As AI accelerates across the clean energy industry, organizations are investing heavily in tools, governance, and capability building. Yet a critical dimension is often overlooked: how AI is reshaping team functionality. AI is not a strategy in itself, but an infrastructure that should serve an organization’s purpose and existing strategy. Drawing on real-world experience, industry conversations, and insights from legal leaders, this piece explores why successful AI adoption ultimately depends on the strength of the team system. The organizations that will lead are those that ensure AI strengthens, rather than diminishes, how their teams work together.
The Clean Energy Paradox: Building AI While Absorbing It
In the clean energy industry, we are in a unique position. We are helping to build the infrastructure that powers AI’s rapid growth, from data centers to grid demand to the broader expansion of computing power.
At the same time, inside our own organizations, teams are being asked to integrate these same technologies into how they work, often without a clear understanding of what that means for how they function together. There is a quiet tension here that is beginning to surface more clearly. We are advancing transformation externally while still learning how to lead it internally.
Part of what is emerging is a subtle but important misunderstanding. Organizations are racing to define their AI strategy, when in many ways AI is not the strategy. It is an enabling infrastructure that should serve the strategy that already exists.
As Charlene Li writes, “The mission of an organization — why it exists, who it serves, what value it creates — that doesn’t change because of AI. What AI changes is your ability to execute on that mission faster, at greater scale, with more precision than was ever possible before.”
That shift toward faster, more precise execution is powerful. And it places new demands on the people and teams responsible for carrying that mission forward.
Through my work with leadership teams across the industry, one truth continues to emerge: technology alone does not create transformation. Teams do.
Early Signals: What Gets Missed in Initial AI Adoption
My first real experience with AI inside an enterprise environment brought both excitement and a subtle sense that something important was being missed. There was a thoughtful foundation in place. A secure internal platform allowed people to explore AI without putting sensitive data at risk. Training was available, and new tools expanded what teams, particularly technical teams, could do. It was a strong and responsible start.
At the same time, there was little space to understand how people actually felt about working alongside AI, how consistently outputs were being validated, or how team dynamics themselves were beginning to shift. What I came to see was that AI was being introduced into teams that had not yet been supported to evolve with it.
If AI is now part of the infrastructure through which strategy is executed, then how teams relate to it, trust it, and integrate it into their ways of working becomes central, not secondary.
What Teams Are Experiencing: Complexity, Capacity, and Change
In recent months, as I have continued conversations across the WRISE community and with leaders in clean energy, these patterns have become more visible. AI is not simply changing what teams do. It is changing how they function.
One of the clearest themes is the growing complexity of AI-enabled work. While AI can support research, drafting, and analysis, it also introduces new layers of responsibility. Teams are spending meaningful time reviewing outputs, checking accuracy, and refining what is produced. The work is different, and in many cases, it is not yet lighter.
Alongside this is a growing conversation about capacity. One industry CEO captured it in a way that reflects what I am hearing more broadly. Teams are being asked to move faster, integrate new tools, and meet rising expectations, yet the underlying capacity of the people doing the work has not expanded at the same pace.
This is where the distinction between AI as strategy and AI as infrastructure becomes critical. If AI is accelerating execution, but the team system is not strengthened alongside it, the result is not transformation. It is strain.
In an industry like ours, this matters. AI can inform decisions and provide powerful inputs, but it does not replace the judgment required when conditions are uncertain, time-sensitive, or high risk. That responsibility still sits with people, and it requires clarity, support, and alignment to hold well.
Redefining High-Value Teams in the Age of AI
This moment invites us to return to a fundamental question that is often assumed rather than examined. What is a team now?
At a basic level, a team shares goals, works together toward those goals, and reflects on how to improve its performance. But high-value teams operate at a deeper level. They are aligned around a shared purpose, coordinate effectively across functions, adapt to changing conditions, maintain a future impact perspective, and create value not only internally but across the broader stakeholder system they serve.
They are not simply groups of individuals working in parallel. They are living systems of relationships, decisions, and shared accountability.
If AI is now part of the infrastructure through which work happens, then these relational and systemic qualities become even more important. They are what determine whether AI strengthens execution or fragments it.
AI Governance in Clean Energy: Necessary but Not Sufficient
There is important work happening within the legal and governance community in clean energy that is helping to create structure around adoption of AI. Leaders such as Natalie Kim[i] and Lauren Haller[ii] are advancing thinking around how organizations move beyond informal or reactive approaches. Their work reinforces the importance of clarity around what is acceptable, who holds decision-making authority, and how oversight and accountability are embedded into operations.
This is essential. When people understand what is safe and supported, they are more able to engage.
At the same time, governance alone does not ensure that AI serves the organization’s purpose or strengthens its teams. Even with strong structures in place, teams can experience misalignment in how AI is used, uneven adoption, strain in decision-making, and a gradual erosion of shared understanding.
Structure creates the container. Teams determine whether that container generates value.
The Hidden Risk: When AI Becomes a Coordination Subtractor
What is beginning to emerge is a more nuanced risk. AI is often described as a force multiplier, and in many ways it is. However, without paying attention to team dynamics, it can also introduce friction that is less visible but equally impactful.
Speed increases, but alignment does not always keep pace. Output expands, but clarity can become diluted. Tools improve, while shared ways of working begin to fragment.
In some cases, individuals turn more toward AI for answers and less toward one another, subtly weakening the relational fabric that strong teams depend on.
This is where the deeper question sits. Not whether AI is being adopted, but whether it is strengthening or diminishing the team system responsible for executing the organization’s purpose.
The Opportunity: Aligning AI with Purpose Through Teams
The opportunity in front of us is not to slow down adoption, but to bring it into alignment with purpose through the way teams operate.
If AI is an infrastructure layer, then the work is to ensure that it supports, rather than distorts, the mission it is meant to serve.
In practice, this means creating space to understand how teams are actually experiencing AI, not just how often they are using it. It means building in moments for reflection so teams can see how their ways of working are changing and adjust to the changes with intention. It means being clear about where human judgment is essential and ensuring those moments are supported and protected.
It also means establishing shared norms so that AI becomes something teams navigate together rather than individually, and grounding all of this in purpose so that the use of AI strengthens what matters most rather than diluting it.
Closing Invitation: A Different Kind of Readiness
We are still early in this “AI diffusion,” and many organizations are learning in real time. What is already clear, however, is the differentiator will not be access to AI. The differentiator will be the ability of teams to work well with it and with each other at the same time.
If you are leading a team or organization right now, there is an invitation here.
Not just to ask how AI is being adopted, but to look more closely at whether AI is truly serving your strategy and purpose, and how your team is functioning within that shift.
Where is energy expanding, and where is it being depleted?
Where is clarity increasing, and where is it becoming more fragmented?
Where are decisions becoming stronger, and where are they under strain?
And perhaps most importantly, where is AI strengthening your team’s ability to deliver on what matters most, and where might it be quietly diminishing it?
These are not secondary questions. They are the foundation of whether AI becomes a source of meaningful advantage or quiet erosion.
This is the work I am increasingly focused on with leadership teams across the clean energy sector. If this perspective resonates, I welcome the conversation.
[i] Article: How We Built an AI-Ready Organization: Inside One Company’s First Year
[ii] Article: Stop Writing AI Policies. Start Building AI Governance: A Framework for In-House Counsel
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