AI Adoption
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95%
of GenAI pilots in companies fail to scale. (MIT)
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70-85%
of GenAI deployment efforts fail to meet desired ROI. (NTT)
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42%
of enterprises abandon AI initiatives before production. (CIO Dive)
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Only <43%
of AI projects are expected to fail due to data quality, lack of technical maturity, and shortage of skills. (Informatica)
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80%
or more of AI projects are expected to fail in 2026. (Pertama Partners)
The human side of AI adoption.
AI adoption is not driven by technology alone—it depends on whether people perceive AI systems as reliable, understandable, fair, and aligned with their needs. Behavioral science provides the insights needed to understand how trust is formed, why it breaks down, and how organizations can design AI experiences that encourage confidence, acceptance, and responsible use.
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Because people—not technology—determine whether AI succeeds. Even the most capable AI system will fail if employees do not trust it, understand it, or integrate it into their workflows. Behavioral science identifies the psychological barriers that influence AI adoption and helps organizations design interventions that encourage confident and responsible use.
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Yes. Resistance is often driven by uncertainty, perceived loss of control, fear of making mistakes, or concerns about fairness and accountability. Behavioral science helps uncover the root causes of resistance and develops evidence-based strategies to address them.
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Trust is influenced by factors such as transparency, consistency, perceived competence, fairness, and user experience. Behavioral science helps organizations understand how people form trust and provides practical guidance for designing AI systems and governance processes that people are more willing to adopt and rely on.
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Workarounds, shadow AI, and low adoption are often symptoms of behavioral friction rather than technical failure. Employees may find systems confusing, disruptive, cognitively demanding, or misaligned with their existing workflows. Behavioral science helps identify these hidden barriers and recommends ways to reduce them.
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Absolutely. People can become either overly skeptical of AI or overly dependent on it. Behavioral science helps organizations calibrate trust so that users know when to rely on AI, when to question it, and when human judgment should take precedence.
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People do not always adjust their behavior after an AI error. Behavioral science explains how factors such as automation bias, confirmation bias, and cognitive inertia influence continued reliance on AI, helping organizations design safeguards that promote appropriate oversight.
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Yes. Successful AI transformation requires changes in behavior, not just new technology. Behavioral science helps organizations understand employee motivations, concerns, and decision-making, making change initiatives more effective and sustainable.
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Customers evaluate AI-enabled products based on more than functionality. Perceptions of trustworthiness, fairness, privacy, ease of use, and control all influence adoption. Behavioral science helps organizations design customer experiences that reduce uncertainty and increase confidence.
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Many change initiatives focus on communication and training but overlook the psychological factors that drive behavior. Behavioral science complements change management by providing evidence-based insights into how people make decisions, form habits, respond to incentives, and adapt to new technologies.
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Behavioral science is especially valuable for challenges involving human judgment and decision-making, including trust in AI, resistance to change, shadow AI, performative human oversight, automation bias, underuse or overuse of AI, poor compliance with AI governance, low employee engagement, and the gap between organizational policy and actual behavior. These are fundamentally human problems—and understanding human behavior is where behavioral science has its greatest impact.
Our engagement offerings.
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BEAR
Diagnostic
BEAR (Behavioral Evaluation & Adoption Risk) is a 21‑day behavioral diagnostic that reveals why AI adoption stalls in real workflows. It observes how users actually interpret, test, override, or avoid an AI system, maps these behaviors to the five dimensions of the AI Trust Axis, and produces a clear profile of failure modes and the conditions required to stabilize appropriate reliance.
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STAR
Transformation
STAR (Scaling & Trust Accelerator Roadmap) is a behavioral transformation program that turns successful AI pilots into safe, predictable enterprise‑wide adoption. It diagnoses behavioral strains using the AI Trust Axis and applies a multi‑layer intervention architecture across workflows, interfaces, change mechanisms, and governance to ensure AI becomes a durable part of daily work.
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B-GRIT
Governance
B‑GRIT (Behavioral Governance, Risk, Integrity, & Trust) is a behavioral governance system that ensures AI produces reliable, intended human behavior. It establishes the policies, accountability structures, and oversight mechanisms needed to keep AI‑supported decisions trusted, fair, and appropriately used across an organization.
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BOLD
Audit
BOLD is a fast, single-case behavioral audit that determines whether one human-AI decision was reasonable at the time it was made—independent of whether the outcome was right — concluding in a Verdict of Behaviorally Sound, Fragile, or Breakdown.
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Always built on the AI Trust Axis.
Across our BEAR, STAR, and B‑GRIT engagements, the AI Trust Axis serves as the analytical backbone that translates interviews, observations, surveys, and workflow evidence into a structured behavioral profile. It reveals where users may misinterpret outputs, over‑rely or under‑rely on recommendations, experience friction, perceive unfairness, or lose trust after errors. The AI Trust Axis helps identify the conditions under which governance succeeds or fails and provides a repeatable way to detect drift, design behavioral safeguards, and strengthen governance.
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An argument for behavioral governance.
Behavioral governance is an approach that treats organizational decision‑making as a human system rather than a procedural one. It focuses on how real people inside institutions perceive risk, interpret signals, respond under pressure, and influence one another—often in ways that diverge from formal policies or rational models. Instead of assuming decisions follow documented processes, it examines the psychological drivers, social dynamics, and cognitive shortcuts that shape how governance actually unfolds in moments of uncertainty, conflict, or crisis.