AI and Sustainability: Two Trends, One Inflection Point

For a few years now, sustainability leaders have asked a fairly narrow question: how can AI help us hit our targets? That question still matters. It’s just not the only one anymore. The sharper version asks two things at once: how can AI drive business value with sustainability built in as an outcome, and what is AI’s own footprint, and who’s accountable for it?

IDC’s research points to the same conclusion from both directions: AI and sustainability have become one conversation, told from two sides: AI for Sustainability (using AI to drive sustainability outcomes) and Sustainable AI (managing the sustainability of AI itself). Here’s what our latest published research says about both, and why the organizations that treat them as one strategy are already pulling ahead.

AI for sustainability: Software grows into the operating layer

Sustainability software used to be a system of record, a place to tally emissions and file disclosures. That’s changing. As IDC’s Amy Cravens puts it in Sustainability Software Usage Trends 2026:

The market is voting with its budget. IDC forecasts the worldwide sustainability and ESG software market growing from $5.98 billion in 2024 to $12.36 billion by 2029, a 15.6% CAGR. And buyers aren’t just adopting AI features because they’re new: 71% say they’re willing to pay a premium for AI-enabled sustainability capabilities, and 80% are increasing their spend. Vendors call that a repricing of what “good” sustainability software looks like, and the buyer data backs them up.

The same shift is playing out in services. Dan Versace‘s take, from IDC’s MarketScape: Worldwide Sustainability Strategy Services 2026:

Where the value shows up: Carbon, energy, circularity, and integrated ESG/sustainability data

Talk to buyers long enough and the abstraction falls away. The value shows up in a handful of concrete places.

Carbon and energy management is the clearest one. IDC’s 2026 sustainability/ESG predictions research projects that by 2027, 40% of manufacturers will use AI-driven analytics and automation to optimize energy efficiency, cutting carbon emissions by up to 30% in the process, through predictive load forecasting, real-time monitoring, and automated adjustment rather than manual intervention. On the software side, vendors are already applying AI directly to carbon accounting: streamlining data ingestion, catching errors, and turning what used to be a quarterly scramble into something closer to a running total. In the datacenter, our Building Blocks of a Sustainable Datacenter report tracks the same pattern showing up in energy and carbon triage: dynamic workload management and grid-carbon-aware scheduling have moved from pilot projects to standard operating practice.

Circularity is the fastest-growing piece of the picture. IDC research shows the share of organizations tackling circularity through their sustainable AI strategy rising from 22% in 2024 to 29% in 2026, a 32% jump, outpacing most other environmental categories, driven largely by the unit economics of AI hardware itself: refurbishment, redeployment, and high-value materials recovery pencil out better for AI accelerators than for general-purpose IT. Zoom out further and the trend holds at the enterprise level too: IDC projects that by 2028, 75% of enterprises will set formal IT asset circularity goals, with 90% of retired assets returned to the circular economy and 20% of new IT sourced as refurbished.

And underneath all of it sits the data problem. Amy Cravens’ AI-Enabled Sustainability Software survey found that ESG data readiness (data quality, integration, and standardization) is the primary constraint to scaling AI for sustainability, with data accuracy ranked the single most urgent issue organizations want AI to fix. Dan Versace and I called this out directly in Navigating the New Sustainability Services Landscape as “ESG Data Management 2.0.” Sustainability data has become mission-critical enterprise data, subject to audit and financial disclosure. Closing the gap between what regulators require and what most organizations’ data infrastructure can actually deliver is one of the most concrete, high-value opportunities in the market right now.

The maturity divide: Why some organizations capture 26x more value

Here’s the number that ties all of this together. IDC’s 2026 AI and Sustainability Survey segments organizations into three maturity cohorts, and the spread between them is enormous: only 3.3% of beginner-stage organizations report significant business value from AI-enabled sustainability, rising to 31.8% at the intermediate stage, and 84.6% among advanced organizations, a 26-fold gap between the least and most mature. As I wrote in my Sustainable Business Value Creation in the Era of AI Everywhere report:

The stages map cleanly onto the examples above. Beginner organizations are still consolidating fragmented ESG data for compliance and disclosure, exactly the data readiness gap Amy’s research flags. Intermediate organizations move into operational efficiency, where AI-driven energy and carbon management start generating hard, CFO-legible savings. Advanced organizations push into circularity, product-level life-cycle design, and ecosystem data-sharing, the territory where sustainability stops being a cost center and starts showing up as revenue premium and lower cost of capital. The gap tracks how deeply AI is embedded into operational decisions and how tightly sustainability data connects back to the maturity cohorts above.

Sustainable AI: The bill comes due

Here’s the part that gets less airtime: AI has a resource cost, and buyers are starting to ask about it directly. As I noted in IDC’s Sustainability/ESG 2026 Predictions:

This is the flip side of the framework: IDC distinguishes between Sustainability through AI (using AI as a lever for broader sustainability goals) and Sustainability of AI (managing AI’s own environmental footprint), plus a third, adjacent lens, AI for Good, where AI is applied directly against societal and SDG-aligned goals. Our Building Blocks of a Sustainable Datacenter, 2026 update tracks how organizations are responding at the infrastructure layer: liquid cooling is now a baseline requirement for AI workloads, and AI-specific ITAD (secure data sanitization for accelerators and recovery of the high-value metals inside them) is now a distinct discipline in its own right. As I put it in that research:

The two questions are converging

Split into two workstreams, “AI for sustainability” and “sustainable AI” can pull in opposite directions: one team pushing to scale AI faster, another asking what that scale costs. IDC’s research on the highest-maturity organizations shows something different. They’re running one capability, AI-enabled data infrastructure accountable for the value it creates and the resources it consumes, and that’s the same pattern showing up in the datacenter and circularity data above.

That’s the real signal underneath all three of our research streams this year. Software vendors are building the operating layer Amy Cravens describes in her research above. Services are repositioning around AI-enabled infrastructure and provable outcomes. Carbon, energy, and circularity have moved from side projects to line items CFOs read every quarter. Infrastructure itself is now being asked to justify its own footprint.

For buyers, the practical takeaway is simple: the sustainability maturity conversation and the AI investment conversation are the same budget line now. Treat them separately, and you’re optimizing half the equation. Treat them together, and the value multiplies. That’s the maturity gap from Bjoern’s data, playing out at portfolio scale.