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Ravens AI Academy Series
Trust by Design: Why AI Projects Fail Before They Start
The Business Case and Governance Blueprint for Enterprise AI — the Roles, Accountability, and Governance Structure That Prevent It
Every disappointing AI initiative has a technical postmortem explaining the model's limitations. Rarely does that postmortem name the actual root cause: the organization never trusted its own data enough to build on it with confidence, and no one had assigned clear accountability for fixing that before the project began.
Trust by Design makes both halves of the argument in one volume — the leadership and financial case for treating data trust as an investment, and the concrete governance structure of roles, committees, and accountability models that sustains it.
Inside the book
- Part I — The Trust Deficit: why AI projects fail before they start
- Part II — The Cost of Bad Data: quantifying data quality and failed initiatives
- Part III — Building the Business Case: ROI models and speaking the CFO's language
- Part IV — The Stewardship Imperative: ownership vs. stewardship
- Part V — Roles and Structures: CDO, data owners, stewards, AI governance committee
- Part VI — Accountability Models: RACI, escalation paths, performance measurement
- Part VII — Committees and Governance Design: councils, AI review boards, data-trust culture