Who’s Training Tomorrow’s Experts?

When I was in art school, we studied the books. We studied the masters. We learned the technical side of photography.

One of my favorite classes was location lighting. That was where the preparation met the actual situation.

You could understand how lighting was supposed to work and still find yourself somewhere the conditions didn’t cooperate. You had to figure out what was happening, adjust and understand why your next choice might work.

Knowing the technique was the starting point. Applying it when the conditions changed was where experience developed.

I think about that as we introduce more automation and AI into our work. We’re finding useful ways to remove manual steps and explore ideas faster. We also need to think about how people will develop the understanding behind those steps.

Who’s preparing them to recognize when the result doesn’t make sense?

01 / Understand what you’re asking technology to do

Over the years, I’ve preferred that my teams learn how to do a process manually before we automate it.

Working through the process helps people understand what’s happening, why the steps matter and how one action affects the next. When we automate it, they can evaluate whether it’s doing what we intended.

If something breaks, they have a better chance of recognizing where it broke and how to fix it.

That doesn’t mean everyone needs to spend months repeating a task a system can handle. The amount of practice should fit the responsibility. But someone accountable for the result needs enough understanding to question it.

Automate the task. Make sure people still understand the consequences.

A successful run tells you the system worked that time. Understanding the process helps you respond when the inputs, requirements or circumstances change.

02 / A convincing image still needs to be thought through

We’re seeing clients use AI to create reference images—go-bys, swipe and mood boards—to communicate what they want. That can be a useful starting point. It gives us something tangible to discuss.

But an image can look convincing while presenting something that would be difficult, expensive or physically impossible to reproduce as shown.

Proportions may be off. Scale may be inconsistent. Materials may behave in ways they wouldn’t in the real world.

AI can show you a rug running up a wall and across a ceiling. Producing that physically takes a conversation about support, installation, materials and cost. The image doesn’t explain any of that.

An experienced team can look at the reference and ask: What part of this matters to the client? The mood? The composition? The exact installation? Would a physical set, a digital approach or a combination deliver the intent?

The reference starts the conversation. It doesn’t resolve the production plan.

People entering the field need opportunities to work through those questions. They need to see how an attractive idea becomes an achievable approach—and what changes along the way.

That understanding develops through exposure to real decisions.

03 / Let people see the reasoning

One of the most useful ways I’ve helped people grow is by bringing them into the process while the work is happening.

Include them in the meeting. Have them help prepare the presentation. Explain what you’re considering and what you expect the client to ask.

That might sound like:

“The client is going to want to understand the cost per asset. I need you to know how we got to that number and what would change it.”

Now they have an assignment with a purpose. They’re learning how the information connects to a business decision.

Afterward, talk through what happened. What did the client ask? What had we anticipated? What did we miss?

I’ve also used an approach I think of as shadow leadership: give someone responsibility, stay close enough to support them and allow them room to work through it.

The person should know where that support is. You agree on the boundaries, let them lead and step in when the consequences require it. Otherwise, give them the opportunity to make the decision and learn from the outcome.

The goal is for them to carry more understanding into the next assignment.

04 / Experienced people need room to experiment, too

Development isn’t only for people starting their careers.

People who understand an established workflow should be experimenting with new technology. They know where the effort goes, which exceptions matter and what a successful result needs to accomplish.

That experience gives them a useful basis for testing a different approach.

I see AI as a force multiplier. It can take on parts of the manual process and give people more capacity for creative exploration and problem-solving.

To find that value, experienced people need time to try things. Can a tool remove a step they’ve spent years managing? Does it improve the result? Does it create additional review or correction somewhere else?

Pairing that experience with someone who brings a fresh perspective on the technology can be especially useful. Both have something to contribute, and both should be learning.

05 / Make coaching part of the operation

If development matters, it needs time on the calendar and attention in the work.

Meet with people. Sit alongside them. Watch how the task actually gets done. Ask what they’re seeing and where they’re getting stuck.

You learn things there that a status report won’t show you: the workaround someone has developed, the requirement they’ve misunderstood or the decision they keep waiting for someone else to make.

Those are opportunities to improve the process and build capability at the same time.

As new methods develop, identify people who are ready to learn them more deeply. Give them time to test, practice and understand the limitations. Then prepare them to teach others.

Teaching the trainers requires more than demonstrating which buttons to press. They need to explain the reasoning, recognize common problems and help someone else work through them.

Set expectations for that responsibility. Make time for it. Follow up to see whether the next group can apply what they’ve learned.

That is how knowledge becomes useful across a team instead of staying with a few capable individuals.

Before you change the workflow

When introducing a new tool or automating a process, include these questions in the discussion:

• What does the person responsible still need to understand? Identify the judgment required to evaluate the result.
• Where will they get practical experience? Build in observation, practice and feedback.
• Who will coach them—and when? Give that responsibility an owner and enough time.
• How will the learning reach the wider team? Prepare people to teach and check that others can apply it.

We should keep improving the way work gets done. There is plenty of repetitive effort worth removing, and new technology gives us room to do things we couldn’t do before.

Alongside that progress, we need people who can recognize an unrealistic reference, explain a production choice, troubleshoot a process and guide someone else through it.

Tomorrow’s experts need opportunities to practice today—with someone willing to make the time to help them learn.