Topic starter
09/07/2026 3:30 pm
TL;DR: Companies often start AI programmes by asking how to use or sell AI instead of defining the business problem first, which causes implementations to stall and obscures when human judgment should remain in the loop, according to Commvault. The practical issue is governance, not enthusiasm: without clear decision boundaries, data rules, and success measures, AI becomes a hammer looking for nails.
Editorial analysis by NHI Mgmt Group, based on content published by Commvault: “AI for AI’s Sake Is Not the Right Approach”.
Key questions
Q: How should teams decide whether AI is the right solution for a business problem?
A: Start with the operational problem, not the model.
Q: Why do AI projects stall when organisations start with the tool?
A: Because the team never defines what success looks like or who owns the decision.
Q: What do identity teams get wrong about data governance in AI platforms?
A: They often treat governance as a data-management layer rather than an identity and access layer.
Practitioner guidance
- Define the business problem first Write the operational pain point in plain terms before selecting any AI use case.
- Map human decision points Identify where a person must approve, override, or interpret output before a workflow can continue.
- Set data upload rules Specify which data classes may never be entered into external AI systems, and require review for anything that could expose customer, employee, or confidential operational information.
Bottom line: AI adoption fails when organisations define the solution before they define the operational problem.
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09/07/2026 3:51 pm
Problem-first AI adoption is now a governance requirement, not a design preference. When leaders start with the tool, they skip the control questions that make adoption safe and measurable. The result is stalled rollout, unclear ownership, and success criteria that track usage instead of outcomes. For identity programmes, the lesson is simple: technology choice comes after governance boundaries, not before.
A question worth separating out:
Q: What should teams do when AI output affects operational decisions?
A: Define explicit approval or override points before the workflow goes live. If the outcome affects people, money, or sensitive information, the model should support decision-making, not silently close the loop on its own.
👉 Read our full editorial: AI adoption fails when teams start with the tool, not the problem