CDO / CAIO GuideTwelve Decisions

From Data Platform to Pharma Decision Infrastructure.

Twelve questions on ontology, architecture, autonomy, governance and compounding intelligence that determine whether AI becomes proprietary advantage in Pharma, not just proprietary spend.

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What's inside the guide.

Twelve CDO/CAIO-level questions, from the Enterprise Decision Portfolio to ontology, build/buy/own, autonomy and governance.

  1. 01Strategy: what should our AI strategy actually optimise for?
  2. 02Data foundation: what does AI actually require?
  3. 03Ontology: why should it become core to the architecture?
  4. 04Proprietary advantage: what stays ours as models commoditise?
  5. 05Build, buy, partner: what goes where?
  6. 06Architecture: how do RAG, graphs, causal reasoning and agents fit?
  7. 07Operating model: centralised or federated?
  8. 08Funding: which bets deserve disproportionate capital?
  9. 09Autonomy: how autonomous should AI become?
  10. 10Governance: how should it change?
  11. 11People: what happens as AI scales?
  12. 12Compounding value: how does AI get more valuable every year?
12 CDO/CAIO-level questionsFrom the Enterprise Decision Portfolio to ontology, build/buy/own, autonomy and governance.
A full target architectureThe seven-layer stack from systems of record to the experience layer, with Enterprise Context at its centre.
A 12-month agendaConcrete moves for 0 to 90 days, 3 to 6 months, and 6 to 12 months, not a five-year programme.

68%

of pharma and biotech leaders cite neglecting data quality and governance early as the main reason AI initiatives fail.

ZS, 2026 CDIO Outlook · cited in the guide

More guides from The Decision Desk.

Seven guides, from the CEO agenda to the plant floor. Each is a ranked, sourced priority sequence, built on research from Bain, McKinsey, Deloitte, BCG, EY, ZS and MIT NANDA.

The Decision Desk · CDO / CAIO Guide

CDO/CAIO Guide for AI in Pharma.

Twelve questions. One architecture. Free, no spam, no sales pitch first.

Backed by ZS, Gartner, Deloitte and MIT NANDA research

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