Cracking the GenAI ROI Paradox: Turning Pilots into Profit-Driving Products

Why do nearly all Generative AI pilots stall before delivering measurable impact? Despite massive investment, most GenAI initiatives never make it past experimentation. Fragmented ownership, weak governance, and underestimated production costs leave product leaders accountable for outcomes they cannot clearly defend.

In this practical webinar, Oracle Product Lead Rakshana Balakrishnan breaks down what actually separates stalled pilots from scalable, profit-driving GenAI products.

In this session, you’ll learn:

• The most common failure patterns that keep GenAI stuck in pilot purgatory
• A proven framework for moving GenAI from experimentation to governed, production-ready products
• How leading organizations align product, engineering, legal, and finance to unlock ROI
• What it takes to scale GenAI initiatives that withstand executive, security, and compliance scrutiny

Product leaders will walk away with concrete, immediately applicable guidance to evaluate GenAI investments, make defensible product decisions, and lead AI initiatives that deliver real business outcomes.

Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s event below, then head on over to our Events page to see which product leaders will be joining us next week.

Show Notes:

1. 95% of enterprise GenAI pilots fail due to unclear ROI measurement, causing them to languish in “pilot purgatory.”
2. A GenAI use case must fundamentally change user behavior/decisions to drive meaningful ROI.
3. Lack of economic discipline is a common pitfall in GenAI adoption—organizations rush pilots to follow trends.
4. Monetization and ROI should be the primary focus before launching GenAI pilots.
5. The PACE framework provides a structured approach for evaluating GenAI opportunities: Prioritization, Audience, Choice, Economics.
6. “P” is for prioritization of problems with measurable metrics; only use cases with tangible, quantifiable outcomes should proceed.
7. Measurable metrics include cost savings, productivity gains, revenue increases, and risk reduction.
8. “A” is for segmentation of use cases by audience—internal (focus on costs/productivity) vs external (focus on revenue/experience).
9. Internal GenAI uses enable “fail fast” pilots and typically drive cost reduction and learning.
10. External GenAI products are higher-stakes, requiring stricter quality, compliance, and greater investment but targeting revenue growth.
11. “C” is the choice between building or buying GenAI, weighing trade-offs in differentiation, resources, development time, and compliance.
12. Building from scratch offers control and potential IP, but increases time, cost, and operational overhead.
13. Buying off-the-shelf GenAI tools is faster, easier to secure, and shifts maintenance/innovation to the vendor.
14. Security, continuous innovation, and regulatory compliance are mandatory concerns for both build and buy strategies.
15. “E” is for economics evaluation—use cases need solid financial business cases, including multi-year ROI projections.
16. Applying PACE results in four strategic categories: starter bets (internal/quick), quick internal wins, long-term external bets, and non-starters.
17. Non-starters (low ROI, long time-to-market, little impact) should be cut early to avoid wasted investment.
18. Cross-functional advisory boards (product, engineering, security, platform) are essential for oversight and go/no-go decisions.
19. Regular monitoring is critical: Use cases must continually meet ROI, quality, safety, and business relevance to continue investment.
20. Numbers matter—decisions regarding killing, pausing, or investing in pilots must be analytically defensible, not just intuition-driven.