My doctoral research was built on a simple comparison. I studied two groups of Fortune 500 leaders side by side, one adopting a new wave of technology, one resisting it, and putting them next to each other made the differences unmistakable. A decade later, those same differences are sorting leaders again, this time around AI. The labels have changed. The two camps have not. This piece is the full account: how the study was built, what each camp looked like from the inside, what did not separate them, why the pattern is repeating at the largest scale we have ever measured, and an honest instrument for finding out which camp you are in right now.
How the study was built
The 2014 work was a qualitative descriptive multiple-case study, and the design deserves a paragraph, because the design is why the findings hold up. The question was a why question: why does one senior leader adopt a new technology and pull an organization forward while another, equally capable, stalls? Why questions about decisions made in context are what qualitative case research exists for. A survey can tell you how many leaders adopted. It cannot sit with a leader for an in-depth interview and find out what they thought the technology was, which is, as it turned out, where the answer lived.
The core of the study was two cases set deliberately against each other: five Fortune 500 leaders who had adopted social business platforms and five who had resisted them. Around those ten, another nine interviews, employees from both kinds of organizations and staff from a technology provider, triangulated what the leaders reported against what the people around them saw. Nineteen interviews in total, analyzed in NVivo to surface the patterns that separated the groups, and eight themes emerged from the comparison (Goodwin, 2014). The side by side structure is the point. Studying adopters alone tells you what satisfied customers say about themselves. Setting adopters against resisters isolates the conditions that separate a decision from a delay, and it lets the contrast carry the findings rather than any one leader’s enthusiasm.
The resister, from the inside
Start with what the resisting leaders were not, because the caricature gets in the way of the finding. They were not lazy, not unintelligent, and not failing. Many ran successful operations. Nothing about resistance looked like resistance from the inside. It felt like prudence, like letting the dust settle, like focusing on the real business instead of the shiny thing.
What set them apart was specific, and it repeated across the themes. The most striking finding was definitional: three of the five resisting leaders could not describe what a social business platform was when asked directly. One thought the term referred to physical meetings. Another thought it meant social media. These were senior leaders at some of the largest companies in the world, being asked about a technology wave their industry was actively moving through, and the honest answer was that they could not picture the thing they were declining.
The second marker followed from the first. Where adopters saw a system, the resisters saw a wide and growing pile of tools, and that dispersion left them with a lower understanding of what any of it did. The third marker was personal: they had not engaged with the technology themselves, and because awareness in an organization comes down from the top, their teams had not engaged either. A leader who has never touched a thing cannot sponsor it, and the study was blunt on the cascade: where the leader was unaware, the subordinates were unaware too. And underneath all of it sat the quietest marker, the one I keep returning to. The fragmentation in their own operations, the disconnected channels and rekeyed information their people navigated daily, had become invisible to them. They were not refusing to fix a problem. They could no longer see it, a mechanism I unpacked in the communication tax your business pays every day.
The adopter, from the inside
The adopting leaders inverted every one of those markers, and the inversions were consistent enough to read as a portrait. They could explain the technology in plain terms and give real examples of it in use, not vendor language, their own words. They had engaged with it personally, enough to hold an informed opinion rather than a borrowed one. Here is the detail that surprises people: the adopters had, if anything, engaged with more tools than the resisters, not fewer. What distinguished them was not restraint. It was that they understood the system underneath the tools, the unified layer all the individual pieces were supposed to serve, so more exposure produced more clarity instead of more noise.
Their starting point was different too. The adopters moved because they had recognized a concrete problem worth solving, usually a communication breakdown, information fragmented across too many disconnected channels, and they saw the platform as the fix for that specific, felt pain. The tool was the answer to a question they were already asking. And their leadership posture completed the picture: clear communication with their teams, empowerment rather than micromanagement, and a situational approach that adjusted to what each moment required, in contrast to resisters who tended to lead narrowly by personal example. Each of these differences is its own lever, and together they make up the 5 drivers of AI adoption.
What did not separate them
A comparison study earns its keep as much by what it rules out as by what it finds, and three obvious suspects failed to explain anything. Budget did not separate the camps. Company size did not. And age, the explanation nearly every participant believed in, did not survive contact with the data. The expectation going in was generational and it was almost universal: younger leaders adopt, older leaders resist. Then one participant broke the pattern in a way averages never could. A Baby Boomer, squarely in the demographic everyone had written off, turned out to be one of the strongest adopters in the entire study, and the reason had nothing to do with his birth year. He had loved technology since before he was ten and had simply never lost the habit of staying ahead of it. That single outlier exposed what the age averages had been measuring all along: early exposure and sustained engagement, not youth. The full case against the age excuse is in you are not too old for AI.
A decade later, the same two camps
If the split were an artifact of one technology wave, it would be a historical curiosity. It is not. The macro data of that era already showed what the adopters were responding to: the McKinsey Global Institute estimated that the average interaction worker spent 28 percent of the workweek on email and nearly 20 percent more hunting for internal information, nearly half the week moving information rather than acting on it (McKinsey Global Institute, 2012). The adopters in my study were the leaders who could see their organization paying that bill.
Now run the tape forward. McKinsey’s current global survey finds that 88 percent of organizations report using AI in at least one function, while only 7 percent have scaled it across the enterprise (McKinsey, 2025). BCG describes the same divide in starker language, calling the leading group future-built and warning that the laggards are caught in a widening value gap, and it puts a number on the leadership half of the story: among the lagging companies, only 8 percent of C-suite leaders are deeply engaged with AI (BCG, 2025). The leadership numbers sharpen it further: McKinsey finds the organizations capturing real value are three times more likely to have senior leaders who own and actively champion the work, and BCG names delegating AI down to middle management as a defining failure of the laggards. A decade later, awareness still comes down from the top, or it does not come at all. Read those numbers against the 2014 portraits and the mapping is one to one. The leader who cannot explain what a model does in their business is the leader who thought the platform meant physical meetings. The executive who delegated AI to IT is the leader whose team was unaware because awareness never came down from the top, the pattern examined in AI is a leadership problem, not an IT problem. The owner drowning in disconnected AI features is the resister staring at the pile of tools, covered in why buying more AI tools is making you slower. Two camps, a decade apart, same dividing line.
The eight themes in one sentence
Stand back from the individual findings and the eight themes keep measuring the same two underlying variables. The first is understanding: what the leader could see, from the definition of the technology itself to the fragmentation in their own operation to the system underneath the pile of tools. The second is posture: how the leader carried the organization, communicating clearly, empowering rather than micromanaging, engaging personally so awareness could cascade down. Every theme is a facet of one of those two, and the one-sentence version of the study is that adoption was decided by what leaders could see and how they carried what they saw.
Honesty about method belongs here too, because it is what makes the findings usable rather than merely quotable. A qualitative study of ten leaders and nine surrounding voices cannot tell you what percentage of executives are resisters, and it does not try. What it can do, and what surveys cannot, is expose the mechanism, the why, in the leaders’ own words: not that resisters existed, but that resistance was made of definitional blindness, tool dispersion, and personal disengagement. Mechanisms are what transfer across technology waves, which is exactly why portraits drawn in 2014 keep predicting behavior in 2026, and why the large-sample numbers above land where the mechanism says they should.
Which camp are you in
The uncomfortable part of the research is that resisters rarely know they are resisting, because from the inside it feels like prudence every time. So the honest test cannot be do I like AI. It has to probe the actual markers the study found, and here is that test, five questions, each mapped to what it measures.
One: can you explain, in plain language and without vendor vocabulary, what AI is doing for a business like yours? This is the definitional marker, the one that caught three of five resisters. Two: have you personally used it on a real task in the last month, not a demo, a task? This is the personal engagement marker, the strongest cascade signal in the study. Three: did your last AI move start from a problem you had named, or from a tool someone was selling? This is the problem-first marker that separated every adopter. Four: who owns AI in your organization right now, you, or someone you handed it to? This is the top-down awareness marker, and if the answer is a delegation, the research says your team’s ceiling is already set. Five: when did you last change your mind about a piece of technology because you engaged with it? This is the sustained openness marker, the one the Boomer outlier embodied. Score yourself without mercy. Three or more uncomfortable answers puts you in the resister camp today, whatever your intentions say.
Positions, not personalities
If that test stung, the most important finding in the study is the hopeful one: adopter and resister are positions, not personalities. Nothing in the data tied either camp to a fixed trait. The markers are all behaviors, explaining, engaging, starting from problems, owning the change, and behaviors move. A resister who spends one month building genuine literacy, the gap addressed in you cannot adopt what you do not understand, and who anchors their first move to a real named problem, has already crossed most of the line. The camps are real, the line between them is real, and it is crossable in either direction starting today, which is exactly why knowing your current position matters more than defending it.
The move
Take the five-question test above and write the answers down, because written answers resist the flattery of memory. Then act on the single worst one this week: use the tool on a real task, name the problem, or take AI back from wherever you delegated it. For a structured read on where you stand today, the Omnine AI Readiness Assessment scores you across these markers in about three minutes.
References
Goodwin, M. R. (2014). A qualitative descriptive multiple-case study: Fortune 500 leaders’ social business platform adoption (Doctoral dissertation, University of Phoenix). ProQuest Dissertations Publishing (UMI No. 3648813).
McKinsey Global Institute. (2012). The social economy: Unlocking value and productivity through social technologies.
McKinsey & Company. (2025). The state of AI in 2025: Agents, innovation, and transformation. McKinsey Global Survey on the state of AI.
Boston Consulting Group. (2025). Are you generating value from AI? The widening gap.