AI Adoption: Driving Change With a People-First Approach

Artificial intelligence (AI) is rapidly transforming organizations across almost every industry, offering untold opportunities for efficiency, productivity and innovation. However, AI adoption often fails—not because of the technology itself, but due to human barriers such as resistance, uncertainty and lack of alignment among employees.

Many organizations focus solely on implementing AI tools without preparing their workforce, leading to poor adoption and wasted investment. In fact, an independent study into personal, team and organizational AI usage conducted by Prosci, surveying 1,107 professionals across multiple industries, found that 63% of organizations cite human factors as a primary challenge in AI implementation. 

Yet, when implemented effectively, GenAI adoption is perceived to drive a number of benefits; the same study revealed that 61% of respondents believed it would make projects more successful, 65% thought it would boost their personal success, and 73% said their organizations would be more successful as a result of GenAI adoption.

By taking a people-first approach to enterprise AI adoption—focusing on communication, training and leadership support—companies can help their people by preparing, supporting and equipping them to adopt this exciting new technology.

In this article, we explore key insights from Prosci’s research on AI adoption, why a people-first approach is essential, and how a structured approach to change management can help your organization integrate AI effectively and sustainably.

AI Implementation vs. AI Adoption

While AI implementation and AI adoption are often used interchangeably, they are not the same. 

Implementation is the technical process—installing AI tools, integrating them into systems, and making them available for use. Adoption, however, is about people. It’s the process of ensuring AI becomes a natural, effective part of everyday work.

Many organizations invest heavily in AI technology but struggle to embed it into workflows. The challenge isn’t the technology itself, but the human response to it. Factors such as fear of job displacement, lack of AI training, and mistrust of AI decisions can cause employees to feel uncertain, hesitant, or even resistant to the change. When these concerns go unaddressed, AI adoption stalls.

This is why a people-first approach is essential. AI adoption succeeds when organizations focus on supporting employees, building mutual trust, and ensuring they have the knowledge and confidence to use AI effectively. When people feel equipped and empowered, they engage with AI instead of resisting it.

According to MIT Sloan, over 60% of companies with more than 10,000 employees have already adopted AI, highlighting its growing role in large-scale operations. As AI becomes increasingly integrated into business processes, organizations that prioritize a human-centric approach to managing this change will be best positioned for long-term success.

Understanding the Human Factor in AI Adoption

For AI adoption to succeed, organizations must ensure employees feel prepared, supported, and confident using AI in their daily work. However, many companies overlook the human factors—such as uncertainty, trust and alignment—that ultimately determine adoption success.

Below we explore the key human challenges organizations face when embedding AI into their operations, as revealed in Prosci’s study, Keys to Unlocking AI Adoption:

Top Challenges of Enterprise AI Adoption

Lack of AI proficiency and training

Our research on AI adoption across the enterprise shows that 38% of AI adoption challenges stem from insufficient training in AI tools, making it a key barrier to successful integration. Without the right skills, employees are hesitant or unwilling to integrate AI into their workflows. 

Providing hands-on training and AI literacy programs directly addresses this gap, increasing the likelihood of successful adoption.

Technical challenges and integration issues

When AI tools feel disconnected from daily workflows, employees become frustrated and resistant. According to our report, 16% of AI adoption challenges result from system integration issues and AI tool functionality. 

For adoption to succeed, solutions should be intuitive and enhance existing processes rather than disrupt them. You can achieve this by focusing on user-centric design and providing practical tools, templates and assessments that change leaders can use to support AI adoption.

Leadership alignment and organizational resistance

Forty-three percent of respondents attributed AI adoption failure to insufficient executive sponsorship, highlighting the need for clear communication from leadership on the AI vision and strategy. 

By aligning AI initiatives with business goals and employee needs—through clear success metrics, role-specific support and strong communication—leaders can enhance clarity, prevent resistance and drive engagement.

Concerns over AI data and information quality

AI adoption depends on reliable, high-quality outputs. Yet, more than 10% of adoption challenges stem from concerns about AI-generated data. Employees may question accuracy, consistency or potential bias, leading to hesitation in using AI-driven insights. 

Strong data governance within an organization ensures AI has access to accurate, well-managed and ethically sourced data, reducing inconsistencies and minimizing bias. By defining who has access to data, setting AI-model training standards, and continuously monitoring AI-generated outputs, organizations can build confidence in AI-driven insights and drive greater adoption.

Trust and confidence in AI decisions

Many employees struggle to trust AI recommendations, especially when they conflict with human judgment. For front-line workers and managers, a lack of visibility into AI decision-making fuels this skepticism. 

Building trust requires transparency and human oversight, particularly for AI systems that make autonomous decisions, such as agentic AI. Organizations must clearly explain how AI models work, what data they use, and the level of autonomy they have. Implementing human review processes alongside AI ensures accountability, strengthens confidence in AI-generated outcomes, and reinforces responsible AI adoption.

Security and ethical concerns over AI adoption

Our research shows that executives focus on data privacy, bias and ethical risks, while employees worry about fairness. Without clear processes for AI implementation, these concerns can slow adoption.

To address this, organizations should establish ethical guidelines, risk assessment frameworks, and transparency measures that explain how AI is trained, monitored and used. Communicating these safeguards clearly—along with the role of human oversight—helps build confidence in AI systems, ensuring employees know they’ll be used responsibly and equitably.

AI adoption challenges across different roles

AI adoption looks different at every level of an organization—and resistance can arise when role-specific challenges go unaddressed. 

According to our Best Practices in Change Management research, mid-level managers are the most resistant group, followed by front-line employees. Understanding the root causes of resistance for each group allows organizations to tailor their approach and drive adoption more effectively.

Our research highlights five primary reasons for employee resistance:

• Lack of awareness about the need for change
• Change in job role
• Fear of the unknown
• Lack of support from or trust in leaders
• Exclusion from change-related decisions

Helping people achieve the Awareness element of the Prosci ADKAR® Model can help to address many of the factors above, providing clarity into the nature of the change and ensuring employees understand the reasons behind it. As a result, organizations can reduce pushback, build confidence, and drive meaningful AI adoption.

The Role of Change Management in Enterprise AI Adoption

Defining change management

Change management is a structured approach to preparing, equipping and supporting people through change. Its goal is to drive successful adoption by preventing or mitigating resistance and ensuring individuals understand, embrace and sustain new ways of working.

The Prosci ADKAR Model—a research-based model for individual change—outlines the five key elements a person needs to acquire for effective change:

Prosci ADKAR Model

The Importance of Change Management in AI Adoption

As with any major change, addressing the human factors involved in AI adoption requires a structured approach to support employees through the transition. Organizations that embed change capabilities into their culture are more likely to achieve project objectives, stay on schedule, and remain on or under budget, increasing the overall success of AI initiatives.

Correlation Between Effective Change Management and Success

By focusing on behavioral and cultural shifts, rather than just technical rollout, companies can overcome issues such as skills gaps and employee uncertainty, and create an environment where AI is successfully integrated and sustained.

To foster an environment open to change, organizations can:

• Provide clear communication and leadership support. Our study, Keys to Unlocking AI Adoption, shows that companies with strong executive sponsorship see greater AI adoption success.
  
  To drive engagement, leaders must clearly communicate how AI will impact employees’ roles and align with business objectives. Prosci Best Practices in Change Management research consistently shows that people and groups have preferences about who they want to hear from during change.
  
  

  When it comes to messages about how change impacts the business or the organization, people want to hear from the person in charge—typically a senior manager or executive.

Preferred Senders of Messages

• Offer training, reinforcement and ongoing learning. User proficiency in AI is a top adoption barrier. Investing in AI education and reinforcing new behaviors over time enables employees to integrate AI into their workflows.
• Balance autonomy with governance. The study illustrated that transparency in AI decision-making fosters trust and long-term engagement. But AI adoption requires both flexibility and oversight. Employees should have the freedom to explore AI tools at their own pace, while governance provides structure through clear policies and ethical guidelines. 

AI adoption succeeds when employees are engaged in the transition. By embedding change management into AI initiatives, organizations create a culture where AI is embraced, seamlessly integrated, and used to drive meaningful business outcomes.

Overcoming AI Adoption Challenges With Effective Change Management

Successful AI adoption requires a clear strategy that supports people through the transition to AI technologies. With its five elements, our ADKAR Model provides a comprehensive roadmap for enabling AI adoption. 

Here’s how you can use our ADKAR Model to facilitate successful AI adoption initiatives through a structured approach to change management:

1. Building Awareness and alignment for AI adoption

The Awareness element is about understanding the need for change. Without clear awareness of why AI is being introduced and how it fits into the organization’s strategy, employees may perceive it as a disruptive force rather than a strategic enabler. 

Leadership plays a critical role in shaping this awareness—and executives tend to be more positive about this technology. By communicating a strong AI vision, setting clear expectations, and actively modeling AI use, leaders can help create alignment across all levels of the organization.

2. Creating Desire for AI adoption

The Desire element focuses on fostering a willingness among individuals to engage in the change process. As well as being aware of its implications, employees need to have a genuine desire to engage with AI. Organizations can foster this desire by demonstrating AI’s benefits, involving employees in AI initiatives, and providing opportunities for hands-on learning. 

When employees see AI as a tool that supports their success and professional growth, they are more likely to embrace it willingly rather than view it as an imposed change.

3. Equipping employees with Knowledge to use AI

The Knowledge element provides individuals with the information and training they need to understand how to implement the change effectively. As we’ve established, a lack of AI literacy among users is a significant barrier to adoption. Structured learning pathways can help employees gain the necessary skills to adopt AI into their workflows. 

The Prosci 3-Phase Process—Phase 1 – Prepare Approach, Phase 2 – Manage Change, and Phase 3 – Sustain Outcomes—provides a clear framework for identifying training needs, delivering targeted learning, and reinforcing skill development.

Prosci 3-Phase Process

4. Building Ability to integrate AI into workflows

The Ability element helps individuals acquire the necessary skills and resources to carry out the change. Beyond AI knowledge, employees need hands-on experience and support to confidently apply AI in their daily work. Developing ability requires practical application, reinforcement, and real-world problem-solving rather than just theoretical understanding.

By fostering internal AI expertise through ongoing coaching, peer collaboration, and structured AI integration opportunities, employees can continue to develop their abilities and apply AI effectively within their given roles.

5. Using Reinforcement in AI adoption for long-term success

Finally, the Reinforcement element involves creating mechanisms to support and reward the new behaviors and practices to ensure that the change is maintained. Sustaining the success of AI adoption efforts requires ongoing reinforcement and leadership support. Prosci research on driving enterprise adoption shows that encouraging AI experimentation improves adoption outcomes, while organizations that create safe spaces for employees to test AI tools see stronger long-term success. 

A structured change management approach embeds AI into organizational culture by reinforcing behaviors, providing continuous learning opportunities, and ensuring AI becomes an integrated part of daily workflows.

Best Practices for a People-Centric Approach to AI Adoption

Let’s explore how organizations can apply the Prosci 3-Phase Process to embed change management principles into AI adoption. The following strategies are based on insights from the our latest Best Practices in Change Management benchmarking study, comprising 25 years of research from 10,800+ professionals globally. 

These best practice approaches provide actionable steps to establish governance, develop AI skills, build trust, and scale adoption across the business.

Establish clear AI governance and leadership alignment

A centrally managed approach prevents inconsistent AI adoption across the organization. To achieve this, organizations can establish an AI change management team responsible for developing policies, ensuring business alignment, and overseeing implementation. This team should include leaders from IT, HR and key business units to ensure AI is integrated strategically and sustainably.

The Prosci 3-Phase Process helps organizations embed governance by defining AI policies early, aligning leadership on AI’s role, and ensuring ongoing transparency and compliance. This structured approach reduces uncertainty, builds trust, and creates a solid foundation for enterprise-wide AI adoption.

Provide targeted AI training and skills development

Because 22% of employees struggle with AI’s learning curve, organizations must provide structured, hands-on training tailored to specific roles. Effective programs could include interactive AI workshops, scenario-based training, and continuous learning sessions to ensure employees can apply AI effectively in their daily work.

The Prosci 3-Phase Process enables organizations to assess skill gaps, deliver tailored AI training, and reinforce learning through real-world application. By embedding AI training into continuous learning and development, organizations can develop AI proficiency across the workforce.

Foster a people-first AI strategy and organizational culture

AI adoption is most successful in a culture that promotes experimentation, empowerment and ongoing learning. Prosci research shows that organizations that actively encourage AI experimentation achieve higher adoption success rates because employees feel more confident integrating AI into their work.

To build an AI-ready culture, organizations should create opportunities for hands-on experimentation, allowing teams to test AI tools in real-world scenarios and refine their use. By applying the Prosci change management methodology, companies can embed AI adoption into daily workflows, ensuring it becomes an integral part of the organization’s culture.

Scale AI adoption across individuals, teams and the organization

For AI adoption to succeed, it must extend beyond leadership and IT; employees at all levels need the skills and confidence to engage with AI. Our research shows that organizations with widespread AI expertise experience smoother adoption, while executives have significantly more autonomy in AI tool selection than front-line employees (-0.80 vs. +0.86), creating misalignment.

The Prosci Change Triangle (PCT) Model, integral to the Prosci Methodology, contributes to success by highlighting the necessary balance across strong leadership, structured processes, employee engagement and support, and other key areas. When these areas align, AI adoption scales effectively, fostering collaboration, trust and long-term integration.

Prosci Change Triangle (PCT) Model

Address trust, security and ethical concerns proactively

AI adoption slows when employees question AI-generated recommendations, data reliability or ethical implications—and over 10% of organizations cite security and ethical concerns as key adoption barriers.

The Prosci 3-Phase Process helps organizations manage AI concerns by establishing clear governance in Phase 1 – Prepare Approach, reinforcing ethical AI usage in Phase 2, and embedding long-term trust strategies in Phase 3. This structured approach ensures AI is adopted responsibly and transparently.

Embracing AI Adoption With a People-First Approach

Technology alone doesn’t drive success—people do. AI adoption isn’t just about introducing new tools and platforms; it’s about creating the right conditions for employees to embrace and sustain change. That’s why, without a people-first approach, even the most advanced AI solutions struggle to deliver real impact.

The Prosci Methodology bridges the gap between AI implementation and successful adoption, providing a structured framework for guiding employees through change. Organizations that embed change management into their AI strategy see stronger engagement, faster adoption, and greater long-term success.

AI’s true potential is realized not when it is simply implemented, but when it is fully adopted and embraced by people. By prioritizing structured change management, businesses can unlock AI’s full value, drive innovation, and create a workforce that’s ready for the future.