Why do 85% of AI projects fail?
Not because the models do not work. Because objectives are fuzzy, data foundations are weak, nobody owns the project after the pilot, and change management gets skipped.
The failure rate is real, and the causes are consistent across industries. Top of the list: unclear objectives. Teams start with "let us try AI" instead of a specific problem to solve, and the project drifts until the budget runs out.
Data quality is the second silent killer. Models are only as good as the data they run on. Incomplete records, inconsistent definitions across systems, and poor access controls all quietly sabotage AI efforts from the start.
The last big cause is integration and adoption. A working model that never makes it into a real workflow is a science project. A workflow that the team does not trust or use is also a failure. Strong consultants spend as much time on scoping, change management, and success metrics as they do on building.
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