Automation where the work happens
Agents wired into the ERP, the inbox and the documents, not a chatbot bolted onto the side of the business.
365 Cloud Advisors
AI Agents & Intelligent Automation
Put AI to work, not just to chat.
We design AI agents and intelligent workflows that work across ERP systems, documents, email, data and business applications while keeping people in control of decisions that matter.
This is for teams where a handful of processes eat a disproportionate share of the week: accounts payable, order entry, purchasing, repetitive customer queries.
Automation that does the work, not just talks about it.
A chat window is a tool a person has to remember to use. An agent is a process that runs whether anyone remembers or not. The difference matters, because the value is almost never in the conversation — it is in the forty minutes a week nobody spends gathering information any more.
The processes worth automating first are dull, frequent, and follow rules somebody could actually write down. They usually span more than one system, which is why they resisted automation until now, and they have outcomes you can measure, which is how you will know whether it worked.
Every agent we design has a defined scope, an explicit boundary on what it may touch, and a human in the loop wherever the decision carries real consequence. Read access and write access are separate decisions, always.
Accounts payable automation
Sales order and quote processing
Purchasing and inventory agents
Customer service automation
Document and email processing
Executive reporting and knowledge search
What you get
Deliverables, not a deck of slides.
A shortlist of processes worth automating
Scored on frequency, hours consumed, how predictable the rules are, and whether the outcome can be measured. Not everything that annoys people is a good candidate.
A working agent, scoped narrowly
One process, built and running, with its limits defined — rather than a platform rollout that has to be justified before anything works.
An explicit permission boundary
Exactly what the agent can read, what it can write, and what it must escalate. Written down, reviewable, and enforced in configuration rather than in good intentions.
Human-in-the-loop checkpoints
Where a person approves before anything commits, chosen by the consequence of being wrong rather than by how confident the model sounds.
Logging you can audit
A record of what the agent did and why, so an unexpected outcome can be reconstructed instead of argued about.
A measured before-and-after
The hours the process took before, and after. Without this you have an anecdote instead of a business case for the next one.
How it works
Where this sits in the route.
Step 1
Assess
Understand your business, systems, processes and AI readiness.
Step 2
Connect
Unify trusted data and make it securely accessible to people, apps and AI.
Step 3
Automate
Deploy AI agents where they deliver measurable value.
Step 4
Govern
Implement responsible AI with security, permissions and human oversight.
Common questions
The things people actually ask.
What is the difference between Copilot and an agent?
Copilot assists a person who remains responsible for the decision. An agent carries out a defined process itself, with oversight. Both are useful and they are not interchangeable — confusing the two is how organisations end up explaining an automated transaction to an auditor.
Will this replace people?
The realistic outcome is that predictable work stops consuming people who are better used on the unpredictable kind. If a process is so routine that an agent can run it end to end, it was never the reason you hired anyone.
What happens when the agent gets something wrong?
It will, eventually. That is why scope, permission boundaries, approval checkpoints and logging are designed in from the start rather than added after an incident. The question to design for is not whether it fails but how visibly and how recoverably.
Related reading
Written on this, at length.
AI + Business Central
10 Business Central Tasks You Can Start Automating With AI
Nobody’s first run of the season is a double black. AI automation does not start with an autonomous ERP either — it starts with the dull thing somebody grinds out every Tuesday morning.
AI Architecture
Building an AI Integration Layer Around Business Central
Connecting an AI model to Business Central takes an afternoon. Connecting it correctly takes an architect — and you find out which one you did in the incident report.
AI Strategy
Microsoft Copilot vs. Custom AI: Which Is Right for Your Business Central Environment?
The answer is usually both — for different runs. The mistake is buying one pair of skis and expecting them to handle the whole mountain.
Start here
Not sure this is the right run for you?
The Trailhead Assessment is a short set of questions about your systems, data and processes. It ends with a practical map of what to modernize, what to automate, and what to leave alone.