Why AI Adoption Fails

Studies consistently show that around 70% of AI projects fail to deliver their expected business value. The reasons are rarely technical. The most common causes are:

No governance: AI tools are deployed without oversight, risk classification, or clear ownership.
No measurement: Organisations cannot demonstrate ROI because they never defined baselines or tracked adoption metrics.
No change management: Employees are handed new AI tools without training, context, or incentives to adopt them.

The 6 Dimensions of AI Readiness

Successful AI adoption requires maturity across six dimensions. Weakness in any single dimension creates risk:

Strategy: Clear AI vision, executive sponsorship, and alignment with business objectives.
Technology: Infrastructure, data quality, integration capabilities, and security posture.
People: AI literacy, change readiness, champion networks, and skills development.
Process and governance: Policies, approval workflows, risk classification, and compliance frameworks.
Culture: Whether it is safe to experiment, and whether teams share what works.
Measurement: Baselines, adoption tracking, value attribution, and reporting that reaches leadership.

Getting Started

The most effective approach to AI adoption follows three steps:

Start with an assessment: Understand where you stand across all six dimensions before investing in tools or training.
Identify quick wins: Prioritise use cases that are high-impact and low-effort to build momentum and prove value early.
Build governance early: Establish agent registration, risk classification, and oversight processes before scaling, not after.