Why Most AI Strategies Fail Before They Start
The biggest risk in enterprise AI isn't the technology. It's the gap between what vendors promise and what your organisation can actually execute.
The biggest risk in enterprise AI isn’t the technology. It’s the gap between what vendors promise and what your organisation can actually execute.
The Promise vs. Reality Gap
Every week I speak with senior leaders who’ve been sold an AI transformation. The pitch decks look incredible. The ROI projections are compelling. The vendor demos are flawless.
Then reality hits.
Six months in, the pilot project is stalled. The data isn’t clean. The team doesn’t have the skills. The business case that looked bulletproof in a boardroom presentation falls apart when it meets operational reality.
Three Patterns I See Repeatedly
1. Technology-first thinking
Starting with “we need AI” instead of “we have this specific problem” is the most common mistake. AI is a tool. If you don’t have a clear problem to solve, you’re just buying expensive software.
2. Underestimating the data challenge
Most organisations don’t have an AI problem. They have a data problem. Before you can do anything meaningful with machine learning, you need clean, accessible, well-governed data. That’s not a six-week project.
3. Ignoring the people
The best AI implementation in the world fails if the people who need to use it don’t trust it, don’t understand it, or weren’t consulted in the design process.
What Actually Works
The organisations I see succeeding with AI share a few traits:
- They start small with a specific, measurable business problem
- They invest in data foundations before buying AI tools
- They bring their people along from day one
- They measure results honestly, not just activity
AI isn’t magic. It’s engineering. And like all engineering, it works best when you start with a clear problem and build from there.