If you’re like most companies, you’ll have looked into AI, and even if you’ve done that tentatively, you’ve probably come across AI agents. For organisations of all sizes, these hold much promise because they’re capable of carrying out defined tasks using your internal data. Typically, they can operate within everyday tools like Microsoft 365 which also makes them accessible – though they do come with a few caveats.
AI is no different to most tech in that it is advancing quickly. To that end, best use cases are still not clearly defined across all sectors, though one thing we do know is that readiness in the organisation is a good predictor of AI success.
What that means is, AI agents can deliver productivity gains, but only when they’re introduced in environments with clear processes, well-structured data and appropriate oversight.
Agents depend on structured processes
One of the most common misconceptions about agentic AI is that it can fix organisational complexity; the truth is that it can’t unless it is applied to processes that are clearly defined.
That’s because they’re designed to execute tasks that follow a clear structure, so if one doesn’t exist, is poorly understood or applied inconsistently an AI agent will simply amplify confusion.
So, if you’re considering AI agents you first need to make sure the workflows you want to automate are well defined, well documented and stable. From there, you can begin with small, clearly defined uses cases where the task and data are well understood.
Early use cases often involve internal knowledge
Some of the better use cases we’ve seen have involved AI agents working with structured internal documentation, like a Teams-based assistant that answers HR questions using policy documents.
In that case, the agent accessed policy documents that were stored in a defined location, and answered staff questions by querying the documents directly.
Some of the most effective early applications involve agents working with structured internal documentation. Because the system reads the underlying files in real time, any updates to the policies automatically change the answers the agent provides.
It’s an approach that would work well in finance because most organisations hold a large number of invoices and financial records. Because these are difficult to analyse quickly, an agent can be purposed to review and identify patterns, allowing finance teams to access insight that would otherwise have to be done manually.
Elsewhere, agents are really well suited to repetitive knowledge work – things like regular tender submissions can use an agent to summarise documents and extract key deliverables, highlight compliance requirements and generate a concise overview of the opportunity for the team reviewing it.
We’ve also seen some organisations use agents to evaluate the tone in written communication and look for signs of frustration or dissatisfaction. This has been used to spot early signals that a customer or stakeholder relationship may require attention.
Data governance remains fundamental
Data governance will always be fundamental to technology, and AI isn’t an exception to that rule. Organisations that use or want to use AI agents should have a very clear understanding of where their data is stored and how it is managed.
If you’re introducing any new tools, then questions around data sovereignty and security are vitally important because you must maintain compliance and regulatory standards. That includes keeping data within appropriate geographic jurisdictions.
Large technology providers such as Microsoft have built mechanisms to support this. Organisations using Microsoft’s ecosystem can choose regional hosting options so that data remains within specific locations, such as the UK, helping to maintain data sovereignty.
Agents still require human oversight
It’s worth keeping in mind that using AI agents isn’t a case of plug, play and forget – it will always need to be monitored, reviewed and refined. You’ll need to do that regularly. In short, they need to be managed like any other operational tool (or even junior staff) performing defined tasks – their outputs should be checked periodically and adjusted if or when they don’t meet required standards.
Right now, AI systems are pretty good at summarising and analysing information quickly, but not so reliable when it comes to decision making. That responsibility, and the responsibility for outcomes, should always remain with the organisation and be people-led.
So, while an AI agent might provide a helpful summary of a complex document, someone should still review the output before relying on it for critical processes. It’s a necessary oversight that ensures automation improves efficiency without introducing unintended errors.
Managing the rise of self-service AI
Microsoft is making it easier for companies and employees to create their own automation tools through Copilot and the wider Microsoft suite. And while it opens the door to a new wave of internal innovation, it also requires governance.
Allowing unrestricted experimentation can quickly lead to uncontrolled systems interacting with sensitive data. To stop this happening, and going wildly out of control, you need to implement a structured process for developing and approving new agents. You should welcome ideas for automation and innovation but also have a process to review them for risk, data readiness and operational impact before they’re rolled out. IF nothing else, it’s good data hygiene.
Over time, you may find yourselves operating alongside a growing number of automated assistants performing specific tasks in the background. These agents can help staff work more efficiently by handling routine information processing, analysis and summarisation.
But, like any other part of the technology environment, they must be managed carefully. Establishing clear processes for creating, monitoring and maintaining AI agents will ensure organisations can benefit from the technology while maintaining the control and accountability required for responsible use.