Why most AI projects fail (and it’s not about the technology)
Across financial services and energy sectors, organisations are investing billions in AI transformation. Yet, research from Gartner indicates that only 54% of AI projects make it from pilot to production, whilst MIT research shows that 95% of organisations are getting no return at all from AI projects.
We ask, why?
The Governance Gap
Most AI failures stem from inadequate or outdated governance frameworks established before development begins. Organisations rush to innovate, deploying pilots and proofs of concept without addressing fundamental questions:
Who owns the model's decisions?
How do we ensure regulatory compliance across jurisdictions?
What happens when the AI produces an unexpected result?
How do we maintain an audit trail that satisfies regulators?
In highly regulated sectors like financial services and energy, this governance gap becomes critical.
The challenge only intensifies as regulatory requirements evolve: looking forward, the EU AI Act and sector-specific guidelines from the likes of FCA and Ofgem, demand robust governance frameworks. Yet many organisations are attempting to retrofit compliance onto AI systems that were designed without these considerations.
The Cost of Getting It Wrong
When AI governance is an afterthought, enterprises face a painful choice: either abandon projects after significant investment, or retrofit governance to significant additional expense.
Beyond sunk costs, poor governance creates operational and reputational risks. Models that drift without monitoring can make increasingly poor decisions. Systems without proper explainability face regulatory action. And in worst-case scenarios, governance failures lead to the kind of headlines that destroy stakeholder confidence.
What Successful AI Transformation Requires
Leading organisations:
establish clear accountability structures that assign ownership for model performance, bias monitoring, and regulatory compliance.
implement robust monitoring frameworks that track model behaviour in production.
ensure AI innovation aligns with regulatory requirements and enterprise risk appetite through cross-functional governance committees.
Critically, they recognise that effective AI governance is about enabling sustainable innovation. With proper frameworks in place, organisations can move faster, knowing their AI systems meet both regulatory standards and internal controls.
The Path Forward
For CTOs, CIOs, and CSOs, the message is clear: AI transformation isn't just a technology challenge. It's a governance challenge that requires executive-level attention and cross-functional collaboration. The organisations succeeding at enterprise AI aren't necessarily those with the most sophisticated algorithms; they're the ones that have built the governance foundations to deploy AI at scale, manage risk effectively and demonstrate compliance to regulators.
Is your AI governance framework ready for enterprise-scale deployment, or are you building on foundations that will cause your next AI project to fail?
How WeBuild-AI Can Help
At WeBuild-AI, we help enterprises to navigate AI transformation successfully.
Our approach combines technical expertise with a deep understanding of organisational change, ensuring that AI capabilities translate into continued business value. We are AI-native and pride ourselves on providing x10 value for enterprises through our solutions.
