Why 80% of AI projects fail (and how to prevent it)
The majority of AI projects come to grief not on the algorithm, but long before that. Organisations invest in tools without knowing exactly which problem they are solving. In this article we share three patterns we see time and again, and how you can break them.
1. There is no clear strategy
Many organisations start an AI initiative because “the board wants it” or because a competitor is doing it. But without a clear question, which business problem are we solving, for whom, and how do we measure success, the project quickly becomes an experiment with no destination. The first step is always: start with the problem, not with the technology.
“Most AI projects fail not because of the technology, but because of the lack of a clear goal.”
2. Too much focus on tools, too little on people
Adopting a new AI tool is easy. Getting people to embrace a new way of working is the real work. Resistance does not come from unwillingness, but from uncertainty. The teams we guide need safety, concrete examples and room to practise. The choice of tool is secondary to the change in culture.
3. No iterative approach
Organisations want too much too soon. They define a large scope, build in silence for months, and only launch once everything is “finished”. But AI applications call for iteration: start small, test quickly, learn from users. A working two-week pilot yields more insight than a perfectly worked-out six-month plan.
Marc helps organisations put AI to meaningful use, from strategy to implementation.
Follow on LinkedIn ↗