Before You Scale Copilot, Read This
AI is exciting. Powerful. Fun and cool. Copilot (of course) is showing it’s face all over town. Businesses big and small are running with it.
Hit the switch and go. No problem, right?
Right?
Oh. Ok, well, maybe a couple of small problems. Nothing we can’t address though. Stay with me here and don’t panic. We’re not suggesting you turn off Copilot access.
In fact, that itself would be a huge problem. Your use of Copilot is not going away. It’s here and we’re here for it.
We just need guardrails. There should be structure to the use, there should be directives on what is the best use case for your organization.
Otherwise you come away with unpredictable, inconsistent and possibly risky Copilot behavior.
Many organizations are focused on what Copilot can do, and I get it. There’s a lot to discover. But really, if we’re going to use our heads, we should be focused on how it should be used.
OK, you said ‘risky behavior’. You got my attention.
Glad to hear it. Now let’s talk consequences of simply turning on Copilot and throwing caution to the wind.
We have various skill levels, a wide variety of departments, and team members with a big range of business acumen. What may seem obvious to a more seasoned team member may be something that a new employee might miss.
When this happens, we have a bit of a ‘wild west’ situation here. We find that rather than supporting our efforts, we’re instead getting inconsistent outputs across teams, possibly sharing or surfacing the wrong information, losing credibility when we fail to vet the output, and a general lack of accountability for AI-generated work.
As with all things, we benefit from structure (why haven’t we fully learned this yet? sigh)
Simple governance is your friend
You probably have a rock solid onboarding program for new hires, huh?
You go over the important things, like setting expectations, defining appropriate business exchanges, creating consistency in effort and outcomes, and reducing risk while maintaining a healthy pace.
No different for Copilot use. Seriously.
Get a little granular with how your organization views the individual work your people are doing with Copilot. Clear expectations are a must-have for empowered, confident employee use with minimal risk.
Suggested guardrails to line up for use
At Excel and Flourish, we are frequently asked about best practices when we provide training. Here are a few no nonsense, easy to follow guardrails:
✅ Verification expectation
AI outputs should always be reviewed before sharing. Before Copilot, we reviewed our work, didn’t we? Why would this be any different?
✅ Data awareness
Users should understand where Copilot is pulling from. Know your sources.
✅ Use case clarity
Not everything needs Copilot—focus on high-value scenarios. Figure out what is the biggest bang for your buck here and work there first.
✅ Security alignment
Respect existing permissions and data access. You already have these. They exist. Remind your team of these permissions and then use them.
Think about it.
When social media came on the scene, how many of our organizations created (pretty quickly) a Social Media Agreement or added this chapter to their employee handbook? Same scene here. We need an AI chapter.
If we create too many rules, we’ll have no adoption.
If we operate with no rules, we come away with chaos.
We’re finding that sweet spot in the middle and working from there.
The goal is guided adoption. Creating the pathway to success as an organization, with the full support and insight of leadership.
When the team knows when to use Copilot (and also, when NOT to), how to verify what they generate, how to know when sensitive information should be protected and how to apply Copilot in workflows safely, we’ve set the stage for organizational success.
We’re not aiming for perfection.
We’re aiming to avoid treating Copilot like it’s an experiment. Your organization and your team deserve better.