Author’s Note: On November 1, I’ll be running the 2026 New York City Marathon in memory of my uncle Chris and in support of the Parkinson’s Foundation. As this newsletter lands in your inbox, I’m likely out on a hot, humid training run. If you’d like to learn more about Parkinson’s disease or support my fundraising efforts, you can visit my fundraising page here.
The current enterprise AI adoption playbook looks like the following:
1. Hire an expensive consultant to tell you how to “implement AI into your business”
2. Sign 10,000 people up for an enterprise ChatGPT or Claude account
3. Roll out training (most likely led by an external consultant or a non-technical team) for said enterprise AI plan
4. Wonder why no one is using AI and there haven’t been any “AI-driven” synergies yet
I think a key problem in this process (well, there are a lot of problems) is that AI adoption is being approached as a singular exercise. Those 10,000 employees spin up 10,000 different AI workflows. Every employee develops their own understanding of where AI is useful, how to prompt it, what context to provide, and how much they can trust its output.
Sure, there may be company training and team-level breakout sessions that get into the tactical adoption of AI. But what tends to happen is that a few power users shine, while the vast majority of employees either ride off their coattails or do the bare minimum, comprehending just enough of how to use AI to get by.
There’s an alternative approach I’ve been thinking about.
It’s called multi-player AI.
Multi-player AI adoption involves multiple humans and AI participating in the same workflow and learning from one another. It starts with viewing an AI agent as a really smart colleague, not a program or “thing” you need to implement into your workflows. AI that has context, can converse with you and your colleagues collectively, and can execute on tasks.
For this to work, there needs to be a mindset shift. AI agents need to be viewed as expected attendees, not party crashers that everyone groans about when they show up and no one understands.
Based on personal anecdotes, multi-player AI adoption comes much more naturally to recently founded companies. In an early-stage environment, pace is critical, so everyone becomes AI-savvy for the sake of growth and keeping the company in business. You think less about finding AI use cases because everyone is using AI all day out of necessity. The multi-player approach (working with each other and AI) is ingrained in company culture.
Now, it’s the legacy enterprises that are starting from a much tougher position. When you have an employee base that has been used to working a certain way for decades, you’re going to encounter friction when changing the way they work. Particularly if the “change management” implies job loss based on what everyone is hearing in the news.
It’s funny. Big companies view AI as in, “we need a strategy,” while start-ups say, “this needs to be done ASAP, let’s get Claude on it.” Who do you think is getting a better ROI on their AI investment?
But I believe large enterprises were just thrown a lifeline when it comes to AI adoption.
Enter, Slack Code.
Last week, Slack (owned by Salesforce) introduced Slack Code, “a new feature that allows users to develop code via shared rooms directly within the messaging platform where AI agents write the software and human teammates watch, steer, and direct the session.”
This is a major announcement because Slack made building software a multi-player game. Now, it’s not just you and your AI harness (i.e. Claude Code or Codex). It’s also you, your colleagues, and your AI model of choice, building together. All with the context of your conversations and documentation in one spot.
I believe this is a critical product feature release because it shifts the mindset in favor of AI adoption. Since Slack Code enables your team to work with AI together, AI essentially becomes your colleague. It has access to all your conversations and documents within Slack, meaning it can respond with rich context and write relevant code.
Let’s shift back to the training component of the multi-player strategy. There’s a higher likelihood that people are going to learn more from seeing how their colleagues interact with AI than sitting through a monotonous weekly, hour-long training session on how to write a prompt (gosh help you if it’s led by HR). I haven’t tried out Slack Code, but it seems that if my colleague and I want to fix a bug in the codebase, we can prompt the AI agent within the same chat. In this case, I will learn from their thought process, and they will learn from me. Not only about the business or technical problems that we are solving, but also how they interact with AI.
This can create a multiplier effect. For example, perhaps two engineers and a product manager are chatting about a customer pain point. The engineers call their AI agent to join the chat, and they all start building a solution. The product manager can provide customer context, while the engineers write more technical prompts to the agent. The team is building together in real time.
This level of AI adoption is likely to compound on itself because those three employees are learning from each other how to work with AI, and then they bring this experience to their next problem with other coworkers. The behavior spreads, enabling fast adoption.
Executives should view AI adoption less as an implementation or training issue and more as a social issue. The power users of AI within a company can create as many tutorials and training guides as possible, but that still doesn’t ensure that a critical mass will comprehend the information.
If you’re leading enterprise AI adoption, I highly recommend you view it as a social experiment. By creating environments where curious employees can work with agents (and each other), their understanding of AI will compound more quickly than if employees are working in a silo. The curiosity will be amplified.
In one line: “show, don’t tell.”
Perhaps the future of enterprise AI isn’t giving every employee an AI assistant and hours of training for them to mindlessly watch. Instead, it’s giving every team an AI coworker. Companies that aren’t seeing much return on their current AI strategy need to be honest with themselves. The fastest way to teach someone how to use AI may not be another training session. It may be letting them watch the person next to them use it. And that’s why the future is multi-player.