Acceptance vs. Usage: What the Data Actually Reveals About Who Is Good at AI — and Why Self-Awareness Is the Answer

Show Notes: Heartwired – Emotionally Intelligent Leadership

Episode Title: Acceptance vs. Usage: What the Data Actually Reveals About Who Is Good at AI — and Why Self-Awareness Is the Answer

Host: Dr. MJ Vignone

Guest: Nicolas Bassan, Psychologist, Entrepreneur & CEO, Omind

Episode Summary

In this episode of Heartwired, Dr. MJ sits down with Nicolas Bassan — psychologist, cognitive scientist, and CEO of Omind — for a conversation that starts with a deceptively simple distinction and ends with one of the most data-grounded arguments for emotional intelligence you'll hear anywhere.

Nicolas has spent more than a decade combining neuroscience, behavioral science, and gamified assessments to make human capabilities measurable rather than intuitive. Omind has worked with over 10,000 leaders across major international organizations. In a landmark study conducted with Accenture Research across five countries, Nicolas and his team set out to answer a question that turns out to matter enormously: is AI acceptance the same as AI effectiveness? Spoiler: it isn't — and the cognitive skills that separate the two are exactly the ones most leaders aren't measuring.

The conversation covers why questionnaires lie, what pumping air into a virtual balloon tells you about risk tolerance, the surprising connection between asking for help and AI adoption, why deep-focus people resist AI, and how a six-month leadership program for a French bank used behavioral game data to personalize development for 20 leaders across 8 countries.

Key Takeaways Questionnaires Tell You What People Think About Themselves — Behavioral Games Show You How They Actually Behave

Most leadership assessments rely on self-report: "Are you a risk taker?" The problem is that the answer depends on context, mood, and the moment. Nicolas's approach replaces those declarations with behavioral tasks — short, gamified challenges based on validated cognitive science paradigms, each backed by hundreds of scientific publications. The balloon task, for example, asks players to pump air into a balloon: more air means better performance, but risk explosion. Three to four minutes of gameplay generates a reliable proxy for financial, social, and health risk behavior. Combine that with self-report and you create something far more powerful: a gap analysis between who people think they are and how they actually behave.

Acceptance and AI Usage Are Not the Same — and the Gap Has Real Consequences

In the Accenture Research study across five countries, Nicolas's team found that acceptance of AI and effective usage of AI are not correlated. An employee who says yes to AI and an employee who uses it well are frequently different people. For organizations deploying AI at scale, this is a critical finding: buying licenses and getting buy-in is only step one. The human skills that determine actual effectiveness are a separate variable — and without measuring them, organizations are flying blind.

The Leaders Best at Asking for Help Are the Best at Using AI

One of the study's most striking findings: people who are comfortable asking colleagues for help in daily work are significantly better at accepting and adopting AI. The cognitive and emotional mechanism is the same — both require trusting that it's okay not to do everything yourself. For leaders who came up as individual experts before becoming managers, this is a direct challenge. Delegating, asking for help, and accepting AI are all expressions of the same underlying skill. Developing one develops all three.

People Who Struggle with Focus Love AI — and Deep Workers Resist It

Nicolas found a counterintuitive correlation: high focus ability is negatively associated with AI acceptance. Deep workers — those who thrive in uninterrupted concentration — don't like the multitasking rhythm of waiting for AI to generate and then redirecting attention. On the other side, people with attentional challenges often embrace AI precisely because it reduces the cognitive load of sustained attention. Neither reaction is wrong — they're expressions of cognitive profile. The implication for leaders: AI adoption isn't one-size-fits-all, and meeting people where their minds actually are will produce better outcomes than mandating universal adoption.

Map the Skills First — Then Match AI Use to the Person, Not the Other Way Around

Nicolas's framework for leaders managing AI transformation: start by mapping your people's cognitive and emotional skills. Make them aware of their own profiles. Then help each person use AI in ways that fit how their minds actually work — rather than expecting everyone to adopt AI the same way. Only then does training make sense, because now it's targeted. Asking for help and trusting others can be trained — but only if the leader first understands where each person is starting from.

The French Bank Case: When You Align Development Data to Strategy, Everything Changes

Omind ran a six-month leadership program for a French bank with over 20 leaders across eight countries. Before designing a single workshop, they mapped participants' behavioral and cognitive profiles, then cross-referenced the results against the bank's published strategic plan. The workshops were designed around the actual gaps between the leaders' profiles and what the strategy required — not a generic curriculum. Participants reported high engagement precisely because the content felt personal and connected directly to the work they were doing the next day. A behavioral reassessment at the end produced measurable evidence of development.

Self-Awareness Is the Core Leadership Skill for AI Transformation

Nicolas's closing message is also his central thesis: for leaders navigating AI transformation, the most important thing is self-awareness — awareness of their own skills, awareness of their people's skills, and awareness of whether both are aligned with the transformation plan. The data to support that awareness exists. What's missing in most organizations is the willingness to gather and act on it. We have more data about customers and finances than about the people doing the work. That imbalance is where leadership transformations fail.

Memorable Quotes

"Acceptance and usage are not correlated. That was our first hypothesis — and it turned out to be a good one."— Nicolas Bassan

"People who are better at asking for help in a daily basis are better at accepting AI. It's the same skill — I'm okay with not doing everything myself."— Nicolas Bassan

"We have so much data about customers, about businesses, about finance. But when it comes to people, the data is just lacking."— Nicolas Bassan

"Almost everybody says they accept AI. Not many people are actually good at it."— Dr. MJ (opening reflection)

About Our Guest

Nicolas Bassan is a psychologist, entrepreneur, and CEO of Omind. For more than a decade, he has been exploring a simple but increasingly urgent question: what makes us distinctly human — and how can we better understand and develop those capabilities in a world increasingly shaped by AI?

His work brings together psychology, behavioral science, neuroscience, and data to study how people think, adapt, learn, lead, and perform under pressure. At Omind, Nicolas works with organizations to make human capabilities more visible, measurable, and actionable — helping them make better decisions about development, leadership, and transformation. Omind has worked with over 10,000 leaders and managers across major international organizations, including a published research collaboration with Accenture and a model validated through Columbia University.

Nicolas also hosts the Orga Nova podcast.

Connect with Heartwired

Email: drmj@drmjheartwired.com

Website: drmjheartwired.com

LinkedIn: linkedin.com/in/maryjeanvignone

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