Catalyst: AI Adoption Reshapes Traditional Apprenticeship and Expert Leaning Models

Evaluating AI Through a Human-Centered Lens

As part of Prophet’s 2026 Catalysts research, we’re exploring how organizations are progressing on their AI journeys through our Human-Centered Transformation Model. Rather than looking at AI adoption through a technology lens alone, the framework examines how strategy (DNA), capabilities (Mind), operating models (Body), and behavior (Soul) must evolve together to enable lasting transformation. This is our third article in a five-part series that explores what each lever means before we launch our 2026 Catalyst report. Read the Catalyst series:

• AI’s Next Challenge Isn’t Adoption, It’s Leadership
• Getting More Value out of AI Means Looking Beyond Tech

Here we focus specifically on our findings under ‘Mind’. Much of the conversation around AI capability has focused on technical skills – prompt writing, tool proficiency, and AI fluency. But our research suggests that organizations are confronting a deeper challenge. As AI takes on more analytical and executional work, organizations are being forced to rethink how expertise is developed, how professional judgement is built, and how employees learn throughout their careers.

Leaders are increasingly asking a different question: what capabilities will matter when AI can perform many of the tasks that traditionally developed expertise?

What we’re seeing from the new research:

AI fluency is becoming the new baseline.

The question is no longer whether employees should learn AI. In many organizations, AI fluency is rapidly becoming a foundational workplace skill rather than a specialist competency. Organizations expect employees at every level to understand where AI fits into their work, how to use it responsibly, and when to rely on human judgement. As AI becomes embedded in daily workflows, AI literacy is shifting from a competitive advantage to a requirement.

“L&D is spearheading AI journey for employees, how they use it, how they can be fluent, understand it, and use it responsibly.”

HR Leader, Software Development

AI expertise is moving beyond prompting to managing agents.

Many organizations have invested heavily in prompt engineering and AI training, but leaders increasingly recognize that prompting is only the beginning. As AI agents become capable of executing increasingly complex workflows, employees will need to move beyond generating outputs to supervising, orchestrating, and evaluating them.

[…on the use of AI beyond prompting] “we’ve been seeing advanced AI tools in the talent attraction space today, for example Eightfold AI provides an agent that calls applications and sifts through applications at any time of the day.”

HR Leader, B2B Marketing & Sales

AI is reinventing the traditional apprenticeship model.

One of the biggest questions emerging from our research is not whether AI replaces early-career work, but how it changes the way expertise is built. AI has the potential to accelerate learning by reducing time spent on repetitive tasks and giving junior employees earlier exposure to higher-value thinking, problem-solving, and insight generation. But this won’t happen by default. Without intentionally redesigning capability-building pathways, organizations risk creating a generation that reaches answers faster without developing the critical thinking and professional judgement that traditionally came through experience. The opportunity isn’t to preserve the old apprenticeship model, it’s to build a better one.

“Apprenticeship has traditionally been with people, now you also have to apprentice the model you’re working with.”

HR Leader, Pharmaceuticals

What’s next for business leaders?

1. Move beyond AI literacy to AI judgement

Last year’s Catalyst research focused on building AI literacy across the workforce. That remains essential, but the next wave of AI adoption is shifting from just fluency to judgement. Organizations now need to focus on helping employees develop the judgement to evaluate outputs, identify appropriate use cases, challenge recommendations, and understand where AI should and should not be applied.

2. Reinvent the apprenticeship model

AI is changing how expertise is built in the workplace. As routine work is being automated, organizations can no longer rely on traditional ‘learn by doing’ alone to develop professional judgement. Leaders need to deliberately redesign early-career development, creating structured opportunities for coaching, simulation, shadowing, and guided problem-solving to develop the skills that AI cannot develop for them.

3. Make learning part of the job

Last year’s research emphasized AI training and capability-building. This year, we’re seeing that one-off courses won’t keep pace with the technology. Organizations should embed learning into everyday work by creating regular opportunities for experimentation and knowledge sharing (i.e. showcasing how teams are using AI, running innovation challenges, carving out time to test new use cases).