The constant in the AI swirl is people, teams, and meaningful contribution.
Amid the swirl of AI headlines promising workplace transformation, a new research finding brought a useful reality check.
The AI Confidence Gap, new research from Kungfu.ai and Wakefield Research, brought us into the room with 300 U.S. C-suite executives at companies with $500M+ in annual revenue. The sobering bottom line: only 14% of the executives surveyed said most of their AI investment has delivered a return.
The line that stayed with me was this: “The throughline is human: people make AI work, or it does not work at all.”
That same day, I was on a webinar with Stephen Klein of UC Berkeley, a leading voice on the realities that can help balance the hype of AI. I am paraphrasing here, but one of the ideas I took from him was this: AI is at its best when it sparks innovation and creativity, when it acts as a thought partner, not a thought replacer. We should be comparing AI to us, not the other way around.
He also encouraged us to build what will not change. Build the constant. Build what helps organizations create value and market momentum even as tools, models, and platforms continue to shift.
Like many of us, I sometimes feel out over my skis in this emerging AI era. The models are changing. The tools are multiplying. The predictions are loud. The pressure to move fast is real.
But even if intelligence becomes a new layer of infrastructure, human infrastructure and human agency will continue to be leading-edge sources of value.
Years ago, I began a parallel track alongside my executive career in renewable energy. I wanted to understand how I could help companies in industries I cared about thrive on the human level. One track of study led to another, which eventually led to Systemic Team Coaching®. That nearly two-year study track is one I completed this year.
The constant of my career now feels very clear: people are the source of innovation, meaning, and enduring organizational strength. My work helps leaders and teams create the conditions in which that human potential can become meaningful contribution.
Why AI Adoption Still Comes Down to People and Teams
How companies, including those in clean energy and clean tech, approach enterprise AI adoption comes down to more than tools. It comes down to people and teams. It comes down to how they learn, decide, collaborate, communicate, use judgment, build trust, and create value together.
How teams function is the heart of my work now, and it is a critical leverage point for AI success. Not the only one, but one that is often overlooked.
Even when dashboards record individual employee AI usage, where is the lens on how teams function together? How do we help teams deliver on what is already in front of them while also layering in AI exploration, learning, and workflow redesign? How do we help the rest of the enterprise systemically learn, iterate, and build success?
Those questions led me to create Preparing Your Team for an AI Uplift: A Practical Guide. How to Avoid Common Pitfalls and Strengthen the Human Infrastructure Before, During, and After AI Adoption.
The guide grew out of a practical question: how should an organization prepare for an AI rollout, so it leads to clearer priorities, stronger collaboration, better decisions, shared learning, and more meaningful human contribution?
This is not a technical implementation manual. It is a practical preparation guide designed to help leaders look across the full team system, surface hidden friction, and strengthen the human infrastructure needed for AI to create real value.
The guide looks across four main areas: Culture, Process, Technology, and Governance, with Learning and Story Capture running across all four.
Five AI Adoption Pitfalls Leaders Should Watch Closely
1. Culture Pitfall: Underestimating hesitation across the organization
AI adoption can create hesitation at every level, not only among employees.
Team members may worry about job security, surveillance, replacement, quality expectations, or being left behind. Leaders may also feel pressure to champion tools they are still learning to understand and use themselves.
As AI flattens access to information and capability, leadership also has to evolve. Organizations need space for honest inquiry, shared learning, and psychologically safe experimentation across roles and levels.
This matters especially in mission-driven sectors, where people are not only doing tasks. They are often deeply connected to the purpose of the work. If AI is framed only as a speed or cost-reduction effort, leaders may miss the deeper question of how AI supports the mission, the customer, the stakeholder ecosystem, and the human contribution that remains essential.
Leadership question: What stories are people telling themselves about AI in this organization, and how are we helping shape those narratives with clarity, honesty, and trust?
2. Process Pitfall: Ignoring the capacity tax before the gain
AI can eventually give time back.
It may help employees reduce repetitive work. It may help managers synthesize information, draft communications, and support team planning. It may help executives prepare board materials, make sense of complex information, and move faster with better visibility.
That upside matters. But before those gains appear, there is often a capacity tax.
People need time to learn, test, prompt, validate, redesign workflows, build new habits, and understand what good use looks like. Managers need to translate strategy, answer questions, model usage, manage anxiety, support learning, and keep work moving. Leaders need to clarify expectations, decision rights, governance, and measures of value.
If leaders do not recognize and resource this transition load, AI adoption can feel like one more demand placed on already full teams.
Leadership question: What capacity is required before the promised productivity gain can actually show up?
3. Technology Pitfall: Creating unequal access to endorsed tools
When employees do not have transparent access to approved tools, clear guidance, and shared resources, AI adoption becomes uneven and informal.
Early adopters may move quickly while others wait. Shadow tools may fill the gap. Shame or uncertainty may increase around who is using what. The organization loses the ability to build coherent capability across teams.
Unequal access also makes it harder to understand what is actually happening. Leaders may see pockets of experimentation, but not a clear picture of where AI is helping, where it is creating risk, or where employees need support.
Leadership question: Do people have clear, equitable, and transparent access to the tools, guidance, training, and support they need to use AI responsibly and well?
4. Governance Pitfall: Leaving ownership vague
Everyone may support AI in theory, but that does not mean anyone clearly owns adoption, governance, workflow redesign, learning, or employee experience.
As AI becomes embedded in more workflows, leaders need to clarify where AI may assist, where it may recommend, where it may not decide, when human review is required, and who carries final accountability.
“Someone will catch it” is not a governance model.
This is where AI adoption becomes a leadership and operating system question, not only a technology question. If decision rights, escalation paths, ownership, review expectations, and governance refresh rhythms are unclear, the organization may have governance on paper but not in practice.
Leadership question: Who owns the decision, the risk, the review, and the learning when AI becomes part of the work?
5. Learning Pitfall: Letting team learning stay isolated
This is one of the areas I believe leaders may underestimate most.
As teams experiment with AI, valuable learning will emerge in real time. People will discover what worked, what created value, what felt confusing, what raised concern, what helped people trust the process, and what changed how the team worked.
If that learning is not captured and shared, it can remain isolated in pockets, “patchwork” as one CEO expressed to me. One team figures something out while another starts from scratch. One employee has a breakthrough, but no one else sees it. One group works through fear or resistance, but the insight never becomes usable for leaders or other teams.
This is not nice-to-have communications. It is part of the adoption system. Learning and Story Capture helps leaders surface what is actually happening, normalize experimentation, reduce fear through peer examples, share practical use cases, make invisible learning visible, strengthen trust, and turn adoption from individual effort into shared organizational capability.
Leadership question: What do we want teams to notice, document, and share as they experiment with AI, so their learning strengthens the whole organization?
The Opportunity: AI that Strengthens What Matters Most
The organizations that benefit most from AI will not only be the ones with the best tools. They will be the ones that prepare their people, teams, and operating systems to learn, adapt, and lead through change.
That does not mean slowing AI adoption down. Thoughtful preparation is not the same as delay. In many organizations, AI adoption and expansion can move faster when leaders have a clear process for aligning the initiative to purpose, culture, workflows, governance, ownership, and measurable value.
AI may provide the uplift. Human infrastructure determines whether the organization can hold it.
At the heart of this work is a simple goal: helping mission-driven organizations use AI to build creative, innovative, strong companies that honor human connection in our AI age.
Free Download: Preparing Your Team for an AI Uplift
Preparing Your Team for an AI Uplift: A Practical Guide. How to Avoid Common Pitfalls and Strengthen the Human Infrastructure Before, During, and After AI Adoption is for leaders navigating AI adoption and expansion in clean energy, climate, infrastructure, and mission-driven organizations.
Use it with your executive team, leadership team, managers, or cross-functional project team to identify where AI adoption and expansion may create value, where it may create hidden strain, and what human infrastructure needs to be strengthened to support innovation, learning, and meaningful contribution.
This guide grew out of my own executive experience, my current work as an organizational advisor, my training in Systemic Team Coaching®, and the conversations I have been having across clean energy and adjacent sectors about what AI is revealing inside teams.
