In the late 90s, while working as a mainframe programmer at a local council, I watched a technology decision that stayed with me. A strong case was made to move away from Oracle. The business argument was sound, the roadmap case was clear, and the alternative looked compelling. The decision was still to stay put.
The reason offered was not technical or strategic. It was protective: the team was invested in the stack. In the years that followed, I saw that pattern repeatedly. Projects were delayed, diluted, or quietly derailed for reasons that had little to do with end-user value.
The Familiar Failure Pattern
When Forbes published a piece on why many AI projects fail, skeptics circulated it as proof that AI itself is the issue. But the key part of that conversation is what organisations can do about failure, not just the failure headline.
The root causes are not new. They are the same factors that have sunk IT initiatives for decades.
- Alignment failure: strategy says one thing while functions execute in different directions.
- Cultural friction: technology change is people change, and most organisations underinvest in that transition.
- Execution weakness: poor design, weak governance, and fragmented ownership compound over time.
Shadow AI Is a Symptom
Many employees already use personal AI tools at work, often ahead of official policy. That gap between lived workflow and formal governance is a signal of organisational disconnect, not just tool novelty. I have seen this play out with every significant platform shift of the last 30 years. The technology almost never fails first. The adoption model does. AI is not exempt from that pattern.
IT teams focus on risk and performance, HR teams focus on culture, and line managers are left to absorb operational tension. Without deliberate integration across all three, adoption stalls even when the tools are capable.
Bottom Line
Most AI project failures are not fundamentally AI failures. They are leadership, alignment, and execution failures. Exactly the same categories that undermined major IT programmes long before modern generative models existed.
Whether you are a founder, executive, manager, or practitioner, the decision to adopt emerging technology is always a gain/loss calculation. That is precisely why the decision deserves more rigor than headline reactions.