The Problem: Organizations often adopt AI through a small number of individuals without establishing shared practices across the workforce. Teams are then left without repeatable methods for selecting use cases, choosing tools, and deciding where human judgment must remain. We addressed this problem across five professional-learning organizations. While professional learning was the setting, the same challenge arises wherever organizations need to turn individual AI use into a capability their workforce can sustain.
Our Approach: We developed a three-stage model:
The Impact: Each of the five coached organizations finished with a working artifact and the materials needed to operate or extend it. All five reported that the engagement addressed a relevant need, advanced a concrete AI goal, and provided value. Across the six embedded lessons, 3 of 4 participants rated the AI instruction “Very Useful” or “Extremely Useful,” while 9 of 10 said its depth and complexity were “Just Right”. Several workflows also became more efficient, including one in which prompt-development time dropped from eight hours to one.
In a 2024 survey of 1,000 executives across 59 countries, BCG found that only 26% of companies had developed the capabilities needed to move beyond proofs of concept to generate tangible value. Roughly 70% of the implementation challenges involved people and processes rather than technology or algorithms. The engagement with professional-learning organizations thus focused on making AI understandable to people (holding varying levels of technical experience), and turning that understanding into practices their organizations could sustain after the instruction ended.
We combined a shared foundation with practice inside existing work and coaching through implementation.
1. Building a shared foundation across differing levels of experience: We delivered three 90-minute workshops covering AI across the professional-learning lifecycle: analysis, design, development, implementation, and evaluation. Participants practiced with the tools rather than hearing them described.
The instruction was supported by market research; we reviewed 400 sector-focused AI tools, then selected approximately 20 to construct a decision guide organized by use case, setup effort, and price.
2. Embedding AI practice inside real workplace tasks: We embedded 20-minute AI lessons into an existing six-part course. Each lesson applied an AI method to a deliverable participants were already producing, and included a worked example, an activity, and direct application to the participant’s organization. The six lessons and their slides are listed in Fig. 3.
3. Coaching teams through implementation: We provided sustained, hands-on coaching to each of five organizations in the professional learning sector. Every engagement began with a problem the organization already had; we helped define the need, built and tested a work product, explained the decisions behind it, and transferred the materials so that the team could operate and extend the result themselves. The resulting artifacts included an AI literacy framework analysis, a career-connected task generator, an AI tool for shortening and evaluating lesson plans, a curriculum-internalization chatbot, and an assessment rubric for evaluating educational technology.
The engagement empowered five professional-learning organizations with AI tools and methods they could independently operate and extend.
All five professional-learning organizations agreed or strongly agreed that the coaching addressed a relevant need, supported adoption of new AI tools, advanced a concrete AI goal, was responsive to their context and constraints, produced useful deliverables, and provided value overall. Nearly nine in ten participants said the lessons struck the right balance of depth and complexity, while more than three in four rated them “very useful” or “extremely useful.”
The work was recognized externally as well. One participating organization acknowledged Ghamut’s AI consultation and expertise in a research preprint, and another published an interview on applying AI to professional practice.
The AI workforce training model (shared instruction, practice within existing workflows, implementation support) we applied to professional-learning can be extended to other domains. The materials from this engagement are publicly available to help other organizations get started.