Enablement & Training

Why Most AI Training Fails

The standard approach to AI training goes like this: bring in a presenter, show some impressive demos, give everyone access to ChatGPT, and hope for the best. Three weeks later, most people have gone back to their old workflows. A few enthusiasts are using AI regularly. Everyone else tried it once, got a mediocre result, and concluded it’s not ready for real work.

This happens because the training doesn’t connect to actual work. Generic demonstrations don’t teach a marketing manager how to draft briefs faster, or show a project coordinator how to summarize status updates from five different tools. People need to see AI applied to their specific tasks, with their actual context, to understand the value.

We build training programs that close this gap. Every exercise uses your tools, your data, your workflows. Participants leave each session having completed a real task with AI assistance—not a toy example.

How Our Programs Work

Discovery. Before we design any training, we interview a cross-section of your team to understand their roles, their pain points, and their current relationship with AI tools. This takes 2-3 days and shapes everything that follows.

Role-specific curriculum. We build modules for each functional group. Sales teams learn different skills than operations teams. Managers need different capabilities than individual contributors. The prompt engineering playbook includes templates and patterns specific to each role’s daily work.

Hands-on workshops. Each session is 70% practice, 30% instruction. Participants work through exercises based on their real tasks—drafting communications, analyzing data, summarizing documents, creating presentations—with direct coaching from our team. We emphasize iterative prompting: how to evaluate AI output, refine your approach, and know when the result is good enough vs. when to do it manually.

Champions program. We identify and train internal champions—people who are both capable and willing to support their peers after we leave. They get additional training, facilitation skills, and materials to run informal learning sessions. This is how adoption sustains itself without ongoing external support.

What People Actually Learn

The core skill isn’t “how to use ChatGPT.” It’s judgment—knowing when AI helps, when it doesn’t, and how to get from a mediocre first result to a useful final output. Specific skills include:

• Writing effective prompts for different task types (drafting, analysis, summarization, brainstorming)
• Evaluating AI output for accuracy, tone, and completeness
• Iterating on results instead of accepting or rejecting the first response
• Understanding limitations—what AI consistently struggles with and when to do it yourself
• Maintaining quality and brand consistency when using AI for content and communications

Building Lasting Capability

The goal of every enablement engagement is to leave your organization more capable than we found it—and to make that capability self-sustaining. The champions network, the playbooks, and the reference library are designed to keep working after the formal training ends.

We also include a measurement framework so you can track adoption over time. If usage drops or quality degrades, you’ll see it in the data and can respond—whether that means a refresher session, updated materials, or addressing a specific team’s blockers.

Most organizations see meaningful adoption changes within 30 days of training. Typical results include a 40-60% increase in regular AI tool usage and a measurable improvement in output quality for AI-assisted tasks. More importantly, teams report that AI stops feeling like an extra tool to learn and starts feeling like a natural part of how they work.