Why initiatives fail — and how to design them for success.
For leaders and senior professionals who sponsor, approve, govern, or deliver Data & AI initiatives.
Inquire About Schedulingof data scientists' time is spent on data quality issues — not building machine learning models.1
of revenue is lost to data that was never right at the source.1
1 Redman, T.C., "Seizing Opportunity in Data Quality," MIT Sloan Management Review, 2017.
Most Data & AI initiatives don't fail because of technology — they fail because they lack clarity: no shared understanding of how enterprise data actually works, no clear view of complexity and common failure patterns, and no reliable way to assess maturity and know what is needed to advance to the next stage. This course builds that clarity and provides a practical framework for building initiatives that succeed.
How enterprise data actually flows — from source systems to reports, dashboards, and AI.
Understanding why initiatives that look simple on paper become unexpectedly complex in practice.
Identify and name the patterns through which data and AI initiatives fail.
A structured, honest assessment of where your organisation sits today and what it takes to advance.
A practical decision framework for designing data and AI initiatives that succeed.
Practical guidance on what Generative AI requires to succeed — and where it genuinely adds value.
Built from real delivery experience — not theory, not vendor frameworks, and not isolated use cases.
12 years building Data & AI capabilities across leading banks in Kuwait — National Bank of Kuwait, Boubyan Bank, and Gulf Bank. Built Kuwait's first AI-powered banking recommendation and spending-insights features at Boubyan Bank, and led enterprise data strategy at Gulf Bank.
PhD in AI, Universidad Politécnica de Madrid, with a Master's in Statistics from Universidad Carlos III de Madrid. AI researcher with hands-on experience across international public and private sector projects, including consulting for the European Commission in Brussels.
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