by Michael Stelzner / May 12, 2026
Are your team members leveraging AI on a daily basis but only tapping into a small fraction of its potential? Have you put money into AI education only to see individuals quietly revert to their previous workflows after a few weeks?
In this piece, you will learn about a structured approach you can emulate to move your workforce from fundamental AI users to advanced AI strategists.
The Case for Prioritizing Employee AI Training Over Massive AI Deployments
The majority of organizations are placing a single, large wager on artificial intelligence: a single, multi-million-dollar solution created by external providers. They channel resources into that project while overlooking the reality that if their staff cannot grasp, add to, or improve upon what has been developed, success is improbable.
However, if the overall AI expertise within a business is roughly at level three, and the project being rolled out demands level eight or nine proficiency to function and sustain, no one in the organization can meaningfully contribute. The only individual who understands the system is the person brought in to construct it—and once that person departs, the initiative falls apart.
The substitute for the single large wager method is comprehensive training for all staff.
The aim of sophisticated AI education is not to transform everyone into a programmer. It is to advance each person from their current position toward creating tools that tackle the challenges only they fully grasp, because they are the ones handling the daily tasks.
When 50, 100, or 200 staff members each develop their own, even fairly basic, instruments within ChatGPT, Claude, or Gemini—the combined effect surpasses a single custom application in both speed and expense. Every individual resolves the issues they know intimately, since no one comprehends a job's pain points like the person performing it.
As individuals progress to levels five and six, another development occurs: their concepts for bigger, more advanced AI applications become genuinely valuable to the business. They have constructed enough on their own to grasp what AI requires to function—what data must be linked, what the model needs to understand, and where the exceptions exist. That shared insight makes the organization a significantly better client for any external developer they eventually hire, and lessens reliance on vendors from the outset.
The broader transition John outlines is a move from AI reluctance to AI interest. When staff create something tangible, they witness the capability directly. They experience a sense of meaningful contribution. Self-assurance grows, reluctance diminishes, and the organization begins producing AI concepts from within—rather than waiting for management to issue the next directive from above.
#1: Set Up Two Kinds of Oversight Before Training Starts
You will require a framework for tracking two aspects at the same time.
The first aspect to track is the advancement of a staff member's AI abilities. You will need to measure how long each employee's tasks currently take and how long they take following training. The comparisons before and after serve as proof that education is yielding tangible outcomes.
The second aspect to track is what staff produce with AI during and after training, because security and supervision must grow as abilities improve. When staff use AI for straightforward questions or to draft blog entries, oversight needs are low. However, when staff begin operating agents linked to outside databases, the security and supervision demands increase substantially.
John's system keeps these two paths moving together, so organizations are not hurriedly implementing controls after the fact.
Pro Tip : For companies that wish to give staff access to various leading AI models within a safe, compliant setting, John endorses platforms such as BoodleBox and NebulaONE. Both are designed with HIPAA and FERPA compliance and offer staff access to multiple AI models via a single secure interface without the data exposure dangers associated with using consumer-oriented tools on corporate networks.
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#2: Structure Blended, Objective-Oriented Training
A training failure pattern John regularly observes is twofold.
First, there is an overreliance on independent study. Most staff left to go through independent training on their own do not advance past the initial few sections. They are occupied, and the pressures of their current roles do not vanish simply because you have requested them to take on AI education.
To address this failure point, John implements a blended model of recorded segments that staff can complete on their own timetable, alongside live support sessions a couple of times each week. Without that live human interaction, drive fades quickly, and the program yields a very costly collection of partially viewed videos.
Second, there is a shortage of personal significance. Without a compelling, individually meaningful objective to aim for, staff frequently treat the education as unimportant and quietly revert to their previous routines.
To neutralize this failure point, John assists each person in identifying five to ten specific items they could realistically create in AI, before any education begins. We will discuss this more thoroughly later in this piece. This pre-idea stage gives individuals a motivation to go through the education. They are already thinking, "If I could get AI to handle this thing that annoys me, that would be a success!" and so they participate in entirely different ways than someone who is instructed by their manager to watch some videos.
Every person who finishes education should leave with at least one tool, workflow, or prompt system that saves them at least three hours per week.
#3: Carry Out Two Team Evaluations
John conducts two distinct evaluations before education starts.
Map Your Team’s AI Proficiency Against Four Stages of Expertise
John organizes AI proficiency into ten levels, grouped into four distinct stages: literacy, fluency, mastery, and stewardship.
Literacy (Levels 1–3) : Staff at this stage comprehend what AI is, what it can and cannot accomplish, and how to use it securely. They understand how to pose a clear question, improve their prompt when the result is insufficient, and assess whether the result is trustworthy. They do not automatically accept the first answer.
Fluency (Levels 4–6) : This is where individuals start using AI regularly within their actual job, enhancing work quality and speed. They have begun constructing simple tools: a custom GPT, a Claude project, or a structured prompt they distribute with colleagues. This is the stage where genuine business impact starts to appear.
Mastery (Levels 7–9) : A staff member at this level is building repeatable workflows, connecting tools, using reusable prompt systems to address ongoing challenges in their specific role, and starting to work with AI agents. At this stage, the level of governance and security oversight required also increases, as staff connecting to external data sources or running API calls need closer monitoring.
Stewardship (Level 10) : The final level is where someone is managing both people and AI systems. Stewards oversee staff who have been authorized to build and run agents, and they are responsible for ensuring AI is being used properly at an organizational level. John notes that no one in his training programs has reached level nine or ten yet, largely because security practices haven't kept pace with what AI can now do.
98% of staff at every organization John trains are at level three or below. To determine where everyone in your organization is, create a questionnaire with about 20 questions designed to understand everyone’s individual AI skill level.
The first 17 should include questions such as: Have you built a knowledge base? Have you created a prompt and shared it with teammates? Have you built a custom GPT? Have you built a Claude project? Have you built an agent? Have you connected two different workflows together? Have you ever turned on secure features in ChatGPT? Can you explain when to use AI and when not to?
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Each of the final three open-ended questions should require employees to submit an actual sample prompt. You need to see exactly how someone structures an instruction.
John uses this assessment to produce a visual heat map showing where an organization's AI capabilities are currently concentrated. As training progresses and people are retested, you should see those dots shift into levels four through six. A smaller group might even push toward seven and eight. That movement gives leaders concrete evidence that training is producing a return on investment.
Determine Your Team’s Role Type Makeup
The assessment, similar to the PAIE assessment, presents employees with 15 to 20 questions in which they read a scenario and identify which response option is most like them and which is least like them. The forced choice prevents employees from simply picking the answer they think their manager wants to hear.
The assessment identifies each employee's primary working style across four categories:
Doers execute. They love getting work done and thrive when given clear tasks.
Administrators organize. They build and follow rules and love creating structure.
Innovators generate ideas. They think creatively and love conceptual problems.
Connectors build teams. They thrive when helping others work well together.
John uses this breakdown for two purposes.
First, it helps him anticipate where each person will need more support during ideation.
Administrators and Doers tend to think inside existing constraints, so when it comes time to imagine a custom GPT or Claude project they'd want to build, they often get stuck. Innovators generate ideas quickly but may need help grounding them in something practical. Knowing these details in advance lets John and his team provide each type with the right direction.
Second, he uses PAEI to build what he calls an AI council, the internal group that oversees AI adoption across the organization. The composition of that council directly determines what kind of culture emerges.
A council composed entirely of Administrators produces overly restrictive policies that get locked down before they ever gain traction. Innovators are essential because they're the ones who champion AI, get people excited, and keep the organization moving forward. Administrators balance that energy with the necessary “not so fast” logic that prevents the organization from moving recklessly. Without Innovators, nothing gets adopted. Without Administrators, nothing gets controlled.
The council needs all four types, or it will be structurally biased from the start.
#4: Give Your People a Problem to Solve Before They Watch a Single Video
As noted, a common reason AI training fails isn't the curriculum. It's that employees enter training without a personal stake in what they're going to build.
Asking people what they want to build produces blank stares. Asking them to name something in their job that drives them up the wall produces immediate, specific, energized answers. John’s team starts the upskilling process by asking every employee one straightforward question: What do you do every week that is repetitive, slow, frustrating, or mentally draining?
The next step is what John calls the Perfect Day Exercise. Employees are asked to imagine their ideal workday—specifically, what are all the tasks they'd love to hand off to someone else, with confidence that those tasks would be done with excellence? That wishlist becomes the raw material for what they'll build.
Once an employee has identified their candidate task or process, the conversation moves to two diagnostic questions. First: could AI realistically help with this? Second—and this is where most people stop short—should we simply speed up the existing process, or should we redesign the process entirely because AI changes what's possible?
John describes this distinction as the difference between bolting AI onto something old and asking, “What would this process look like if it were designed with AI from the start?” The second question consistently surfaces the bigger wins.
The tools employees build don't have to be technically complex. A custom GPT, a Claude project, a document analyzer, or a structured prompt workflow are all within reach for someone at the fluency level. The goal isn't novelty. It's practical time savings, repeatable quality, and confidence.
Three examples from John's training programs illustrate the range:
Patent Analyzer : A chemical industry professional who files 20 to 30 patents per year was spending $30,000 annually in legal fees. He built a patent analyzer that could review a patent he was preparing to file, cross-reference it against existing patents to identify potential conflicts, and help him rewrite the application before handing it to his attorney. His legal fees dropped by 90%, and he eliminated a $15,000 software subscription entirely.
Home Construction Cost Estimator : A woman working in real estate built a home construction cost estimator that delivered estimates within 3% of a $20,000-per-year software application she had been paying for. She hadn't started with that idea. She came in planning to build something that analyzed market buying signals, but as she worked through the training, she pivoted to the tool that would actually solve her biggest problem.
RFP Assessment : An office furniture CEO who works with businesses filling 20,000–40,000 square feet of space came through John's training already familiar with ChatGPT.
His sales team received 350-page RFPs to bid on large commercial projects, and just determining whether to bid the go/no-go decision took three to six hours per document. If the decision was yes, the team would put two and a half people on it for two to three weeks to write the response. The result: they could realistically bid on only three projects per year, each worth between $250,000 and $1.5 million.
By the end of training, the CEO had built a tool that could digest a 350-page PDF, surface the furniture-relevant sections, and deliver a go/no-go recommendation in 20 minutes. If the decision was yes, the same tool helped him generate a complete RFP response in two hours with one person himself. He immediately saw that bidding on three to five projects per month, instead of three per year, was now possible.
John Munsell is an AI transformation expert and author of Ingrain AI: Strategy Through Execution The Blueprint to Scale an AI-First Culture . His course, AI Mastery for Business Leaders , guides organizations through structured AI training and governance. Explore his AI Impact Analysis and follow him on LinkedIn .
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