AI pair programming sounds like a developer’s dream. GitHub Copilot promises faster development, cleaner code, and less time spent slogging through boilerplate. It auto-suggests functions, writes unit tests, and fills in repetitive logic so your team can spend more time on the best parts of their jobs.
Organizations are understandably eager to adopt any new tool that promises increased productivity, more secure code, and happier dev teams. However, Copilot rollouts can be tricky, and not all of them pan out.
Sure, some development teams have welcomed our new robot overlords with open arms. Still, others are skeptical… even as company leaders are trying to roll out Copilot to the entire organization. As it turns out, adopting Copilot isn’t just a licensing decision; it’s a culture shift. It asks developers to work differently, and it demands that leadership understands what the tool actually is (and isn’t). Some teams are getting it wildly right. Others? Not so much,
We’ve read through developer forums, Reddit threads, and case studies to bring you stories of real Copilot adoptions: the good, the bad, and the chaotic. If you’re thinking about rolling out Copilot across your org, take a look. These stories and lessons might save you a lot of time (and drama).
In 2024, GitHub partnered with Accenture to study its GitHub Copilot rollout. The study followed two groups of developers: one group had access to Copilot, and one did not. Most of the devs with access to Copilot seemed excited to use it: 81.4% installed the Copilot IDE extension on the same day that they received a license, and 96% of those who installed the extension started receiving and accepting suggestions that same day.
The developers with Copilot access also say they used it regularly: 67% used Copilot at least five days a week, with an average usage frequency of 3.4 days per week, and 70% of respondents say they relied on GitHub Copilot for coding tasks in familiar programming languages.
Developers are tinkerers. Allowing your team access to a tool, letting them play around with Copilot, and adopt it on their own is an easy way to encourage adoption.
Rather than insist all developers start using Copilot, Accenture made licenses available. Github actually recommends this approach. By offering a self-service Copilot model, you can make it easy for developers who are already interested in trying Copilot on their own, without approval. When their teammates notice their wins, they may be tempted to try Copilot themselves.
It’s also important to invest in some way of measuring success. Accenture’s pilot was set up so that Copilot use could be measured against a control group, which helped the company see clear outcomes.
You’re obviously not going to find any case studies about teams that struggled to implement AI. However, devs aren’t shy about discussing negative experiences on Reddit. One user discussed a Copilot initiative that wasn’t going well. The reason? Training. Everyone in their organization was given the same generic onboarding and didn’t quite understand how they should be using Copilot to help them in their specific roles.
The lack of examples led to unclear expectations about how to use Copilot and which tasks it’s best suited for. In this case, it meant team members tried to force Copilot to handle tasks it wasn’t designed for, and when these tasks didn’t work, some users lost faith in the tool.
If you’ve invested in Copilot, it’s important to make sure your teams know how to use it well. By designing your pilot program around well-defined tasks and measuring adoption, you can help everyone understand how Copilot should be used and where it excels. Clear onboarding means the devs who use it are more likely to succeed. By measuring that success, you can duplicate it in other groups.
Using a dashboard to measure adoption also means your team will know which tasks are actually being streamlined and which are being made more complex.
No, some company leaders just don’t know. However, they have read up on the benefits of AI pair programming, and they believe that since they’ve invested in Copilot, their teams will now be producing a week’s worth of work in a day. After all, the company invested in Copilot; shouldn’t work be flying along immediately? Some managers don’t quite get that Copilot isn’t doing all of a developer’s work for them; it’s a tool that helps them do their work. It’s also supposed to increase developers’ job satisfaction, not overwhelm them.
Sometimes, even developers fall prey to the “Copilot is magic” mindset. Rather than trusting their own experience and knowledge, some devs are just accepting that Copilot knows better than they do. This leads to accepting suggestions they haven’t read through first. This is also not the way Copilot should be used. It’s there to automate repetitive code, not replace programmers. Developers should absolutely be reading through their code, including suggestions from Copilot.
It’s important that everyone, from management to development, knows exactly what Copilot does. It’s equally important that they know what it doesn’t do.
Unrealistic goals and expectations from management will only stress out your team. It sends the message that you’re trying to automate their jobs, and that management doesn’t know (or care) what they actually do for your company.
Additionally, no good can come from devs who think Copilot is a coding sorcerer that knows better than they do. It may lead to less productivity if team members are accepting suggestions they haven’t read through first. Unfortunately, if you’re not measuring Copilot usage, accepted suggestions, and actual productivity, you can’t know.
A lot can be learned from other teams’ mistakes (and successes). The lessons here?
We all know the quote about management and measurement by now, right? Without visibility into your Copilot rollout you may end up with pandemonium rather than productivity. However, by using a dashboard that gives you relevant metrics, like Opsera, you can see what’s going right and wrong with your Copilot pilot.
Opsera’s Unified Insights is the only platform that shows the full impact of Copilot on developers, teams, projects, and on your organizational business goals.
By connecting all your DevOps tools across the entire software delivery process, Opsera gives your team a complete view of your software delivery process, allowing you to analyze and continuously improve your process.
The platform allows your team to make baseline comparisons with data from up to a year in the past, letting you easily chart your organization’s adoption journey and clear before and after results.
