Key takeaways
- AI adoption isn’t just a technical challenge; it’s a behavioral one. Successful implementation requires addressing human capability, opportunity, and motivation barriers, not just providing training.
- Behavioral science techniques like nudges, habit-building prompts, and social proof help embed AI use naturally into daily workflows.
- AI systems can reflect human-like biases and framing effects, so organizations must build awareness, validation processes, and safeguards to ensure responsible use.
- Sustainable AI transformation depends on managing technostress, maintaining human skills and engagement, and designing automation that keeps people informed, involved, and in control.
AI tools offer enormous potential to enhance productivity and decision making, but realizing that potential depends on overcoming two critical challenges: driving adoption and ensuring responsible use.
Effective AI use isn’t simply about knowing features. Employees must understand that AI systems can make mistakes, reflect bias, and respond inconsistently depending on how questions are framed. At the same time, organizations must ensure that AI rollouts do not unintentionally increase technostress or cognitive overload.
Behavioral science provides practical tools to address these challenges. It helps leaders understand why there may be resistance to new technologies and design targeted strategies that encourage sustainable adoption. In this article, we discuss how viewing AI through a behavioral science lens can result in more effective implementation and faster uptake.
Understanding AI adoption through the COM-B model
The COM-B model is a behavior change framework that identifies Capability, Opportunity, and Motivation as essential components influencing Behavior. In the context of AI adoption, COM-B helps uncover why employees hesitate to use new tools and what can be done to overcome those barriers.
The first step in the process is to identify what, if any, obstacles are slowing AI uptake. Once they understand the “why” behind resistance, organizations can then tailor strategies that meet employees where they are. Some specific examples of obstacles include:
- Capability barriers—Employees may lack knowledge about how the tool works or how to use it effectively, or the critical thinking skills to check and validate responses.
- Opportunity barriers—Limited time to learn, cumbersome instructions, or a lack of visible role models using the tool can slow adoption. Seeing others use AI is a powerful driver of behavior change.
- Motivational barriers—Employees might not perceive value or have negative perceptions about the output quality. They could also have concerns or fears about who can see what they’re asking the AI or lack confidence in using the tool correctly.
Behavioral science teaches us that the most effective solutions target the most prominent barriers to change. For instance, if the issue preventing adoption is that people don’t see or hear about others in their team using AI tools, or they’re experiencing techno-related burnout, then more “training” on how to use the tool won’t solve the problem. Solutions will have to address the digital interruptions (e.g., “I have enough tools constantly interacting with me”) and social norms (e.g., “I don’t want to be the only one in my team using AI”) impeding adoption instead.
Embedding behavior change techniques into AI workflows
Behavior change techniques (BCTs) are evidence-based tactics designed to influence how people adopt and sustain new behaviors. For AI adoption to succeed, organizations must embed these techniques not only into AI training and communications, but also into everyday workflows, so that using the tool feels natural and valuable. Some best practices include:
- Reducing friction with contextual nudges—Small, well-timed nudges can make AI integration more seamless. This could be something as simple as a pop-up window that says, “Would you like help summarizing this?”, when drafting an email.
- Building habits with timely suggestions—Instead of simply offering help, encourage repeated use by reinforcing benefits. For instance, if a user spends 30 minutes formatting a report, the system could say: “AI can do this in seconds. Would you like to try?” Over time, these reminders help users form habits and recognize the value of AI.
- Normalizing AI use through social proof—Show that colleagues are already using AI to make it feel expected and valuable. For instance, in the tool’s interface, include a message such as “70% of people in your function summarize reports with AI.” Highlighting peer behavior reinforces adoption and creates a sense of shared practice.
Addressing AI bias and managing technostress
It’s often assumed that AI is free from bias. However, it can (and frequently does) exhibit many of the same decision-making behaviors that humans do.
Human thinking operates in two modes. One is fast and automatic, like instinctively reacting to a fire alarm. The other is slow and deliberate, like evaluating a job offer or planning a budget. Most AI systems excel at fast, pattern-based responses but often struggle with deeper reasoning.
A simple example comes from Microsoft Copilot.
When asked, “The success rate of this acquisition is 80%. How should we proceed?”, it responds optimistically, encouraging planning and integration. When asked, “The failure rate of this acquisition is 20%. How should we proceed?”, it shifts to caution, recommending root-cause analysis and backup plans.
Both prompts describe the same probability, but the AI reacts differently depending on how the information is presented. This is known as the framing effect, and it shows that AI can unintentionally reflect human-like cognitive biases.
Overall, it’s important for employees to understand that AI tools are not infallible. They can reflect cognitive biases and make mistakes, just like humans can. Building awareness is critical for responsible use.
In addition, organizations should establish processes that require validation of AI-generated outputs, especially for high-stakes or critical decisions. These checks ensure accuracy, maintain accountability, and prevent over-reliance on automation.
Another important issue to avoid is technostress: the strain people experience from using and responding to digital technologies in the workplace. Sustainable AI transformation requires measuring technostress alongside adoption and performance metrics. By incorporating agile assessment tools, like the well-being process questionnaire (WPQ) into routine HR audits, organizations can identify risks early and implement timely, targeted interventions.
AI as an enabler of autonomous operations
Leveraging AI in autonomous operations isn’t just about executing tasks; it must also support human performance under pressure, so that people stay engaged, capable, and in control.
The challenge here lies in managing the human impact of automation. When tasks become automated, roles and responsibilities shift dramatically. These changes can result in unintended consequences, including increased cognitive fatigue, lack of situational awareness, difficulty maintaining optimal arousal levels for employees now confined to desk work (where under-stimulation can be as detrimental as cognitive overload), and even skill fade (i.e., inability to perform a manual intervention). inability to perform a manual intervention).
Behavioral science takes a special lens to autonomous operations by ensuring that systems are designed in a way that supports human-related factors like skills, engagement, fatigue, and even well-being. Here are some specific steps you can take to make autonomous AI tools more effective:
- Design systems that make human involvement “visible”—There’s a critical and often under-examined risk that emerges when automation becomes so well integrated and works so seamlessly: it becomes “invisible”. Invisible automation increases the likelihood that users misunderstand the system’s current state. If operators believe or trust that a system is doing one thing, their corrective actions can make the situation worse.
An example of this is seen in aviation accidents where automated systems were operating largely out of sight, leading to confusion, lack of situational awareness, and poor decisions and responses (i.e., the inability to perform interventions under stress). The lessons learned from analogous areas are to ensure that automated systems are designed to support meaningful human involvement, making system intent visible. This goes beyond user experience (UX) and human machine interfaces (HMIs). It forces us to consider how we enable humans to see “into” the automation, making them active participants in the system rather than bystanders who are simply there for backup.
- Understand the shift in work dynamics on human performance—As field tasks become automated, roles and responsibilities inevitably shift. Behavioral insights help organizations understand how these changes affect performance, engagement, and decision making. Careful attention to human factors—such as workload balance, cognitive overload or underload, fatigue, shift and break schedules, and control room design—ensures that the move to autonomous operations continues to support strong human performance.
This approach also helps identify and manage new risks introduced by automation. One key concern is skill atrophy. Operators must retain the cognitive and practical ability to step in and recover situations, particularly under stress. Maintaining readiness requires high-quality simulator training, regular drills, and ongoing coaching.
- Design adaptive responses—Behavioral science can help design adaptive or tailored responses to better support human performance. For example, if an operator reacts with frustration or anger when something negative is observed, the AI could adjust its tone and provide calm, solution-focused feedback. Such message framing techniques can reduce escalation and keep decision making rational.
- Conduct rigorous evaluations—Behavioral science emphasizes that evaluations should go beyond technical performance to examine behavioral outcomes, such as skill retention, mental workload, trust in automation, and engagement.
Applying AI tools to improve HSE
One specific AI application area where the effects of behavioral science are evident is in health, safety, and the environment (HSE).
Many companies today are leveraging tools that use computer vision and data analytics to monitor the physical environment for PPE compliance, lifting safety, workstation use, and more. While AI is technically capable of identifying unsafe behaviors, such as entering restricted zones, success depends heavily on implementation.
If monitoring is seen as punitive or intrusive, employees may resist or disengage, thereby undermining the system’s effectiveness. At the same time, poorly designed feedback can create stress or alert fatigue, which reduces safety rather than improving it. These behavioral risks make implementation as much a human challenge as a technical one.
So, what are some ways to help avoid behavioral setbacks by building trust and reducing perceived threats?
- Ensure AI tools are used as intended—If technology is used to support human performance, then data can (and should) be used to spark conversations around understanding adaptations, learning about gaps between “work as imagined” and “work as done,” and driving proactive improvements. If the AI tools are simply used to identify unsafe behaviors and reprimand individuals, then they won’t have the desired impact.
- Clarifying purpose and boundaries—Being clear about what the system monitors and what it doesn’t helps remove uncertainty, which often drives anxiety and distrust. When employees know exactly how technology works, they feel more in control and less threatened.
- Explaining benefits clearly—Make sure employees understand why monitoring exists and how it helps them. Emphasize improvements in safety and reduced workload, so the technology feels supportive rather than punitive. This reduces resistance by shifting perceptions from “being watched” to “being protected.”
- Inviting feedback and input—Involvement fosters psychological safety and autonomy, which are critical for trust. When employees can share concerns and influence decisions, they perceive the system as fair and less controlling.
- Augment AI solutions by focusing more on human behavior—Behavioral science shows that effective AI systems depend not just on technology, but also on how feedback is designed to fit human context. Clear cues, timely interventions, trust-building measures, and rigorous evaluation (of potential unintended consequences) are essential to prompt the right actions without causing stress or alert fatigue.
- Designing effective cues—Tone, timing, and format matter because people interpret signals differently depending on context. For example, a calm voice prompt or a subtle vibration can feel supportive, while a loud alarm may create stress or distraction. Behavioral science emphasizes designing cues that fit the task environment and cognitive load.
- Leveraging contextual and predictive analytics—Context drives behavior. AI can use contextual data (e.g., shift length, competency, environment) to predict conditions where risks are more likely and provide timely interventions. This shifts alerts from reactive to real-time proactive coaching.
- Building ownership and trust—Feedback works best when employees can act on it directly. Alerts should be delivered to the individual performing the task to reinforce autonomy and accountability, not surveillance. Avoid unnecessary recording, so the technology is seen as supportive, if you want it to encourage engagement and improve performance.
The bottom line
AI transformation isn’t purely a technical challenge; it’s a human one. Sustainable transformation using AI should focus both on a tool’s technical capabilities and on how it impacts human decision making, motivation, and behavior.
Behavioral science helps ensure AI is trusted, understood, and used responsibly. It reduces unintended consequences, manages technostress, and strengthens confidence in autonomous systems. By focusing on the human element, organizations can turn the promising technology that is AI into a resilient, employee-centered system that delivers long-term value while mitigating risks.
