Involve Before You Mandate

The Only Trust Move That Closes the AI Perception Gap

The Only Trust Move That Closes the AI Perception Gap

The organizations that are struggling with AI adoption have usually made the same diagnosis: employees are not using the tools enough. The response is predictable. Mandate harder. Add measurement. Tie adoption rates to performance reviews. Enforce through the management layer. Adoption rates go up. The dashboard looks better. And then, quietly, the resistance goes underground. Many employees don’t know if they’ll lose their job to AI. Few feel involved in the decisions about how AI gets deployed in their work. These two realities explain more about the failure of AI mandates than any tool-selection decision or change management curriculum. The organizations that have cracked AI adoption are not the ones that mandated better. They are the ones that involved before they mandated, and that sequence changes everything.

The Mandate Reflex

There is a logic to the mandate approach. Leadership has data showing that AI tools produce significant productivity gains for the people using them deeply. Leadership also has data showing that most employees are not using the tools deeply. The gap between what the tools can do and what employees are actually doing with them is money left on the table. So the reflex is to close that gap through authority. Roll it out. Train everyone. Track the dashboards. If usage is low, mandate. If mandates are not working, mandate with more specificity. What the mandate approach misses is that the resistance is not behavioral. It is psychological. The mechanism is straightforward incentive psychology. Executives who authorized the AI investment have staked their credibility and budget on the claim that AI makes the organization more productive. They need it to be true. Frontline workers who read job-loss headlines every morning need it to not be true, or at least to be less true than leadership claims. Both sides are filtering identical information through fundamentally different personal stakes. No mandate resolves that. You can compel tool use. You cannot compel the psychological safety that makes tool use productive.

What 12% Means

Very few employees feel involved in decisions about how AI gets deployed in their work. That gap is worth holding for a moment. Most of the people who will be expected to use AI transformation tools… are not in the room when those tools are chosen. It is describing how organizations are making decisions. Eighty-eight percent of the people who will be expected to use AI transformation tools, to build their work around them, to advocate for them or resist them through daily action, are not in the room when those tools are chosen. They are handed the outcome of a decision they did not make and told to adopt it. In most organizations, this is not malicious. It is an artifact of how transformation projects are structured. A small group, usually IT and senior leadership, evaluates tools, secures a contract, builds a rollout plan, and then executes that plan on the rest of the organization. Employees are the recipients of the transformation, not the designers of it. This is not a neutral design choice. It is the choice that predicts resistance. When people feel that something is being done to them rather than with them, they comply at the minimum threshold that avoids punishment. They use the tool in ways that satisfy the dashboard while preserving their actual workflow. They wait for the initiative to lose steam, because most do. They interpret mandatory AI adoption as the latest version of a long history of top-down transformations that did not deliver what was promised to the people who had to live inside them.

Shadow Adoption Is the Message

The data point that most organizations misread is their own shadow adoption rate. According to Microsoft and LinkedIn’s 2024 Work Trend Index (a global survey of 31,000 knowledge workers across 31 markets), 75% of knowledge workers are already using generative AI at work, and 78% of AI users are bringing their own AI tools rather than waiting for IT-sanctioned options. They are not waiting for the rollout plan. They are solving their own problems with whatever tools they can reach. (news.microsoft.com, ‘Microsoft and LinkedIn release the 2024 Work Trend Index on the state of AI at work,’ May 8, 2024.) Organizations typically see this as a governance problem to solve. Stop the unauthorized use. Create a sanctioned list. Block the unapproved tools. The shadow-adoption numbers mean most of your workforce has already told you where the AI leverage is. The question is whether you are treating that signal as intelligence or as a compliance problem.

What “Involve” Actually Requires

The word “involve” is doing a lot of work in AI transformation conversations, and most of what it is doing is insufficient. A town hall is not involvement. A feedback survey after the decision is made is not involvement. An optional AI showcase where the tools are already chosen is not involvement. Involvement that changes outcomes happens before the commitment is made. Vizient, a healthcare performance improvement organization, made this concrete. Before designing any AI-augmented roles or workflows, the company built what it calls a Persona-Based Adoption Model: a phased, empathetic approach to mapping what employees actually wanted from AI tools before rolling anything out. (Sam Fredin, Director of AI Strategy & Technology, Vizient, “Vizient’s AI Optimization Strategy Choice Cascade,” LinkedIn, Oct. 21, 2024.) The questions seem almost too simple. But they reframe the entire design problem. Instead of starting with AI capabilities and asking where they fit, Vizient started with human preferences and asked where AI could serve them. The difference in outcomes is structural. When workers feel that the transformation is built around what they said they needed, resistance looks different. They are not defending their turf from an imposition. They are trying to get the implementation right on something they asked for. This is not the same as letting employees veto every AI decision. It is the difference between involving people in problem definition and involving people in solution approval. You can involve people in the first without surrendering authority over the second, and the organizations that make that distinction get dramatically different adoption outcomes. Red Hat’s own account of its AI rollout describes the same dynamic. Engineers choose their own tools from a bounded set of options, treat AI experimentation as bottom-up rather than mandated, and the company frames the result as ‘a capability multiplier,’ not a threat. The result, in Red Hat’s own telling: an ‘explosion of internal experimentation’ and a workforce that is curious about AI possibilities rather than defensive against them.

The Hallways Know Before the Meeting Does

There is a pattern that shows up consistently in organizations struggling with AI adoption. Leadership believes adoption is low because employees need more training or clearer tools. When you talk to the people doing the work, you find something different: pockets of sophisticated AI use, distributed unevenly, often invisible to the management layer above them. The practitioners using AI deeply are generating the practices that could scale. They are also, almost universally, not sharing what they know with the organization. At a Voltage Control executive dinner, Taran Lent, then leading product at Illumia, described a move that illustrates how shadow practice becomes organizational signal. He had written a five-page AI strategy paper with the model prompt pasted directly at the top, making the AI use visible rather than buried. He sent it to his parent company’s CEO with a TL;DR. The response was immediate: “This is what I mean by AI first. You guys get it.” Making the AI use legible made it safe to share. It turned a private practice into a signal the organization could see and build on. Rachel Brown, Managing Director of Innovation at CIBC Global Asset Management, made the complementary observation: a discovery sprint across every team sounds like a way to surface the real picture, but it ends up asking the people who are following the official story rather than the ones doing the work. What you actually need are domain leaders who understand the art of the possible in their specific context. Those people exist in most organizations. They are simply not in the room where the strategy is being set. The involve-before-mandate sequence requires knowing who those people are, which means you need a way to see the hallways before you design the mandate.

The Sequence Is the Prescription

Mandate-then-involve creates the adoption loop most organizations are caught in: announce, train, mandate, measure, discover that measurement is gaming the metric, mandate more specifically, discover deeper resistance. Involve-then-mandate runs the sequence differently. The first move is to ask before you commit. Vizient’s persona-mapping approach is a good starting structure: find out what people want to do more of, what they’d do with more time, and what they’d rather not do at all. The answers tell you where the AI intervention has actual demand, which is where it will stick. The second move is to make shadow adoption visible rather than punishable. The three-quarters of your workforce already using AI without sanction are your best evidence of what the tools are good for in your specific context. Create a channel that makes their discoveries legible without requiring them to confess a policy violation. The policy may need to change before that channel can work. The third move is to frame the transformation as a two-way deal with explicit terms on both sides. What the organization commits to: transparency about what AI will and will not replace, involvement in decisions that change how work is designed, honest communication when the plan changes. What employees commit to: learning in real workflows, sharing what works, raising problems early when the implementation is not delivering. These three moves do not require new tools. They require a different set of decisions about who is in the room before the decisions are made.

What This Looks Like in Your Organization

If most of your workforce does not feel involved in AI decisions, you have not yet done the first move. The dashboard numbers are showing you adoption of the tool, not adoption of the transformation. Those are different things, and only one of them compounds. The leaders who will have functioning AI-enabled organizations in 2028 are the ones who used 2026 to ask the questions before they committed to the answers. The leaders who will be mandating harder in 2028 are the ones who committed first and are still trying to get everyone on board. Involvement is not the slow path. It is the only path that does not end in the loop. Want to explore what this looks like for your organization? Learn more about our AI transformation programs.