Data Strategy in the Age of AI: Building the Retrieval-Ready Enterprise

Most enterprise AI failures are data failures one or two steps removed. Pilot demos land, production rollouts stall, retrieval quality drifts, and the diagnostic always points back at the same place: the data the organisation had learned to live with was never retrieval-ready. Fixing that is not a data-lake line item. It is a discipline.

Data Strategy in the Age of AI installs that discipline. It sits on the data layer of the enterprise AI stack and treats it with the same principle-first rigor the earlier volumes used on architecture, programs, security, and strategy. Written for the CDO, CIO, and CTO who have to make the data layer actually work under an AI program, the book names the consumer discontinuity between BI and retrieval, walks the seven canonical pipeline stages, and prescribes the capture, quality, retrieval, governance, and measurement moves that produce retrieval-ready, AI-grade data.

• Data debt as a distinct category: Why BI tolerated it silently and AI cannot.
• A stage-by-stage diagnostic across capture, ingestion, cleaning, transformation, storage, indexing, and retrieval.
• The capture decision as the upstream leverage point.
• Cleaning as an adversarial, not janitorial, move under a probabilistic consumer.
• Retrieval-readiness as a measurable property: the retrieval-eval discipline the model-switch will not save you from.
• The data-product mindset and lineage as operational control planes.
• Proprietary data as moat, with realistic erosion timelines.
• Synthetic data: where it helps and where it harms.
• The second-order flywheel: decided by the correction-signal architecture.
• Data governance under AI: the CDO-CIO-CTO triangle that AI destabilises.
• The CFO-legible data dashboard: the metrics that survive a model-switch.
• Industry and region patterns across healthcare, finance, industrials, media, retail, and the public sector.

CDO, CIO, CTO, Chief AI Officer, Chief Transformation Officer, and the CFO who is about to approve another tranche of data-platform spend. Senior data leaders (heads of data platform, heads of analytics, data engineering leads) and management consultants advising at this altitude.

This is Book Five of The AI Black Book, a principle-first reference series for enterprise leaders. Book One (Enterprise AI Agents) installs the architectural vocabulary. Book Two (The AI Transformation Playbook) moves programs out of pilot purgatory. Book Three (AI Security for the Enterprise) clears the security gate. Book Four (AI-Native Business Models) frames the board-level reinvention. Book Five drops back to the substrate every other volume depends on: the data layer.

Principle-first. Vendor-free. Written to survive two model generations.

Mehul Jain is an AI practitioner and founder who helps enterprises move AI from strategy to production. His work spans the full arc of The AI Black Book.

Across two decades of consulting and founding work, he has advised large enterprise clients across financial services, insurance, consumer goods, and telecommunications.

He currently runs three companies: Finigami, a document extraction platform; Geology, a GEO product that helps brands appear in AI-powered search; and Litmus Check, an AI-first platform quality assurance automation.

He holds a degree in Computer Science from IIT Bombay and an MBA from INSEAD with an exchange at Wharton. His first book, Introduction to Gen AI for Curious Developers, was published in 2024 and sells in five countries.