AI Governance
Your Business Central environment is making AI decisions every day. Nobody has decided who’s in charge of them.
AI in Business Central doesn’t arrive with a governance framework. It arrives with a set of capabilities — and leaves the question of who controls them, what they’re permitted to do, and who is accountable when they go wrong entirely to you.
Ask most BC customers who decides what their AI tools are allowed to do, and you get one of two answers. The first is a name — usually someone in IT or finance who enabled the feature and hasn’t thought about it since. The second is silence, followed by the slow realisation that nobody has actually made that decision.
This is the governance gap. And it’s more consequential than most organisations recognise until something goes wrong.
AI governance in a BC context isn’t an abstract compliance exercise. It’s a practical set of decisions about which processes AI is trusted to run autonomously, which decisions require human review, what happens when AI output is wrong, and who is accountable for the consequences. These decisions don’t get made by default. They get avoided by default — until a specific incident forces the conversation that should have happened months earlier.
“The question isn’t whether AI will make consequential decisions in your BC environment. It already is. The question is whether anyone has decided what it’s allowed to decide.”
The questions nobody is asking
Six governance questions every BC AI deployment needs answered.
These aren’t hypothetical risk management questions. They’re practical decisions that determine how safely and reliably your AI operates — and what your exposure is when it doesn’t.
1. Which decisions is AI permitted to make autonomously — and which require human sign-off?
Bank reconciliation suggestions accepted without review. Sales order automation triggered without approval. Vendor payment scheduling based on AI recommendations. Each of these is a decision with financial consequences. Whether AI makes it alone or as a recommendation to a human is a governance choice — and most organisations haven’t made it deliberately.
2. What is the threshold for human review?
Even teams that have defined autonomous AI decisions rarely define the threshold at which a human should intervene. Above what transaction value? What data confidence level? What deviation from historical pattern? Without explicit thresholds, “human oversight” becomes whatever individual users happen to notice.
3. Who is accountable when AI output is wrong?
An AI-generated vendor suggestion leads to a bad purchase. A Copilot reconciliation match creates a posting error. An automated workflow approves something it shouldn’t. In each case, someone is accountable — but in most organisations, who that is has never been formally determined. Accountability gaps are governance gaps.
4. How is AI output audited and reviewed?
Manual processes leave paper trails. AI-driven processes often don’t — unless someone has specifically designed the audit layer. Without audit logging of AI decisions and outcomes, you can’t detect systematic errors, can’t demonstrate compliance, and can’t learn from failures.
5. What data is AI permitted to access — and what is off limits?
Copilot and connected AI tools pull from your BC data to generate output. Not all of that data should be equally accessible. Customer payment history, employee-related financial data, commercially sensitive margin information — access permissions for AI tools are rarely reviewed with the same rigour as user permissions.
6. How does governance keep pace with capability changes?
Microsoft adds AI capabilities to BC in every release. Each new capability is a new governance question. An organisation that established its AI governance framework in January may find it materially incomplete by July. Governance that doesn’t update with the platform isn’t governance — it’s documentation of a past state.
What the gap costs you
The four risks of unmanaged AI governance in BC.
|
Financial exposure AI making autonomous financial decisions without defined limits or review thresholds creates direct financial risk — particularly in payment runs, credit approvals, and procurement automation. |
Compliance vulnerability Regulatory frameworks increasingly require documented controls over automated decision-making. An undocumented AI governance approach is an audit finding waiting to happen. |
|
Accountability vacuum When AI output causes a problem and nobody is clearly accountable, the investigation is slower, the fix is harder, and the root cause is more likely to recur. |
Eroded trust Teams that experience AI errors without a clear response process stop trusting AI output entirely — and work around it, undermining the investment and leaving the risk in place. |
“AI governance isn’t about slowing AI down. It’s about creating the conditions in which AI can be trusted to go fast — because the boundaries are clear and the accountability is defined.”
A framework worth building
Five components of a practical BC AI governance framework.
Governance doesn’t need to be complex to be effective. The organisations managing AI risk well in BC have typically established five things — none of which requires significant investment, but all of which require a deliberate decision to do them.
|
✓ A decision register A documented list of every decision AI is permitted to make autonomously in BC, with the threshold, the review trigger, and the accountable owner named for each. |
|
✓ An AI audit log A systematic record of AI decisions and outcomes in BC — not just activity logs, but a structured review of whether autonomous decisions are producing the intended results. |
|
✓ Defined accountability A named owner for each AI capability — responsible for monitoring performance, reviewing the audit log, escalating anomalies, and initiating the response when AI output goes wrong. |
|
✓ Data access policy for AI An explicit review of which BC data AI tools can access — aligned to user permission principles and reviewed whenever new AI capabilities are enabled. |
|
✓ A governance review cadence A scheduled review — at minimum aligned to BC release cycles — that updates the governance framework as new AI capabilities are introduced and existing ones evolve. |
None of this is technically complex. All of it is organisationally difficult — because it requires conversations about accountability that organisations prefer to defer. The role of a trusted BC partner in this context is to facilitate those conversations before an incident forces them, and to translate the outputs into a practical framework that the BC environment can actually enforce.
At NAV SEAL, we work with customers to build AI governance frameworks that match the pace of Microsoft’s platform development — not as a compliance exercise, but as a practical foundation for using AI confidently and accountably.
Using AI in Business Central without a governance framework? That’s a risk worth addressing.
NAV SEAL helps BC customers establish practical AI governance frameworks — covering decision boundaries, accountability, audit, and data access — before an incident makes the conversation urgent.
Visit navseal.com or connect with us on LinkedIn to start the conversation.
#BusinessCentral #Dynamics365 #AIGovernance #ERPStrategy #AIStrategy #MicrosoftPartner #NAVSEAL #DigitalTransformation
