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Old school sustainability is obsolete

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    • Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
    • To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data cybersecurity to tech investment strategy to customer experience.
    • This is part 11: Sustainability.

Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.

To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data cybersecurity to tech investment strategy to customer experience.

This is part 11: Sustainability.

Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.

To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data cybersecurity to tech investment strategy to customer experience.

This is part 11: Sustainability.

Generative AI can help scale sustainability—ushering in a new era of responsible growth.

At times, the climate crisis can seem insurmountable. As emissions targets are set, then missed, then set again, some are left wondering if anything can tip the scales. Add pollution, deforestation, and species loss to the list of environmental problems and the outlook for life on our planet becomes even more ominous.

Enter generative AI. With its whiz-kid capabilities, it can analyze environmental data in an instant, uncovering patterns that lead to game-changing insights. It can offer solutions to stubborn problems across the sustainability spectrum. It’s no silver bullet, but if used strategically, generative AI could help companies achieve sustainability aspirations at scale.

As AI-first solutions inspire business model innovation, CEOs have a chance to center sustainability in a way that wasn’t previously possible. Generative AI can optimize operations for both sustainability and profitability, helping leaders avoid suboptimal tradeoffs. It can explore new materials, simulate new designs, and evaluate product lifecycles in a fraction of the time, eliminating the costly process of trial-and-error.

Generative AI can also help make businesses more resource-efficient, reducing costs, emissions, and waste. For example, it can use data from energy grids, weather patterns, and usage trends to predict and adjust energy distribution in real time. This helps companies limit their carbon footprint while also boosting the bottom line, strengthening the business case for sustainability across the board.

In this environment, good intentions will no longer be good enough. With generative AI on the scene, CEOs can make the enterprise sustainable by design—not as an afterthought—and translate ideals and ambitions into the actionable strategies and measurable business results that stakeholders expect.

The IBM Institute for Business Value has identified three things every leader needs to know:
1. Generative AI can help make ideals real.
2. Sustainability is a team sport—and generative AI is a star player.
3. Sustainable AI isn’t a given.
And three things every leader needs to do right now:
1. Turn tradeoffs into win-wins.
2. Make 1+1=3 with ecosystem partners.
3. Use generative AI to make a net-positive impact.
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An updated version of this chapter with 2024 data is now available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.

Additional content

Meet the authors

Anthony Marshall

Connect with author:


, Senior Research Director, Thought Leadership, IBM Institute for Business Value


Christian Bieck

Connect with author:


, Europe Leader & Global Research Leader, Insurance, IBM Institute for Business Value


Cindy Anderson

Connect with author:


, Global Executive for Engagement and Eminence, IBM Institute for Business Value


Karl Jacob Dencik

Connect with author:


, Chief Economist and Global Sustainability Research Leader, IBM Institute for Business Value

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    Originally published 28 November 2023

    1. Strategy
    + Generative AI
    What you need to know
    Generative AI can help make ideals real

    What will it take to bridge the gap between sustainability aspirations and action? This question has plagued CEOs for decades, as financial pressures have precluded progress toward many sustainability goals. Even today, while 86% of executives say their organization has a sustainability strategy in place, only 35% have acted on it.

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    Integrating the principles of sustainability into the core business has been a key challenge. Recent IBM IBV research revealed that 72% of executives approach sustainability as a revenue enabler rather than cost center. Yet, 64% think they must continue to make tradeoffs between financial performance and sustainability. But now, generative AI has cast sustainability commitments in a new light.

    Rather than existing on two sides of a great divide, sustainability and profitability goals have begun to merge. Generative AI, powered by transparent data, can help leaders turn intel into insights faster than ever, enabling businesses to hit both sustainability goals and financial targets with one strategic strike. For example, generative AI can analyze historical sales data, market trends, and other factors to predict future demand more accurately, helping companies optimize production levels, reduce overstock, and minimize waste.

    Of course, generative AI can’t do all of this on its own. It must be used as a complement to traditional AI, IoT, and other emerging tech. We’ve identified four pillars of success: data and ecosystems, digital technology, process and business integration, and skills and decision-making. Organizations with higher maturity in these areas are 43% more likely to outperform their peers on profitability—and 52% more likely to say their sustainability efforts drive profitability. Not coincidentally, they’re also 33% more likely to use AI for sustainability reporting and performance.

    This data hints at generative AI’s potential. Today, 61% of executives say generative AI will be important for their sustainability agenda and 69% of organizations plan to increase their investment in generative AI for sustainability.

    But will these investments deliver results—or are leaders chasing a mirage? That all depends on the data. Roughly three in four executives say manually processed data is holding back their sustainability reporting and performance efforts—and inadequate data is the top barrier to sustainability progress overall. What’s more, 83% agree that high-quality data and transparency are necessary to achieve sustainability objectives.

    What you need to do

    Turn tradeoffs into win-wins

    Use generative AI to address key sustainability data gaps, make reporting processes more efficient, reduce risk, and comply with rapidly changing requirements. Tap data to find opportunities to automate processes, design products and services, cut energy costs, and limit resource consumption in a way that delivers positive sustainability outcomes and financial gains.

    • Drive sustainable business outcomes through dramatically improved insights. Activate sustainability data and insights for improved performance across the enterprise and ecosystems, understanding where specific generative AI use cases add value or introduce risks. Use generative AI to find patterns that inform better pricing, budgeting, and incentive mechanisms based on sustainability metrics and data.
    • Embed sustainability across the enterprise. Align sustainability, business, and AI strategies to avoid advancing generative AI in isolation. Integrate sustainability-driven generative AI initiatives into all business units and your corporate governance framework. Use generative AI to augment and enrich your sustainability data for reporting and operationalization of sustainability goals.
    • Innovate, don’t replicate. Use generative AI as a source of innovation for sustainability to change how things get done. Don’t just automate existing, suboptimal processes and ways of working.
    2. Ecosystem
    + Generative AI
    What you need to know
    Sustainability is a team sport—and generative AI is a star player

    No single organization can solve the world’s sustainability problems. Natural resources are shared across borders and boardrooms, and every organization plays its own role in preserving them for future generations.

    Protecting the ecosystem takes an ecosystem—and every player is essential. AI experts, data scientists, environmental specialists, business strategists, and policymakers must all work in unison to develop and implement sustainability solutions.

    The need for collaboration isn’t new, but generative AI has changed the game. Now that it’s on the roster, organizations can collaborate faster and more effectively. In fact, ecosystem collaboration is the top benefit executives expect from using generative AI for sustainability. For example, generative AI can help manufacturers, material scientists, and consumer products companies develop more eco-friendly packaging by using desired properties and environmental criteria to suggest innovative compositions and designs.

    Harnessing the power of advanced algorithms enables the entire ecosystem to make more sustainable game-time decisions. Organizations are assembling a new playbook in response, with 65% co-creating generative AI capabilities for sustainability with ecosystem partners or suppliers.

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    What you need to do

    Make 1+1=3 with ecosystem partners

    Scale impact across the enterprise and through your ecosystem to pursue sustainability and profit as complementary business goals. Co-create generative AI capabilities with partners to limit environmental impact and advance sustainability initiatives.

    • Work with strategic ecosystem partners to drive greater impact. Make partners integral to your sustainability data and generative AI efforts. Share sustainability data with partners for collaboration and co-creation.
    • Democratize, don’t centralize. Give employees access to relevant sustainability data and AI capabilities. Empower them to adjust their daily tasks and decisions based on data-enabled insights. It is through thousands—or millions—of daily actions that an organization’s sustainability strategy is brought to life.
    • Upskill, don’t stand still. Invest in people with the right mix of sustainability and generative AI skills as part of your business strategy. Use generative AI to educate employees about sustainability concepts.
    3. Environment
    + Generative AI
    What you need to know
    Sustainable AI isn’t a given

    No path forward comes without pitfalls. As organizations leverage generative AI en masse, new sustainability concerns are coming to the forefront.

    For example, generative AI is resource-hungry. Training one large language model (LLM) can consume a lot of water and emit a massive amount of CO2. Organizations can minimize impact by fine-tuning existing generative AI models rather than training new ones. For leaders planning ahead to limit generative AI’s carbon footprint and water use, applying existing foundation models and reusing resources can be a valuable tactic.

    Another way to limit the environmental impact of generative AI is to switch programming languages. This can reduce the energy consumption of an application by up to 50%. Running workloads in a container platform instead of in a classically deployed virtual machine environment can also cut annual infrastructure costs by 75% thanks, in part, to increased energy efficiency.

    For its part, generative AI can take these sustainability strategies to the next level. Rather than just switching code, it can develop more energy-efficient algorithms and software by analyzing code performance. It can also identify which workloads can be most efficiently containerized—and even rethink the entire data center, designing and optimizing layouts, cooling systems, and server configurations to minimize energy consumption.

    Generative AI can even investigate itself to find new ways to limit its impact—but it needs human minds to light the way. Partnering with research institutions, technology providers, and other businesses to share knowledge, resources, and best practices can help organizations use generative AI to advance their sustainability strategies without blowing past carbon caps.

    Generative AI amplifies some of the risks of traditional AI, and its use in domains such as sustainability reporting needs to be tightly governed. But executives aren’t sure their teams have been properly conditioned, citing insufficient organizational readiness as the #1 barrier to using generative AI for sustainability.

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    What you need to do

    Use generative AI to make a net-positive impact

    Minimize the environmental impact of generative AI by building on existing foundation models rather than starting from scratch. Use generative AI to create better code with lower environmental impact and redesign data centers to consider sustainability.

    • Recalibrate your approach to generative AI. Make your generative AI capabilities more sustainable before they become more pervasive. Upgrade and fine-tune existing models as much as possible. Use less energy-intensive computational methods.
    • Design IT for sustainability. Monitor energy consumption, hardware utilization, and data storage to identify opportunities to become more energy efficient. Tap generative AI and hybrid cloud to limit your IT carbon footprint.
    • Don’t take opportunistic shortcuts. Put data governance in place to ensure the use of generative AI follows principles that advance sustainability and align with your organization’s values.

    The statistics informing the insights on this page are sourced from proprietary IBM Institute for Business Value (IBM IBV) data. These include insights from 500 executives across 22 countries in August 2023 regarding their perspectives on operationalizing sustainability; published data from the IBM IBV study The ESG data conundrum, and previously unpublished data from the related survey of 2,500 executives across 34 countries in April 2023; data from the IBM IBV IT sustainability beyond the data center study published in May 2022; and data published in the IBM IBV study Sustainability as a transformation catalyst in January 2022.


    Bookmark this report


    default alternate image text
    An updated version of this chapter with 2024 data is now available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.

    Additional content

    Meet the authors

    Anthony Marshall

    Connect with author:


    , Senior Research Director, Thought Leadership, IBM Institute for Business Value


    Christian Bieck

    Connect with author:


    , Europe Leader & Global Research Leader, Insurance, IBM Institute for Business Value


    Cindy Anderson

    Connect with author:


    , Global Executive for Engagement and Eminence, IBM Institute for Business Value


    Karl Jacob Dencik

    Connect with author:


    , Chief Economist and Global Sustainability Research Leader, IBM Institute for Business Value

    Download report translations


      Originally published 28 November 2023

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