Executive Summary
Successful leadership narratives fail when built in internal vacuums rather than being tested against external reality. True resonance requires closing the gap between what a leader wants to say and what the world is prepared to hear. By integrating AI-native sentiment analysis with deep human relationship capital, organizations can move from “instinct-based” pitching to precision-engineered earned media. In the age of Generative Engine Optimization (GEO), this third-party credibility is no longer just for reputation—it is the primary data source that informs how AI discusses your organization.
Key Takeaways
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Test for Readiness, Not Just Resonance
Internal conviction does not equal market readiness; a message must find “white spaces” in a saturated public conversation to break through.
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What to do next: Use AI tools to map sector narratives and identify unanswered questions before finalizing your pitch.
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Pair Machine Intelligence with Human Persuasion
AI identifies the opening and refines the message, but seasoned professionals with established trust are required to “close the deal” with journalists.
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What to do next: Balance your tech stack with “strategist-mediated” insights to ensure data-backed pitches land with the right human touch.
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Prioritize GEO as Strategic Infrastructure
Since ~77% of generative AI citations stem from earned media, your presence in AI responses depends largely on credible, third-party coverage.
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What to do next: Shift earned media from a “comms line item” to a strategic priority to ensure your organization is cited accurately by AI systems.
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For 25 years, I’ve watched leaders make high-stakes decisions about human behavior based on instinct. They’ve guessed how communities will respond to change. They’ve assumed which stakeholders will resist transformation. They’ve made decisions in crisis based on what they hope people will do, not what evidence shows they’ll actually do. Today, AI has matured enough that we can reasonably anticipate behavioral reactions, especially when it comes to groups. This can be done with a precision that was impossible a decade ago. The only question that matters: Are you building your strategy on what you want people to do, or what data says they are likely to do?
The Assumption Trap
Most leaders don’t think of themselves as intuitive decision-makers. They believe they’re data-driven. But when it comes to understanding how stakeholders will respond to change—how a community will receive new policy, how employees will adapt to transformation, whether a board will support a strategic shift—even the most rigorous leaders still default to assumption. Historically, about 50% of business decisions fail to deliver the intended value by rushing to judgment without information or failure to explore alternatives.
I’ve personally seen this pattern across more than 50 countries. A government leader responds to a crisis, or decides to implement a major institutional transformation, and assumes opposition will come from one constituency when the actual resistance emerges from another. A corporate board plans a restructuring and bets on rapid buy-in, then faces delays because they misread how middle managers would actually respond. A health system redesigns patient pathways based on what leaders think people want, only to discover the actual friction points elsewhere. The failure rates speak for themselves: most transformation initiatives fail not because the vision is flawed, but because leaders misread human response. What is more challenging is that 49% of businesses have developed stagnant decision styles that obscure decision results and reduce the ability for the organization to learn over time.
The cost of this gap is real. When communications miss, stakeholder alignment takes months longer than it should. When an advertisement fails to resonate with the target audience, revenue goes down, investors pull back, and competency is questioned. When influence strategies are built on wrong assumptions about motivation, organizations spend resources on the wrong levers. When leaders carry high uncertainty about how their decisions will land, they move more slowly. They hedge. They over-communicate to cover their uncertainty. They make decisions defensively instead of decisively.
There’s also a reputational cost. In an age of scrutiny, decisions that visionary leaders make about institutional change, especially in bedrock organizations serving citizens and community, get questioned. Those decisions hold up longer when they’re grounded in evidence. They get undermined faster when they rest on intuition.
The pattern is systematic. And it’s because the capability to systematically see behavior hasn’t existed until now.
Leadership Decision | Operating on Assumption | Operating on Intelligence |
Stakeholder response to major change | Predict based on relationships and past experience | AI-surfaced analysis of behavioral response signals across comparable contexts |
Influence campaign design | Build messaging around what you think people care about | Design based on mapped behavioral triggers, simulations, and documented real objections |
Transformation timing and sequencing | Launch when the strategy is ready | Sequence based on behavioral readiness signals across stakeholder groups |
Crisis and risk anticipation | React when public sentiment shifts | Anticipate behavioral response and make informed decisions within the same news cycle. |
Coalition and alignment building | Identify allies from known relationships and engage | Map behavioral indicators to identify the persuadable middle and the real resisters |
Case Study
In January 2012, JCPenney CEO Ron Johnson, fresh from building Apple’s retail empire, made a strategic decision that seemed, from the inside, entirely rational. He eliminated all coupons and promotional sales, replacing them with “fair and square” everyday low pricing. The logic was clean: if customers want lower prices, why confuse them with artificial markdowns? Why train them to wait for sales that dilute margins? Give them honest prices.
What Johnson was actually doing was applying the behavioral model of Apple’s customers to JCPenney’s customers. They are not the same audiences, and they don’t make decisions the same way. His own post-mortem made it explicit. In a Businessweek interview, he said: “I thought people were just tired of coupons…our core customer, I think, was much more dependent and enjoyed coupons more than I understood.”
What behavioral intelligence would have surfaced: his core customer wasn’t shopping for price. They were shopping for the win. The coupon was a psychological reward loop. The “How Much You Saved” line at the bottom of the receipt was the score. Strip it away, and you haven’t given them a better deal. You’ve taken away the experience that they came in for.
Revenue fell 24% in a single year—from $17 billion to $12 billion. Q4 2012 same-store sales dropped 32%, which analysts called one of the worst quarters in retail history. The company posted a $1.38 billion net loss. The stock collapsed from over $40 to under $10 a share. Johnson was fired 17 months after he started, and JCPenney spent years clawing back the ground it lost.
What AI Now Reveals
Human behavior may be irrational, but it is not random. It’s discoverable. And recent developments in AI and data science have demonstrated that behavioral indicators can be surfaced with measurable accuracy that changes what is possible before we ever make the decision.
Recent research published in Nature in 2025 showed that AI models trained on behavioral and psychological data can predict human responses across more than 160 distinct psychological studies with significant precision. These aren’t simple models. They’re integrating multiple data streams—demographic patterns, communication history, prior behavioral response, organizational context, institutional constraints—to surface what will actually happen, not what we guess will happen.
This validates a belief I’ve held throughout my career: psychological indicators exist and can be surfaced, with the right combination of data, expertise, and algorithmic rigor. For decades, I built behavioral forecasting approaches on that premise. I worked with organizations that couldn’t afford to guess; federal agencies making policy decisions affecting millions, government leaders navigating complex stakeholder environments, enterprises managing transformation across thousands of people. We had to understand actual behavior, not assumed behavior. We built methods to forecast it.
What’s changed is the speed and scale at which this is now possible. What used to require months of structured stakeholder research, behavioral mapping, and iterative testing can now be accelerated through AI models trained on human responses across contexts. The capability matured. The tool sharpened. Evidence became more accessible.
How Visionary Leaders Apply This: Three Strategic Moves
Clarity on Stakeholder Response
The first move is the simplest and most consequential: before you design a message, post, campaign, advertisement, speech, or legislation initiative, understand how your stakeholders will actually respond.
Before behavioral intelligence, the answer was: you’ll find out when you get there. You’ll test, measure, iterate, adjust. You’re making decisions with uncertainty. You’re communicating before you understand what people actually need to hear
“Using Decision Support Tools (DSTs) that are built on predictive analytics can provide a significant benefit by reducing guesswork. We can increase the speed of decisions and be first to market, we can increase the success rates of decisions, we can increase accuracy of forecasts, we can increase profitability, we can get faster market response times, we can reduce risk, and we can increase the success rate of new innovations. It requires some expertise to build the support tools correctly and credibility behind the tool is important, but it can be done and it is incredibly powerful when done correctly.”
With behavioral intelligence embedded into the research phase, you move differently. You enter the Transformation ArcSM with clarity. You know where the real resistance lives. You understand which stakeholders need different communication approaches. You can design influence strategies that actually map to how people respond, not how you assume they’ll respond.
Accelerated Influence
The second move is speed. Influence campaigns built on assumptions, MISS. Influence campaigns built on intelligence, HIT. When you understand how stakeholders actually respond, you can design communications, engagement strategies, and timing that work.
My work with the governor on Oklahoma’s AI Strategy is a direct example. We didn’t just write a policy document. We understood the behavioral intricacies of key stakeholders—which constituencies would be early adopters, which would need more evidence, which would resist, and why. We designed a strategy for building a coalition and moving stakeholder alignment. We designed communications that matched how different groups actually respond. The result: faster buy-in, clearer strategy, a Chief AI Officer role that stuck.
“When it comes to understanding human behavior we combine predictive analytics with the theory of mind, which is the ability to associate mental states and beliefs, desires, and intentions to others. This is important as traditional predictive analytics, based on data alone, normally relies on simple pattern recognition and extrapolation, which has a tendency to fail when human behavior is involved.”
Influence that’s grounded in behavioral evidence moves faster because you’re not fighting human nature—you’re moving with it. You understand the real objections, not the assumed objections. You know which stakeholders are persuadable and which aren’t. You understand which evidence resonates and which doesn’t. You time engagement when stakeholders are ready to listen rather than when your calendar says it’s time. The cycle time compresses dramatically.
This matters because the speed of influence affects the speed of momentum. In a high-stakes transformation, the first phase of stakeholder alignment determines whether the rest of the initiative moves with momentum or against friction. Behavioral intelligence lets you win that first phase faster, then ride the momentum forward.
Systemic Resilience
The third move is institutional. Decisions built on assumption get undermined when scrutiny increases. Decisions built on behavioral evidence hold up.
This has been true throughout my work with federal agencies, State Department, Department of Defense, Department of Justice, on complex multi-stakeholder decisions. When you can ground a decision in behavioral evidence, as in, “we understand why stakeholders are responding this way because we’ve mapped the actual patterns,” the decision becomes defensible. It holds up in the room. It holds up in the press. It holds up when stakeholders push back because you’ve already anticipated how they’ll respond.
Behavioral Intelligence as Transformation Infrastructure
Here’s what often gets missed: behavioral intelligence isn’t a separate analytical practice bolted onto transformation. It’s embedded into how transformation happens.
Saxum’s approach maps directly to the Transformation ArcSM—Clarity, Vision, Momentum, Influence, Adapt.
Arc Stage | Without Behavioral Intelligence | With Behavioral Intelligence | What Becomes Possible |
CLARITY | Define the problem through internal perspective and stakeholder interviews | Map how stakeholders actually experience the situation — surfaced through behavioral and AI-driven analysis | Start from truth, not working theory |
VISION | Design the future state as the ideal outcome, then build buy-in | Design the future state accounting for how people actually respond to change | Vision people move toward, not away from |
MOMENTUM | Activate the strategy and hope for alignment | Sequence activation based on behavioral readiness signals — move with the grain of stakeholder response | Faster first phase; momentum that doesn’t stall |
INFLUENCE | Communicate, measure reaction, adjust messaging | Predict response before full deployment; adapt communications to understandable reactions | Influence at the speed of evidence |
ADAPT | Learn from what broke after it breaks | Monitor behavioral shift signals before they become visible failures | Continuous intelligence, not post-mortems |
The integration matters because transformation is fundamentally about moving people through change. If you understand how people actually respond to change at the foundation level—and you’ve embedded that understanding into every stage of your strategy—the entire arc accelerates.
This is where our model becomes differentiated. My 25-plus years of behavioral forecasting expertise, paired with SaxumOS (our AI collaborators), and our transformation team. A team that has behavioral intelligence embedded into how we work. It’s not a side analysis. It’s the spine of the strategy.
What This Means for Your Decisions
I’ll close with two questions for you to sit with.
First: Are your decisions about institutional change, stakeholder alignment, and transformation built on evidence or assumption?
Most organizations still default to assumption. They talk to a few stakeholders, they trust their instincts, they move forward, hoping their read on the room is right. That approach used to be acceptable because the alternative didn’t exist. Now it does.
Second: Do you have the institutional capability to systematically understand how your stakeholders will actually respond?
Most organizations don’t. They lack the integration of behavioral expertise, data access, and analytical rigor to do this at scale and pace. Gaining that capability takes partnership, not hiring another analyst.
The evidence is clear. Behavioral modeling is real and can be done at scale. The only question is whether you’ll include this insight and capability in your strategy as a competitive differentiator.
