AI Product Metrics: Why Adoption and Engagement Are No Longer Enough

The Core Argument

Guided by host Sanjay Saini, Senior Product Manager Tanvi Goel explained that traditional software metrics—like daily active users, session length, and pure adoption—are rapidly losing relevance in the AI era. Because Generative AI is non-deterministic, measuring "success" now requires evaluating the intelligence of the output itself. Tanvi argued that Product Managers must evolve their KPIs to track groundedness, precision, hallucination tolerance, and autonomous tool-selection rates to guarantee they are building trustworthy, high-value AI products.

Detailed Answers

1. Could you give a brief intro about yourself and your professional background?

Tanvi Goel brings nearly 15 years of tech industry experience. Starting as a Business Analyst, she transitioned into Product Management, spending the last decade building robust B2B and B2C SaaS products specifically within the FinTech and enterprise banking domains. She currently serves as a Senior Product Manager.

2. Why do you say we should rethink traditional product metrics?

For decades, PMs relied heavily on acquisition and retention metrics, assuming that the longer a user stayed on an app, the more value they were getting. Tanvi argued that AI flips this paradigm. An AI's core value proposition is speed and personalization. If a user spends ten minutes navigating screens to find an answer, the AI has failed. The new goal is delivering precise intent matching in seconds.

3. What is the difference between B2B and B2C product metrics?

While the core objective of solving customer problems remains the same, the environments are vastly different. B2C (like Zomato or Uber) focuses on individual downloads and direct engagement. B2B (like CRM systems) deals with long, highly negotiated sales cycles and multiple user personas (procurement, legal, end-users), requiring metrics centered on Annual Recurring Revenue (ARR) and Average Contract Value (ACV).

4. How does AI change the game of product measurement?

Traditional software is rules-based and deterministic. Generative AI is probabilistic. If a company builds an internal Knowledge Assistant using RAG (Retrieval-Augmented Generation) for HR policies, the system must not only retrieve the correct version of the document but also synthesize it perfectly. This introduces data science metrics into product management, specifically: Accuracy, Precision, and Recall.

5. How should recommendation engines impact Click-Through Rates (CTR)?

Using Netflix as an example, the overarching corporate goal is increasing viewers per week. An effective AI recommendation engine must achieve an incredibly high CTR on the very first screen. If users are forced to flip through multiple pages to find content, it indicates the AI has failed to understand their intent based on demographic and watch-history data.

6. How do you handle and measure AI hallucinations?

Hallucinations occur when an LLM loses context and fabricates facts. To mitigate this, PMs must enforce "explainability" by displaying citations linked to the grounded source material. Furthermore, tolerance levels must be strictly measured based on the domain. A creative writing assistant can safely tolerate a 2% hallucination rate, whereas a banking or legal tool requires near-absolute deterministic certainty.

7. What new metrics apply to Agentic AI and autonomous agents?

Agentic AI differs from conversational AI because agents have autonomy, memory, and the capability to trigger external tools. Tanvi detailed entirely new metrics required for orchestration: Tool Calling Rate, Retry Rate, and Tool Selection Rate (e.g., measuring how accurately a multi-agent orchestrator decides to route a customer complaint to the Payment Engine versus the Order Management System).

8. What is a Forward Deployed Product Manager?

The role of the PM is evolving rapidly alongside AI coding tools. A "Forward Deployed Product Manager" no longer stops at creating wireframes and mockups. Armed with advanced "spec-to-code" AI development platforms, these PMs can now rapidly build, test, and deploy production-ready applications themselves directly for the client, merging the roles of product and engineering.

Tanvi's shift from engagement metrics to groundedness, precision and tool-selection rates is a measurement skill most product teams don't have yet — it is the evaluation work our Artificial Intelligence training program covers.