Currently, the most common question we get asked isn’t about a dropped Teams call or a printer that has stopped communicating with the network. It’s, “What are you actually doing about AI?” And it’s a completely reasonable question to put to anyone you’re paying a monthly retainer to.
It deserves a proper answer, so here’s what AI expertise actually looks like in practice.
The Job Has Changed
Managed IT support used to be a fairly well-defined proposition. You paid a monthly fee, someone fixed things when they broke, and your backups got checked. That model still exists, but it isn’t sufficient any more.
The businesses we look after need to know whether the AI tools they’re considering are secure, worth the subscription, and how to use them without exposing client data or creating compliance risk.
None of that falls outside the remit of a good managed service provider (MSP). Most of it sits squarely inside it.
What Most Businesses Are Dealing With Right Now
Across our client base, businesses are curious about AI (enthusiastic, even) and usually unsure where to start. It’s not a criticism; it’s just that there’s a lot of noise that has landed on decision-makers in a very short space of time.
A few things come up repeatedly:
- Someone in the team has started using ChatGPT or Copilot without telling anyone, and now there’s a question about what data has been going into it.
- The MD wants to “do something with AI”, but there’s no clear starting point.
- A software vendor has added AI features to an existing subscription, and nobody has switched them on or evaluated whether they’re worth using.
- Someone has found a tool that looks useful but doesn’t know whether it’s compatible with their setup or safe to use with client data.
While these aren’t dramatic, the longer they’re left unaddressed, the bigger they become.
What We Actually Do About It
When AI comes up with a client, we don’t open with a product recommendation. We start by looking at what they already have in place and what they’re actually trying to achieve.
Security and Governance First
Before any AI tool goes into active use, it needs to be assessed against the business’s data handling obligations. That means checking the vendor’s data processing terms, understanding where data is stored, and putting clear internal guidelines in place around what staff can and can’t enter into the tool.
Identifying Where AI Helps
The government’s Department for Science, Innovation and Technology (DSIT) published AI adoption research that found 71% of surveyed businesses hadn’t identified a clear use for AI in their organisation. That is the gap a good MSP should be closing, working through where AI-assisted workflows would actually save time or reduce risk in a specific business rather than recommending tools for the sake of activity.
Making Existing Tools Work Properly
The AI most likely to deliver value in the short term is often already inside software a business is paying for. Microsoft Copilot, Xero, HubSpot, HR platforms. Most have added meaningful AI features over the last twelve months as standard, and in most businesses they haven’t been configured or switched on.
Evaluating New Tools With the Right Questions
When a client wants to add something new, we help them check the data processing terms, whether the vendor is ISO 27001 certified, and where data is stored. These are questions vendor homepages rarely answer directly, but they matter before you sign up.
What This Means for Your IT Partner
If the IT support company you’re paying isn’t having this kind of conversation with you, it’s worth asking why. The businesses getting real value out of AI are the ones with a partner who is helping them ask the right questions before committing to anything.
A managed service provider worth keeping in 2026 should be able to:
- Review any AI tool you’re considering and give you a clear view on cyber security and data handling.
- Tell you which Microsoft 365 AI features you already have access to and whether they’re worth turning on.
- Help you put simple internal governance guidelines in place so your team knows what they can and can’t do with AI tools.
- Tell you honestly when a tool isn’t worth the subscription and why.
This requires an IT partner who has done the work themselves and is paying attention to what’s actually happening in the market.
A Note on What We’re Building
Inside Platform 365, we’ve been going through this ourselves: evaluating tools, updating our own processes, and building a clearer picture of where AI is genuinely useful across the types of businesses we support.
Some of what we’ve found has been impressive. Some of it hasn’t matched the vendor’s version of events. We’re putting together a more detailed account of that later in the year.
Get a Clear View on AI for Your Business
Book a call with Dan and find out exactly where AI can help, what’s genuinely worth pursuing, and what isn’t.
FAQs
- What does a managed service provider do about AI?
An MSP should assess AI tools for security and compliance, identify where AI would save time, and advise on safe internal use. If yours hasn’t raised any of this with you, it’s a conversation worth starting. - How can an IT support company help with AI adoption?
Your IT support company brings the security and data compliance context that most AI vendors leave out, starting with a proper review of data processing terms before you commit to anything. That’s the groundwork that stops AI adoption becoming a data handling problem. - What should I expect from my IT partner on AI in 2026?
At minimum, your IT partner should be proactively flagging AI features in tools you already pay for and giving you an honest view on anything new you’re considering. If that conversation hasn’t happened yet, raise it directly. - Is my IT company qualified to advise on AI tools for my business?
An MSP with ISO 27001 certification and hands-on experience across a range of business environments is well-placed to advise on AI tool security, data handling, and integration. - What is AI governance and why should my IT partner be involved?
AI governance means having clear guidelines about what data can go into AI tools, who can access outputs, and what happens when something goes wrong. An MSP helps because those rules need to fit your existing data policies and any sector-specific compliance requirements.
