Why Instruction Is the Ugly Version of Inspiration
We like to believe that adoption follows enablement. It sounds disciplined and responsible: give people access, train them properly, distribute the materials, explain the features, host the workshops, and measure participation. From a governance perspective, it checks every box.
And yet, enablement is often the laziest form of transformation.
Years ago, I taught Excel to an accountant as part of an informatics course for families in need in Brazil. She entered her data carefully and diligently, following every instruction. The formatting was correct. The structure was clean. She was doing everything “right.” And then, almost instinctively, she reached for her calculator to add everything manually.
In that moment, I realized I had turned Excel into her new supervillain. I had introduced a tool without relieving the pain she had carried for decades. Until I showed her a simple SUM formula.
What changed was not the software. It was the removal of a deeply ingrained frustration. Decades of repetitive addition were reduced to a single function. Her discomfort dissolved. Her posture changed. She was no longer complying with the tool. She was leveraging it. She wasn’t catching up anymore. She was sprinting ahead.
That moment was not enablement.
It was proof of personal value.
The Corporate Calculator
Organizations repeat this pattern more often than they admit.
Frameworks are rolled out meticulously. Roles are defined, ceremonies are scheduled, decks are presented with conviction. Progress is reported upward in language that signals momentum. From the outside, it looks like movement. Inside, however, employees often experience an increase in cognitive load rather than a reduction in friction.
I have seen transformations that were technically implemented but practically resented. Middle managers caught between executive mandates and operational reality. Teams participating in rituals they did not believe improved their outcomes. Engineers attending meetings because they were required to, not because they were convinced.
On paper, change had occurred.
In practice, people were carrying the wheel instead of rolling it.
The same pattern now appears in AI adoption. Licenses are purchased at scale, internal tools are developed with impressive technical sophistication, and training materials are distributed comprehensively. Yet usage remains shallow. The assumption persists that once people understand the features, valuable use cases will naturally follow.
That assumption is not irrational. It is simply incomplete.
Use-Case-First Is Human Nature
In a workshop with a B2B sales department, the original ambition was to use AI to scan 800-page tenders for keywords. The proposal sounded innovative. It also avoided the real question.
Why not simply search the PDF?
The silence that followed revealed the gap. The actual burden was not keyword detection. It was the exhausting manual effort required to compare tender requirements with the company’s own feature set and determine whether pursuing the opportunity made sense. That was where time disappeared and frustration accumulated.
Once that friction was acknowledged, the solution became clear. AI could eliminate most of the comparison work and dramatically accelerate the go/no-go decision. The team found an off-the-shelf solution aligned with procurement constraints and skipped unnecessary custom development.
Adoption did not need to be forced.
It followed the relief.
Human behavior responds to pain reduction more reliably than to feature education.
The Anxiety You Don’t See
Years earlier, Pebble’s Timeline interface embodied the same principle in a quieter, almost elegant way. It asked a deceptively simple question: if a watch tells you the present, what should timekeeping look like in a connected world?
The answer was contextual. Past, present, and future were always one button away. What was coming up next? What had just been missed? No menus. No phone. No mental juggling.
The impact was not dramatic in a marketing sense. It was psychological. In a sales conversation, I could confirm availability with a glance at my wrist. Five seconds regained. No interruption of flow. No visible distraction. That small moment removed a layer of quiet anxiety — the fear of forgetting, of double-booking, of losing track.
It felt like a superpower not because it added complexity, but because it removed it.
Later, when I led product initiatives, that logic shaped my thinking. I became convinced that the most valuable feature a system can offer is one that reduces interaction. A contextual layer that anticipates what the user needs before the user consciously asks for it. Engagement through reduction, not accumulation.
The highest form of product design is not more usage.
It is more leverage.
Acceptance Follows Proof of Personal Value
In conversations about AI enablement strategies, I have encountered the view that use cases will naturally emerge once employees are trained and exposed to the technology. There is reasoning behind that belief. Domain expertise combined with technical knowledge can, in theory, produce innovation.
But experience suggests a missing ingredient.
Without an early and tangible proof of personal value, enablement often results in compliance rather than exploration. People attend the sessions. They log into the tool. They experiment briefly. And then they revert to the familiar because the cost of continued uncertainty outweighs the perceived benefit.
Behavioral economics explains why. Loss aversion is powerful. Change introduces risk. If the first interaction with a tool does not immediately reduce friction, it increases it. And increased friction rarely inspires curiosity.
Adoption is not a feature problem.
It is a trust problem.
Trust is built when a tool demonstrably removes a recurring pain. When it shortens a task. When it reduces cognitive strain. When it eliminates the small anxieties that accumulate over a workday.
Acceptance follows proof of personal value. And once acceptance is established, engagement becomes self-sustaining rather than enforced.
Designing for Leverage
As technologies like AI move closer to individual users, the responsibility to design for these early moments of undeniable value becomes more acute. Their impact is often incremental at first — a time saving here, a friction removed there — but those increments compound across teams and organizations.
Leaders who understand this shift their focus. They stop asking how to enable everyone at once and start asking where the first undeniable win lies. They look for the SUM formula moment. The eighty percent reduction in manual comparison work. The five-second confirmation that removes quiet anxiety.
Transformation does not begin with instruction.
It begins with leverage.
The caveman who invented the wheel did not need a better training manual on how to carry it.
He needed to see that it could roll.
