AI Consulting
AI Governance Consulting
Build the accountability structure that lets AI grow without outpacing the business around it.
Governance that holds up when it’s tested
A framework is only as strong as its policies, and policies are only as strong as the controls that enforce them. Our AI governance consulting services put accountability at the center of how AI gets used, not as an afterthought to it.
Governance framework
Define how AI decisions are made, what oversight is required and how the structure will be reviewed as AI use evolves across the business.
Responsible AI policies
Define clear, practical policies that give employees the confidence to use AI responsibly, with defined guardrails around acceptable use, approvals and risk.
Controls and risk alignment
Establish operational controls that keep AI use aligned within your policies and ensure risks are identified, assessed and managed consistently.
Roles and forums
Assign clear accountability for AI decisions and create the forums that keep governance active over time, not just documented and forgotten.
What sets our approach apart?
Governance your teams can use
A governance framework sitting in a document repository doesn’t govern anything. We turn policies and controls into practical ways of working that fit how your teams actually operate.
Confidence without unnecessary friction
Good governance should give your organization the confidence to move forward. The instinct is to start with tight restrictions and relax them later, but that can drive AI use underground, where risks become harder to see and manage. We start with clear guardrails and the freedom to operate within them, then refine those guardrails as the evidence builds.
Clear ownership for AI decisions
AI governance works best when everyone knows who owns each decision. We define roles, decision rights and governance forums so teams know what requires approval, who is accountable and how risks are managed. Assumed accountability is not the same as assigned accountability, and the gap between the two is where governance breaks down.
Evidence the business can stand behind
When a board, regulator or external stakeholder asks how an AI decision was made, the answer should already exist. We put the audit trails, assessments and governance records in place before they’re needed.
Case studies
AI governance looks different in every organization. Explore how we’ve helped global clients across a range of industries.
Get the right controls in place
Fill in the form and one of our consultants will give you a clear view of what governance should look like for your business.
FAQs
AI governance is the set of policies, controls, roles and decision-making structures that determine how AI is used across an organization. It defines who is responsible for AI decisions, how new use cases are reviewed, what risks need to be considered and how issues are escalated.
Many organizations have an AI policy. Far fewer have the governance needed to make it part of everyday decision-making. Overly restrictive governance doesn’t eliminate risk; it often pushes AI use underground, where it becomes much harder to oversee.
AI governance responsibilities are typically shared. Overall accountability usually sits with senior leadership, supported by teams across risk, legal, technology, data and the wider business.
The critical thing point is clarity. Everyone involved should understand what they own, what they can approve and when decisions need to be escalated. Assumed accountability is not the same as assigned accountability, and the gap between the two is where governance tends to fail.
AI governance monitoring means regularly reviewing how AI is being used across the organization. That includes checking whether new use cases have followed the right approval process, whether risks are being identified and documented appropriately, and whether the governance framework continues to reflect emerging risks.
Higher-risk applications require closer oversight, regular reviews and clear escalation routes. Lower-risk use cases need lighter-touch monitoring, provided agreed policies and controls continue to be followed.
AI ethics defines the principles that should guide how AI is used, including fairness, transparency and accountability. AI governance is the operational framework that puts those principles into place.
The two should work together. Without clear governance, ethical principles remain aspirational. Without ethical principles, governance risks becoming a compliance exercise rather than a guide for better decisions.
An AI governance audit assesses whether your governance framework is operating as intended and whether AI is being used in line with established policies and controls.
Audits may be conducted internally or independently, giving leaders, boards and external stakeholders confidence that AI is being governed responsibly and with appropriate oversight.
An AI governance framework should define how AI decisions are made, who is accountable and what standards teams are expected to follow.
This typically includes approval processes for new AI use cases, clear policies on acceptable use, risk assessment processes, operational controls and defined decision rights. It should also include a regular review cycle, because needs evolve, as AI capabilities, risks and regulations continue to change.
