If your LinkedIn feed is like mine, 80% of the content is gushing about how the latest AI model will revolutionize their business. But for me, this matters almost zero – folks have got it backwards. The thing that will most significantly determine the extent to which a business will benefit from AI is their culture – it’s a change-management issue, not an issue of using this AI model or that.

More accurately, it’s a change-management opportunity. It’s an exciting time in history when people at every level can be empowered by technological changes – and you, as a business leader, can be the one motivating your organization and helping navigate this change process successfully. To do that, I’ve found, means going back to basics.

My own “basics” include a degree in psychology from well before I started building AI products, and I’ve increasingly been drawing on foundational psych concepts to drive better results as customers I work with adopt AI in their business. Here are the principles of change management that can also empower your organization as it undertakes the AI transformation.

Turning uncertainty into understanding

One of the biggest hurdles faced by organizations implementing AI is fear. The fear of needing new skills, of innovating faster than one can keep up, and above all of AI making roles redundant – these are all understandable and worth tremendous empathy. But re-framing how these feelings about AI arise and are conceptualized is crucial to future success.

As the school of Positive Psychology has established, human concerns need a human approach: more than simply alleviating negative emotions, it’s most effective to emphasize strengths, well-being, and growth. In other words, shifting your messaging from pathology to compassion, from pessimism to optimism, is the only way to address very real fears and get genuine buy-in to new ways of thinking.

I honestly believe the most effective communications strategy now is to put AI in the context of earlier points in time like the Industrial Revolution or the early internet. It’s unpredictable, but it’s exciting: people are gaining new skills and achieving new things no one on Earth has ever done before. Your genuine passion for doing new things and making real society-wide progress will be the basis of a company culture that embraces experimentation without fear and with enthusiasm – setting the tone for all the following AI transformation efforts your team undertakes.

The most underrated link in AI adoption

Those efforts, though, will need to build on your positive company culture by grasping the nuts and bolts of how individuals learn new skills and habits. Giving your company the base set of skills that everyone needs in the AI era can’t just be a PowerPoint; measuring outcomes can’t just be a survey. Your adoption plan has to be a fleshed-out, long-term initiative driven by observational learning and leveraging principles of conditioning.

Familiarity breeds contentment

Observational learning is social learning, how children to top athletes model behaviors based on what they’ve already seen others do. Simply put, show me things I can relate to, and I’ll adapt – maybe not flawlessly, but comfortably. We, as humans, are built to work this way.

This is how our company, Make, constructed our in-house AI adoption program. We sent a detailed questionnaire to each employee – Which tools are you already familiar with? What are the pain points you’d like to address? Who do you already come to with questions about AI? The responses formed the basis for individually tailored learning plans, giving every team member an actionable roadmap for building useful AI skills in a way familiar to them.

From concept to habit: Making AI stick

Ensuring skills actually take root requires showing real value to the learner. For this, AI needs to be ingrained in existing tools and processes – it can’t be some burdensome “separate thing”. For example, built-in AI capabilities in Slack can do wonders to supplement my thinking and maximize results in an application where I’m already spending my time.

Ultimately, identifying where AI will produce the most value needs to be determined by how specific roles see maximum usability – giving everyone the latitude to identify individual bottlenecks and the AI skills to solve them, either on their own or collaboratively. This positive reinforcement will confirm the thinking that AI actually does let you accomplish tasks quicker, better, or entirely new.

Showing this value in day-to-day processes consistently will go even further: with time, classical conditioning will automatically make AI top of mind whenever a complex or unfamiliar task needs solving.

Trust but verify

AI agents should be go-to tools, but they can’t run on autopilot; AIs citing nonexistent legal cases or columnists recommending fake books are two recent examples of the risks that come with removing the human element altogether. Assessing risk appetite for every AI use case is crucial to implementing the best solution.

Yet, as any parent of a teenager can assure you, humans come with built-in unconscious biases that make accurately assessing risks difficult. The availability heuristic, for example, pushes us to overestimate the likelihood of particularly memorable events; its near-opposite, the optimism bias, leads us to believe that negative events are less likely to happen to us than to others. And these are just two of a very long list.

Tools that help you verify your AI agents’ outputs and orchestrate how they work together is one step toward cutting through biases and managing risks, and forward-thinking AI companies are developing these. But knowing which biases exist in the first place and how they work will be how you make sense of issues that pop up and safeguard against them.

How a Psychology Background No. 1 Boosts Better AI Adoption with Human-Centered Insights

Artificial Intelligence (AI) has rapidly moved from being a futuristic concept to becoming a vital part of everyday life. From personalized recommendations on streaming platforms to decision-support systems in healthcare, AI is shaping the way we work, learn, and interact. But as powerful as AI systems are, their success doesn’t depend solely on algorithms or computing power—it depends on people. And this is where psychology enters the picture. A psychology background doesn’t just enrich the technical field of AI; it actively boosts adoption by ensuring systems are designed with empathy, trust, and human behavior in mind.

Why Psychology Matters in AI Adoption

At its core, psychology is the study of human thought, emotion, and behavior. AI adoption, on the other hand, isn’t just a technical challenge—it’s a human one. Many organizations struggle not because AI tools lack capability, but because employees, customers, or stakeholders don’t fully trust or understand them. By applying psychological principles, businesses can bridge this gap.

Psychology provides insights into how people perceive technology, what motivates them to embrace change, and how they cope with uncertainty. These insights are crucial in guiding the design, communication, and deployment of AI systems. In short: AI may provide the brainpower, but psychology provides the heart and soul that make adoption smoother.

Understanding Human Behavior in the Age of AI

One of the biggest hurdles to AI adoption is fear—fear of job loss, fear of privacy invasion, or fear of the unknown. A psychology background equips AI professionals with the ability to recognize these anxieties and address them proactively. For example:

• Cognitive psychology explains how people process information. If an AI interface overwhelms users with complex dashboards, adoption will be low. Psychologists know the importance of simplicity and clarity in design.

• Social psychology highlights the role of group dynamics and influence. If respected leaders within an organization advocate for AI adoption, employees are more likely to follow.

• Behavioral psychology emphasizes reinforcement and habit formation. Offering positive feedback when employees successfully use AI tools can build confidence and long-term engagement.

By applying these principles, AI adoption strategies can be tailored to human needs rather than forcing humans to adapt to machines.

Building Trust Through Transparency

Trust is the cornerstone of any successful technology. Without it, even the most powerful AI systems will face resistance. Psychology helps us understand how trust is built—and broken.

For example, people are more likely to trust AI if they feel it is transparent and fair. A psychologist working with AI teams might emphasize the importance of explainability: making sure users know why an AI made a recommendation. If a healthcare AI suggests a diagnosis, patients and doctors will feel more confident if the system explains the reasoning in human terms.

Moreover, psychologists understand the impact of bias and perception. If users sense that an AI system is biased—whether in hiring, lending, or criminal justice—they will reject it. A psychology-informed approach ensures diverse user perspectives are considered during development, helping to minimize bias and increase fairness.

Human-Centered Design and User Experience

AI should not feel cold or mechanical; it should feel intuitive, supportive, and human-centered. A psychology background contributes to better user experience (UX) by focusing on how people interact with systems.

Take voice assistants like Siri or Alexa. Their success lies not only in their technical ability but also in how natural, friendly, and approachable they sound. That’s psychology at work—understanding tone, language, and emotional resonance.

In workplaces, AI tools designed with empathy—anticipating stress points, avoiding information overload, and offering supportive feedback—will be adopted faster than those that ignore human factors. The psychology principle here is simple: when people feel understood, they are more likely to engage.

Encouraging Organizational Adoption

AI adoption isn’t just about individuals—it’s about culture. Organizations often resist change, even when it brings clear benefits. Psychologists specializing in organizational behavior play a critical role in helping companies transition smoothly.

• Change management strategies rooted in psychology ensure employees feel included in the process rather than sidelined by technology.

• Motivation theories highlight the importance of aligning AI with employee goals—showing workers how AI can reduce repetitive tasks and free up time for creative work.

• Conflict resolution skills help address tensions between employees and technology, ensuring smoother integration.

Ultimately, organizations that adopt AI with psychological insights see less resistance, higher morale, and better long-term outcomes.

Education, Training, and Confidence

A critical factor in AI adoption is education. People need to feel capable of using new tools. A psychology-informed approach to training focuses not just on technical instruction but on confidence-building.

For example, breaking learning into small, manageable steps aligns with cognitive load theory, which suggests humans can only process a limited amount of new information at once. Incorporating gamification, rewards, and interactive learning leverages motivational psychology to make training engaging.

By prioritizing emotional readiness and skill development, psychology ensures that AI adoption doesn’t intimidate but empowers.

The Broader Social Impact

Beyond businesses, psychology also plays a role in societal AI adoption. Public perception of AI—shaped by media, culture, and personal experience—can influence everything from government policy to consumer behavior. Psychologists help demystify AI for the general public, framing it not as a threat but as a partner.

Consider mental health applications: AI-powered chatbots provide support for anxiety or depression. Psychology ensures these tools are empathetic, ethical, and respectful of human dignity. By designing AI that aligns with psychological principles of care and empathy, adoption rates increase significantly.

The Positive Future of AI + Psychology

Bringing psychology and AI together creates a synergy where technology is not just smarter but also kinder. Instead of focusing only on efficiency, we can design AI that supports human well-being, respects individuality, and empowers creativity.

With a psychology background, AI adoption becomes less about enforcing change and more about inspiring growth. Employees feel valued, consumers feel understood, and society feels more confident about embracing the future.

Conclusion

The No. 1 boost to AI adoption doesn’t come from more data or faster processors—it comes from understanding people. A psychology background provides the insights needed to build trust, encourage adoption, and design human-centered experiences.

In a world where technology often feels impersonal, psychology ensures AI adoption remains rooted in empathy, transparency, and human connection. Together, these disciplines hold the key to a future where innovation doesn’t just advance machines—it uplifts humanity.

Conclusion

In the end, navigating the AI transformation isn’t going to be much different from the digital transformation many of us have already lived through. As we found then, coming out better, faster, and stronger actually means mastering the low-tech principles of change management: understanding how the human mind works will help us adapt to – and get the most out of – this brave new world.

Vice President of Market Strategy at Make

Darin is Vice President of Market Strategy at Make. He showcases Make’s latest innovations so clients stay at the bleeding edge of workplace automation and agility, and previously led Make’s Product team.
In addition to his expertise in product, Darin is deeply interested in how technology and operations can be deployed for social good, having worked with a wide array health and human service providers to maximize their impact.

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