A customer sends an email asking why an invoice has not been processed. Someone reads it, searches for the invoice, checks an approval, asks a colleague for an update and writes a reply.
None of those steps is particularly difficult. The problem is that they cross several applications and depend on context.
This is the type of work autonomous AI assistants are being designed to handle.
Instead of answering one prompt and waiting for another, an autonomous assistant can work towards a defined objective. It can inspect available information, decide which permitted action comes next, use connected applications and continue until it reaches a stopping point or needs human approval.
That distinction matters. It moves AI from helping with individual tasks towards coordinating parts of a business process.
How an autonomous AI assistant differs from a chatbot
A conventional chatbot mainly works within a conversation. You ask a question, it generates a response, and you decide what happens next.
An autonomous assistant can be given a goal and a set of permitted tools.
For example, an accounts assistant could be asked to investigate overdue customer invoices. Depending on how the system is configured, it might:
identify invoices that meet defined conditions;
retrieve relevant customer and transaction information;
check previous correspondence;
prepare a summary for the accounts team;
draft a customer email; and
request approval before the email is sent.
The important part is not that AI can write the email. Generative AI has been able to do that for some time.
The significant change is that the assistant can coordinate several steps and applications as one piece of work.
Why workflows across apps are difficult
Most business processes do not live inside one application.
A sales enquiry might arrive through Outlook. Customer information may be held in a CRM or ERP system. Supporting documents may sit in SharePoint. Staff may discuss the issue in Teams. An approval could involve another business application.
People connect these systems manually every day.
They copy information between screens, search for documents, check statuses and send reminders. They also carry context in their heads. An experienced employee often knows which system to check next when something does not look right.
An autonomous assistant needs those boundaries made explicit.
It needs to know what information it can access, which actions it can perform, when it must stop and who should approve sensitive actions.
That makes workflow design just as important as the AI model itself.
Where Microsoft tools fit
For organisations already using Microsoft 365 and Dynamics 365, AI assistants can sit alongside tools employees already use.
Microsoft 365 Copilot provides AI capabilities around workplace information and applications. Microsoft's broader agent capabilities can also be used to build assistants that work with approved data sources and actions.
Dynamics 365 Business Central provides another useful source of operational context. Depending on the integration and permissions provided, a workflow might need information about customers, vendors, sales documents, purchase documents, inventory or financial transactions.
The assistant should not receive unrestricted access simply because the technology allows connections.
The same principle we use for ordinary system permissions still applies: give the process only the access required to perform its job.
Start with bounded work, not complete autonomy
The word "autonomous" can suggest software operating without supervision. That is not the most useful starting point for most companies.
A better first project has a narrow objective, clear rules and an obvious point for human review.
Consider a purchasing process. An assistant could monitor an agreed source for requests, collect the supporting information, check whether required fields are present and prepare the request for an authorised employee.
That removes repetitive coordination without giving the assistant authority to make every purchasing decision.
The same approach can work in finance. An assistant might investigate an exception and assemble the evidence, while the accountant still decides how the exception should be treated.
This distinction becomes more important as the financial or operational consequence of an action increases.
Controls need to be part of the workflow
A common mistake we see with automation is focusing on the normal path. The process works well when every document is complete and every transaction matches expectations.
Real business processes contain exceptions.
A supplier name may not match. A customer may dispute an amount. A purchase could exceed an internal approval threshold. A required document may be missing.
An autonomous assistant therefore needs clear escalation rules.
Businesses should define which actions the assistant can complete automatically, which require approval and which should always be handed to an employee.
Access permissions also matter. If an assistant can use several applications, its effective access can become broader than expected. IT teams should review the identities, permissions, connectors and data sources involved rather than treating the assistant as an isolated application.
Logs are equally useful. Teams need enough information to understand what the assistant did, what information it used and where a person intervened.
Choose the first workflow carefully
The best candidate is usually not the biggest process in the company.
Look instead for repetitive work that crosses applications and already follows reasonably clear rules. The process should also have a measurable output.
Invoice follow-up, document collection, internal request routing and routine status checks can be useful areas to examine. The exact choice depends on how your organisation works and where its data sits.
Before building anything, write the process down.
Identify the trigger, each system involved, the information required, the actions permitted and every point where human approval is necessary. Then list the common exceptions.
That exercise often exposes process problems before AI is introduced.
Autonomy should be earned one step at a time
An assistant does not need permission to complete an entire workflow on its first day.
It can begin by gathering information and preparing recommendations. Once the organisation understands its behaviour, selected low-risk actions can be automated. Higher-impact decisions can remain subject to human approval.
For Nigerian businesses considering this approach, a practical next step is to choose one repetitive workflow that currently crosses two or three applications. Map its steps, permissions and exceptions, then identify exactly which parts an AI assistant could perform without changing who remains accountable for the final decision.
