Getting Your GenAI Adoption Right

Whenever organizations introduce innovation, it inevitably brings challenges. Certain individuals are quick to engage, while others seek to avoid change altogether. The integration of GenAI into IT delivery processes follows the same pattern. As such, establishing a solid adoption strategy is of vital importance. This section examines the approach required to achieve successful adoption.

 

Whether you’re introducing your own wrapper GPT, rolling out Microsoft Copilot 365, integrating any new generative AI tool or even blocking online GenAI tools due to security risks, one thing is clear: the success of the technology depends as much on how it’s introduced as what it can do.

 

People don’t adopt tools just because they’re available, they adopt them when they see value. And if employees are already using GenAI tools like ChatGPT in their personal workflows, their expectations are already set. When they’re given something new like Copilot 365, which operates within a different context and set of constraints they may immediately notice what’s missing, not what’s possible. They may focus on potential drawbacks rather than possible benefits and might not be aware of the reasons for introducing this tool instead of the one currently in use.

 

Without a clear story around why a new tool is being introduced and how it will help them, adoption can stall. Worse, users may seek out unofficial alternatives that feel more familiar (like mentioned before), creating security risks and data visibility gaps.

 

A Plan for Engagement

To avoid that, you need a plan. Not just for rollout, but for engagement:

1. Explain the “why” clearly
   Employees or users need to understand the reason behind the limitations. If the tool doesn’t have image analysis or file uploads, explain how that decision protects company data and how it still can amplify their workflow.
   To foster an environment where employees are fully engaged, it is crucial to clearly articulate the organization’s reasons for adopting the new technology and to define the expected outcomes and contributions.
2. Acknowledge the trade-offs
   Don’t oversell internal tools as better than public or commercial alternatives. Be transparent: it’s not about competing on flashy features, but delivering secure, integrated functionality that fits your organization’s needs.
   Whether it’s a Wrapper GPT or Microsoft Copilot 365, focus on how the tool adds value in your specific context through control, customization, or compliance.
3. Involve users early
   Engage power users and champions in testing and feedback. They can help refine the (user) experience and explain it to others in their teams. Always keep the user in mind, no matter how secure or compliant a tool is, if it’s hard to access or unintuitive, people will abandon it.
4. Make support visible and ongoing
   Don’t just launch and leave. Offer training, create feedback loops, and show progress. When people see their input leads to improvements, they’re more likely to stick with it.
5. Position it as a living product
   AI tools, whether internal or commercial, don’t have to be seen as “set and forget.” They should evolve. Share roadmaps, highlight updates, and celebrate wins. Show how feedback shapes improvements. Adoption improves when people feel part of the journey.
6. Align with real use cases
   Adoption improves dramatically when tools are introduced in the context of real problems users face, or applied to use cases that really make a positive impact on productivity or workflows. Instead of generic training, show employees how the tool(s) can help with their actual tasks. If the tool feels directly useful, adoption becomes organic.
7. Address fear and resistance
   Some employees or users may see generative AI as a threat to their job or skills. Others may be skeptical due to past tech rollouts that didn’t deliver. Create space for these concerns. Acknowledge them in town halls, internal communications, or Q&As. Adoption improves when resistance is met with empathy, not pressure.

 

Ultimately, the best technology in the world can fall flat without adoption. But when people feel the tool is built for them, solves their problems, and evolves with them, they’re far more likely to embrace it and help others do the same.

 

Starting with a Purpose: Adoption Strategy

Before diving into generative AI, start with a simple but important question: What do you want to achieve, and how does this support your organization’s goals? Once the purpose is clear, you can define an adoption strategy that fits. But remember, there is no one-size-fits-all approach. The right strategy depends on your specific context.
In some organizations, it makes sense to start small. You might focus on a few well-chosen use cases and work with a group of early adopters or “champions.” These champions gain experience and build confidence, eventually helping to expand the use of Generative AI across teams and departments.
In other cases, a broader rollout from the start may be the right move. For example, you could decide to make a tool like Microsoft Copilot available to everyone. To support this, you might offer targeted training to specific groups, turning them into champions who can guide and support others.
However, just making the tool available isn’t enough. It’s not unusual to see only 10% (or less) of people using it initially. With the right interventions such as training sessions, workshops, 1-on-1 support, or simply giving people time to experiment—adoption can grow. These efforts might raise usage to 30%, but if your goal is 70%, you’ll need to keep adjusting and asking: What’s still missing? What else do we need to do to move forward? Making progress visible is key here. Noticeable progress motivates people to keep being engaged and to also engage co-workers/peers.

 

Communication is a key part of any adoption strategy. A good
practice is to think in two timelines:

1. Adoption activities: What training and support are needed, when, and for whom?
2. Communication moments: What specific communication moments are needed to support the organization to achieve their desired goals? (e.g. what message are we sharing, via which platform and by whom?)

 

Successful adoption isn’t just about tools. It’s about people, purpose, and progress. And that requires a thoughtful, ongoing approach.

Communication is Key

Many leaders step in the pitfall of assuming that introducing new technology is as simple as sending an email to the organization. But in reality, that rarely leads to meaningful adoption. Especially with something as transformative as generative AI. A top-down message often stays too abstract. People struggle to connect it to their daily work and ask themselves: What’s in it for me? That question needs a clear answer.
For people to truly engage, they need more than just information they need involvement. One-way communication (like an announcement or even a speech from a metaphorical soapbox) has limited impact if it doesn’t feel relevant or personal. Real adoption happens when people feel they are part of the process, not just on the receiving end of it.
Create space for feedback, dialogue, and co-creation. Invite people to share their ideas, concerns, and even their doubts. When employees see they can influence the journey, they’re much more likely to step in and help shape it.
Consistency is also essential. A one-time message isn’t enough. Keep showing up with clarity and honesty, especially when it comes to concerns like job security. Some people may fear that AI will replace them. That’s why communication must also address these emotions. Show how the technology can reduce repetitive tasks and free up time for more valuable and enjoyable work. The message should feel personal, and it should fit the needs and mindset of each employee.
Tailored communication helps reduce uncertainty, build trust, and increase willingness to engage. Because at the end of the day, adoption isn’t just about rolling out tools—it’s about guiding people through change, with empathy and intention.

Adoption Curve

When introducing generative AI into an organization, it helps to understand that not everyone embraces change at the same pace. The adoption curve offers a helpful way to think about this, with five distinct groups:
Innovators and Early Adopters, roughly the first 16%, are usually the ones who are naturally curious and eager to explore new technology. They don’t need much convincing. These are your champions: enthusiastic, forward-thinking people who can help bring others on board. Use their energy wisely. Highlight their successes, involve them in pilots, and position them as visible advocates to influence the wider organization.

But if you want to achieve real, lasting change, your main focus should be on the Early Majority and Late Majority, together about 68% of your organization. Research and case studies consistently show that once you’ve engaged about 25% of this broader group in the right way, momentum builds. When people see peers adopting the technology and contributing positively, they’re much more likely to join in. Change then starts to scale naturally.
Laggards, the last 16%, are typically resistant to change sometimes actively so. You can invest a lot of energy here, but it often leads to limited results. At a certain point, the question becomes: do they adapt, or do they opt out? But in general, it’s often more effective to focus your efforts on those who are ready, or nearly ready, to engage.

 

The key takeaway? Don’t try to convince everyone at once. Start with those who are willing, build momentum, and let adoption grow through involvement, trust, and peer influence. Focus your energy where it makes the biggest impact.

Formal and Informal Leaders

 

We’re all social beings. Whether we realize it or not, we tend to mirror the behavior of people we identify with often more so than those above us in the hierarchy. In the workplace, this means that employees are more likely to follow the lead of trusted colleagues than that of managers or executives. That’s why informal leaders play such a crucial role in successful adoption.
Every team has people who naturally step into the role of role model those who others turn to for advice, validation, or inspiration. These informal leaders may not have official titles, but they have influence. And that influence can be more powerful than any formal training session or tutorial. If one of these respected colleagues explains or demonstrates how they use a new tool like Microsoft Copilot, even just once, it can have more impact than ten formal communications or workshops.

 

That’s why it’s essential to identify and involve both formal leaders (like managers) and informal leaders (the respected voices on the floor) early in the adoption process. Equip them, involve them, and give them the space to lead by example. They may propose valuable ideas to encourage adoption, and it can be as simple as setting Microsoft Copilot as the default homepage in users’ browsers to help maintain awareness of the tool. When people see someone they relate to embracing the change, they’re far more likely to follow not because they’re told to, but because they want to.

 

Visual Management

Once your goals are clear and your adoption strategy is in motion, the next critical question is: Are we seeing the results we expected? Whether it’s increased adoption, improved productivity, or another target, you need to track progress and adjust course when needed.
Visual management helps make this possible. It turns strategy into something visible, actionable, and shared. One powerful method for this is Obeya, a Japanese term meaning “large room.” It refers to a physical or digital space where teams come together to align on direction, track performance, and drive progress. You don’t need anything fancy even a whiteboard with sticky notes can serve as a strong foundation.
An effective Obeya setup typically includes three key areas:

1. Strategic Direction
   What are we aiming for?
   Example: “Achieve 70% adoption of Microsoft Copilot within our organization.”
2. Performance Indicators
   How will we measure success?
   Example: “Daily Copilot usage.” “Skill development: Progress in GenAI competencies within teams, measured through assessments or certifications.” or “Faster time to market.”
   The goal is to track real, measurable behaviors that show whether the adoption is taking hold along with clear indicators of how these behaviors will be measured and validated.
3. Plan to Value
   What actions will get us there?
   This includes initiatives like:
   ▸Running training sessions
   ▸Setting up and maintaining a Copilot platform
   ▸Organizing knowledge-sharing sessions

 

These actions should regularly connect back to performance data. If adoption isn’t increasing as planned, it’s a signal to adjust not just keep going as-is.
Whether you use a digital dashboard or a whiteboard with Post-its, the power lies in making your strategy visible and using it to guide decisions together.

Aligning on People, Process and Technology

To truly succeed with adoption, you need to consider three
essential pillars: People, Process, and Technology.

• People
  This is where emotion and uncertainty live. Some team members may worry, “Will AI take my job?” These concerns are real and valid and addressing them takes time, empathy, and open conversation. Moreover, achieving your goals with AI often requires more than just new tools, it calls for real behavioral change. People will need to start doing different things to realize different results. That shift doesn’t happen overnight, and guiding people through that change is just as critical as the technology itself.
• Process
  New technology may change how work is done. That means rethinking workflows and guiding teams in how to work differently not just faster, but smarter and more effectively.
• Technology
  The tools you choose must support what people actually need. If you’re giving someone Microsoft Copilot, it should help them do their job better, not just be another tool they don’t use.

Visualizing your goals and tracking progress across all dimensions of change turns strategy into results.

The Generative AI Adoption Journey

Adopting generative AI isn’t a one-time switch it’s a journey. Organizations move through different stages, each with its own challenges and opportunities. Understanding where you are now helps you decide what’s needed to take the next step forward.
This stage model doesn’t measure how “advanced” you are, but rather how deeply generative AI is embedded into your way of working—from early experiments to trusted daily use.

 

 This way, generative AI becomes a tool that produces at scale and supports strategic alignment when guided correctly. In doing so, they ensure that generative acceleration does not come at the cost of strategic misalignment.

 

Moving Through the Stages: What Helps?

Each step builds on the one before it. Here’s what helps move from one stage to the next:

 

• From Stage 1 to 2: Find high-value use cases. Start small pilots with clear goals and feedback loops.
• From Stage 2 to 3: Develop a formal strategy include communication, training, IT involvement, and success metrics.
• From Stage 3 to 4: Appoint champions, build internal support materials, create visibility with success stories.
• From Stage 4 to 5: Use analytics to track impact, embed the tools in culture and processes, and evolve based on real usage.

 

This model focuses on progressing in a way that suits your organization, not just aiming for the top.