argues for. Click Next or the on-screen buttons to step through them right here.
A scripted claims conversation that surfaces the agent's reasoning trace live — intent detection, a blocked disclosure, an escalation to a licensed specialist, and a channel switch from chat to WhatsApp with full context carried over.
A full governed agentic journey — recommend, decide (AI or specialist hand-off), pay, recover from a failed payment, confirm and book — with a built-in recovery path when payment fails, so the customer is never left stuck.
This case study reflects how I approach product strategy—starting with a customer and business problem, identifying opportunities, evaluating risks and trade-offs, and translating them into a structured roadmap with measurable outcomes. While the example focuses on AI governance, the framework is applicable to building any enterprise product.
Defining the customer problem, business context, and why existing approaches fall short.
Identifying the product opportunity, target users, success metrics, and long-term product direction.
Breaking down the end-to-end experience, key workflows, governance requirements, and customer touchpoints.
Evaluating technical feasibility, business value, compliance considerations, and prioritization trade-offs.
Defining success through measurable outcomes, continuous monitoring, customer feedback, and iterative improvements.
Planning phased delivery, stakeholder alignment, adoption strategy, and scalable product evolution.
The full 13-slide "Agentic AI Governance & Validation" narrative, in PowerPoint.
Download .pptx ↗