Practical resource for using AI inside the firm

Pattrn Data resources

AI governance consultant vs IT provider: what is the difference?

How to decide whether an AI governance issue needs an IT provider, an AI governance consultant, an MSP, compliance support or a combined approach.

Short answer

An IT provider can help with systems, permissions, security and tool configuration. An AI governance consultant helps decide which AI uses should be allowed, how human review works, how supplier and data risk is documented, and how the firm turns policy into usable workflow controls.

Next step

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1

What the IT provider is best placed to handle

IT providers and MSPs are important when the issue is technical control: identity, device management, Microsoft 365 configuration, access permissions, retention, security settings, approved applications and support. They can help make the environment safer, but they may not own the business decision about whether a workflow should use AI, what professional judgement must remain human, or what evidence the firm needs to keep.

2

What an AI governance consultant is best placed to handle

An AI governance consultant works across operations, risk, leadership and delivery. The work is to map current AI use, classify the data, define allowed and prohibited use, design approval routes, set human review expectations and turn governance into practical records. The point is not a policy document for its own sake. The point is that staff know what they can do and leaders can explain why the controls are proportionate.

3

When you need both

Most professional services firms need a joined-up approach. IT can configure controls, while governance work defines the rules those controls support. For example, an MSP may restrict unapproved tools, but the firm still needs a route for approving useful AI workflows, checking supplier terms, training staff and reviewing whether the controls are working.

4

How Pattrn Data fits around your existing providers

Pattrn Data does not need to replace your IT provider. The useful role is often to translate AI risk and workflow decisions into a practical governance model, then work with internal teams or providers to implement the controls through AI governance consulting, secure AI implementation and governance retainers.

Practical checklist

Turn the guide into an internal action.

Current AI use mapped
IT controls understood
Business owner named
Data classes agreed
Approved tools listed
Human review model written
Supplier checks recorded
Exception route published

How to use this inside the firm

Use this guide as a working note rather than a finished policy. Share it with the person who owns the process, the person who understands the risk, and at least one person who does the work every week.

The next useful step is usually a short workshop: pick one specific issue, write down the trigger, the inputs, the systems involved, the decisions made, the exceptions and the evidence that needs to be kept.

Warning signs to watch for

Be careful if the proposed answer depends on staff copying client data into unapproved tools, if nobody owns the output, if the supplier cannot explain data handling, or if the process has no clear review point.

Also be careful with projects that promise broad productivity gains but cannot name the process, the users or the measure of success.

Related Pattrn Data support

If this is an active issue inside your firm, the next step is usually to turn the guidance into a scoped process review, risk review or implementation plan.

Questions

What people usually ask next

Can our MSP handle AI governance?

They may handle important technical controls, but governance also needs business decisions about allowed use, client data, human review, supplier approval, evidence and ownership.

Is AI governance just an IT security issue?

No. Security is part of it, but professional services firms also need controls around confidentiality, judgement, client communications, records, oversight and staff behaviour.

Where should we start?

Start by mapping where AI is already being used, what data is involved, which tools are approved and where staff need a clear approval route.

Want to apply this to your firm?

Start with the issue, the data and the risk. Pattrn Data can help you decide what is worth automating and what needs stronger controls first.