Nearly four in 10 companies worldwide are falling behind on their climate commitments, even as they deploy artificial intelligence to optimize sustainability. A new study from BearingPoint reveals a troubling disconnect: while 95% of organizations have committed to science-based climate targets, 37% are already experiencing delays in achieving them. The culprit isn't a lack of planning,it's a failure to translate strategy into action, compounded by AI's growing appetite for energy .

Why Are Companies Struggling to Meet Climate Goals?

The gap between climate ambition and execution runs deep. BearingPoint's research, titled "Achieving environmental goals in the AI era: Winning blueprint for sustainable technology," identifies a cascade of structural and operational barriers that prevent companies from delivering on their sustainability promises .

At the leadership level, the disconnect is stark. Only 36% of businesses have fully integrated their sustainability and technology strategies, while 40% of chief information officers and chief technology officers are not involved in sustainability decision-making at all. Even more concerning, just 20% of organizations co-develop climate goals at the executive level, meaning sustainability decisions are often siloed from the broader business strategy .

The operational challenges are equally significant. Half of organizations lack the right environmental, social, and governance (ESG) tools to manage sustainability effectively, and only 33% have sufficient supplier data to support their emissions reduction targets. These measurement gaps make it nearly impossible to track progress or hold teams accountable .

How Is AI Making the Problem Worse?

Here's the paradox: AI is simultaneously a solution and a problem. While artificial intelligence can optimize energy consumption, improve supply chain efficiency, and enable more accurate sustainability reporting, it also requires significant computing power, creating a new layer of environmental impact .

Currently, digital technologies account for less than 5% of corporate emissions for most companies. However, this share is expected to rise significantly as AI adoption accelerates. In fact, 38% of organizations expect AI's carbon footprint to increase by more than 30% in the coming years .

"AI is emerging as both a sustainability enabler and a new source of emissions, requiring stronger governance and measurement," noted Matthias Roeser, Global Leader Technology at BearingPoint.

Matthias Roeser, Global Leader Technology at BearingPoint

The challenge is that many companies are deploying AI without fully understanding or measuring its environmental cost. They're using machine learning to cut waste in one area while simultaneously increasing energy consumption in their data centers. Without proper governance and measurement frameworks, these tradeoffs remain invisible .

Steps to Bridge the Gap Between Climate Strategy and Execution

BearingPoint outlines a five-part strategy designed to help organizations deliver on climate targets in the AI era. These steps address both the governance failures and the measurement challenges that currently plague corporate sustainability efforts .

• Introduce Net CO2 Delta Analysis: Move beyond simple emissions tracking to measure the net environmental impact of AI projects. This means calculating both the emissions saved by AI optimization and the emissions created by the computing power required to run the AI system.
• Prioritize AI Projects with Measurable Sustainability Gains: Not all AI projects are created equal. Two-thirds of leaders believe technology could reduce emissions by 6 to 30%, while nearly half say 11 to 25% of AI projects already deliver net-positive environmental impact. Focus resources on these high-impact initiatives.
• Bring CIOs and CTOs into Core Decision-Making: Technology leaders must move beyond green IT initiatives and become central players in sustainability strategy. This requires embedding sustainability into digital transformation and helping organizations track and reduce their environmental impact.
• Align Digital Strategy Directly with ESG Goals: Establish joint governance structures that connect technology decisions to environmental, social, and governance objectives. This ensures that every major IT investment is evaluated for its sustainability implications.
• Train Teams in Energy-Efficient Coding and Green Architecture: Embed sustainability key performance indicators (KPIs) into employee roles and performance evaluations. Engineers and developers need the skills and incentives to build systems that are both powerful and efficient.

The research also highlights the critical role of data and transparency. Currently, 41% of CIOs lack sufficient supplier emissions data, and 50% lack effective ESG tools to manage sustainability. However, 67% of leaders believe dedicated ESG platforms will become the standard for sustainability management by 2030. These platforms enable real-time tracking, compliance, and measurable progress .

"AI can become one of the most powerful enablers of sustainability, but only if it is deployed responsibly. Organizations need a holistic approach that integrates sustainable technology architecture, transparent measurement of digital emissions and stronger collaboration," explained Rémy Sergent, Global Leader People and Strategy at BearingPoint.

Rémy Sergent, Global Leader People and Strategy at BearingPoint

The message from BearingPoint's research is clear: the challenge is no longer setting climate targets, but delivering them in an AI-driven world. Organizations that integrate sustainability into technology strategy, governance, and data systems will be best positioned to turn ambition into action. Those that fail to adapt risk falling further behind as regulatory pressure and stakeholder expectations intensify .

As 2026 unfolds, companies are learning that purpose, resilience, and profitability are not competing priorities, but increasingly inseparable. The organizations that succeed will be those that treat sustainability not as a compliance checkbox, but as a core element of how they design, deploy, and measure the impact of their technology investments.