Your organization bought the AI tools. Adoption metrics look healthy. So why hasn’t anything actually changed?
Most organizations measuring AI adoption in 2026 are asking the wrong question. They track licenses deployed, people trained, and teams “using” the tools. These metrics feel reassuring. They fit neatly into a quarterly review. And they almost entirely miss the point.
The uncomfortable truth: the technology is no longer the bottleneck. The models are remarkably capable. The tools are increasingly accessible. And they keep getting better, faster than most leaders can track. Yet according to McKinsey, only 39% of organizations report achieving measurable EBIT impact from their AI investments. Gartner predicts that by the end of 2026, 60% of AI projects will be abandoned before reaching production.
The gap between AI investment and AI impact is not a technology problem. It is an organizational readiness problem. And closing it requires a fundamentally different approach than most companies are taking.
Adoption means people have access to a tool and occasionally open it. That is a start, but it is not the thing that matters. What actually matters is something harder to measure and harder to achieve: has the way your organization works actually changed?
We call this absorption. Absorption is the point at which new capabilities become so embedded in how people research, decide, draft, and deliver that going back to the old way would feel like a step backward. It is the difference between a team that has an AI assistant and a team that has genuinely rethought its workflow because AI made a better one possible.
We worked with an eCommerce team at a global consumer health company that had deployed an AI-powered analytics tool. The tool was live, prompts were distributed. The team leader told us he'd used it three times and got frustrated. One team member had never heard of it. But one colleague had quietly started using a different AI tool to redesign her own monthly reporting process, not because anyone asked her to, but because she saw how it could fit her actual work.
That is the difference between adoption and absorption. And it cannot be manufactured by a training program.
When we talk to leadership teams about what is blocking AI progress, we hear a predictable set of answers: our strategy is not clear, we cannot prove ROI, people are resistant, people need better tools, people need new skills.
These are real challenges. But they are surface-level descriptions of much deeper organizational dynamics. If strategy, tools, and training were sufficient, most companies would have cracked this by now. They have not, because the real blockers live underneath.
From our work with organizations navigating this transition, we see three interconnected gaps that keep companies stuck between adoption and absorption.
Most organizations treat AI as a project with a start date and an end date. But AI is not a rollout. It is an ongoing adaptation. The technology is evolving faster than annual planning cycles can accommodate. Organizations that succeed will be the ones that build adaptive capacity into their culture: the ability to sense what is changing, respond with intention, and reshape their approach as they learn. The organizations that get stuck tend to be the ones waiting for a “final” strategy, treating AI as something to get right once rather than something to learn their way into.
Ask a room of executives what AI value looks like for their organization and you will get ten different answers, or, more often, an awkward silence. Without a clear, shared vision for what outcomes you are actually pursuing, every initiative competes for attention and none of them get the sustained focus they need. This means getting specific: not just “efficiency” or “innovation,” but a concrete picture of where hard impacts (cost reduction, revenue growth, speed) and softer impacts (customer experience, employee capability, decision quality) intersect for your business. Value you cannot define is value you will never capture.
This is the gap that catches most organizations off guard, because it is not one problem. It is three, and they reinforce each other.
The trust deficit. Frances Frei and Anne Morriss describe trust as a function of four elements: reliability, empathy, authenticity, and logic. When any one of these wobbles, trust collapses. AI tools wobble on all four at once. People are not sure the outputs are reliable. They do not feel the tools understand their context. The marketing claims feel inauthentic. And the logic of how the tools reach their conclusions is opaque. This is not irrational resistance. It is a rational response to unearned trust. Building real trust in AI requires the same thing it requires everywhere else: transparency about limitations, consistency over time, and honesty even when you do not have all the answers.
Invisible workflows and the equity lens. Every organization runs on informal workflows, unwritten processes, and relationships that do not show up on any org chart. This is the human infrastructure that holds things together, and it is exactly the thing that most AI strategies ignore. If you do not understand how work actually gets done (not how the process map says it gets done), your AI tools will solve problems nobody has.
And the workflows most likely to remain invisible are the ones held by people with the least organizational power. When AI strategies are designed from the top, they tend to reflect the priorities and processes of leadership. The people closest to the work, often women, people of color, and frontline employees, are the last to be consulted and the first to feel the impact. An AI strategy without an equity lens is an AI strategy that reinforces the gaps it claims to close.
The bandwidth problem. The binding constraint for most knowledge workers is not skill or willingness. It is bandwidth. They are already running at capacity. Expecting them to experiment with new tools, redesign their workflows, and learn new ways of working in the margins of already-full calendars is not a strategy. It is wishful thinking. Structured time for discovery is not a nice-to-have. It is a prerequisite.
This is the phrase that tends to land hardest when we share it with leadership teams. Because most of them know it is true. They have seen the dashboards that show healthy adoption numbers. And they have also seen those numbers fail to translate into any meaningful change in how work gets done, how decisions get made, or how value gets created.
The conventional adoption playbook solves the wrong half of the equation. Getting people to log in is not the hard part. Getting an organization to genuinely change how it operates: that is the work. And it is organizational transformation work, not technology implementation work.
If you are a senior leader looking at your AI investment and wondering why the returns are not materializing, consider the possibility that you do not have an AI problem at all. You have an organizational readiness problem. The technology is ready. The question is whether your culture, your definitions of value, and your ways of working are ready to absorb it.
If you turned off every AI tool tomorrow, which teams would notice, and which would not? What does that tell you about where absorption has actually happened?
Do your people have structured time to experiment, or are you expecting transformation to happen in the margins?
Can you articulate what AI value means for your organization in terms your board would recognize?
Who in your organization is invisible to your AI strategy? Whose workflows have not been mapped? Whose voice has not been heard?
When people do not use the tools you have given them, are you meeting that with genuine curiosity, or calling it resistance?
The organizations that get this right will not be the ones with the best technology. They will be the ones that treated AI as what it actually is: a catalyst for changing how work gets done. And they will be the ones that invested in the organizational capacity to make that change stick.
Let's continue the conversation. Email me directly at erica@aug.co to schedule a call!