The AI Strategy Roadmap: Moving from Pilots to Profit

Overview:

For business success, you need to create a measurable value story so you can move from prototypes to production and demonstrate ROI within the first 12 months. Unfortunately, AI pilots are easy to start and hard to scale. This session breaks down the most common and preventable reasons AI pilots aren’t successful and don’t survive contact with the business. You’ll learn how to structure an AI Strategy Roadmap that aligns stakeholders and prioritises use cases for business value. The session will also review ways to de-risk AI delivery.

Why you should Attend:

If your AI pilots keep producing demos instead of business outcomes, this webinar will show you how to turn experimentation into an execution plan that delivers measurable ROI within 12 months. You’ll leave with a practical roadmap structure, governance guardrails, and a way to prioritise use cases so you stop funding “interesting” work or vanity projects, and start funding outcomes.

FUD liner:

• If you can’t prove ROI in 12 months, your AI budget is already at risk
• Most AI pilots fail because there’s no roadmap, no ownership, and no path to value

Areas Covered in the Session:

• The "AI Pilot Trap": why pilots stall after the demo
• The 5-7 failure patterns behind most pilot programmes (people, process, data, governance, measurement)
• How to define ROI that the business will actually accept (not vanity metrics)
• Use-case prioritisation: selecting work that can deliver value in 12 months
• Data foundations: what must be true before scaling (and what can wait)
• Operating model + ownership: who needs to own what (and where it usually breaks)
• Governance and risk: avoiding Shadow AI and unmanaged model sprawl
• A practical 12-month roadmap structure (phases, milestones, decision gates)
• What to measure at 30/60/90 days to prove momentum early
• Q&A + "next steps" checklist

Who Will Benefit:

• CIO / CTO
• Chief Data Officer (CDO)
• Chief Digital Officer
• VP/Director of Data & Analytics
• Head of Data Science / Head of AI
• Head of BI / Analytics
• Enterprise Architect / Head of Architecture
• Director of Transformation / Digital Transformation Lead
• Product Leaders for Data/AI products
• COO / Operations Director (where AI is tied to operational efficiency)
• Risk / Compliance leaders involved in AI governance (where relevant)