Why AI Projects Fail After the Pilot and How to Scale
At IBM TechXchange, Data Products led Tech Talk 06: “Why Most AI Projects Fail After the Pilot and How Not To!” Our Chief AI & Data Strategist, Dr. Mechie Nkengla, shared the stage with Michael Pompey for a fast, high-energy session that pulled a full crowd. We opened with a live poll: Why did your last pilot stall? The responses matched our slide—no executive owner past the demo, data not production-grade, governance friction, weak user adoption, and costs spiking at scale. From there we pivoted from problems to patterns that actually ship.
Captive Audience
What we covered:
• Ownership past the pilot. Appoint a business exec with budget, KPIs, and decision rights.
• Production-ready data & integration. SLOs, lineage, rollback paths, secure connectors—before feature work.
• Lean, risk-tiered governance. Guardrails as an enabler, with auditable controls and fast paths for low-risk use cases.
• Adoption by design. Champion users, change enablement, task-level uplift metrics, and usage goals per release.
• Unit economics for AI. Track run-cost, reliability, and value per workflow so scale follows ROI, not hype.
The audience Q&A pushed into execution: defining POC exit criteria, running a two-sprint hardening phase, and launching to 2–3 early teams with clear service ownership and an ops playbook (observability, rollback, data quality gates). We also walked through our “pilot-to-program ladder”: Discovery → Proof → Hardening → Production → Scale, with measurable gates at each step.
If this resonates, if your pilot is stuck, or you’re mapping a 2026 roadmap, let’s turn insight into an action plan! We’ll run a 60-minute diagnostic, score your readiness across data, governance, adoption, and economics, and outline a 90-day path to production.
Ready to do a deep dive on your Data & AI Strategy? Connect with us now to discuss.