Tagged: AI adoption

$When organizations introduce AI into the workplace, there's often a moment of genuine confusion at the leadership level. The technology is obviously faster: it can summarize a document in seconds, draft an email almost instantly, and analyze data that used to take hours. Leadership assumes adoption will follow naturally. Why wouldn't people embrace something that saves them time? And yet, in nearly every rollout I've watched, plenty of them don't. It's tempting to conclude that people don't like change, or aren't willing to learn something new. I don't think that's it. The real explanation is less judgmental and more human: our brains are built to conserve mental effort, not physical effort, and definitely not willingness. That's an important distinction. This isn't about whether employees are willing to work hard. It's about cognitive economy, the brain's constant, mostly unconscious calculation of whether a task is worth thinking about. The human brain burns roughly twenty percent of the body's energy while accounting for a small fraction of its weight, so over a long evolutionary timeline we've gotten good at spending that energy carefully. We build habits, recognize patterns, and automate repetitive decisions, because thinking deliberately about every action all day would be exhausting. The workplace runs on the same wiring. Most organizations assume employees are hunting for the most efficient way to get work done. Behavioral science paints a different picture. More often, people default to whatever requires the least immediate mental effort, even when a better option is right next to it. Psychologists sometimes call this being a "cognitive miser." It isn't laziness. It's one of the reasons we stay productive through a day full of constant, low-stakes decisions. Think about answering a routine email. An experienced employee doesn't consciously walk through the steps; they read it, draft a reply almost automatically, and move on. Years of repetition turned that into a habit that costs almost nothing. Now put AI in the middle of it. Should I ask Copilot to draft this? How do I word the prompt? Do I trust what it generated? Do I need to edit it? Did it miss something? Even when AI genuinely reduces the total work involved, it often increases the thinking required just to get started. For a lot of employees, that's exactly where adoption quietly stalls. The first interaction is usually the hardest One of the most persistent misconceptions about AI is that people feel the productivity gain right away. Most don't. The first several interactions typically feel slower than doing the task themselves. Prompting is unfamiliar, reviewing AI-generated content takes concentration, and verifying accuracy is one more decision layered on top. Organizations tend to measure long-term efficiency while employees are living through short-term friction. Both are true at once, and people make decisions based on what they're experiencing today, not what they might experience after a month of practice. Features aren't the same thing as a workflow Software companies love demonstrating features. Employees care about finishing their work, and that gap matters more than it gets credit for. A demo might show AI summarizing a meeting in thirty seconds flat. The employee watching is thinking about everything on either side of it: where do I open this, do I need to copy and paste, can it see my notes, how carefully do I check this, how do I share the result. Each question is small on its own. Together, they decide whether the new workflow feels lighter than the old one or heavier. That's a big part of why embedding Copilot directly inside Outlook, Word, Teams, Excel, and PowerPoint has mattered. Employees aren't being asked to learn a new destination. The less someone has to think about where or how to use AI, the more likely they are to actually use it. Every decision has a cost, even a small one We tend to talk about AI in terms of the work it removes. Less attention goes to the decisions it adds along the way, and every time someone pauses to decide whether AI should help with a task, they're spending cognitive energy. Multiply that across dozens of emails, meetings, and documents in a single day, and the cost adds up fast. Ironically, AI can create more decisions before it starts eliminating any. The organizations getting this right aren't telling employees to "use AI whenever it makes sense." They're identifying specific, recurring moments where it reliably helps: summarize every meeting, draft the first pass of a status update, organize research notes, outline a presentation. Removing the decision removes most of the load that was sitting in front of it. Good adoption makes AI disappear The clearest sign that AI has become part of the job isn't that people are talking about it constantly. It's that they've stopped thinking about it at all. Nobody proudly announces they used spellcheck. Nobody celebrates saving a file to the cloud instead of a hard drive. Those tools succeeded by fading into the background, and I expect AI will follow the same arc. The organizations that pull off widespread adoption won't necessarily have the sharpest models or the biggest budgets. They'll be the ones that make AI feel like the natural continuation of work instead of an interruption to it, because people aren't out looking for the smartest tool available. They're looking for the easiest path to whatever already needs to get done. When AI reduces the thinking required to get started instead of adding to it, adoption stops feeling like change. It just becomes how the work gets done.$

AI / Change Management

July 28, 2026

Why Easy Isn’t Enough

When organizations introduce AI into the workplace, there’s often a moment of genuine confusion at the leadership level. The technology is obviously faster: it...

When AI Challenges Professional Identity

AI / Change Management / Culture

July 16, 2026

When AI Challenges Professional Identity

This topic really hits home. I’ve had this conversation with a dozen or more fellow Microsoft MVPs, and so I thought I’d expand on...

July 13, 2026

Uncertainty Is More Expensive Than Change

In nearly every AI adoption workshop I run, someone offers the same explanation for why their organization is struggling: people resist change. It’s a...

Your AI Training Might Now Be Fundable by Microsoft

AI / Microsoft Copilot

July 8, 2026

Your AI Training Might Now Be Fundable by Microsoft

Back in June, I wrote about digital debt and why so many organizations buy Copilot licenses without ever building the fluency to use them...

Overcoming Digital Debt and Embracing AI in the Workplace

AI / Automation / Events and Stuff

June 24, 2026

Overcoming Digital Debt and Embracing AI in the Workplace

This Friday, I’ll be speaking at my first AgentCon event, part of the Global AI Community that runs gatherings for practitioners and builders all...

May 21, 2026

Your Organization Has an AI Strategy. Does It Have a New Operating Model?

There’s a pattern playing out in enterprise technology right now that should feel familiar, because it has happened before. Organizations acquire the new capability,...

The Entry-Level Problem Nobody in AI Wants to Talk About

AI / AI Agents / Change Management / Copilot / Culture / Future of Work

April 10, 2026

The Entry-Level Problem Nobody in AI Wants to Talk About

Over the past two years, I have facilitated AI at Work workshops for organizations across a range of industries, including financial services, healthcare, manufacturing,...

The AI Adoption Blueprint - Where to Begin

AI / Copilot / Microsoft Copilot

September 5, 2025

The AI Adoption Blueprint: Where to Begin

AI has moved from the sidelines to the center of business strategy. Boards are asking about it, competitors are experimenting with it, and employees...

Tagged: AI adoption

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