A few weeks ago I wrote about building a personal AI toolbox — using Claude, ChatGPT, and GitHub Copilot for different stages of work instead of arguing over which one is "best." That post landed with a lot of people individually. But it also surfaced a question I didn't expect: okay, but how do I get my team to actually work this way?
That's a harder problem than it sounds, and it's worth separating from the personal version.
Why "just use AI" doesn't work as team guidance
Most teams I've seen approach AI adoption the same way they approach a new tool rollout: pick one platform, license it, tell everyone to use it. That works fine for something like a ticketing system, where the whole point is that everyone uses the same one.
It doesn't work for AI, because — as I wrote last time — different tools genuinely do different jobs well. Standardizing on a single platform for a team doesn't eliminate the "which tool for which job" problem. It just hides it, because now everyone is quietly using the wrong tool for at least some of their work, and nobody's saying so out loud.
The result is usually one of two failure modes: people quietly stop using the mandated tool for the tasks it's bad at, or they force it to work anyway and produce mediocre output while telling themselves "well, that's just what AI does."
What actually changes at the team level
The individual "routing" skill I described last time — knowing which tool fits which stage of work — still matters. But at the team level, three additional things become the real work:
- Shared vocabulary for the stages. If I say "let's ChatGPT this" and my teammate says "let's Claude this," we're actually agreeing on something useful — that this is early-stage shaping work versus deep project work — even if it sounds like we're just naming brands. Getting a team to name the stages of work, not just the tools, is what makes routing something you can talk about instead of something everyone does silently and differently.
- Permission to use more than one tool. This sounds obvious, but it's the single biggest blocker I've seen. Procurement and IT naturally want to minimize the number of licensed tools, for good reasons — cost, security review, support burden. But if a team is only allowed one AI platform, you've reintroduced the exact problem the personal toolbox approach was meant to solve. Part of the leadership job here is making the case that two or three well-chosen tools, used deliberately, produce better outcomes than one tool used for everything.
- Visibility into how people are actually using each tool. Not surveillance — just enough shared practice that the team learns from each other. If one engineer has figured out that Copilot handles a particular kind of refactor well, and another is fighting through the same kind of refactor manually, that's a five-minute conversation that saves real time. Most teams have no mechanism for this kind of exchange because "how I use AI" isn't normally treated as something worth discussing out loud.
What this looks like in practice
On my own team, the shift wasn't a policy — it was a habit. We started naming, casually, which "mode" a task was in before jumping into a tool. Is this a strategy conversation, or is this project work? Is this something that needs deep context, or is it a quick inline assist? Once that became a normal question to ask out loud, people naturally reached for different tools without needing a mandate.
The other change was simply normalizing "I used two tools for this." Early on, there was a faint sense that switching tools mid-task meant you'd picked wrong the first time. Once that stigma went away — once "I sparred with ChatGPT on the approach, then built it out in Claude" became a completely unremarkable thing to say in a standup — the whole team's AI use got noticeably more effective.
The leadership takeaway
If you're leading a team through AI adoption, the temptation is to simplify the decision for people: pick one tool, roll it out, move on. I understand the appeal. But the simplification is actually removing the thing that makes AI valuable in the first place — the ability to match a fluid, capable tool to the actual shape of the work in front of you.
The job isn't to pick the tool for your team. It's to build a team that knows how to pick.
#ArtificialIntelligence #ChatGPT #ClaudeAI #GitHubCopilot #Leadership #SoftwareDevelopment #Technology
Thanks,
Michael Cronin
Website: https://www.michaelcronin.info
LinkedIn: https://www.linkedin.com/in/michaeltcronin/details/experience/
