What ChatGPT Means for Enterprise Teams

So what does this actually mean for your enterprise team? Not the hype version. The honest version.

Let me be direct. ChatGPT is impressive. It's also a consumer product running on someone else's infrastructure, with someone else's data policies, producing outputs that sound authoritative and are frequently wrong.

That doesn't make it irrelevant. It makes it the starting gun for something much bigger.

Here's what I'd tell any enterprise leader right now:

This is a certainty. People across your organisation are pasting client data, internal documents, and proprietary information into ChatGPT right now. They're not doing it maliciously. They're doing it because it's useful and there are no guidelines.

Action: Set basic guardrails immediately. Not a ban - that won't work and it sends the wrong signal. Simple rules: no client data, no confidential information, no using outputs without review. You can refine the policy later. You need something now.

ChatGPT is a general-purpose tool. It doesn't know your business. It can't access your systems. It has no concept of your processes, your compliance requirements, or your customers. The gap between "a useful writing assistant" and "AI integrated into enterprise operations" is enormous.

That gap is where the real opportunity lives. And it requires real work to bridge.

The enterprises that will benefit most from AI in the next 18 months aren't the ones rushing to adopt ChatGPT. They're the ones quietly getting their data in order. Your knowledge bases, your process documentation, your institutional expertise - that's the raw material AI needs to be genuinely useful.

If your data is scattered across SharePoint sites, email threads, and people's heads, no AI tool can help you. Start there.

Every software vendor in the market is adding "AI-powered" to their product descriptions. Most of them bolted on a ChatGPT API call last week. Some of them are doing genuinely interesting work with language models. Telling the difference requires asking specific questions about architecture, data handling, and accuracy measurement.

If a vendor can't explain how their AI works, what data it uses, and how they measure accuracy, they don't have an AI product. They have a marketing claim.

We've been testing ChatGPT and adjacent tools against real enterprise work for the past several weeks. Here's the framework that's emerging:

High value, available now: Individual productivity. Drafting, summarising, brainstorming, explaining technical concepts. Low-stakes tasks where "roughly right" is useful and a human reviews the output.

High value, not yet ready: Enterprise knowledge retrieval, document processing, decision support. These need integration with your systems and data, governance frameworks, accuracy measurement, and change management. The technology is moving fast but the enterprise plumbing takes time.

Low value, lots of noise: Replacing humans with chatbots, "AI strategy" without clear use cases, vendor-driven implementations looking for a problem to solve.

We're doing what we always do with emerging technology: testing it against real work, documenting what works and what doesn't, and building a point of view grounded in evidence rather than excitement.

We're not an AI company. We're an enterprise technology company watching the most significant platform shift since mobile. And we're being honest about what we know, what we don't know, and what we think is coming.

More to follow as we learn more. For now: stay curious, set guardrails, start thinking about your data, and ignore anyone promising you the future for a licensing fee.