With a keen eye for turning vast datasets into compelling visual stories, Chloe Maraina has established herself as a leading expert in business intelligence and data science. She joins us today to discuss a topic that has moved from the corporate periphery to the boardroom’s center stage: the CIO’s role in driving environmental sustainability. We’ll explore how this mandate reshapes IT governance, the complex infrastructure choices leaders now face, and how to manage the environmental footprint of emerging technologies like AI. Chloe will also shed light on practical, high-impact actions for reducing digital waste and the crucial partnerships required to cut through greenwashing and ensure genuine progress.
Sustainability has become a board-level mandate, moving beyond a simple compliance exercise. How does this shift a CIO’s priorities beyond traditional metrics like cost and performance? Please share a step-by-step example of embedding these new goals into IT governance and team performance incentives.
It’s a fundamental identity shift. For decades, a CIO’s world revolved around uptime, speed, and cost optimization. Now, a fourth pillar—environmental impact—has been added, and it’s not optional. It forces a re-evaluation of what “value” and “risk” even mean. As Anuj A. Shah noted, once sustainability becomes part of governance, it’s no longer a siloed compliance task; it’s embedded in how you operate. A great example of this in action starts with a board-level commitment, say, to reduce Scope 2 emissions. The first step for the CIO is to translate that broad goal into a concrete IT metric, like moving 50% of workloads to data centers powered by verified renewable energy. Second, this metric is formally integrated into the IT department’s annual operating plan and reviewed quarterly, just like budget adherence. The crucial final step is embedding it in incentives. The head of infrastructure’s bonus might now be tied not just to system stability but also to successfully migrating those workloads. An architect’s performance review could include a scorecard on the energy efficiency of their new designs. This is how you make an abstract goal a tangible, everyday reality; it’s about making sustainability part of the operational heartbeat of the IT organization.
Choices around infrastructure placement have direct emissions consequences. When evaluating cloud, colocation, and on-premises solutions, what specific sustainability metrics should CIOs prioritize alongside latency and security? Could you share an example where the most energy-efficient choice presented unexpected operational tradeoffs?
This is where the conversation gets incredibly nuanced. It’s easy to assume hyperscale cloud is always the greenest choice, but that’s a dangerous oversimplification. Beyond the standard PUE, or Power Usage Effectiveness, CIOs must dig deeper. You need to ask about the carbon intensity of the local grid powering that data center. A state-of-the-art facility is not a sustainable choice if it’s drawing power from a coal-fired plant. You should also be asking for audited emissions data and proof of renewable energy attestations or Power Purchase Agreements. I remember a case with a retail company that was choosing a cloud region for its new e-commerce platform. One region had a fantastic PUE and was incredibly cost-effective. On paper, it was the “most efficient” choice. However, it was located in an area prone to extreme weather, leading to higher resilience risks and forcing them to architect a much more expensive and complex multi-region failover strategy. A second, slightly less energy-efficient option was located in a more stable climate with a greener energy mix. They ultimately chose the second option because true sustainability has to account for operational resilience and total cost, not just a single efficiency metric in a vacuum. It’s a multi-variable equation where sustainability, latency, and control all have to be balanced.
As energy-intensive workloads like AI scale rapidly, they can quietly expand a company’s environmental footprint. How should CIOs proactively account for this in their strategy, and what architectural decisions can they make early on to manage these emissions without stifling innovation?
This is the silent creep that can undo years of sustainability progress. An AI model in training can consume an immense amount of energy, and if it’s not accounted for, you can find your carbon footprint ballooning unexpectedly. The first step is visibility. CIOs need to work with their teams to measure and forecast the energy consumption of these new workloads from the very beginning. This isn’t an afterthought; it should be part of the initial business case for any AI project. Architecturally, the key is making smart choices early. This means evaluating whether your own data centers or a specific cloud provider is better equipped to handle these specialized, high-density workloads efficiently. It also involves designing for efficiency from the start—for example, choosing the right model size, optimizing data processing pipelines to reduce unnecessary computation, and implementing scheduling that runs large training jobs during times when the energy grid is supplied by more renewables. It’s not about stopping AI innovation; it’s about architecting it responsibly so that your technological progress doesn’t create a significant environmental debt.
The text notes four practical actions: optimizing workloads, managing data sprawl, modernizing infrastructure, and reducing digital waste. Which of these typically offers the biggest immediate impact, and what are the primary obstacles IT leaders face when trying to implement it across the enterprise?
While all four are critical for a long-term strategy, modernizing infrastructure—specifically by moving workloads to facilities powered by renewable energy—offers the biggest and fastest win. As Jenny Gerson from DataBank pointed out, choosing a facility backed by renewables can eliminate a huge chunk of your operational emissions almost overnight. It’s a single, powerful decision that can have a massive cascading effect. However, the biggest obstacle to this and the other actions is often organizational inertia and a lack of clear ownership. For instance, tackling digital waste by rationalizing redundant applications sounds straightforward, but it requires deep cross-departmental collaboration. Every one of those legacy systems has a business owner who is resistant to change, fearing disruption to their team’s workflow. Similarly, managing data sprawl by implementing clear retention policies seems logical, but it runs into a culture of “keep everything, just in case.” The primary challenge for the CIO isn’t technical; it’s cultural. It’s about breaking down silos and convincing the entire organization that the environmental and operational benefits of simplification and modernization outweigh the perceived risk of changing the status quo.
Effective ESG initiatives require cross-functional collaboration. How can CIOs partner with procurement to cut through “greenwashing” and verify vendor sustainability claims? Please describe the key data points and attestations you would look for to ensure a partner’s environmental commitment is genuine.
This partnership is absolutely essential, as the CIO becomes the chief technical validator for procurement. Greenwashing is rampant, and it’s easy for vendors to make beautiful, aspirational statements on their websites. The CIO’s job is to demand proof. The first thing I would look for is audited, third-party verified emissions data, not self-reported numbers. Ask to see their Scope 1, 2, and 3 emissions reports. Second, if a data center claims to be powered by renewable energy, don’t just take their word for it. Ask for documentation, such as renewable energy attestations or, even better, evidence of direct Power Purchase Agreements (PPAs) that show they are actively funding new renewable projects. Third, look at their infrastructure design choices. Are they transparent about their cooling technologies and water usage? A truly committed partner will have this data readily available and will be eager to discuss their entire lifecycle approach, from construction materials to end-of-life hardware recycling. The collaboration with procurement becomes a one-two punch: procurement manages the contractual relationship, while IT provides the deep technical diligence to ensure the sustainability claims are backed by hard engineering and verifiable data.
What is your forecast for IT sustainability?
Looking ahead, I believe IT sustainability will evolve from a specialized reporting function into a core tenet of operational excellence, just like cybersecurity or financial discipline. We will see the emergence of “FinOps” and “DevSecOps” equivalents for sustainability—perhaps a “GreenOps” model—where environmental impact data is integrated directly into developer and operations dashboards in real-time. Choosing a cloud region or spinning up a new virtual machine will immediately display a carbon cost alongside the financial cost. This will empower every technologist to make more informed, sustainable decisions daily, rather than waiting for an annual report. Accountability will also become much more granular and transparent. Instead of just a corporate-level ESG score, I foresee a future where individual business units, and even specific applications, will have their own sustainability performance metrics. For CIOs, this means their role will continue to shift from being a manager of technology to being a strategic business leader who wields data to drive efficiency, innovation, and genuine environmental responsibility across the entire enterprise.
