Insights

Successful AI Adoption Begins with Change Management 

Author: Joao Labre

AI promises speed and smarter ways to work, but real success does not begin with models or tools. It begins with people. Every shift in how a company operates depends on trust, clarity, and the willingness to try something new. Technology plays a role, but it cannot carry the full weight of change. 

This is the focus of my role as I work with companies to think about change management prior to adopting new technology. Together with my team, we work with our clients and their extended teams to build confidence  and develop habits that help them get real value from AI. Our principle  is simple: AI adoption is a business outcome that depends on human behavior, not a technical rollout. 

The following is the distilled view of how my team, and I use change management methodology and research from Prosci on the conditions that help people embrace better ways of working. 

Start With People, Not Technology 

Many organizations assume change begins with new technology deployment and licensing. They assume user adoption and the positive change they envisioned will follow. We see this pattern often, and it always leads to the same outcome: the technology arrives, but the behavior does not. 

The Prosci Calatlyst Report (March 2025) emphasizes that AI won’t transform your organization, but the people who use AI will. Effective change management starts with bringing people along in the transformation journey, ensuring they understand the purpose of the change, how their work will shift day to day, receiving support during the initial stages, creating a cadence to check in on progress, and seeing clear sponsorship from leadership. 

Act with common sense and empathy. There are better times to initiate projects that require change: avoid holidays, large events, or companywide stress periods. Understand culture, current workload, and earlier change cycles before deciding on timing. 

When any of these pieces are missing, teams struggle to connect the new technology to their work, and they may view it as yet another tool to learn. 

Change Management: A Program, Not Just a Project 

AI does not settle into place and then stay fixed. Models and requirements evolve. New features appear. Training content and best practices become outdated and need to be revised faster than expected. 

We encourage leaders to treat AI adoption like a long-term program. It requires updated training, sustained communication, and repeated opportunities for employees to practice and ask questions. Teams also need clarity on direction so they can see how their work fits into the larger plan. 

When companies automate manual tasks, important knowledge can disappear if it is not captured. We recommend interviewing power users and domain experts documenting how work happened before automation and tracking improvements over time. This ensures companies understand whether AI genuinely supports the team. 

Governance only works when it supports how people adopt the change. It should create clarity, guide decisions, and help teams stay aligned as the program evolves. Strong governance makes it easier to adjust plans, manage risk, and keep momentum. In programs that succeed, the organization treats adoption with oversight as a shared journey rather than a single rollout. 

Middle Managers Set the Example  

People look to their leaders to understand how serious the organization is about change. Middle managers have an especially powerful influence because they understand the specific pressure points, dependencies, workplace culture, and habits of their teams and are well acquainted with the ‘messy middle’. 

In AI projects, the messy middle is the phase between early excitement and real outcomes. It is where the pilots are done, the risks are clearer, and the work gets harder. Teams are adjusting processes, cleaning data, rethinking handoffs, training people, and sorting out what will scale. Questions about ownership, quality, security, and day-to-day use arise at the same time. 

It is messy because people are learning and unlearning while the organization is still figuring out how AI fits into the work. We emphasize that this is also the point where momentum can stall if leaders do not give clear direction, reinforce priorities, and help teams stay focused on what matters. If middle managers feel confident and supported, their teams follow. 

AI Can Strengthen Change Management Efforts 

In the transition to AI enabled work, AI can help change managers accelerate their efforts, working as a tool to communicate clearly, and create informational and training content. My team and I use AI to prepare training materials and tailor them to each user persona, analyze usage data, simulate user groups and flag possible blind spots, and create targeted, department-specific communication plans. 

Strong AI Programs Avoid False Starts 

I have seen companies adopt AI too quickly. Some purchased licenses early and moved straight to deployment. Without thoughtful training or leadership involvement, employees used only the most basic features. The potential return never materialized. 

Organizations that succeed take a different approach. They begin with a defined business outcome, involve multiple sponsors, and recognize that teams must change how they work. They also invest in readiness, program planning, and follow through. 

Two recent customer stories of AI change management projects our team had led at Beyondsoft illustrate the difference: 

• Eneva: Engineers gained back 20 hours of routine work per month by learning how to use Microsoft 365 Copilot with purpose. 

• Globo: A major media company that successfully introduced Microsoft 365 Copilot with clear sponsorship, structured communication, and a focus on real work scenarios. 

Both show that the effort, planning and enablement around technology matters as much as the technology itself. 

Culture Determines Readiness 

During the first weeks with a new client, we look for signals that show how prepared the organization is. We check whether there is a dedicated change team, whether adoption is funded, and whether leaders understand that training alone will not ensure success in the full program. 

Prosci research reinforces this. Culture influences trust, confidence, and the rate at which people adopt new habits. 

Personalize the Journey for Each Team 

No two teams adapt the same way. Engineers want hands-on examples. Sales teams want use cases that support client conversations. Global teams need cultural adjustments and local context. The tools to socialize the change with workers are different for hybrid and remote workers, versus employees who work in the office five days a week. 

We encourage companies to treat each group as its own audience and find ways to connect and roll out the change in person whenever possible. 

Address Budget and Timeline Gaps Early 

Change management is about people. It is not about the technology or the tools a project brings in. Leaders often miss this. A quick way to see it is to look at a project budget and notice how little is set aside for adoption. That tells you where the focus really is. When teams put users at the center and communicate clearly from the start, they create conditions for real transformation.

João Labre, Change Management Specialist and Director of Modern Workplace 

Leaders often want a people-first program but allocate resources for a technology first rollout. We encourage teams to explain the risk clearly. AI depends on user skill. Without training, feedback loops, and leadership involvement, the return decreases. 

Prosci findings show that projects with strong change support meet their goals more often: 
 
56% of AI challenges are human, not technical. Yet too often, the “people side” gets handed off to IT or innovation teams, when it’s leadership that must guide the transformation.

Prosci Catalyst Report, March 2025 

Make New Habits Stick 

Sustained adoption requires repeated support. Companies must keep communicating, training, and reinforcing value. Metrics help teams see progress. If a program needs to shift direction, leaders should explain it openly to maintain trust. 

When adoption slows, I recommend starting with empathy. Listen closely, understand concerns about automation or workload. Identify what each group values and show real examples of improvement in those areas. People respond when they feel heard and supported. 

Prove Value with Clear Metrics 

We remind teams to measure progress early and often. Quick wins help maintain interest. Long term work gives direction. Leaders need to plan both paths from the start for any change endeavor. It’s important to share progress, learnings, and setbacks openly, keep the big picture mind by continually reinforcing the the larger goal to keep teams informed and connected.

Short-term indicators include: 
• Attendance in training 
• Engagement with training materials 
• Usage patterns of the new technology 

Long-term indicators include: 
• Productivity gains by team 
• Time savings 
• Quality improvements 
• Team satisfaction j

From a qualitative point of view, culture also reveals progress: when teams reference new habits in daily conversations, adoption is taking hold. 

Adjust Communication for Remote and Hybrid Work 

The biggest communication mistake is assuming you already know what users need. Spend time listening. Ask questions. Give people a chance to express concerns. Explain the reasons behind changes and how individuals will benefit. 

AI adoption depends on people building new habits. Communication is the foundation. Remote work changes how adoption teams communicate. When digital channels replace physical spaces, engagement needs to be considered accordingly, for instance remote workers won’t see flyers posted on bulletin boards and may not be able to attend lunch and learn sessions. Despite this shift, where possible, in person AI training remains the most effective, partly because shared experiences build confidence and momentum. 

Use Global Delivery Centers to Scale Adoption 

Global delivery centers help organizations scale AI programs across regions. They provide templates for training, communication materials, program planning, and knowledge sharing. They also create community and consistency across markets and can act as hubs for remote workers. 

Companies benefit from a delivery center when they need speed and consistent structure across multiple teams to ensure that a strategic change management project achieves its long and short-term goals. 

Final Thoughts 

AI delivers value only when people feel confident enough to use it. When organizations invest in communication, training, and leadership involvement, employees respond with curiosity and trust. When effort is sustained programmatically, new habits take root. 

With the right approach, companies can help their teams build skills that support long term success with AI and create meaningful change across the business. Contact us to make your next major change successful in the short and longer term.