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Welcome to the Friday edition of our newsletter. We spend Fridays going deeper into tools and trends related to generative AI (and Tuesdays sharing news updates). This week, Professor Porter writes about being an AI Catalyst at your organization.

Organizations increasingly know that they need to figure out AI. The harder question is who, exactly, helps turn that ambition into actual changes in how people work.

That question has been coming up for us from several directions: in conversations with companies about the kind of help they need to successfully deploy AI, in thinking about how we should prepare our students as they enter the workforce, and in considering how roles inside organizations are likely to evolve as AI changes the nature of work.

You may have already seen some new AI-oriented titles cropping up in job listings: AI facilitator, AI enablement specialist, forward-deployed AI engineer, and others.

We’re going to go out on a limb and propose another term, one that we think better captures a role that may become increasingly important inside organizations.

We call it the AI catalyst.

And importantly, we’re not necessarily talking about a new full-time job title. An AI catalyst is a role someone can play inside an organization: the person who helps AI move from something people are vaguely interested in to something they can actually use.

Read on below to learn what an AI catalyst is, what an AI catalyst does, and how you might fill that role in your own organization.

A catalyst is something that precipitates change. It’s the spark that starts a reaction, or helps one happen much faster than it would on its own.

In our context, an AI catalyst accelerates the productive adoption of AI within an organization.

But the role is more specific than that.

Most existing terms tend to describe either a process role, such as a facilitator, or a technical role, such as an engineer. Neither quite captures someone whose job is to help other people turn ideas into action.

An AI catalyst is present without doing everyone’s work for them. They’re technical enough to understand what AI can and cannot do, but not so technical that the conversation immediately turns into APIs, infrastructure, and model architecture.

Their job is to connect possibilities with people.

So what does the role look like day to day?

An AI catalyst:

In practice, this looks less like:

“I built you an AI tool that summarizes your weekly reports.”

And more like:

“I helped your team figure out how to summarize those reports themselves. Now three people who had barely used AI before are doing it independently.”

The underlying problem is the same, but the second approach builds lasting capability within the organization.

The second version creates capability inside the organization. The catalyst does not become a permanent bottleneck; ideally, the organization becomes less dependent on the catalyst over time.

You don’t need “AI” in your job title to become an AI catalyst for your organization.

You might be someone in operations who understands where work gets stuck. You might be a marketer who is constantly experimenting with new tools. You might be an IT professional who is unusually good at explaining technology to nontechnical colleagues. You might be a project manager who already works across departments and knows who to bring into a room.

What matters is less your formal role than how you use your position to help other people work more effectively with AI.

Start by looking for friction.

Where are people spending time on repetitive tasks? Where are teams copying information from one system to another? Where are employees producing similar reports, summaries, drafts, or analyses over and over again? Where are people already experimenting with AI, even informally?

You don’t need to begin with a grand transformation strategy. Find one problem that is specific, frequent, and low-risk. Then help a small group test whether AI can make that work faster, better, or less frustrating.

A good AI catalyst is curious enough to keep experimenting, skeptical enough not to fall for every new tool, technically fluent enough to understand what is possible, and organizationally savvy enough to understand how change actually happens.

The key is to make useful work visible. When someone finds an application that works, help document what they tried, what they learned, and where human judgment still matters. Share the example with colleagues facing a similar problem, and invite them to adapt it rather than simply handing them a finished solution.

Your goal is not to become the person everyone depends on to use AI for them. It’s to help more people become capable of using it themselves.

That means connecting leadership’s focus on strategy and productivity with technical teams’ concerns about tools and security, and with frontline employees’ questions about whether AI will actually help them do their work.

Most importantly, help create a culture where experimentation is safe, practical, and connected to real problems. Celebrate useful discoveries, be honest about failures, and know when not to use AI.

You can start by finding a real problem, running a small experiment, sharing what you learn, and helping someone else try it next.

That’s how you become an AI catalyst.