From Data to Decisions: Turning Enterprise AI into Real Business Outcomes
summary
Claim: Gartner finds 5% of projects deliver value despite million‑dollar bets and promised 20x ROI. Root causes are concrete: misdefined problems (the elevator/mirror example), opaque models and black box at scale, absent or uninformed humans‑in‑the‑loop, and organizational unreadiness on tech and culture. Recommended fixes: decision‑first design, involve stakeholders early, match initiatives to current capabilities, build feedback loops, prefer explainable solutions and multidisciplinary governance with responsible‑AI guardrails.
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chapters (10)
- 0:00Introduction
- 3:51AI projects failing value
- 7:43AI failures real problem
- 11:12Organizational AI readiness
- 13:16AI strategy beyond hype
- 15:19AI implementation pitfalls
- 17:06Company culture AI adoption
- 19:55AI literacy paths
- 21:59Responsible AI for teams
- 23:04AI readiness next steps
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