Managing Teams Through AI Transformation: Before, During, and After Implementation

When companies decide to integrate AI into their operations, the conversation often starts with tools and algorithms. Yet the real challenge usually lies elsewhere: choosing where AI can truly make a difference and ensuring people are ready to work with it. The MIT Sloan Management Review 2025 report, State of AI in Business, highlights that one of the top barriers to scaling AI successfully is what they call the “learning gap” (the lack of training, alignment, and redesigned workflows).

In our experience at Making Sense, the key to sustainable impact is connecting both sides of the equation: strategy and adoption. Defining where AI adds the most value, preparing teams to work differently, and managing the change that follows are what transform experimentation into real outcomes. Success begins long before deployment; it starts with listening, communicating, and building confidence.

Next, we explore three stages of AI transformation: before, during, and after implementation. Within each stage, six essential steps guide organizations toward alignment and long-term success.

Before implementation: listen, involve, and communicate

Every transformation begins with understanding: of data, systems, and, most importantly, people. Before designing or implementing any AI solution, leaders should take the time to understand how it will influence employees’ daily work, tools, and processes.

1. Give employees a voice in the process

When teams are included early, they bring valuable insights and help uncover where the real opportunities for improvement lie. Employees often understand better than anyone what slows down their work and what could make it more effective. Including those perspectives creates solutions that feel intuitive and relevant from day one. Early involvement reduces resistance later and often uncovers process inefficiencies leaders didn’t see.

From our work with clients, we’ve learned that the first step in any AI initiative is understanding how people actually work. Through interviews and journey mapping, we uncover real workflows, pain points, and opportunities to simplify daily tasks. But the true Discovery process goes beyond collecting feedback. It’s about identifying what really makes sense to build, not just improving what already exists. This mindset allows us to challenge assumptions, explore alternatives, and design solutions that transform the way teams operate instead of simply optimizing the status quo.

2. Communicate with transparency

Open communication is equally essential. Many companies hesitate to announce AI initiatives too early, fearing speculation or confusion. But transparency fosters trust. Sharing what’s being developed, why it matters, and how it will benefit both the business and its people is crucial.

If an external partner like Making Sense is leading the project, it’s important to communicate that openly. People should know who’s involved and why. Being transparent about the collaboration builds trust and avoids confusion about who is driving the change. Clear communication helps teams see AI as something they’re part of, not something happening to them.

During implementation: build engagement and skills

Once development begins, engagement should remain at the center. The implementation stage is when collaboration, feedback, and learning truly make the difference.

3. Empower teams through learning

AI adoption requires new ways of thinking. This is the moment to provide targeted training that helps employees understand how AI fits into their workflows and decision-making processes. Training should not only explain how to use a tool but also what business goals it supports.

As we explored in this article about building a culture of continuous learning in tech organizations, fostering a learning mindset turns uncertainty into curiosity. It helps employees see technology as a source of growth and opportunity.

4. Align leadership and execution

Middle managers and team leaders play a pivotal role in this stage. They are the bridge between strategy and execution, translating vision into daily routines. Their involvement is essential for keeping communication consistent and helping teams understand how new tools will change the way they work.

This is also the right time to start making the change visible. Sharing early versions of AI tools or prototypes helps employees see progress and understand how these solutions will support their goals. Designing the trainings together, with feedback from the people who will actually use the tools, ensures that learning is practical, relevant, and aligned with everyday tasks.

This shared design process turns adoption into co-creation, making AI feel like part of the culture, not a top-down change.

After implementation: manage change with intention

The moment a new AI solution goes live marks the start of a new way of working. Sustained success depends on continuous change management that keeps people aligned as systems evolve.

5. Normalize the new workflows

Even when a tool performs perfectly, teams may default to old habits unless they feel supported. To build momentum, organizations should reinforce the benefits of the new system, celebrate early wins, and gather feedback to fine-tune adoption.

In our experience, projects that include regular feedback sessions and visible leadership involvement maintain higher engagement and faster adoption rates. These actions help the new processes become part of the organization’s culture, rather than an isolated initiative.

6. Establish governance and ownership

AI projects thrive when ownership is clear. A defined governance model helps teams understand who is responsible for maintaining data quality, monitoring performance, and scaling successful pilots. Establishing this structure early ensures that new systems don’t lose direction once implementation ends.

Clear governance also creates space for learning and iteration. When teams know who oversees outcomes and how progress will be measured, it becomes easier to track impact, share lessons, and adjust strategies as the organization evolves.

The cost of neglecting people in AI transformation

When organizations overlook the human side of AI, the impact becomes evident in how slowly adoption scales. The 2025 MIT Sloan Management Review report identifies the “learning gap” (limited training, misaligned structures, and lack of workflow redesign) as a primary reason why many AI pilots never reach enterprise scale.

Coverage from Fortune notes that as many as 95% of corporate generative AI pilots fail to deliver measurable impact, often due to poor change management and insufficient employee involvement. Companies that invest early in communication, learning, and structured adoption strategies are the ones turning AI from a proof of concept into a sustainable business capability.

The risks of neglecting people are:

• Low adoption rates. Employees disengage from tools that don’t reflect their needs.
• Loss of talent. Teams feel disconnected when they’re excluded from decision-making.
• Slower ROI. Projects stall when people lack clarity or confidence.
• Strategic disconnect. Technology evolves faster than the organization’s ability to adapt.

By contrast, companies that engage their people early see AI become a catalyst for innovation. It strengthens trust, fosters collaboration, and amplifies human potential.

Building a future-ready workforce

Preparing people for AI is an ongoing journey. The skills and mindsets needed to work effectively with these technologies evolve constantly, and so must the organizations that use them.

Investing in structured learning programs gives companies a measurable advantage. At Making Sense, this philosophy inspired the creation of our AI Enablement & Learning Program: a framework designed to help leaders and teams combine strategy, capability, and culture, turning AI adoption into a continuous learning process. The program blends strategy, capability, and cultural transformation, guiding leaders and teams to align vision with execution while tracking tangible outcomes.

This approach helps organizations strengthen collaboration, speed up decision-making, and build a shared understanding of how AI creates value. It also helps them recognize their current level of AI maturity, so they can identify the most effective next steps to accelerate adoption.

AI amplifies what people can achieve. When guided with empathy and structure, it becomes an opportunity to redefine how teams work, learn, and grow together.

Key takeaways for AI transformation team alignment

The path to effective AI adoption is built on people, communication, and structure. These key takeaways summarize the six steps that help organizations turn change into measurable, lasting impact.

1. Give employees a voice. Understanding their needs early helps ensure that AI solutions address real problems and make daily work simpler.
2. Communicate with transparency. Clear, ongoing communication builds trust and alignment across teams.
3. Empower teams through learning. Continuous training keeps people confident and capable as they adapt to new tools and workflows.
4. Align leadership and execution. Managers should model curiosity, consistency, and openness to change.
5. Normalize the new workflows. Reinforce positive behaviors, celebrate early wins, and gather feedback to sustain adoption.
6. Build governance for sustainability. Define ownership, metrics, and feedback loops from the start to ensure long-term impact.

Conclusion

AI transformation represents a cultural shift that blends empathy, collaboration, and clarity. Companies that understand this don’t just implement AI, they embed intelligence into the way their teams think and operate.

At Making Sense, we help mid-market and private equity-backed organizations bridge the gap between vision and execution. Through our discovery process, AI Enablement & Learning Program, and hands-on approach to change management, we empower teams to move forward with confidence, alignment, and measurable results.