Crowdsourced insights from the WRISE Bay Area Women’s Leadership Workshop

When our WRISE SF Bay Area community gathered for the third workshop in our interactive series: Thriving in the Age of AI, we started with this question: How do teams actually work with their new “teammate,” AI?

In conversations with workshop participants and leaders who could not attend, similar concerns were shared. AI is here. It is powerful but it is often unevenly applied, and organizations are still defining what thoughtful integration looks like at the team level.

This session was not about tools. It was about teams, and how artificial intelligence is impacting how they function.

Starting with a Team Definition Baseline

We began with a simple baseline.

What actually makes a team a team?

• Do we share clear objectives?
• Do we truly work together toward them?
• Do we meet regularly to reflect and improve?

Only then did we ask the deeper question: How does AI change team functionality?

Not productivity in isolation, or headlines and hype. But how people collaborate, trust, learn, and create value together. How teams are supported and the ecosystem in which they exist determine the value they are able to create for the organization and its stakeholders. It is critical to look at how AI is shaping this core organizational structure.

The Five Human-Centered Challenge Areas

Drawing from research and lived experience in the room, we explored five dimensions of AI integration:

1. Purpose and Human Connection
2. Trust and Quality
3. Capability and Equity
4. Governance and Boundaries
5. Workflow and Value

The Post Its filled quickly at the small table discussion.  What are the challenges teams face with AI?

The Top Three AI Challenges Impacting Team Functionality

After table discussions and report-backs, three themes rose to the top:

1. Time to Validate AI Output

Many teams shared they are spending more time checking AI-generated work than they would have spent doing it themselves. This includes factchecking, rewriting, correcting “fluff” and dealing with what one table called the “dyslexic analyst” effect.

There is also the subtle creativity drain. Prompt engineering can anchor thinking in the center. It can reduce dissenting opinions. It can create echo chambers.

This is not a technical issue alone. It is a trust issue. Who decides when AI output is good enough? What still requires human nuance?

2. Lack of Training and Guardrails

Across the table groups, the same challenge appeared. AI tools are being pushed forward faster than training. This results in early adopters racing ahead and late adopters feeling behind or hesitant, with some feeling shame about using “shadow” tools.

Many organizations do not yet have a clear enterprise strategy for AI use.

Without shared literacy and shared guidelines, teams fragment. Knowledge becomes uneven. Equity concerns surface. This is not only about access. It becomes about coherence.

3. Lack of Enterprise Strategy for AI Use

(and the ripple effects on connection and cohesion).

The third challenge the room named directly was the absence of a coherent enterprise strategy.

With a lack of enterprise strategy AI outputs have no clear North Star. Companies are not identifying use cases, shared metrics for how success is defined, nor have they articulated boundaries.

When AI adoption is decentralized without alignment, teams begin to fragment. People stop consulting one another. Workflows shift unevenly. Shadow tools emerge. Governance gaps widen.

Over time, this lack of strategic alignment can erode something deeper, human connection. As one AI company president said recently, “The things that make us human will become much more important instead of much less important.”

When teams are not aligned around shared goals, shared tools, and shared expectations, collaboration weakens. Trust dissipates and interpersonal capability is not intentionally cultivated.

While the headline challenge is strategic, the downstream impact is cultural. It becomes not just about machine created intelligence, but how we work together, and grow our human genius.

Moving from Symptoms to Systemic Solutions

We did not stop at naming challenges. We applied two lenses to co-create solutions.

Lens One: Building Future-Fit, Purpose-Driven Teams in the Age of AI

Using the Systemic Team Coaching® model, discussed the characteristics of a high-value creating, future-fit team. It is one that:

• Operates from a clear shared purpose
• Systematically engages with the teams in its organization
• Engages stakeholders beyond itself
• Considers ecological impact
• Builds future value
• Integrates continuous learning

If AI is reshaping how we work, then the response cannot be piecemeal. It must be anchored in purpose and stakeholder value.

Lens Two: Moving from Transactional Fixes to Transformational Solutions

We also asked whether our proposed solutions were transactional or transformational.

Transactional solutions are symptom-level: A new tool. A new policy. One person is working harder. Transformational solutions require shifts in communication patterns, accountability, assumptions, and alignment with purpose.

This distinction changed the conversation.

Solutions the Room Proposed

Across tables, these crowdsourced solutions took shape:

Strategic Clarity

• Executive leadership defining clear goals and milestones
• A North Star for AI use
• Defined use cases before broad deployment
• KPIs to measure real productivity gains

Training and Shared Literacy

• Company-wide training
• Transparent access to endorsed tools
• Ongoing education and stress-testing applications
• Building and maintaining a shared bibliography

Governance and Alignment

• Clear company policies and guidelines
• Board-level or expert AI oversight
• Cross-functional AI teams
• Surveying baseline usage to understand reality before setting policy

Cultural Care

• Addressing inequality of access
• Reducing shame around use
• Protecting diversity of thinking
• Continuing to invest in interpersonal capability

And importantly, one table raised a larger truth: AI consumes significant energy. In clean energy, this cannot be ignored. It is both driving critical business in a challenging time, and it gives us the opportunity to exercise operational and ecological responsibility.

What This Means for Our Renewable Energy Industry

Renewable energy and sustainability organizations are uniquely positioned. We understand systems, the long-term impact and that technology alone does not create transformation. Teams do.

If we integrate AI without purpose, we risk fragmentation. When we integrate it with purpose, shared literacy, and stakeholder awareness, we can increase value without losing humanity.

That is the work.

A Personal Reflection

What moved me most was not the sophistication of the insights. It was the honesty, the collaboration and the joy.

Facilitating these workshops has bolstered my already high confidence in our community.  Tackling this very timely topic showed our ability to name challenges and fears, gaps we see in enterprise adoption and to design solutions together. All while supporting each other.

When a room of women across our ecosystem gathers to think systemically, something powerful happens. Clarity strengthens. Connection deepens. And the path forward feels less isolating. This is inspiring!

Beauty is in us. Purpose helps it bloom.℠ Even in the age of AI.

If you are leading a team right now and feeling the tension between innovation and cohesion, this is exactly the moment to step back and work at the level of team purpose, alignment, and stakeholder impact.

AI integration is not just a technical rollout. It is a team evolution. And it deserves to be led that way.