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Most promising AI tools for sustainability professionals
AI tools can enhance sustainability with optimized energy use, improved predictive maintenance and streamlined supply chains while managing their own environmental impact.
As sustainability initiatives become increasingly data-driven, AI can automate labor-intensive processes, improve forecasting and uncover efficiencies that would be difficult to detect through traditional analytics.
However, for senior IT leaders, AI presents a paradox. The same technology that can help organizations reduce waste, optimize energy consumption and improve environmental, social and governance (ESG) reporting also requires significant computing resources. Large language models (LLMs) consume substantial amounts of electricity and water, while the rapid expansion of AI infrastructure places new demands on power grids worldwide.
The challenge is no longer whether AI belongs in sustainability programs, but how organizations can deploy it responsibly. Enterprise leaders must evaluate not only what AI can accomplish, but also whether its environmental benefits outweigh its own operational footprint.
The most successful organizations approach AI as a practical optimization tool rather than a silver bullet. Instead of pursuing AI for its own sake, they apply it to measurable operational problems where they can quantify improvements in energy savings, reduced emissions or lower resource consumption.
What can AI do for sustainability teams?
Enterprise sustainability programs generate enormous amounts of operational, environmental and supply chain data. AI can help organizations transform that information into actionable insights across several core areas.
Energy grid optimization
Machine learning (ML) models can forecast electricity demand, optimize energy distribution and improve building and data center efficiency. AI can also continuously adjust HVAC systems and cooling infrastructure based on changing environmental conditions and equipment loads.
AI creates the greatest value when it is applied to physical systems with measurable outcomes, according to Jackie Swanson, managing partner at Gartner.
"The common thread is a measurable physical output [such as] kilowatt-hours, miles [or] downtime -- data clean enough to trust and a decision someone will act on," she said. "Anything missing those three, and the value tends to live in the demo, not the P&L."
Predictive maintenance
Predictive maintenance remains one of AI's most mature sustainability use cases. By analyzing telemetry data from industrial equipment, ML models can detect anomalies before failures occur, extending asset life while reducing waste, emergency repairs and unnecessary replacement.
It also offers both financial and environmental benefits because organizations replace equipment only when necessary, rather than after catastrophic failures, said Stanislav Kazanov, head of GRC, cybersecurity, sustainability and data at Innowise.
"Businesses save costs associated with waste, transport emissions and manufacturing overhead related to having to replace equipment on an emergency basis," he said.
Supply chain and resource optimization
AI also helps organizations improve logistics, inventory management and demand forecasting. Better forecasting reduces overproduction, minimizes waste and lets companies optimize transportation routes and procurement strategies.
Logistics optimization and demand forecasting offer measurable results because organizations can directly quantify reductions in fuel consumption, mileage and excess inventory, Swanson said.
Carbon footprint tracking and ESG reporting
Many organizations use AI to automate the collection and classification of sustainability data from suppliers, invoices, shipping records and procurement systems. Natural language processing tools can significantly reduce the manual effort required to gather Scope 3 emissions data across complex supply chains.
However, both Swanson and Kazanov cautioned against overestimating AI's capabilities for ESG reporting. Many AI-powered ESG platforms rely on inconsistent or incomplete data, Swanson said.
"Point a model at data you don't trust, and you get confident, beautifully formatted estimates," she said.
While AI can accelerate data collection, organizations should not rely on generative AI (GenAI) to create sustainability disclosures, Kazanov said.
"A spreadsheet that has been validated by a compliance officer will always be more reliable than a summary created from an energy-consuming LLM-based system," he said.
AI tools for sustainability
The market for AI-powered sustainability software has expanded rapidly as organizations seek better ways to measure emissions, automate reporting and optimize operations. While features and pricing vary considerably, the following platforms, presented in alphabetical order, are among the leading options available to enterprise sustainability teams.
CO2 AI
CO2 AI focuses on enterprise carbon management and decarbonization planning. Its key components include product carbon footprint calculations, supply chain emissions management, reduction scenario modeling and AI-assisted emissions analysis.
The platform is cloud-based and primarily targets large enterprises. Pricing is not readily available on the website, yet a free trial is offered.
Diligent ESG
Diligent ESG helps organizations collect ESG data, manage governance requirements and automate sustainability reporting across multiple frameworks. Its key capabilities include AI-assisted ESG reporting, regulatory compliance management, board and governance reporting and risk management dashboards.
The platform is delivered as a cloud-based SaaS system, and pricing is available upon request.
IBM Envizi
IBM Envizi is the vendor's ESG suite. It aims to help organizations collect sustainability data from multiple business systems while supporting ESG reporting and carbon accounting. Its core functions include automated data reporting, emissions tracking, ESG reporting and performance analytics.
Envizi is available as a cloud service with enterprise pricing.
Microsoft Cloud for Sustainability
Microsoft Cloud for Sustainability integrates environmental data across Microsoft business applications and Azure services. Its key features include carbon accounting, sustainability dashboards, emissions reporting and AI-powered data insights.
The platform operates as a cloud service within the Microsoft ecosystem.
Persefoni
Persefoni is an enterprise carbon accounting platform designed to measure, analyze and report greenhouse gas emissions. Its primary capabilities include carbon accounting, regulatory reporting, AI-assisted emissions analysis and management of Scope 1, 2 and 3 emissions.
Persefoni is available as a SaaS platform. Pricing is available upon request, with a free trial available.
Pulsora
Pulsora combines sustainability data management with AI-powered ESG reporting and compliance capabilities. Its main features include ESG reporting automation, sustainability performance dashboards, carbon accounting and regulatory compliance tracking.
The platform is delivered as SaaS with enterprise licensing. Pricing is available upon request.
Salesforce Net Zero Cloud
Salesforce Net Zero Cloud manages emissions data alongside operational and customer information. Its primary functions include carbon accounting, supplier emissions tracking, ESG reporting and sustainability dashboards.
The platform is offered as a SaaS application within Salesforce, and pricing is available upon request.
Watershed
Watershed offers enterprise carbon accounting and sustainability management with AI capabilities. Its key functionality includes enterprise emissions management, supply chain carbon tracking, climate target management and reporting automation.
Watershed is delivered as a cloud platform with custom enterprise pricing.
The environmental cost of AI
Although sustainability platforms continue to add GenAI capabilities, organizations must evaluate whether those features solve real business problems or simply automate existing workflows.
Swanson recommended asking four questions before investing in AI initiatives:
- Is there a measurable physical outcome?
- Is the underlying data trustworthy?
- Will someone actually act on the AI's recommendations?
- And could a simpler technology deliver most of the same benefit?
And despite AI's potential to improve sustainability performance, the technology itself carries environmental costs.
Training and operating large AI models requires enormous computing power. Expanding data center capacity increases electricity demand, while cooling systems consume substantial amounts of water. As organizations deploy AI more broadly, sustainability leaders must account for those effects alongside any emissions reductions AI enables elsewhere in the business.
"The headline tradeoff is AI's own footprint," Swanson said. "Training and inference are energy and water-intensive."
Many enterprise sustainability applications don't require the largest available models, she added.
"Most enterprise sustainability use cases need a small model or classical optimization, not a frontier LLM," she said.
Organizations often deploy resource-intensive models where smaller, more specialized models would produce comparable results using far less energy, Kazanov said.
He also recommends considering when AI workloads run, suggesting organizations schedule compute-intensive processing during periods when renewable energy contributes more heavily to the electrical grid.
Measuring AI's environmental impact requires looking beyond traditional data center efficiency metrics such as power usage effectiveness, Kazanov said. Organizations should also track marginal carbon intensity, water usage, embodied carbon in AI hardware and the full lifecycle of specialized computing equipment.
Organizational accountability presents another challenge, said Arif Gasilov, partner for natural resources and the built environment at Gasilov Group.
"IT enables access, departments pay for licensing, facilities pays the electric bill [and] sustainability reports on emissions," he said.
As a result, no single group fully owns AI's environmental costs.
Organizations should measure the overall effect of their AI infrastructure on the broader energy system rather than focusing solely on internal sustainability metrics, Gasilov said. This can involve calculating AI's net environmental impact by comparing the emissions reductions it enables against the energy and resources required to build and operate the technology, Swanson said.
Her final metric may be the simplest -- and the most revealing.
"Measure whether anyone acts on the model's output," she said. "A model nobody uses is pure cost, carbon included."
Christine Campbell is a freelance writer specializing in business and B2B technology.
