Copilot Vs ChatGPT - Approach Determines Outcome

Tools like Copilot Do Not Fail. The Approach Often Does.

Post 5 of the series: Starting with Copilot Chat

If we were sitting in a restaurant right now and reflecting on this whole Copilot vs ChatGPT investigation, I don’t think we would be arguing about which tool is “better.” That hasn’t really been the story.

The story has been about context, structure and intent.

Over the last four posts we’ve looked at where to start, how perception can mislead us, where Copilot actually earns its keep inside Microsoft 365 and why security and compliance are leadership conversations rather than technical ones. So now we arrive at the real question.

If organisations already have access to AI, why are so many still not seeing meaningful business impact?

The Illusion of Access

What you often find instead is what we call AI adoption without AI advantage.

Adoption looks impressive because it creates a feeling of being up to date, relevant and progressive. Adoption is the easy part. You make a buying decision, purchase licences or a subscription, enable Copilot, allow your teams to experiment, perhaps even trial an enterprise subscription. That creates momentum, it looks modern and it feels like progress.

• Advantage only shows up when specific tasks are done better, faster or more consistently.
• When productivity increases, when the quality of work improves.
• When teams become more efficient and more effective in their roles.

And that only happens when the use of the tool is deliberate.

Without structure, adoption just creates expectation, unwanted friction that results in additional noise.

People use AI occasionally and they get some helpful outputs. They feel positive about it. But when you look for measurable improvement in turnaround time, in accuracy, in stakeholder satisfaction, the reality is often weak, below expectation and a questionable investment of time, effort and investment.

Access on its own does not change behaviour and without behaviour change, business impact remains marginal.

Why Most AI Training Fails to Translate

There is another uncomfortable reality that shows up again and again.

AI training sessions are often engaging, the room is attentive, the examples are clever and people leave thinking, "This is powerful."

Then they sit down at their desk and now the abstract becomes real. The inbox is still messy, the spreadsheet is complicated and incomplete, that contract amendment the legal team are working on is nuanced and the stakeholder expectation is unclear.

What was shown in the session does not immediately map onto what they are facing in front of them. So they revert back to what they have always done, to what they know.

Not because the tool is weak. Not because AI is overhyped. But because the training was not anchored in their actual tasks.

Feature-led training creates awareness. Task-led training creates capability.

When learners practise on generic exercises, they leave informed. When they practise on their own tasks, they leave enabled, that distinction matters far more than most organisations realise.

AI Works at the Task Level

One of the most important conclusions from this entire series is that AI productivity tools operate at the task level.

They do not operate at the job title level and not at the department level, that's for business process optimisation, for approaches like Agentic AI or for applications like your ERP or CRM to have inbuilt AI capabilities. This isn't the domain for productivity tools like Microsoft Copilot, OpenAI ChatGPT or Google Gemini

Their domain, the area they add huge value to your area of responsibility, to your business is the task level.

Tools like Copilot help sales teams with planning, storyboarding, drafting and refining proposals. They help speed up and improve the accuracy of CRM entries so the extended team has clearer visibility around opportunities. They assist with summarising long email threads before critical follow-up meetings and aligning messaging to stored product documentation.

• In Legal, used well, they assist with reviewing customer amendment clauses, analysing contractual risk, communicating the impact of that risk to different stakeholders and helping translate changes in law into business implications that leadership can understand.
• Procurement teams benefit from being able to process large volumes of internal requirements, structure RFPs clearly, respond to supplier questions consistently, compare vendor solutions objectively and support unbiased scoring of commercial offers. When this work happens inside the same Microsoft 365 environment that already houses emails, spreadsheets and documents, the friction reduces significantly.
• Marketing teams use these tools to refine messaging, structure campaign narratives, extract themes from internal strategy documents, and iterate on presentation decks without moving content across multiple platforms.
• Leadership teams rely on summarising board packs, comparing strategy versions, reviewing performance dashboards and clarifying action points across departments.

None of these examples are dramatic. They are practical and they are some of the value ares that individuals, teams and departments benefit from everyday. That is precisely the point.

AI advantage does not come from spectacular one-off outputs. It comes from steady, repeated improvement across everyday tasks.

Copilot in a Microsoft 365 Environment

When we step back and consider the Copilot vs ChatGPT comparison through this lens, something becomes glaringly obvious.

In a Microsoft 365 embedded organisation, Copilot sits inside the same ecosystem that already handles your emails, your OneDrive files, your SharePoint documents, your Excel sheets and your PowerPoint presentations.

It inherits the same data handling model, the same permission structures and the same vital governance framework.

That alignment does not remove responsibility. It does not eliminate the need for judgement. But it does create a structurally sensible foundation for AI use in a business environment.

This is not an argument that other tools have no place, they absolutely do.

It is an argument that where work already lives should influence where AI begins.

So What Actually Determines Success?

If we strip everything back, the story is remarkably consistent. It isn’t the tool that determines the outcome but the operating discipline around the tool.

When we look at organisations that are genuinely seeing impact, they tend to share a few common behaviours. None of this is complicated but it is intentional.

1. They decide where AI should and should not be used.
They don’t leave it to chance. They define which tasks benefit from AI support, where Copilot is the default inside Microsoft 365, and where other tools might be better suited. That clarity removes random experimentation and replaces it with purpose.

2. They anchor AI to real work, not abstract capability.
AI is introduced as support for specific tasks. The RFP that needs structuring. The supplier response that needs comparing. The contract clause that needs reviewing. The board summary that needs tightening. When AI is connected directly to everyday workflow, it stops being impressive and starts being useful.

3. They train at the task level.
Not feature tours. Not generic prompt examples. Real documents. Real inboxes. Real deadlines. When people practise on their own work, learning turns into capability. The gap between the training session and the desk disappears.

4. They set simple, usable guardrails.
Clear direction. Use this tool for internal work. Do not upload sensitive documents to personal accounts. Review outputs before sending externally. Keep confidentiality front of mind. Simple rules that people can actually follow reduce risk far more effectively than complex policies.

5. They measure what matters.
Not number of prompts. Not licence activation. But time saved on defined tasks. Fewer revisions. Faster turnaround. Clearer communication. Adoption metrics make organisations feel modern. Impact metrics make them effective.

When these elements are in place, the results are not dramatic. They are consistent.

• Tasks get completed faster.
• Quality improves incrementally.
• Confidence replaces hesitation.

AI does not create advantage on its own. But when applied deliberately, at the task level, inside the environment where work already happens, it stops being an experiment and starts becoming part of how the organisation operates.

And that is the difference between having AI and actually using it well.

What Comes Next

In this first series, we focused on where to start, how to compare Copilot and ChatGPT, where value appears inside Microsoft 365 and why leadership clarity around security and governance matters.

In the next series, we will move from principle to practice.

We will go department by department, exploring what Copilot looks like when used properly in Sales, Legal, Procurement, Marketing and Leadership and we will also be honest about when tools like ChatGPT or Gemini may be better suited for certain types of thinking.

This conversation is not about promoting a platform but about understanding the role of each tool properly.

That is where AI shifts from adoption to advantage.