How to make sure your AI Project Succeeds

After years of managing technology transformations, I've learned that you cant treat AI like "just another IT project". There are critical key differences, including mindset and approach.

AI projects aren't traditional tech projects with a new label. They operate under fundamentally different rules:

✅ Traditional projects deliver deterministic solutions to well-defined problems ❌ AI projects develop probabilistic systems that learn, adapt, and require continuous stewardship

This distinction matters across every phase of delivery:

✴️ 𝐏𝐡𝐚𝐬𝐞 1 - 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧: Not every business problem needs AI. Success starts with asking: "Is this the right AI investment?" rather than "How do we use AI?"

✴️ 𝐏𝐡𝐚𝐬𝐞 2 - 𝐎𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬:  You can't retrain developers into data scientists. AI demands specialised skills, sophisticated data governance, and a culture that embraces experimentation over zero-defect deployment.

✴️ 𝐏𝐡𝐚𝐬𝐞 3 - 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧: AI pilots aren't scaled-down deployment, they're learning experiments designed to test edge cases, identify bias, and validate assumptions about data quality.

✴️ 𝐏𝐡𝐚𝐬𝐞 4 - 𝐏𝐢𝐥𝐨𝐭 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: Unlike software bugs that get fixed, AI models require iterative refinement. Validation must test fairness, user trust, and real-world performance degradation, not just functionality.

✴️ 𝐏𝐡𝐚𝐬𝐞 5 - 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: AI requires MLOps, not just DevOps. Models degrade over time. Deployment must include continuous monitoring for model drift and human-in-the-loop workflows.

✴️ 𝐏𝐡𝐚𝐬𝐞 6 - 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐬𝐚𝐭𝐢𝐨𝐧: AI is never "finished." Successful AI transforms from a project into an organisational capability through continuous learning, strategic roadmapping, and ethical auditing.

The bottom line? Organisations that recognise these differences avoid predictable failures: models that work in testing but fail in production, governance gaps that create ethical risks, and ROI that never materialises.

I've created a comprehensive guide breaking down the 6 critical phases of AI project delivery and exactly where AI considerations diverge from standard project management.

📥 Download the full presentation to learn: • The questions executives must ask at each phase • AI-specific considerations that traditional frameworks miss • How to build organisational capabilities for AI success

What's been your biggest challenge in AI project delivery?