Catherine Brumbaugh had one week. Lexicon’s chief human resources (HR) officer needed a leadership summit to upskill the company’s HR team on continuous improvement, developing others, interviewing and strengthening HR business partnerships, all tied together under one theme.

Brumbaugh, Lexicon’s director of training and development, had built programs like this before, but never on this timeline. With her usual process, a customized program took 6-8 weeks to build: selecting courses, deciding what to cut, reworking case studies with subject matter experts (SMEs) and rebuilding facilitator guides so there wouldn’t be gaps.

This time, she used DDI CoLab Studio^SM, a leadership development artificial intelligence (AI) tool that builds customized learning content based on an organization’s business context and training needs for any stated audience and length of time. The summit came together in roughly four hours.

“That was CoLab,” Brumbaugh said. “I could not have done that as quickly as I did without it.”

That’s the kind of turnaround most learning and development (L&D) teams are only beginning to imagine. What matters isn’t just the hours saved, but the capacity created, including room to say “yes” to more urgent, business-critical needs and spend more time on the work that requires human judgment. Which raises the real question: What becomes possible when six weeks of work can happen in an afternoon?

The Difference Between Fast and Right

That kind of scale only matters if the output is good quality, and not every tool clears that bar. General-purpose AI is good at sounding right. On its own, and without any real grounding in an organization’s context or how people learn, it can produce a leadership scenario, facilitator guide or discussion prompt that looks finished. But a fluent paragraph and an effective learning experience are not the same thing. Content built this way can miss which behaviors move the needle for a given audience, or how to sequence practice.

This matters more than it might seem. Only 30% of leaders say they have enough time to do their job, and 71% report increased stress, according to DDI’s “Global Leadership Forecast.” Development aimed at people under that kind of pressure has to work.

That’s part of what made Brumbaugh’s experience different. CoLab Studio isn’t a general-purpose model guessing what leadership training should look like. It’s built on DDI’s research and content, grounded in leadership science and instructional design.

Brumbaugh built a check into her process regardless. Every piece of AI-generated content gets reviewed by a human and against established models and practices, a habit that has as much to do with good practice as it does with any particular tool. That discipline hasn’t dimmed her enthusiasm for what the tool has done for the value she can now deliver.

“This isn’t just about saving me time. It’s about what Lexicon can ask of its training team now,” she said. “We can say yes to requests that used to be impossible on this kind of timeline, and still deliver something built specifically for the people in the room.”

Where the Time Really Goes

That distinction points to what’s really changing in L&D. It isn’t the underlying work: knowing what’s worth building, which behaviors matter, how learning aligns to a business problem. Those are still judgment calls a human needs to make.

What’s changing is the ceiling of what’s possible. Most training teams couldn’t create a summit at a moment’s notice. Production, including drafting scenarios, building materials, formatting slides and designing effective practice, has always taken longer than the calendar allows. That level of customization often didn’t get done at all, or got done at a fraction of the depth. AI tools like DDI CoLab Studio mean more organizations can get genuinely customized, high-quality development instead of a generic program stretched to fit. And it means one person can support far more of that work than before.

That doesn’t eliminate the L&D professional’s job role. It reallocates their time toward the parts of the job a tool cannot touch, like understanding what the business needs, sitting with a stakeholder to determine the real problem and deciding how a learning strategy should evolve as that problem does.

3 Questions to Consider Before Trusting a Tool

A few questions are worth asking before handing training content development to an AI tool.

1. Does it start with real business context, or just your prompt?

The most useful output comes from tools that know an organization’s priorities, audience, goals and culture upfront.

2. Is it optimizing for behavior change or content?

The biggest risk AI creates for L&D isn’t bad content but, rather, an increase in plausible, polished content that never changes how anyone leads. A general-purpose AI model predicts the next likely word. It doesn’t inherently know what turns information into a lasting leadership habit. That has to come from leadership research and evidence-based instructional design.

3. Is a qualified person still reviewing everything?

Every AI-generated output, from case studies to slide decks, requires expert review to identify missing context, misleading information or inaccuracies before it reaches learners. As AI tools become more sophisticated, expert review becomes even more critical to ensure plausible-sounding scenarios are also accurate.

Where L&D’s Value Really Lives

AI isn’t making L&D professionals’ roles less important, but it is changing what they should be valued for. When content was expensive and time-consuming to produce, creating high-quality learning was a significant part of the job. Now that AI enables faster, more scalable content creation, L&D professionals must bring additional value through their expertise, judgment and ability to ensure learning drives meaningful business results.

L&D’s value shifts upstream and downstream: Identifying the business problem worth solving, understanding the behavior that needs to change, designing the right intervention, deploying it quickly and knowing whether it worked.

That takes more judgment, not less. It means understanding the business deeply enough to challenge a request for “a course” when a course isn’t the answer. It means knowing enough about how people learn to distinguish polished content from an experience that will change behavior. And it means measuring success by what leaders do differently, not by what learning was delivered.

AI can dramatically accelerate the work in between, but speed isn’t the end goal. The promise of AI is that it gives L&D the capacity to spend less time producing learning and more time making sure it matters.