GitHub Copilot Changes How We Request Fixes — How Do We Assess the Impact?

Hello, I’m Mia Sato, an AI Researcher at GDX.

I recently read about a new integration between GitHub Copilot and Microsoft Teams. The idea is simple: when a bug or development issue appears in Teams, the team can ask AI to investigate the cause and start working on a fix without first moving the conversation into another tool.

My first reaction was that this could remove a lot of friction. I sometimes receive feedback on a screen in Teams, summarize what the team eventually agreed on, and then rewrite those conclusions as instructions somewhere else.

But it also raised another question for me. Even if the requested change is communicated perfectly, how much does the person making the request really know about everything else that change might affect?

This GitHub integration is not designed specifically for e-commerce. It can be used across websites, applications, and other projects whose code is managed in GitHub.

Still, e-commerce provides a useful example for thinking about the difference between making something easier to change and making it safer to change.

From a Teams conversation to a proposed fix

On October 23, 2026, GitHub announced an update to its Microsoft Teams app. GitHub is widely used by development teams to manage code and track changes, and the update brings GitHub notifications and Copilot-powered development work closer together inside Teams.

Imagine that a bug is reported in GitHub and the notification appears in Teams. Someone in the conversation can mention @GitHub and ask Copilot to investigate the problem or work on a fix. Copilot can then use the issue report and the surrounding discussion as context for the task.

The proposed changes are submitted as a Pull Request for the team to review. In other words, the AI can prepare a change, but it does not simply rewrite the live site without human review.

GitHub also describes a workflow where Copilot can use the Teams conversation as context while investigating the issue, making changes, and running tests. The team can review the work plan and see which files were changed.

What I find interesting is that the people who first see the problem in Teams can stay closer to what happens next. Instead of handing the issue off and waiting, the same team can more easily follow the investigation and the proposed fix.

Turning a conversation into instructions is not just copy and paste

A normal Teams conversation rarely starts out as a clean development request.

A discussion might begin with someone saying that a button is confusing. Someone suggests changing the color. Later, the team decides that moving the button would solve the problem better. When the development request is finally written, it needs to reflect the final decision rather than every idea that appeared along the way.

I often go back through conversations myself and summarize what should change and what should stay as it is. Thinking about it this way, that work is not simply shortening a discussion. It also involves deciding which comments represent the conclusion.

Even if Copilot can use the full conversation as context, we should not assume it will always distinguish an early suggestion from the final decision. Leaving a clear message such as “this is the option we are going with” at the end of a discussion will probably remain valuable.

And even if the AI understands the requested change correctly, there is still another problem: that change may affect parts of the system nobody mentioned in the conversation.

How far can one small change reach?

On an e-commerce site, a small change on one screen can be connected to several other parts of the buying experience.

Suppose a team wants to change how a campaign discount is displayed. At first glance, it may look like a product-page update. But the same change could also relate to price calculations in the cart, coupon conditions, or the way discounts appear on the order confirmation screen.

Shopping feels like one continuous experience to the customer, while behind the scenes many different processes are working together. That makes me hesitant to look only at the screen we changed and conclude that the job is finished.

The current team may not understand every one of those connections either. The longer a site has been running, the more likely it is that features have been added over time and ownership has changed. Sometimes a rule exists for a good reason, but nobody currently involved remembers why it was introduced.

I naturally tend to focus on what I want to change on the screen in front of me. But simply using the interface does not tell me what may be affected behind it. And it is not always safe to assume that a developer will immediately know either. If the original design decisions were never documented, the investigation can be difficult for everyone.

The easier it becomes to request changes in natural language, the more important another question becomes before making the change: what else should we check?

Ask AI where to investigate, not just how to fix the problem

Thinking about this made me realize that one of the most useful things AI could do may happen before it writes the fix: investigate the related code and the history of previous changes.

Is the component we are changing also used elsewhere? Are there other processes that rely on the same data? If AI could trace those relationships and identify areas that may be affected, it could help teams notice dependencies they did not know existed.

It would be even more useful if the AI could explain why each area might be affected and point to the relevant code or previous changes. “Check this too” is helpful, but understanding the reason makes it much easier for people outside the development team to follow the logic and take part in the decision.

Test planning is another area where this could help. A normal purchase might work perfectly after a change, while a less common combination of multiple discount rules could still fail. Suggestions for cases that are easy to miss in everyday testing could make the review of an AI-generated change much more reliable.

Of course, GitHub’s announcement does not claim that AI can automatically discover every possible impact. Some important rules exist outside the code, including internal operational procedures and business decisions.

That is why it would also be useful for AI to show what it could not verify. Developers could review the technical impact, while e-commerce operators could check what the change means for real workflows. The final decision can then bring both perspectives together.

Until now, when asking for a change, I have mostly focused on explaining what I want changed. If AI can also support the investigation, we may be able to understand why certain areas need to be checked before moving ahead. To me, that is a different kind of value from simply making the work faster.

Faster fixes only help if they are also safer

If requesting a change becomes easier, teams may finally have time to address small improvements that used to stay at the bottom of the backlog.

But as the number of changes increases, it also becomes more important to record what was checked and what happened.

Ideally, the record would include more than the code that changed. Which functions might have been affected? What tests were run? What were the results? Keeping that context would make it much easier for the next person to understand why a decision was made.

If AI can help organize and preserve that information as part of the workflow, the value may extend well beyond the current fix. For an e-commerce site that will be maintained for years and handed between different people, avoiding the need to reconstruct every past decision from scratch can be surprisingly important.

When I first saw this news, I focused on how convenient it would be to request a fix directly from Teams. Now I think the more interesting opportunity may be using AI to understand the possible impact before a change and preserve the reasoning behind the decision afterward.

If AI can help us not only make changes faster, but also organize the information we need to keep improving safely, that may become especially valuable for e-commerce sites that are expected to keep evolving for a long time.

References

• GitHub Changelog, “Turn notifications into code with GitHub Copilot in Teams” — October 23, 2026
  https://github.blog/changelog/2026-10-23-turn-notifications-into-code-with-github-copilot-in-teams/
• GitHub Changelog, “Shared agentic work with GitHub Copilot in Microsoft Teams” — August 21, 2026
  https://github.blog/changelog/2026-08-21-shared-agentic-work-with-github-copilot-in-microsoft-teams/
• GitHub Docs, “Integrating Copilot cloud agent with Teams”
  https://docs.github.com/en/copilot/how-tos/copilot-integrations/integrate-cloud-agent-with-teams