Find the podcast here.

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A thoughtful group of executives gathered to wrestle with a question many organizations are now facing: Is AI destroying sustainability or reinventing it?

What followed was not a technical debate. It was a candid conversation about tradeoffs, uncertainty, and the human dynamics that will ultimately determine what happens next.

Our Guest Speakers Set the Stage

Ralph Loura, former CIO of HP, Clorox, and Lumentum and co-founder of SustainableIT.org, and Kai Martin, Chief Sustainability Officer at The Pasha Group, grounded the conversation in both scale and reality. They helped the group move past the headline question and into the harder organizational challenge: how leaders govern, prioritize, and bring people along will matter as much as the technology itself.

Short-Term Pessimism, Long-Term Optimism

When asked whether they felt more optimistic or pessimistic, participants landed in both camps, and that tension felt honest given how much remains unknown.

The J-curve framing resonated: a period of increased cost, complexity, and disruption before longer-term benefits emerge. As one participant put it, “Up front investment is required.” That’s true of AI broadly, and it’s especially true when sustainability outcomes are part of the equation.

What Remains Unresolved

Participants surfaced key uncertainties:

• What sustainability will look like through AI rollout and adoption
• Who owns:
  • execution
  • measurement
  • data collection
• Whether accountability sits internally or is pushed to third parties and suppliers
• How increasing politicization of sustainability is shaping expectations and leaving organizations to define their own path forward

The Limits of Individual Action

Even when ownership is defined, leaders are grappling with the limits of their influence.

• A small group of major players are driving the majority of AI infrastructure investment
• Most organizations are operating at a very different scale
• Sustainability outcomes are shaped by an interconnected ecosystem, not any single actor

As one participant reflected, “The scale difference in AI infrastructure spend by the 10-15 top players and everyone else is such a chasm. If major players don’t act, it kind of defeats the purpose.”

Progress depends not just on what any single organization does, but on collective movement across the system.

Which raises an important consideration: How do leaders and organizations take meaningful action in a system where they do not control the biggest levers?

People Will Determine the Outcome

The clearest insight from the room: AI is as much a people challenge as a technology program. As leaders, we have to always be considering how we bring people along and how we ensure we have people that can adapt to the increasing pace of change.

The conversation pointed to the human systems that will shape success:

• Executives working together across silos
• Incentives that reinforce desired behavior
• Hiring and exiting aligned to new realities
• Leveraging AI to fill capability gaps

A Practical Path Forward

Even amid uncertainty, a grounded orientation emerged: think globally, act locally. Focus on what’s within your control. Experiment and learn. Share what you discover.

The question isn’t only whether AI will undermine or reinvent sustainability. It’s whether organizations can align their people, incentives, and decision-making fast enough to shape the outcome before it gets shaped for them.

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By 2030, AI infrastructure will add between 24 and 44 million metric tons of CO2 to the atmosphere
annually. Yet if deployed strategically, AI could reduce global emissions by 3.2 to 5.4 billion tonnes
of CO2-equivalent annually by 2035. That potential benefit is three to four times larger than data
centers’ entire carbon footprint.

The question isn’t whether AI will reshape your sustainability strategy. It’s whether you have
the organizational capability to capture the opportunity before the costs overwhelm you.

What the Research Show

Companies using AI to help reduce emissions are 4.5 times more likely to see significant
decarbonization benefits. The leaders capturing these gains report economic returns averaging $221
million per company. Yet only 7% of large companies comprehensively measure their emissions, and
just 13% have set comprehensive targets according to the BCG + CO2 AI 2025 Climate Survey. The
gap between potential and execution has never been wider

3 Barriers Blocking AI-Enabled Sustainability

Shadow AI and Measurement Blind Spots:

Teams across your organization are already using AI tools: ChatGPT for drafting, Copilot for coding,
free generative tools for design. Most of this usage is untracked, making it nearly impossible to
establish a realistic baseline for AI’s carbon footprint within your operations. Without knowing what’s
being used and where, you can’t measure impact or optimize for efficiency.

The IT-Sustainability Divide:

In most organizations, the people making AI infrastructure decisions (CIOs, IT teams, infrastructure
architects) are physically and organizationally separated from the people who own sustainability
targets. CIOs optimize for performance and uptime. Chief Sustainability Officers focus on emissions
reduction and ESG reporting. Nobody owns the integration. This structural disconnect means AI
deployment decisions that will lock in emissions for years get made without sustainability input, while
sustainability teams set ambitious targets without understanding the carbon implications of the AI
infrastructure being built to support them.

The People Problem:

Research shows that 80% of AI adoption challenges are people issues, not technology issues. Tech
leaders have the vision and understand the potential, but they lack the organizational capability to
execute. Bold transformation visions fail not because leaders got the strategy wrong, but because
organizations haven’t identified and empowered the internal change agents who can translate strategy
into action. For AI and sustainability, success requires coordinating across IT, operations, procurement,
and sustainability teams. Companies invest in carbon accounting platforms and AI tools, but if the
people who could drive adoption don’t have mandate, skills, resources, or recognition, nothing scales
beyond pilot projects.

Questions You Should Be Asking

• How are you measuring AI’s dual impact (both its carbon cost and emission reduction potential)?
• Who connects AI infrastructure decisions with sustainability strategy, and are they talking at the
  planning stage or only in cleanup mode?
• Who are the internal change agents across IT, sustainability, and procurement who could drive this,
  and do they have the mandate, skills and resources to succeed?
• These aren’t technology questions. They’re transformation questions, and the answers will
  determine whether your organization captures the opportunity or gets buried by the cost.

Looking Ahead: AI & Sustainability Roundtable

On March 19, Catalyst Constellations will convene senior leaders driving AI and sustainability initiatives to work through these challenges. Ralph Loura (former CIO of HP, Clorox, Lumentum; co-founder of SustainableIT.org) and Kai Martin (Chief Sustainability Officer, The Pasha Group) will share what’s actually working inside global enterprises.

This isn’t a lecture. It’s a working session on identifying internal change agents, building cross
functional coalitions, and executing when resources are scarce. Save your seat now!

Join us on March 19, for a working session on driving AI and sustainability initiative.

The post The $5.4 Trillion Question: 
Will AI Accelerate or Undermine 
Your Sustainability Goals? appeared first on Catalyst Constellations.

Listen to the full episode here.

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Here’s what the most effective leaders are really doing and why it matters now more than ever.

1) Transformation Isn’t a Project. It’s the System You Build

Too many transformation efforts focus on tools, initiatives or technology rollouts that look great on a slide deck but never shift the underlying system. What separates enduring change from dashed expectations isn’t faster plans, it’s systemic conditions that help people think and act differently.

In 2026, organizations that win will be those that design systems that consistently generate insight, connection, and foresight rather than just solve tactical problems. For example, agile approaches and iterative strategy development are replacing rigid long-term planning because they allow teams to respond in real time to disruption rather than react after the fact.

How to Lead in 2026: Redesign how work flows across the organization so insight, feedback, and decision making happen continuously rather than at fixed milestones. 

2) Leadership Is Shifting From Command to Orchestration

Research on collective intelligence shows that distributed leadership systems outperform traditional command systems because they harness diverse perspectives and drive adaptive decision-making.

What we are consistently hearing in our interviews with senior transformation leaders is that this shift toward orchestration is being paired with a much stronger emphasis on measuring transformation value from the very beginning. Leaders are no longer willing to treat outcomes as something to assess at the end. They are building clarity early on what success looks like, how progress will be measured, and how value will be demonstrated across the system, which serve as strong guide posts in constantly shifting landscapes. 

The leaders who excel today are not the ones who “own all the answers.” They are the ones who:

• Connect dots across domains, human, technological, and strategic
• Enable teams to turn uncertainty into clarity
• Hold shared purpose more tightly than rigid plans

How to Lead in 2026: Shift from owning answers to orchestrating outcomes by aligning on success measures early and using them to guide teams through uncertainty.

3) AI Is Table Stakes. But Human Fluency Is the Real Differentiator

AI isn’t just another tool; it’s reshaping how work gets done and how decisions get made. But the organizations that simply apply AI tactically are already plateauing. The next frontier, and the harder one, is to embed AI in ways that amplify human capability without undermining dignity or purpose.

Recent leadership trend research shows that leaders now need to be fluent in AI not as technologists, but as integrators. They must design AI into the human processes of sense-making, autonomy, and judgement rather than outsourcing strategy to algorithms.

Leaders who succeed will treat AI not as a cost center or productivity hack, but as a transformation catalyst that reinforces human strengths like curiosity, empathy, and strategic foresight.

How to Lead in 2026: Deploy AI where it improves decision quality, sense making, and focus, and be explicit about where human judgment must remain non negotiable.

4) Matrix Teams Are Where Transformation Actually Lives

Most transformation work no longer happens within a single function or reporting line. It happens in matrixed teams: strategic programs, tiger teams, cross enterprise initiatives that sit outside the org chart but carry enterprise-level expectations.

This is not a small leadership challenge. Matrixed teams are often flatter but more political, with accountability that is implicit rather than explicit and wins that are poorly articulated or unevenly rewarded.

Yet these teams are rarely treated like what they are: real teams doing the hardest work in the system.

The to be successful matrix teams must have leaders that:

• Clarify shared purpose, success criteria, and decision rights early
• Build trust and psychological safety across reporting lines and power structures
• Lead through influence, not authority, in environments with competing priorities

In practice, this means organizations must invest more, not less, in leadership and team development for matrixed teams. Without that investment, transformation slows, energy drains, and the most capable leaders burn out trying to navigate ambiguity alone.

How to Lead in 2026: Invest in matrix teams as real teams with clear purpose, decision rights, and leadership support.

5) Transformation’s Biggest Derailer: Mindset, Not Strategy

Strategy without the right mindset rarely fails loudly. It fails quietly.

Organizations often assume that stalled transformation is the result of flawed strategy, insufficient planning, or poor execution. But in practice, what derails transformation most often is a leadership mindset that prioritizes certainty over learning and control over adaptation.

As change accelerates, success depends less on having the perfect plan and more on whether leaders are willing to question assumptions, adapt in motion, and create space for learning as conditions evolve. This requires an adaptive mindset, not just adaptive processes.

In 2026, leaders who sustain momentum are those who move beyond performative change and actively model curiosity, flexibility, and reflection. Executives we spoke to highlighted the importance of developing three key attributes across their orgs:

• Trust 
• Vulnerability
• Psychological Safety

How to Lead in 2026:  Lead transformation by shifting mindset before strategy, and by modeling the trust and adaptability required to learn and adjust in motion.

Leading Change in 2026: What It Takes

Transformation used to be about delivering outcomes. Now it’s about creating environments where better outcomes can continuously emerge.

To do that, leaders must:

• Shift from managing projects to shaping conditions of possibility
• Combine human judgment with AI-augmented insight
• Build culture and adaptive mindset as the real engine of execution
• Lead with empathy, agility, and systems awareness

In a world of accelerating change, the leader who can read not just patterns, but people, and who places purpose at the center of progress, will not only navigate uncertainty, they will shape it.

The post Transformation Leadership in 2026: 5 Priorities Leaders Must Get Right appeared first on Catalyst Constellations.

Sustainability initiatives are under pressure from every direction: shifting regulations, tighter budgets, and competing enterprise priorities. 

At the same time, AI is rapidly becoming one of the largest drivers of energy consumption and infrastructure expansion, putting sustainability commitments at risk. Yet these technologies also represent one of the most powerful tools organizations have to reduce waste, cut operational costs, and accelerate sustainability outcomes.

That’s the paradox.

And IT and sustainability leaders are caught in the middle. How do you scale AI responsibly when teams are under-resourced, expectations are rising, and both success and failure carry enterprise-level consequences?

This is what organizations are grappling with right now. So, to ground this conversation in reality, we’ll be joined by Kai Martin, Chief Sustainability Officer of The Pasha Group a $1B+ integrated logistics and transportation company and Ralph Loura, former Chief Information Officer of HP, Clorox, and Lumentum, and co-founder of SustainableIT.org. Together, they will share real stories from inside global enterprises on what leaders are actually doing, what’s working, and where theory breaks down in practice.

This Roundtable Explores HOW to:

• Build coalitions that drive results: Identify and activate passionate change agents across the organization, forge strategic partnerships with key executives (CFO, CHRO, COO), and create momentum when small teams need to lead enterprise-wide change
• Turn constraints into a strategic advantage: Reframe energy-efficient AI from a compliance requirement into a competitive differentiation, lowering costs, attracting talent, and creating market separation as regulations shift.
• Navigate AI as both the problem and the solution: Explore real examples where organizations are using AI to reduce operational and environmental impact while still scaling critical capabilities.

Who Should Attend: CIOs, CTOs, Chief Sustainability Officers, and transformation leaders making AI infrastructure decisions in the next 12-18 months.

Format: Expert perspective + peer discussion on real implementation challenges

Bring your toughest “how do we actually do this?” questions. Leave with frameworks for identifying Catalysts, building stakeholder buy-in, and creating the cultural movement needed to execute when resources are scarce.

Please apply here and we’ll get back to you within 48-hours. Only 20 slots are available.

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Listen to the full episode here.

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Our Guest Speakers Set the Stage

Vijay Chauhan, GlobalSTL Lead at BioSTL, opened with a clear signal: nearly 70% of leaders say cultural, not technical, barriers are the biggest challenge to AI adoption.

Kevin Swagerty, Chief Technology Officer, and Kurt Hasbrouck, Vice President, Culture, at MVP Health Care, shared how they’re putting people, not platforms, at the center of their strategy.

• They spotlighted a Digital Twin project born from empowering internal change agents to explore real problems and design solutions.
• The big unlock: a clear way to source, vet, and prioritize AI use cases based on value, cost, and complexity, anchored in strategic alignment and executive sponsorship.

Then we opened it up for a full group discussion.

A Shared Reframe Emerged

As the conversation progressed, a shared insight emerged: the focus is not on “AI” itself but on solving meaningful organizational problems. Participants reflected on previous technology shifts: desktop computing, email, mobile, and recognized that while AI introduces new speed and scale, the underlying patterns of organizational change are familiar.

Cultural Barriers Leaders Are Grappling With

Participants surfaced several common barriers shaping AI adoption across the sector:

• Employee fears, including job loss, failure, and over-reliance on AI
• Misalignment between new tools and actual organizational needs
• Balancing top-down direction with bottom-up innovation
• Lack of executive sponsorship and alignment
• Cost-to-value concerns

Strategies Leaders Are Using to Break Through

Leaders shared concrete practices that are already advancing adoption and impact:

• Focus AI initiatives on solving employee-relevant problems with measurable impact.
• Align investments with enterprise strategy and secure visible executive sponsorship.
• Take time to understand and address fears directly and transparently. One participant offered: “FEAR = False Evidence Appearing Real.”
• Model desired behaviors, particularly among middle managers, who are essential in operationalizing change.
• Create experiential learning opportunities through AI Labs, live use-case demonstrations, and collaborative solutioning.
• Use disciplined metrics and 30-60-90-day checkpoints to rapidly evaluate initiatives and discontinue low-impact efforts.
• Pair innovation with strong governance to build trust and confidence.

This was a lively, solution-oriented conversation that reflected both the urgency and the opportunity of this moment. If you missed it, you’ll want to be in the room for the next one.

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