Watch Desk posted an update
Enterprise AI projects can work technically and still fail to deliver in the business, argues BMC executive Abhijit Kakhandiki. His prescription: start with a measurable business result, not a more elaborate model stack.
Why it mattersKakhandiki says organisations should build traceability and accountability into workflows, then treat AI like other mission-critical systems, with monitoring, recovery plans and service-level commitments. One example in his Forbes essay: an AI model worked as intended, but the service around it was not covered by the company’s SLA, and business commitments were missed. A useful reminder that “the model worked” is not the same as “the business worked”.
Discuss: Should companies prove business value before scaling AI, or does scaling help them discover where the value is?
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