Point of view for executives navigating technology change.
In-depth perspectives on AI governance, operating models, transformation and risk — written from two decades inside ECB-supervised European financial services. Not commentary. Practitioner notes.
AI Change Management in Financial Services: From Tool Adoption to Governed Human-AI Work
A practical AI change management framework for financial institutions: AI literacy, workflow redesign, trust, governance and measurable value.
AI in Financial Crime Prevention: From Rules-Based Monitoring to Evidence-Based Intelligence
How AI can strengthen financial crime prevention through graph analytics, better detection, investigator support, explainability, governance and measurable risk value.
Measuring AI Business Value in Banking: From Productivity Claims to Evidence-Based Value Realisation
How banks can measure AI business value with baselines, workflow redesign, risk-adjusted costs, Finance validation and board-level value governance.
Convolutional Neural Networks in Banking: From Pattern Recognition to Model Risk Governance
Convolutional Neural Networks, usually called CNNs, are one of the most important architectures in the history of modern artificial intelligence.
AI Governance in Banking: The Operating Model for Controlled AI at Scale
AI governance in banking is no longer a policy exercise — it is the operating model connecting the EU AI Act, DORA, GDPR, model and third-party risk.
AI in Banking: Europe’s Real Advantage Is Controlled Execution
Artificial intelligence in banking has moved beyond the innovation lab. The strategic question is no longer whether banks will use AI — but whether they can scale it without losing control.
The Hidden Cost of Technical Debt in Regulated Environments
Technical debt has stopped being an engineering concern. In regulated environments it has become a governance, resilience and compliance issue — and increasingly a board-level responsibility.
Your AI Pilot Was a Success. So Why Did It Never Scale?
There is no shortage of successful AI pilots. What fails is the transition from isolated success to enterprise capability — and that is a question of organisational maturity, not technology.
The Enterprise AI Operating Model
Most companies don't have an AI problem. They have an operating model problem. Everyone focuses on the models; almost nobody focuses on how AI should actually operate across the organisation.
Why Most Enterprise AI Projects Fail — and How to Build AI That Actually Scales
Enterprise AI rarely fails because the model is not clever enough. It fails because the organisation around the model is not ready.