Business & technology / AI Consulting Services Wiki

Research and resources

Explore sources on AI services, adoption, evaluation, data engineering, governance and implementation, with reading paths and downloadable chart data.

Reading paths

  • Services and deliverables

    Compare advisory, development, integration, training and managed services.

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  • Strategy and readiness

    Connect business objectives, data, risk and adoption to a staged roadmap.

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  • Data and technology

    Understand predictive models, generative AI, RAG and MLOps.

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  • Governance and security

    Map framework concepts to responsibilities and testable controls.

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  • Adoption and research

    Keep business-use surveys separate from worker experience and experiments.

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  • Provider selection

    Compare proposals through scope, evidence, ownership and operating support.

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How to interpret the evidence

Government surveys describe defined populations and observation periods. Experimental research evaluates particular tasks and conditions. Technical documentation explains methods and operational choices. Provider descriptions establish what a provider says it offers; they do not independently verify business results.

The source directory matches the article. The chart data CSV retains the values and populations behind the three original graphics.

Versions and updates

The consulting experiment uses Harvard Business School’s April 2026 published-study recap. Worker-use data incorporates the Census Bureau’s September 2026 weighting revision. Observation dates remain distinct from the date a source was checked.

The editorial policy explains how examples and recommendations are labeled. Material updates belong in the revision record.

AI Consulting Services Wiki · Editorial reference
Sources reviewed October 26, 2026