Key Points:
AI adoption succeeds when it builds on familiar tech adoption principles while addressing new trust-related concerns.
Tailored strategies matter: industry norms, organizational culture, and AI type all shape what works.
Trust challenges go beyond automation—spanning transparency, fairness, and perceived impacts on roles.
Social influence is powerful and often underestimated; peer behavior shapes adoption even when not acknowledged.
AI adoption builds on many familiar lessons from past technology rollouts—but it also raises new, trust-centered challenges that demand fresh thinking. From concerns about transparency and fairness to uncertainty about how AI will affect roles and responsibilities, trust emerges as a critical differentiator. This article explores where traditional adoption strategies still apply, where they fall short, and how to support effective, context-sensitive AI integration.
Why Defining AI Is a Crucial First Step in Driving Adoption
While many people report having heard of artificial intelligence, studies show that more than half feel unsure about what AI actually is—or how and when it’s being used. This uncertainty presents a significant, often overlooked, barrier to adoption.
Understanding AI shapes how people perceive its value, how much they trust it, and whether they believe they can successfully use it. In that sense, defining AI isn’t just an academic exercise—it’s a strategic first step in preparing an organization for successful implementation.
At its core, artificial intelligence refers to systems capable of performing tasks that typically require human intelligence. Some example definitions include:
“AI is the imitation of human behavior (ability to think, solve problems, learn, correct oneself, etc.) by computer.”
“Artificial intelligence devices can simulate human behaviors (e.g., talk, walk, express emotions) and/or intelligence (e.g., learning, analysis, independent consciousness).”
“Artificial intelligence refers to computers and robots doing things that traditionally require using human intelligence.”
Beyond a basic definition, it’s also helpful to clarify the specific type of AI being implemented in your organization, and the capabilities users will encounter directly. This could include explaining whether the system makes recommendations, automates decisions, or interacts using natural language—and doing so in plain, practical terms.
Creating this shared understanding early on helps people feel more confident, more in control, and more open to engaging with AI meaningfully—all of which are essential for adoption.
Usefulness and Ease of Use Are Still Top Drivers of AI Adoption
Early research in AI suggests that the many key drivers of AI adoption mirror those of broader technology adoption. Across studies, two core factors most strongly shape people’s willingness to use AI systems:
Perceived usefulness – Will this AI system improve my job performance? Will it make specific tasks easier or more efficient?
Ease of use – Is the system intuitive and user-friendly? What kind of training or support will I need to use it effectively?
These factors have long been central to the success of new technology adoption—and they remain highly relevant when introducing AI. If people don’t see clear value or feel confident using a system, adoption is unlikely, no matter how advanced the underlying technology.
That said, AI also introduces new layers of complexity, such as trust, transparency, and perceived fairness. While usefulness and ease of use are foundational, they're no longer sufficient on their own. Leaders must design adoption strategies that address both the familiar drivers and the unique challenges AI presents.
The Trust Factor: A Unique Challenge in AI Adoption in Organizations
While traditional technology adoption emphasizes perceived usefulness and ease of use, AI introduces an additional and complex variable: trust. Research to date frames trust in AI as multifaceted, involving both human-like and functional dimensions.
Human-Like Trust Factors
These refer to how users perceive the AI’s intentions and “character”—in ways that resemble how we judge other people:
Competence – The belief that the AI can perform tasks effectively.
Integrity – The sense that the AI follows acceptable rules or principles.
Benevolence – The belief that the AI acts in the user’s best interest.
Functional Trust Factors
These relate to how well the AI operates and how its behavior is understood:
Reliability – Consistent, dependable performance over time.
Explainability – The extent to which users can understand how the AI reaches its conclusions.
Transparency – Clarity about the AI’s capabilities, limitations, and data sources.
Current studies indicate that functional trust is especially influential in AI adoption decisions—because it’s closely tied to whether the system is seen as capable and dependable. For example, in managerial or decision-support contexts, users must trust the AI to provide accurate, consistent analysis based on available data.
That said, in domains like healthcare, education, and customer service, where empathy, interaction, and care matter, human-like trust factors may carry more weight. In these settings, users may be more influenced by how well the AI mimics human qualities or appears to act with positive intent.
To build trust, change leaders can focus on:
Demonstrating consistent performance to establish reliability
Explaining how AI decisions are made to improve explainability
Being transparent about the system’s scope and limitations
Clearly communicating how AI use is governed within the organization
These actions help establish the credibility and confidence needed for users to adopt AI as part of their daily work.
Context Matters: Tailoring Trust-Building Strategies
Building trust in AI isn’t one-size-fits-all. Studies suggest that the importance of trust—and which trust factors matter most—varies based on context, including industry, national culture, and the specific type of AI in use.
For example:
Type of trust: In domains like financial services or customer-facing applications, human-like trust factors—such as perceived integrity and benevolence—may play a larger role. In contrast, workplace tools that employees depend on may shift focus toward functional factors like reliability and result accuracy, particularly when users have little choice about whether to engage with the system.
National culture: Survey data shows that individuals in emerging economies (e.g., India, China, Brazil) report higher general trust in AI than those in developed economies such as Finland, the Netherlands, and Japan.
Organizational domain: Willingness to trust AI also differs by functional area. For instance, studies report lower comfort with AI in Human Resources than in areas like healthcare diagnosis and treatment.
These variations suggest that trust-building strategies should be tailored, not templated. Change leaders may benefit from researching AI implementations in their specific industry, cultural context, and domain. Google Scholar is a good entry point for finding academic studies, while reports from consulting or AI firms may offer more current—though potentially commercially biased—insights.
Building Institutional Trust to Support AI Adoption
In addition to individual perceptions, institutional trust plays a key role in shaping how people engage with AI. A 2023 study from the University of Queensland found that people often rely on authoritative institutions to signal the safety and reliability of new technologies.
This trust is shaped by mechanisms such as:
Government regulations
Corporate ethics guidelines
Organizational governance practices
However, the study also found that many people perceive these safeguards as insufficient—creating a gap between expectations and reality. For organizations, this presents an opportunity: demonstrating visible, credible AI governance practices can build trust and set them apart.
Rather than waiting for external regulators, change leaders can take proactive steps to:
Define internal AI usage standards
Communicate clearly about governance processes
Align AI deployment with organizational values and ethics
By reinforcing institutional trust alongside functional and human-like trust factors, organizations can strengthen the foundation for successful AI adoption.
The Complex Role of Social Influence in AI Adoption
While trust plays a central role in shaping individual attitudes toward AI, social norms and peer behavior also have a powerful—though nuanced—impact, especially in high-contact settings like healthcare, education, and customer service.
Studies suggest that social influence is most potent during the early, pre-adoption phase, when people are still forming opinions. Over time, as individuals gain experience with AI, its direct influence may decline in favor of personal judgment.
However, a broader body of research across domains—from physicians' prescribing habits to community water conservation—suggests a counterintuitive insight:
People often believe they are guided by personal values or rational arguments, but are more influenced by peer behavior than they admit.
Moreover, these shifts don’t tend to spread through one charismatic “influencer.” Instead, behavior change travels most effectively through broad, well-connected networks—where many peers, across groups, demonstrate similar behaviors.
Implications for AI Adoption Strategies:
Create visible examples of AI being used successfully by respected colleagues.
Embed AI tools into team workflows, particularly across cross-functional groups.
Leverage peer learning and normalization, rather than relying only on persuasive messaging or top-down encouragement.
By shaping the social context around AI use—not just the individual decision—you can increase adoption momentum in more durable, organic ways.
Bridging the Intention–Action Gap in AI Adoption
Even when people express willingness to adopt AI, there’s often a gap between intention and actual use. This is a common challenge in technology adoption more broadly—and one that AI initiatives must anticipate.
Most AI adoption studies focus on intentions, not behaviors. But research suggests that intentions alone don’t always lead to action. Instead, a range of "facilitating conditions"—both organizational and technological—can make the difference.
These include:
Leadership commitment and support
Hands-on training and ongoing guidance
Dedicated time, resources, and infrastructure
Some AI-specific research echoes these findings, though results have been mixed. Still, the overall pattern is clear: lowering friction and boosting support increases the odds that AI tools will move from pilot to practice.
To close the gap between intention and action:
Provide hands-on experience and reinforce with training and support.
Identify and remove adoption barriers—such as lack of time, tools, or conflicting signals from leadership.
Highlight visible investments in infrastructure to support AI use.
Clarify how success will be measured and share ongoing progress and lessons learned.
Celebrate early wins and model adoption broadly, especially among leaders.
Acknowledge concerns and skepticism openly, with transparent responses.
By combining these operational enablers with attention to trust, context, and social influence, organizations can move beyond isolated interest toward meaningful, widespread AI adoption.
Tips for Change Leaders to Overcome AI Adoption Challenges
Successful AI adoption requires more than technical implementation—it demands a change strategy that accounts for both what’s familiar about technology adoption and what’s uniquely challenging about AI. While foundational drivers like usefulness and ease of use remain relevant, change leaders must also address trust, context, and the social dynamics that shape behavior.
Below are practical actions change leaders can take to build the organizational conditions for sustained AI adoption:
Define AI clearly: Provide specific definitions and examples of the types of AI being implemented in your organization. Consider creating brief videos to illustrate the AI's functionality and how users will experience it.
Build Multi-Faceted Trust: Develop strategies that address various aspects of human-like and functional aspects of trust in AI. This might include demonstrating the AI's reliability, explaining its decision-making process, and showcasing its positive impact on work processes.
Strengthen Institutional Trust: Develop and communicate clear ethics guidelines and governance practices for AI use in your organization. Consider partnering with respected external bodies to validate your approach.
Tailor Your Approach: Research AI adoption specific to your industry, country context, and the type of AI you're implementing. Use these insights to customize your change management strategies to fit your change context.
Leverage Social Influence: Identify and support early adopters, ideally groups of them, who can serve as visible examples of successful integration of AI into work practices. Create opportunities for peer-to-peer learning and sharing of AI experiences.
Monitor and Bridge the Intention-Action Gap: Regularly assess both intended and actual AI usage. Follow-up to identify barriers to adoption and develop targeted change interventions to address them.
Communicate Transparently: Be open about the capabilities and limitations of the AI solution. Address concerns proactively and create safe spaces for employees to express their doubts or reservations.
Provide Ongoing Support: Recognize that AI adoption is a journey, not a destination. Model the use of AI, at all levels where possible, and offer continuous learning opportunities, technical support, and channels for feedback and improvement suggestions.
References
Abrahamse, W., & Steg, L. (2013). Social influence approaches to encourage resource conservation: A meta-analysis. Global environmental Change, 23(6), 1773-1785.
Bach, T. A., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2024). A systematic literature review of user trust in AI-enabled systems: An HCI perspective. International Journal of Human–Computer Interaction, 40(5), 1251-1266.
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182.
Bedué, P., & Fritzsche, A. (2022). Can we trust AI? an empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), 530-549.
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.
Centola, D. (2018, November 7). The Truth About Behavioral Change. MIT Sloan Management Review.
Chi, O. H., Gursoy, D., & Chi, C. G. (2022). Tourists’ attitudes toward the use of artificially intelligent (AI) devices in tourism service delivery: moderating role of service value seeking. Journal of Travel Research, 61(1), 170-185. (1)
Choung, H., David, P., & Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 1–13.
Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. (2)
Gillespie, N., Lockey, S., Curtis, C., Pool, J., & Akbari, A. (2023). Trust in Artificial Intelligence: A Global Study. The University of Queensland and KPMG Australia.
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925.
Kelley, S. (2022). Employee perceptions of the effective adoption of AI principles. Journal of Business Ethics, 178(4), 871-893.
5 charts that show what people around the world think about AI. World Economic Forum. (2022, 5 January) (3).
Manning, C. (2020, September). Artificial Intelligence Definitions. Stanford University Human-centered Artificial Intelligence.
Noonan, D. (2024, February 20). The 25% revolution--how big does a minority have to be to reshape society? Scientific American.
Russo, D. (2024). Navigating the complexity of generative AI adoption in software engineering. ACM Transactions on Software Engineering and Methodology.
Tomoko Yokoi, M. R. W. (2024, May 30). How organizations navigate AI ethics. I by IMD.
AI Statement: Consensus was used, in addition to Google Scholar and Google Search, to identify relevant research on this topic. Claude was used to develop initial drafts from an outline and notes provided by the author. Claude was used as an assistant to interpret statistical findings from sources. ChatGPT was used to refine structure and mark-up to support search visibility.
This article was original published on September 13, 2024 and updated on July 15, 2025.
Photo by Pavel Danilyuk