Somewhere in your business right now, someone is pasting a customer contract or a set of financial figures into ChatGPT to save themselves an hour.
Nobody told them to. Nobody is watching what happens to that data afterwards. That, more than any spreadsheet of “AI use cases”, is the starting point for most boardroom conversations about generative AI.
You do not need to become an AI expert to lead well here. You need a clear view of what generative AI is, what it is genuinely good at today, where the real risk sits, and what it looks like when a leader sponsors a first project properly. That is what this guide covers.
What Generative AI is
Generative AI is software that creates new content, text, images, summaries, code, based on patterns it has learned from vast amounts of existing material. Ask it to draft an email, summarise a report or answer a question, and it generates an answer word by word, based on probability, not understanding in the way a person understands.
That is different from the AI most businesses already use without thinking of it as “AI”. Your finance system flagging an unusual invoice, or your spam filter catching a suspicious email, is AI too, but it is built to spot patterns and make a decision within fixed rules. Generative AI produces something new every time, in response to whatever you ask it. Tools such as ChatGPT, Microsoft Copilot and Google Gemini are all generative AI. The name you will hear most in a Microsoft 365 business is Copilot, an implementation of generative AI built to work inside the Microsoft tools and permissions your business already has, rather than a separate consumer app.
What it’s genuinely good at, and where the hype outruns reality
| Reliably useful for | Not automatically reliable for |
|---|---|
| Drafting a first version of a document | Up-to-date facts |
| Summarising a long report or email thread | Exact figures |
| Pulling together a first pass answer to a common question | Accountable decisions, unless it is connected to trusted sources and the output is checked |
| Helping service teams find information faster | Anything going straight to a customer, a contract, or a set of accounts |
These are jobs that involve language and pattern, not judgement calls with real consequences.
It will produce a confident, fluent answer even when that answer is wrong, a problem often called “hallucination”: the tool inventing something that sounds plausible but is not true. That is why every generative AI output used in your business needs a person checking it before it goes anywhere near a customer, a contract, or a set of accounts. Treat it as a fast first draft, not a finished decision.
The real risk isn’t the technology, it’s shadow AI
The risk that should concern you isn’t generative AI itself. It’s staff already using free, consumer versions of these tools on company laptops, with no oversight of what data they are putting in or where it goes. This is sometimes called “shadow AI”: AI tools being used across your business without your knowledge or any agreed rules, in the same way “shadow IT” describes unapproved software creeping into a business outside its normal IT setup.
Once customer information, financial data or unreleased company plans go into a public AI tool, you have lost control of where that information sits and who can see it. That is not a reason to ban generative AI outright; staff who are told “no” continue using it on personal devices, out of sight, regardless. It is a reason to give people a sanctioned, governed way of using it instead, so the business can see what is being used and set boundaries around company data.
A governed entry point, such as Microsoft Copilot running inside your existing Microsoft 365 setup, keeps AI use inside the permissions and access controls you already manage, rather than sending company data out to an open consumer tool with no oversight. It’s one option among several, and it is worth understanding properly before you rely on it.
What “sponsoring a first initiative” means
Sponsoring generative AI in your business does not mean buying a platform or announcing a company-wide rollout. It means three things, and none of them require a technical background.
Pick one governed use case, not a technology.
Choose a single, well-defined task, drafting standard customer responses, summarising board papers, or supporting a service desk, where the volume is high and the risk of a wrong answer is low. Resist the urge to solve everything at once.
Set the rules before you set the tool.
Decide what data is off-limits, who checks outputs before they go external, and who owns the decision if something goes wrong. This takes a short conversation, not a policy document nobody reads.
Ask for evidence, not enthusiasm.
After a defined period, ask what the pilot achieved: time saved, quality of output, and whether people would miss it if it stopped. That evidence is what earns the next budget conversation with your board.
This is a smaller commitment than most leaders expect. It’s a contained decision about one use case, not a strategic bet on a whole technology category.
Where this fits with the rest of your AI plan
Generative AI is one part of a wider picture that includes AI managed services and AI strategy for SMEs, and it sits alongside AI in mid-sized operations more broadly. If your team is already asking about Microsoft Copilot specifically, TSG’s guide to Microsoft Copilot goes into what it does and how it’s licensed. If you’re weighing up how to get outside support for any of this, choosing an AI provider sets out what to look for.
TSG’s AI services exist for exactly this stage: helping a leadership team turn “we should be doing something with AI” into one governed use case with a clear owner and a clear measure of success, before any wider spending decision. Accelerate AI, a free introductory session, is the natural starting point if you want that conversation without committing to anything first.
Your board conversation, made simple
You don’t need a technology answer for your board. You need three things:
A plain description of what generative AI is and isn’t.
A clear statement of how you’re managing the shadow AI risk.
One governed use case you can point to as evidence you’re moving deliberately rather than reacting to hype.
That’s a stronger position than most of your competitors have reached, and it is one you can build without a large internal IT team behind you.
If you want to talk through where generative AI fits in your business, we’re happy to have that conversation.
Get in touchFrequently asked questions
It’s software that creates new text, images, or answers by learning patterns from vast amounts of existing material, then generating a response word by word when you ask it something. Tools such as ChatGPT and Microsoft Copilot are both generative AI.
Used with rules around what data goes in and who checks the output, yes. Used ungoverned, with staff pasting company information into free consumer tools, no. The safety question is about governance and oversight, not the technology itself.
AI broadly covers any software that spots patterns and makes decisions, such as fraud detection or spam filters. Generative AI is a specific type that creates new content, text, summaries, images, in response to a request, instead of flagging or sorting existing information alone.
A single, well-defined task with high volume and minimal risk if it goes wrong: drafting standard responses, summarising reports, or supporting a service desk. Set clear rules on data and checking before you start, then measure the result before deciding what’s next.
Banning it outright rarely works; people use it anyway, on personal devices, out of sight. Giving staff a governed, sanctioned way to use generative AI, with clear rules on company data, is a more realistic way to manage the risk.
No. Copilot is one governed entry point if you already run Microsoft 365, but the more important first step is agreeing one use case and a set of rules. TSG’s guide to Microsoft Copilot covers the detail if that’s the direction you’re considering.
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