Over the last 18 months, sustainability has become a topic du jour for the advertising industry. At the same time, many companies making sustainability a core piece of their mission are also very publicly leaning into AI.

The challenge is that AI and sustainability efforts are diametrically opposed to one another.

Sustainability in programmatic advertising pivots largely around supply path optimization efficiencies, which in concept have been around for a long time. If you make the buyer and seller of media closer, with a more direct path and some decent business rules between them, emissions are lower.

AI, on the other hand, is not particularly sustainability-friendly. Creating the hardware used to run these models requires vast energy resources, and there is substantial data exhaust emitted in using and training the models, which can take months and even years. According to one UMass Amherst study, just one AI model can emit more than 626,000 pounds of CO2, equivalent to more than 60 gasoline-powered cars, in its life cycle.

Think about how many models one advertiser, one agency or one tech provider will run, then start multiplying across advertisers, agencies and tech providers.

There are more and more data centers being built. Where available, they are being built near sustainable resources. However, there is not enough real estate in those areas to meet demand and sustainable resources aren’t infinite.

In media and advertising, there are many dimensions to the campaign lifecycle that can leverage AI. These include creative, social, search, programmatic bidding and media mix modeling for budget allocation. Add these up across campaigns, companies and countries, and carbon emissions will grow exponentially.

While other industries are focused on this natural friction between AI and sustainability, it doesn’t seem the marketing industry is doing so. Given its major implications, we should most certainly be having a more open dialogue about this inevitable friction. 

Are brands going to hold agencies and vendors to account? Will agencies be willing to sacrifice some performance in the name of sustainability, even though that may put them in a less competitive position?  Should we have guardrails around generative AI, to keep emissions low or neutral in the short term? 

Limiting AI emissions will almost certainly stifle potential creativity, progress and performance, but it is technically the right thing to do for the environment. This is a harder conversation to have than our current sustainability discussions—and the harder problem to solve—because supply path optimization is just better business, and always has been. 

With AI, it’s not as cut and dry. Purposeful use of AI requires a framework of value, meaning that businesses should focus AI on business problems that are most critical to solve, and measure the impact of financial growth or efficiency on one side while measuring the carbon footprint it creates on another.

Start with the end goal in mind

To implement a framework within your organization, start with the end goal in mind and be intentional about what your organization’s north star is. While many companies are talking a green game, in private they aren’t as bought in. As with advertising, it’s better to be honest: If you are a company that believes that you will not get to zero emissions, be deliberate as to what your goal truly is. Then, work backward from there.

Establish business discipline and structure

Whatever the goal, business discipline and structure is an absolute must to achieve it. Once the goal is set, identify the choices needed to leverage AI to improve business. These can include optimization of operational workflow or building tools to drive better outcomes through media spend.

Include an emission quota with each initiative with the business upside in mind. For example, a company might determine that while it can’t be carbon neutral for a specific initiative, the value of the project is still worth it.

Set goals and measure against them

Many organizations set goals then don’t measure against them, except for top-line revenue and perhaps margin. But in the world of cloud technologies and data-driven media, measurement must be part of an organization’s DNA. When considering the explosion of emissions and costs that AI will place on organizations, it’s the companies that have the most discipline around AI that will win.

AI is a tool to get to an answer, it isn’t itself the answer. You can swing a golf club and miss the ball entirely or knock it in for a hole-in-one. It’s how the tool is used, and by whom, that matters. By harnessing AI and sustainability with forethought and intention, success can be achieved.

None of this is to say that over time, AI and sustainability can’t coexist more efficiently. In the meantime, we should learn our lesson from the programmatic supply chain conversations: When we start measuring later, it’s harder to fix. Let’s for once solve the problem we know will come before we create it.