A practitioner's case for universal AI fluency — built from 25 years in the trenches, verified with real research, and backed by the numbers.
A post making rounds in L&D circles made the argument that the fastest way to waste an AI budget is to train everyone on AI. The recommendation: target specific roles with measurable performance gaps. Be strategic. Be surgical. It got a lot of reactions. I disagreed — not because I don't believe in performance consulting, but because I've seen what happens when organizations decide only some people are worth the investment. So I stopped arguing from instinct and ran the numbers.
"Opportunity is missed by most people because it is dressed in overalls and looks like work."
— Thomas EdisonI have spent 25 years working inside organizations — regulated healthcare, financial services, fintech, contact centers. I have talked to the people doing the work when nobody else thought to ask them. I have seen what gets left on the table when organizations decide that only some people are worth the tool. I have also seen what happens when you give everyone access, build the infrastructure to catch what surfaces, and get out of the way.
This is not what I read in a book. This is what I have watched happen, in real organizations, under different CEOs, in multiple industries, repeatedly. The pattern is consistent enough that I decided to stop saying "I've seen this" and start showing what the numbers say when you actually model it out.
I have worked inside organizations where some of the most elegant operational solutions came from people no one would have called a high-potential. They built tools that standardized outputs across entire departments. They created workarounds that turned a 20-minute process into a 3-minute one. Nobody asked them to. Nobody identified them in a Hi-Po program. They just knew the work and found a better way. When only some people get the tool, you only get some of the ideas. The rest stay locked inside people the organization decided not to ask.
The argument for selective training makes more sense for specialized tools — a CRM platform, a financial system, a project management suite. AI is not that. AI is infrastructure — the same way the telephone was infrastructure, the same way email is infrastructure. It doesn't belong to a department. It doesn't respect a job title. Deciding which employees need to understand it is like deciding which employees need to understand email.
There is also a risk that extends well beyond the workplace. When people use AI without understanding what it actually is — that it generates based on pattern matching, not retrieval of verified facts, that it hallucinates with complete confidence, that it reflects the biases baked into its training data — bad information doesn't stay at the desk. It travels home. It travels through social networks. It reaches people outside the workplace who still consume and share information through smartphones and social networks.
The first place most people will encounter AI in a structured, intentional way is at work. And when they go home and talk to their families — their parents, their kids, their grandparents — they carry what they learned with them. That's why I loved using cybersecurity content designed for the whole household. One program trained employees on phishing and social engineering. But the same content had activities for kids who didn't even have a phone yet, guidance for seniors about password safety, simple checks for grandparents who forward everything in their inbox. Many cybersecurity programs now treat household behavior as part of the threat surface, recognizing that the risk lives in living rooms as much as in boardrooms. AI literacy works the same way.
"Bernie Madoff's fraud didn't stay in the boardroom — it flowed through social networks to individuals, families, and charities who heard about steady returns and trusted the narrative. Misinformation from confidently misused AI spreads the same way — through social ties and trusted channels — which makes it a citizenship problem as much as a workforce-productivity problem."
— Supported by UT Dallas affinity-fraud research (15) and Harvard Misinformation Review plus NPR/Reuters reporting on AI misinformation (12–13).This is not a doomsday argument. It is not a conspiracy. It is the same logic that made us decide everyone needs to understand how email works, how to recognize a suspicious link, how not to give their password to someone who sounds official on the phone. AI is that foundational now. The question isn't whether people will encounter it. They already are. The question is whether they'll encounter it with enough understanding to know what they're dealing with.
To stop arguing from instinct and start arguing from numbers, I built a model. 2,000 employees. Conservative assumptions throughout. Three approaches to AI training. Here's what happens at 12, 24, and 36 months — and why the gap between them keeps growing.
Hi-Po first approach. Trains identified high-potentials and selected "AI-impact roles" first. Executes exactly as planned. Everyone trained by Year 3. This is the best-case version of the selective approach — the one the argument assumes will happen.
Same plan as Ideal, but follows what enterprise training data actually shows. Something else becomes important at month 14–16. Momentum stalls. 48% of the organization never trained by Year 3. This is what usually happens.
Full fluency push in Year 1. Champions network. AI hangouts. Prompt library. Shoutouts published org-wide. Cross-functional coordination. No performance pressure in Year 1. New hires onboard into a living system from day one.
Company A Realistic ends Year 3 with 48% of the organization never trained. 960 people. Some of them had the next workflow improvement. You will never know which ones.
Cumulative net returns. Matisse's Year 1 return alone ($2.46M) exceeds Company A Realistic's entire 3-year total ($2.90M). The gap is not about which company worked harder. It's about which one built infrastructure.
| Metric | Co. A — Ideal | Co. A — Realistic | Co. B — Matisse |
|---|---|---|---|
| % Trained by Year 1 | 25% | 17% | 100% |
| % Trained by Year 2 | 65% | 38% | 100% |
| % Trained by Year 3 | 100% | 52% | 100% |
| Total 3-yr delivery cost | $717,600 | $655,000 | $1,408,520 |
| Net Year 1 | $1,025,900 | $445,520 | $2,459,480 |
| Net Year 2 | $2,661,300 | $978,280 | $5,766,000 |
| Net Year 3 | $4,270,500 | $1,474,480 | $9,491,500 |
| 3-year total net return | $7,957,700 | $2,898,280 | $17,716,980 |
| 3-year ROI | 11× | 4.4× | 12.6× |
| Innovation infrastructure | No | No | Yes |
| Risk reduction (hallucination/bias) | Partial | Minimal | Full |
| New hire integration | Delayed | Inconsistent | Day one |
| Knowledge asset created | No | No | Yes |
The training itself is assumed. What most organizations skip is everything around it. These eight elements are what turned training into a system — each one feeding the next.
None of this runs without someone whose only job is to make it run. Not a committee. Not a champion with a day job. One person — sitting inside the organization, not outside it — whose explicit responsibility is cross-functional coordination, visibility, and continuity. That is the difference between infrastructure and a good idea that fades.
The condition that makes everything else possible. Fluency first. No metrics tied to AI use in Year 1. Psychological safety intact. People explore, make small mistakes, build real understanding. Accountability comes in Year 2 — after the foundation exists.
The first point of contact. Open, recurring, themed sessions — email drafting, transcript tactics, power prompts. Anyone could attend. No registration required. This is where fluency starts building visibly and communally.
What the hangouts produce. The best thinking gets captured here. Living document. Crowd-sourced. Not generic internet downloads — built in-house, reviewed by champions, specific to this organization's systems and language.
People submit what they did with AI, what department, what task, how much time it saved. Published org-wide. Visible. Celebrated. The prompt library stops feeling like templates — it starts feeling like proof of what's possible.
What keeps the shoutouts coming. Small rewards, public acknowledgment, people seeing their name and their work surfaced to the whole organization. Psychological reward drives more sharing than monetary value. People contribute because they're seen and thanked.
The human infrastructure that validates, curates, connects, and carries everything across departments. Every team has one. They meet cross-functionally. Ideas don't die in the team they were born in — they travel.
Champions now have data. Time saved by task, adoption patterns by team, trends in poor prompts feeding coaching updates. The system starts informing itself. Champions bring specific examples to team meetings — not generic encouragement.
The proof point. A new hire walks in and everything above already exists. They don't onboard into a blank slate. They onboard into a living system — prompt library, shoutouts, hangouts, champions. Speed to fluency is measurably faster from day one.
The objection I hear most often: not everyone will contribute something meaningful. Correct. That is not the point. The 1% framing is not a pessimistic estimate — it is a deliberate floor. I want to show you what happens even at the absolute minimum, before I show you what actually happens when the infrastructure is there to catch more.
In business, a ripple is what happens when one person's idea doesn't stay with that person. It gets caught, validated, documented, and embedded into how the work gets done — until everyone is operating on a better process, whether they know where it came from or not. The size of the ripple is determined by the infrastructure that catches it.
At Kruze Consulting — a fully remote accounting and finance firm of 170 people — I rolled out an AI program built exactly like the Matisse model. We captured 20+ documented AI shoutouts in just the first couple of months. The shoutout tracker started from the beginning of the rollout, during busy season, when people were still ramping up and getting used to the idea. The prompt generation form produced dozens of real, usable prompts within weeks — which champions then verified and pushed into the library for everyone to access. And this was the floor. We were just getting started. Once people see their work published, once they see someone else's idea and recognize they have one too, it compounds. That's not speculation. That's what happened. The 1% is never going to stay at 1%.
The same 20 ideas. Company A Realistic's ripple doesn't just stall — it decays in Year 3 as the program loses momentum and people move on. Company B Matisse compounds because the ideas are baked into training, onboarding, and standard operating procedure.
The 1% is never only 1%. But even if it were — even at the absolute floor — the number says the same thing. Train everyone. Build the infrastructure to catch what surfaces. Get out of the way.
— Michelle DeshotelsThere are scenarios where training everyone on AI produces bad outcomes. I'm going to name them honestly. Each one has an interactive question inside — take a guess before you read the answer. Then I'll show you why none of them are arguments for training fewer people.
You teach people AI is a search engine. You don't teach hallucination — that AI generates responses based on pattern matching, not retrieval of verified facts. You don't teach bias — that early AI systems were trained on internet data full of human assumptions and prejudices, including some deeply wrong ones. You don't teach prompt construction. You create 2,000 people who are confidently wrong. That is worse than 2,000 people who are appropriately skeptical because they don't understand the tool. Bad AI literacy is more dangerous than no AI literacy because it comes with false confidence and no brakes.
The ideas come up and go nowhere. A champion who gets pulled back to their real job the moment the big customer calls. A prompt library that nobody owns. A committee that meets monthly to talk about AI ideas — but it's everybody's side job, not anyone's actual responsibility. When it's everybody's job, it's nobody's job. That is where good ideas go to die, every single time, in every industry, in every organization I have ever worked in or worked with. The training wasn't the problem. What surrounded it — or didn't — was the problem.
Someone uses AI, makes a mistake, gets called out publicly. Now nobody uses it. You have trained 2,000 people to be afraid of the tool. Research on AI adoption links poor communication, exclusion from decisions, and weak psychological safety directly to worse adoption outcomes. This is exactly why a Year 1 with no performance metrics is not a soft decision — it is the structurally correct one. People need room to explore, make small mistakes in low-stakes environments, and build genuine fluency before you hold them accountable for outcomes. Fluency first. Accountability after. The sequence is not optional.
Training everyone doesn't fail. The conditions around it fail. These are solvable problems with known solutions. None of them are reasons to limit who gets access to the tool.
Your tooth starts to ache. It isn't going away. The sooner you address it, the better off you are — and the cheaper it will be. The longer you wait, the more expensive the solution becomes. This is not a metaphor. This is how delayed intervention works in every context. AI training is not different.
This pattern is not unique to AI. It repeats with every enterprise software rollout, every new process initiative, every change management effort that gets real traction and then loses its champion. Organizations invest in building something that works — training, infrastructure, accountability — and then something shifts. A budget cycle. A leadership change. A "more urgent" priority. The investment stops. The gains stop. And then months later, someone notices the gap and the conversation starts over from scratch, usually at a higher cost and with lower goodwill from the people being asked to go through it again. You don't get a refund on the momentum you lost.
I started with instincts built over 25 years. Then I ran the claims through independent research before publishing anything. Here's exactly what the evidence supports, what's partial, and where I'm speaking from experience rather than citation. I'd rather be honest about the distinction than cite something that doesn't hold.
| Claim | Research says | What the evidence actually shows |
|---|---|---|
| Training everyone outperforms selective training for AI adoption | Supported | Organizations training both employees and leadership show a 23-point advantage in high-value AI outcomes. Broader transformation participation correlates with better results. Best evidence-based framing: universal baseline plus role-specific depth. |
| AI will reshape most jobs across most industries | Partial | BCG: 50–55% of U.S. jobs reshaped in 2–3 years. Microsoft research shows high AI applicability across diverse occupations. "Every job equally" is overstated — impact intensity varies by role. The direction and the breadth are real. |
| Underutilization of enterprise technology is measurably costly | Supported | Organizations waste $15,000+ per employee per year on inefficient ways of working. Underutilized licenses, inactive users, and poor governance create direct and hidden costs. Well supported across multiple sources. |
| Frontline workers are rarely included in decisions about their own work | Supported | Only 23% of frontline workers believe senior leaders understand their day-to-day. 90% of organizations say frontline decisions matter; fewer than 1 in 4 actually equip frontline workers to make them. 85% of executives believe their orgs are bad at diagnosing problems. |
| Enterprise training often fails to reach its full intended audience | Partial | No specific failure percentage is sourced to a clean primary study — and this report does not claim one. What is supported: only 2% of employees are directly involved in a typical enterprise transformation. Involving 7% or more doubles positive outcomes. The direction is real. The specific figure is not. |
| Excluding people from AI training creates fairness and trust problems | Supported | 2025 Nature research links poor communication, exclusion from decisions, and weak psychological safety with worse adoption outcomes. MIT Sloan identifies this as one of the most common AI strategy failures. |
| AI misinformation spreads through social networks, not just organizations | Supported | Harvard misinformation research and NPR/Reuters AI reporting document how AI-generated misinformation spreads through social channels. Research links overconfidence in the medium (polished AI output) with sharing behavior. Citizenship framing is increasingly supported by policy analysts and researchers. |
Several claims in this report come from 25 years of working inside organizations — not from published studies. I have named them clearly throughout. They are not anecdotes used to replace data. They are examples of patterns the research also identifies. I have lived them across multiple industries, multiple organizations, and multiple leadership environments. The experience and the evidence point the same direction.
What the research cannot yet fully capture: the specific dollar value of the idea that never surfaced because the person who had it was never given the tool, never asked, never in the room. That line item doesn't exist. It never will. But that doesn't mean the cost isn't real.
San Diego, CA
25 years across regulated healthcare, financial services, fintech, and contact center operations
I am not a thought leader who reads about organizations. I am a practitioner who has worked inside them — in QA, in operations, in L&D, in AI enablement, across industries that don't forgive mistakes. I have built training programs from scratch. I have implemented enterprise software. I have talked to the people doing the work when nobody else thought to ask them.
This report is not a framework. It is not a methodology with a catchy acronym. It is what I have seen, what the research supports, and what the numbers show when you run them honestly — without assuming the conclusion before you start. I went looking for data that would prove me wrong. The data pointed the same direction I was already facing.
I built and ran the Matisse model in a real organization. 100% AI curriculum completion. 50% voluntary adoption. 70–80%+ monthly LMS engagement sustained over time. I know what it takes to build something like that. I know what the infrastructure requires. And I know that none of it happens by accident.
If you want to talk about what it actually takes to build AI fluency across an organization — not the version that sounds good in a proposal, but the version that holds up — I am not hard to find.
All sources verified independently through Perplexity research prior to publication. Claims that could not be verified against primary sources are clearly marked in the Evidence section as coming from experience rather than citation.