AI investment without governance: what is blocking productivity?

TL;DR: European companies have invested heavily in AI models but still lack the organisational adaptation needed to turn that spend into productivity, according to Asenion. The gap is not model capability alone, but the weak alignment between technical teams, risk teams and business teams.

Editorial analysis by NHI Mgmt Group, based on content published by Asenion: “(AI) People Podcast”.

Key questions

Q: How should organisations govern AI programs before scaling them enterprise-wide?

A: Organisations should define ownership, data quality checks, approval gates and retirement criteria before AI use cases proliferate.

Q: Why do AI programmes often fail to deliver productivity gains?

A: Because capability alone does not create usable value.

Q: What are the signs that AI governance controls are not keeping pace with adoption?

A: Common warning signs include unclear ownership for AI use cases, inconsistent approval processes, limited visibility into where sensitive data enters models, and weak evidence for audits or assessments.

Practitioner guidance

• Define AI governance ownership Assign named owners for use-case approval, risk review, exceptions, and retirement so responsibilities do not diffuse across technical and business teams.
• Build a live AI use-case register Track each AI use case, its purpose, risk tier, approval status, and review date so leaders can see where adoption is outpacing control.
• Align operating teams before scaling deployment Bring technical, risk, legal, and business stakeholders into a shared decision path so production use does not depend on informal coordination.

Bottom line: AI programmes do not fail only because the models are weak. They also fail when governance, ownership and business alignment remain immature.

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