AI for a human-powered future

In his digest series Ask David, David Carlin explores the challenges sustainability teams face today, giving actionable advice on demonstrating financial value, strengthening business strategy, managing risk, and driving real organizational impact.

AI has undeniably entered the world of sustainability, often accompanied by a familiar instruction: make do with less. Perhaps it can write the report faster. Perhaps it can replace a team member. For sustainability folks already stretched between growing demands and shrinking budgets, this sounds like another turn of the screw. The problems we work on need more resources not less.

Seeing AI merely as a cost-reduction strategy is not only unsustainable it is also setting sights far too low. AI’s real potential lies in capability growth: expanding the work we can undertake, the people we can reach, and the impact we can have. Sustainability should be one of the clearest demonstrations of that potential. There is more important work than ever and less time to get it done.

AI can transform how we work and give us the leverage we need to be value-adding systems thinkers. Goldman Sachs analysts considered a range of possible AI futures and, even under their baseline assumptions, estimated that AI could add 1.5 percentage points to annual labor productivity growth over a ten-year adoption period. Most of that gain came from increasing the productivity of people who remained in their jobs. For sustainability teams, more productivity should mean a greater capacity to understand change and bring our knowledge into key strategic decisions.

Earlier this year, I wrote about three new roles of a sustainability leader: cartographer, teacher and business person. We already know more acronyms than most of our colleagues would ever wish to learn. As sustainability professionals, we need to transmit our stores of knowledge across our firms. In my experience, AI gives us the capacity to go further in each of these new roles.

As cartographers, we help our businesses navigate a changing landscape. A shift in insurance availability can alter the attractiveness of an asset. A new technology can change the economics of a product. Policy developments can reshape a market. These changes never arrive in the smooth and predictable ways we hope they would.

Recently, my team worked with an organization on developing a model to examine how changes in insurance and insurability could affect its assets. AI helped process the large quantities of information needed to spot emerging developments. The human skills are knowing what to look for and understanding what a signal means for a particular business. Earlier warnings give leaders more time to prepare effective responses.

The teacher’s challenge is getting sustainability knowledge to the people whose decisions give it practical effect. Procurement teams, relationship managers and risk officers encounter these issues differently. A generic presentation can leave everyone informed in principle and unsure what to do on Monday morning.

In a training program with a financial institution, we developed examples and used AI to help illustrate interactive scenarios that participants could explore. In my work with LinkedIn on a climate risk course, we considered how AI can help challenge assumptions and uncover risks we might otherwise miss. These applications enabled us make learning relevant to the specific responsibilities of a given role. A teacher still needs to judge what matters, recognize confusion and help colleagues work through difficult questions. AI gives us more ways to connect with those colleagues.

As sustainability professionals, we know how urgent climate change and environmental breakdown are. Yet we can struggle to communicate their significance to our firm’s leadership. We need to explain what these changes mean to our bottom line and our competitive position. We need to bring our knowledge into discussions where business priorities and investment decisions are set.

Tools such as the International Finance Corporation’s MALENA can help professionals extract environmental and social information from company documents. Faster review leaves more room to investigate concerns and discuss them with decision-makers. This is how AI-powered efficiency gains can begin to influence decisions. Here’s how we think about evaluating AI tools: begin with a recurring problem: an investment review, a procurement decision, a conversation with a client. Consider what information is missing and where the process gets stuck. Then examine whether AI can help, how its outputs can be checked, and who will use them. A tool is worthwhile if it can accelerate and enhance the work we need to do.

That is why I believe sustainability leaders must be ambitious about AI. We have knowledge that our firms and communities need, and ways to put it into practice. I want to spend more of my time helping people make sense of change and act on it. AI gives us the capacity to reach further and engage people more effectively. We should use it.

For more information, visit the D. A. Carlin & Company or explore David Carlin's Substack.