What comes after AI literacy training?

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For the past two years, organizations have invested heavily in AI literacy.

Employees have learned to use LLMs, experiment with agents, and automate parts of their jobs. AI Champion networks have helped people share what works and learn from one another.

That investment matters. And it isn't finished. Plenty of people are still figuring out how to use AI well.

But for teams that have made progress with adoption, a new question is coming up: What should we teach people next?

A recent Harvard Business Review article, What Top Performers Do Differently with AI—and Why They See the Biggest Benefits, caught my attention. Researchers studying sales professionals found that top performers were getting particularly strong benefits from AI.

Rather than treating AI primarily as a way to complete tasks faster, they used it as a thinking partner. They tested different sales strategies, anticipated customer objections, prepared for difficult conversations, and explored approaches they might not have considered on their own.

This helped them go beyond efficiency gains. AI supported the judgment, preparation, and strategic thinking that were already important to their performance.

And that got me thinking about AI training.

If some of the greatest benefits come from using AI to improve how we think and make decisions, what capabilities should we be developing next?

What capabilities should L&Ds & AI Enablement leaders develop next?

If AI can help people think through problems, explore alternatives, and make better decisions, perhaps the next stage of AI training should focus less on what the tools can do and more on what people can accomplish with them.

At Design Sprint Academy, we've been exploring three capabilities that we believe are becoming increasingly relevant: problem framing, workflow redesign, and facilitation.

1. Problem framing and critical thinking

Once people see what AI can do, it's natural to start spotting things to automate.

But a promising automation idea isn't always the same as a meaningful business opportunity.

AI Problem Framing helps teams make that distinction. It's a structured method for examining AI opportunities through different lenses and applying a series of filters to challenge assumptions, evaluate potential value, and refine decisions together.

We use it in a one-day workshop, where the people responsible for deciding which AI opportunities to pursue work through this thinking process together.

And this is where something else happens.

People have to hold opposing ideas in their heads without rushing to conclusions. They need to consider different perspectives, explore possible outcomes and risks, challenge their own assumptions, and question each other's reasoning.

It requires everyone to bring their best thinking to the table.

By the end of the day, the team has a clear direction. Sometimes that means identifying AI use cases worth investing in. Other times, it means deciding to drop an idea. Either way, they've reached a decision together, with a shared understanding of why.

But the value goes beyond that decision.

Every time people work through this process, they're exercising their critical thinking skills. They're practicing how to evaluate complex problems, navigate uncertainty, and make decisions together.

And the more often they do it, the stronger those capabilities become.

2. Workflow redesign and ownership

General AI training often starts with the tasks people already do. Workflow redesign starts by asking whether the steps, handoffs, and responsibilities still make sense.

What I found interesting in the HBR research was how top performers used AI to explore different approaches to their work, rather than simply speeding up what they were already doing.

I think there's a parallel here with how organizations approach AI.

When we look at an existing workflow, it's tempting to ask which steps AI could automate. But what if we questioned the workflow itself? What if some steps were no longer necessary? What if responsibilities could shift, information could move differently, or people and AI could work together in entirely new ways?

That's what we explore through the AI Workflow Sprint.

Over four days, the people involved in a workflow examine how it currently works, identify opportunities for change, design alternative ways of working, and prototype a new approach they can test.

And just like with problem framing, there's another capability being developed in the process.

People practice questioning established ways of working, considering alternatives, and designing solutions together. They're not simply learning how to use AI within an existing process. They're learning how to rethink the process itself.

But something else happens when the people affected by a change are involved in designing it.

They have a say in what changes, understand why decisions are made, and can raise concerns before those decisions become final. Instead of having a new workflow imposed on them, they've helped shape it.

That creates ownership and reduces resistance to change. People are more likely to support and adopt a new way of working when they've been part of creating it.

3. Cross-functional facilitation and collective intelligence

Even when a team agrees that a workflow needs to change, people may disagree about how.

Bringing everyone into the same room is a good start. But it doesn't guarantee a productive conversation, let alone a good decision.

Without guidance and structure, familiar group behaviors tend to emerge.

The loudest person dominates the discussion. The most senior person influences the direction, sometimes without intending to. The group gravitates toward the first reasonable idea because it's easier to agree than to explore alternatives. People hold back concerns or avoid challenging assumptions because they don't want to create tension.

And when people come from different departments, there's another layer of complexity.

They bring different priorities, experiences, perspectives, and ways of communicating. They may not know each other well enough to understand where someone else's concerns are coming from. Even when everyone has something valuable to contribute, those contributions aren't always heard or understood.

This is where social sensitivity becomes important: the ability to notice how others are responding, listen carefully, recognize different perspectives, and create enough trust for people to disagree constructively.

Research on collective intelligence suggests that a group's ability to perform well together isn't simply the sum of its members' individual intelligence. How people interact matters. Whether they listen to one another, share speaking time, and respond to social cues can influence how effectively they solve problems together.

AI Facilitation is the practice of guiding cross-functional teams through structured conversations and decisions about where AI creates value, how workflows should change, and what to explore next. That's why we see it as a capability worth developing.

An AI Facilitator guides the group through a structured process that gives different perspectives a chance to be heard, challenges assumptions, prevents premature agreement, and helps people make decisions together.

This is a capability that AI Champions and AI Transformation Leads can develop and bring into their existing roles. Many already help colleagues explore AI opportunities or coordinate change. Learning to facilitate structured conversations gives them a practical way to bring the right people together and help those ideas become shared decisions.

And this is another capability that develops through practice.

People learn to listen beyond their own perspective, navigate disagreement, recognize group dynamics, and build on each other's thinking.

The goal isn't just to bring smart people together. It's to help them become smarter together.

So what comes after AI literacy?

Perhaps the next step isn't another training course focused on what AI tools can do. It's creating more opportunities for people to practice the capabilities that help them put AI to good use.

When teams work together to evaluate an AI opportunity, they're practicing critical thinking.

When they redesign a workflow together, they're learning to challenge established ways of working and take ownership of change.

And when they navigate different perspectives to reach a decision, they're developing the skills that make collective intelligence possible.

What's interesting is that these capabilities don't have to be developed separately from the work. They can be developed while teams are solving real business problems, making decisions about AI investments, and redesigning how their organizations operate.

That raises a different question for L&D and AI Enablement leaders.

Instead of asking only what people need to learn about AI, perhaps we should also be asking how to create more opportunities for them to learn through AI transformation.

If you've already invested in AI literacy and AI Champion programs, what capabilities are you thinking about developing next?