Advisory support for regulated teams, grounded in the reality of AI delivery.

AI governance has stopped being a side project because AI has stopped behaving like a single programme.

Common use cases ensure that AI turns up in small, localised decisions, whether that’s a GenAI feature added to a vendor tool, a model used to prioritise alerts, or a support workflow rewritten around automation.

On the other end of the spectrum, firms are also launching enterprise-wide AI transformation programmes that reshape operating models, decision-making structures and customer interactions at scale.

Each initiative may have a clear business case in isolation. Together, they create an accountability problem that cuts across governance, risk, compliance and operational control.

What does this mean for today’s regulated firms?

What’s changing in day-to-day governance

Most regulated organisations already run frameworks for data, models, third parties, and operational risk. AI puts strain on the joins between those frameworks.

This strain emerges in:

  • Change control: model updates land inside product cycles, not committee cycles
  • Consistency: the same tool can behave differently across teams, prompts, and inputs
  • Third-party exposure: AI enters through procurement, but oversight assumes standard software risk
  • Ownership: decision rights blur across legal, risk, product, data, and engineering
  • Evidence: controls exist, but proof lives across tickets, documents, and vendor portals

Controls might look fine on paper, but operating them takes time, and time is the first thing to go when adoption picks up.

The baseline is higher now

Regulation isn’t the only driver, but it has changed what good looks like.

The EU AI Act has helped standardise risk-based expectations around classification, documentation, oversight, and accountability. Even firms outside scope use it as a reference point because it forces clarity: what’s in use, what risk category it sits in, and what controls follow.

In Broadgate’s conversations with audit functions, the same questions keep circling:

  • Which AI systems are in use, including vendor tooling?
  • Who owns each use case, and who can pause it?
  • What control checks run across data, evaluation, access, monitoring, and change?
  • Where is the evidence, and can it be produced without a scramble?

What audit-ready AI governance looks like

A strong programme produces a small set of artefacts and routines that people can run.

  • A maintained AI register, including third-party tools
  • Named owners for each use case, with decision rights written down
  • Controls mapped to the workflow: data sourcing, evaluation, deployment, monitoring, change control
  • Records of approvals, exceptions, incidents, and changes
  • Evidence routines that run as part of delivery work
  • A third-party oversight approach that covers AI-specific risk
  • A capability plan that keeps ownership stable as adoption grows

This is the difference between governance that exists and governance that operates.

The Trust Challenge Continues

The market may have moved on from the black-box AI anxiety of 2021, but that does not make today’s outputs automatically more reliable, defensible or safe to use in regulated decision-making.

In financial services, where reputation is both a commercial asset and a regulatory obligation, the pace of model development has reopened the innovation vs regulation tension.

Firms know AI can improve detection, triage, monitoring, customer experience and operational efficiency. They also know that a poorly governed model, unclear ownership structure or missing evidence trail can create regulatory, reputational and conduct risk.

Broadgate and the Integration Advantage

Alongside Broadgate’s expertise across governance, risk, compliance and transformation, sits DeepRec.ai, the group’s specialist AI and deep tech recruitment business.

Broadgate understands regulated hiring across risk, compliance, governance and change. DeepRec.ai brings specialist knowledge of AI, machine learning and deep tech talent markets.

Together, they help firms think beyond vacancy filling and towards the decision points where capability, control and confidence need to meet.

The result is a more consultative talent partnership: identifying the roles, interim support and specialist expertise required to make AI governance work in practice, whether that means strengthening model risk, building AI assurance capability, improving vendor oversight or placing experienced leaders into critical transformation moments.

AI governance support for regulated firms

If your organisation is reviewing AI governance frameworks, implementation plans, ownership structures or regulatory readiness, Broadgate can help assess where operational, control and capability gaps exist.

Broadgate works with regulated firms across AI governance, risk, compliance and transformation, alongside DeepRec.ai’s specialist AI and deep tech network.

Contact Broadgate to discuss AI governance assessments, implementation support and specialist capability planning.