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AI & Data Governance Advisory

Three focused advisory engagements, each addressing a distinct layer of governance. Start where the friction is loudest and add more as your AI program matures.

Bring structure to AI as is scales

Three focused advisory engagements, each addressing a distinct layer of governance. Start where the friction is loudest and add more as your AI program matures.

  • AI Program Advisory

    Governs how AI is approved, owned, and scaled. Decision rights, accountability, and value realization across your organization.

    Available as a standalone engagement

  • Data Governance Roadmap

    Governs what data AI can access and rely on. Ownership, access controls, compliance alignment, and data risk.

    Available as a standalone engagement

  • AI Agent Governance

    Governs how agents behave once deployed. Visibility, runtime controls, and access boundaries across your agent landscape.

    Available as a standalone engagement

Three offers, not three phases

AI governance and data governance are deeply connected in practice. AI is only as reliable as the data it works with, and data controls shape what AI can access. But they involve different questions, different buyers, and different parts of your organization.

That is why we have structured these as three distinct engagements:

  • one for how AI is approved and owned,
  • one for what data AI can trust and use, and 
  • one for how agents behave once they are live.

You can engage any of them independently, or layer them over time as your program grows.

Govern how AI is approved, owned, and scaled

AI Program Advisory

Decision rights, accountability structures, AI intake, and value realization. The organizational layer your AI program needs to grow responsibly.

What this engagement addresses

  • Who owns AI decisions and is accountable for outcomes
  • How AI use cases are proposed, evaluated, and approved
  • Role clarity across business, IT, and transformation teams
  • How to scale AI without governance lagging behind
  • Optional Center of Excellence design, tailored to your needs

Govern what data AI can access and rely on

Data Governance Roadmap

Data ownership, access controls, compliance alignment, and risk. The foundation that determines whether AI has trustworthy, governed data to work with.

What this engagement addresses

  • Who owns data assets and how access is controlled and enforced
  • Whether the data feeding AI is reliable, governed, and compliant
  • Data security and risk alignment across your AI use cases
  • Gaps between current-state maturity and where you need to be
  • How governance tools like Microsoft Purview can be operationalized

Govern how agents behave once they are deployed

AI Agent Governance

Agent visibility, runtime behavior, and access boundaries. Practical oversight for organizations where agents are already in motion.

What this engagement addresses

  • Visibility into which agents are live and what they can access
  • Agent monitoring and compliance across custom and low-code builds
  • Runtime behavior controls and access boundary definition
  • Agent identity and access management across your environment
  • Guardrails that support delivery speed rather than limiting it
Where to start

Find the right entry point

  • Start with AI advisory when...

    AI is scaling faster than your governance can keep up

    - Ownership and decision rights around AI are unclear

    - Copilot or AI tools are live but there is no intake or accountability model

    - AI initiatives feel inconsistent across teams or business units

    - Leadership wants structure before expanding AI investment further

  • Start with data governance when...

    Data quality or access is undermining AI confidence

    - Teams do not trust the data AI is working with

    - Data ownership is fragmented or undefined across the business

    - Compliance or risk concerns are slowing AI adoption

    - You want data governance in place before expanding AI use cases

  • Start with agent governance when...

    Agents are already deploying faster than you can keep track

    - Teams are building agents in parallel with limited central visibility

    - There is no clear picture of what is live and what it can access

    - You need guardrails that do not slow down the teams building them

    - Agent sprawl is ahead of any governance model

"The reason this approach is modular is straightforward: most organizations do not need everything at once. We start where the friction is greatest and intentionally layer governance as AI matures."

Kari Reynolds Consulting Director

Ready to bring structure to your AI program?

Tell us where the pressure is most acute and we will help you find the right starting point.

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