GLOSSARY

AI Strategy

An AI strategy defines where AI creates value, how the firm builds or buys capability, and what governance (NIST AI RMF, ISO/IEC 42001) protects data and brand.

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Quick answer
An AI strategy is a written plan that ties AI investment to business outcomes. It covers the use-case portfolio, build-versus-buy posture, data readiness, governance, talent model, platform choices, and a sequenced roadmap — so AI spend becomes a program, not a pilot graveyard. Strategies aligned to NIST AI RMF and ISO/IEC 42001 travel further with boards and auditors.

WHAT IT IS

A credible AI strategy covers five components: a use-case portfolio scored on value and feasibility; a build-vs-buy posture per use case; a data and platform foundation; a governance model aligned to frameworks like the NIST AI Risk Management Framework (AI RMF 1.0) and ISO/IEC 42001; and an operating model that clarifies who owns AI inside the business. Without all five, pilots proliferate and production value stalls.

HOW IT WORKS

Strategy work typically runs in four stages: diagnosis (AI maturity, data readiness, opportunity mapping), portfolio design (prioritized use cases with expected impact), target architecture (data, models, integrations, human-in-loop), and change plan (roles, training, vendor decisions, responsible-AI controls).

WHEN TO USE

Commission AI strategy when pilots are multiplying without production outcomes, when a board or regulator asks for responsible-AI governance, or when the next three-year plan depends on AI-differentiated offers.

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Related questions.

What is an AI strategy?
An AI strategy is a written plan that ties AI investment to business outcomes. It covers the use-case portfolio, build-vs-buy posture, data readiness, governance, talent model, platform choices, and a sequenced roadmap — so AI spend is a program, not a pilot graveyard.
Who owns AI strategy inside an enterprise?
In practice, a CTO, CIO, or Chief Data Officer sponsors it, with the CEO signing off on the business cases. A lasting AI practice also needs a named accountable leader — often titled Head of AI, Chief AI Officer, or Director of AI — who owns the roadmap and reports on outcomes.
How long does an AI strategy take to build?
A rigorous strategy takes eight to sixteen weeks: three to four weeks of current-state assessment, three to four weeks of use-case discovery and sizing, and the balance on roadmap, governance, and business-case writing. Anything faster is a slide deck, not a strategy.
How does AI governance fit into strategy?
Governance is part of the strategy, not an afterthought. It defines data policies, model-risk tiers, approval workflows, monitoring, and incident response. Strategies aligned to frameworks like NIST AI RMF and ISO/IEC 42001 travel further with auditors and boards.
How does NUUN AI run strategy engagements?
We run a standardized 8–12 week AI strategy engagement covering use-case portfolio, data readiness, platform decisions, governance, and a sequenced roadmap tied to a financial model. The output is a plan the board can fund, not a think-piece.

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