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    Topic cluster

    AI Strategy

    Making defensible investment decisions about AI.

    What does ai strategy cover?

    Deciding what to automate, in what order, with what budget — and how to prove it worked.

    Sub-topics

    • Governance & Risk

      Policy, oversight, data protection and audit trails for AI systems operating inside a business.

    • Models & Providers

      Comparing foundation models, providers, pricing and capability trade-offs for business workloads.

    Definitions in this cluster

    • Return on investment

      Return on investment for AI automation is the net benefit — labour hours recovered, revenue gained, error cost avoided — divided by total cost of ownership, including build, licences, maintenance and change management.

    • Total cost of ownership

      Total cost of ownership is the full lifetime cost of an AI system: implementation, model and infrastructure spend, integration upkeep, monitoring, retraining, and the internal time spent supervising it.

    • Build vs buy

      Build vs buy is the decision between developing an AI capability in-house and licensing an existing product. The deciding factors are whether the capability is a competitive differentiator, how specific your data is, and whether you can staff maintenance.

    • Proof of concept

      A proof of concept is a time-boxed build that tests whether an AI approach can meet a defined success threshold on real data, before committing to production investment.

    Guides & analysis