From AI Pilot to Enterprise Impact: How to Scale AI the Right Way

What separates AI pilots that make headlines from AI products that actually transform the business? In this webinar, Executive Director of Product Management Nirmaljeet Malhotra will be speaking on how to move from AI Pilot to enterprise impact. Drawing on experience leading large-scale AI and product strategy initiatives, he unpacks why most AI projects stall at the pilot stage and introduces a practical maturity framework to help organizations build AI that drives operational efficiency, revenue impact, and long-term trust.

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Show Notes:

1. Most enterprise AI failures are not model failures, they are product and system failures.
2. Over 80% of AI projects never reach production, highlighting a scaling—not experimentation—problem.
3. AI pilots optimize for technical feasibility; scaled AI requires operational accountability.
4. Success in AI depends more on product maturity than model sophistication.
5. The AI Product Maturity Curve: Experiment → Embed → Scale → Govern.
6. Experiment proves possibility, but it does not prove business impact.
7. Embedding AI into real workflows is the most fragile and critical stage.
8. Trust breaks down when AI outputs lack explainability or ownership.
9. Scaling AI is about platformization, not duplicating isolated use cases.
10. Reusable infrastructure (data pipelines, monitoring, governance layers) compounds ROI.
11. Governance enables scale—it does not slow it down.
12. Explainability, bias detection, and auditability are non-negotiable in regulated industries.
13. AI transformation is a product leadership initiative, not just a data science initiative.
14. The product leader’s role evolves: translator → workflow architect → platform strategist → trust steward.
15. AI systems designed for augmentation outperform those designed for full automation.
16. Model metrics (precision, recall, latency) matter—but business KPIs determine success.
17. AI roadmaps must connect directly to P&L levers like cost reduction, revenue lift, and risk mitigation.
18. Leading and lagging indicators should be clearly defined before scaling any AI initiative.
19. Data readiness and governance are foundational investments, not overhead.
20. Long-term AI advantage comes from strong systems integration, organizational alignment, and trust by design.