AI can reduce complexity and reduce energy waste, and professional services organizations may be the key to overcoming the barriers to proper adoption
Generative AI (GenAI) is not going away any time soon. Indeed, more than three-quarters (77%) of professional services workers said they believe AI and GenAI will have a transformational or high impact on their professions within the next five years, according to the 2024 Future of Professionals Report from Thomson Reuters.
At the same time, however, as GenAI begins to move rapidly from hypothetical to reality, its introduction creates a host of related issues — perhaps none greater than the energy that the operation of GenAI consumes. AI data centers’ annual power consumption is expected to reach 90 terawatt-hours by 2026, a roughly a 10-fold increase from 2022 levels and representing one-seventh of all data center energy consumption, according to a Deloitte report from late-2024. Further, that figure will only increase in the coming years as GenAI becomes more prevalent within professional settings.
As a result, many companies may find it more difficult to meet their energy transition goals to lower overall emissions as they increasingly embrace AI. In fact, the recent Industrial AI x Sustainability report, produced by Reuters Events in cooperation with Siemens, found that nearly half (46%) of all respondents said they believe their organization is at risk of missing interim energy transition targets.
Clearly, AI does not occur in a vacuum. As GenAI implementation continues apace, the next necessary step is to incorporate its use into energy targets — a natural fit for the type of advice typically given by professional services organizations. Indeed, legal, tax, and risk & fraud professionals can help design their clients’ architect for a future in which AI usage can even be a positive step towards their energy goals.
“Sustainability is interconnected with all other business objectives, simultaneously influencing and being influenced by them,” said Pina Schlombs, Sustainability Lead for DACH (the region comprising Germany, Austria, and Switzerland) for Siemens Digital Industries Software, within the report. “These relationships can either compete with or reinforce progress, meaning businesses must develop a thorough understanding of the cause-and-effect chains. This is a complex feat, but mastering it allows organizations to leverage the right strategies and technology stack at the right time and maturity, to scale progress fast.”
Simplifying the complex
One of the primary goals of AI systems is to simplify complex actions, breaking them down into more repeatable, automatable components. Consider the research component of any sort of legal case or tax problem — by leveraging data to anticipate questions and more quickly identify the type of information needed, the time required for the research process can be drastically reduced.
Industrial AI can take these concepts and apply them on a large scale for the goal of reducing emissions. The report gives an example of a digital twin for an entire factory, which allows adjustments to be modeled and tested before implementation. Through AI-powered trial and error, companies can find ways to reduce industrial complexity, process and analyze data quickly, and streamline tasks to remove unnecessary use of energy.
James Cole, Chief Innovation Officer at the Cambridge Institute for Sustainability Leadership (CISL), points to the start-up Monumo, which has developed AI to run through 10 mission motor design simulations in one day, 200-times faster than the industry standard. Even traditional industries such as construction have found uses: Titan Cement, for example, reduced its energy consumption by 5% to 10% through AI modeling of its manufacturing operations.
“We’ve never had such powerful tools to solve the challenges we face in sustainability,” said Cole within the report. “Artificial intelligence systems have the potential to help us understand the world in all its complexity and optimize industrial processes not only for strong business outcomes, but also for holistic social and environmental outcomes.”
The result is that as these systems progress, industrial AI is set to dramatically transform industry energy transition in the near future. Only 14% of survey respondents said they believe industrial AI has a high impact on accelerating industry transitions today; but at the same time, 50% said they believe industrial AI will do so within the next three years. This quick adoption will require buy-in and coordination from all levels of the organization.
Overcoming adoption challenges
So, what does all this mean for professional services? As noted above, no AI occurs in a vacuum, and similarly, no business runs without legal, tax, or risk & fraud input. Thus, as professional services organizations are increasingly looked upon as strategic partners and business generators rather than simply cost centers, there is an opportunity to counsel clients on the energy opportunities afforded by AI.
As noted previously, AI adoption can be sorted into three main categories: financial, partnerships, and internal capabilities and skills. By focusing on overcoming all three types of barriers, professional leaders can influence clients to not only adopt AI, but to do so in a way that encourages energy-neutral use.
“AI is not optional, it’s out of the bag, it’s going to happen, and it has the potential to transform business models,” CISL’s Cole said. “So, companies that are assuming everything is broadly going to stay the same except for their investment in AI risk missing the point.”
Legal, tax, and risk and professionals — both within corporate functions and at outside firms — should make sure they are a part of the AI ecosystem and have their thoughts heard.
First, by encouraging greater measurement and data collection, professionals can help their clients determine exactly the financial impact and risk they could face associated with AI adoption. Just 20% of professional services organizations actively measure return-on-investment from GenAI tools, according to our internal research, which also showed that data collection around these tools is poor across the board — and that includes their impact on ESG.
Second, legal, tax, and risk and professionals — both within corporate functions and at outside firms — should make sure they are a part of the AI ecosystem and have their thoughts heard, keeping in mind ESG as an element of the overall impact. If a company is using industrial AI to transform factory operations, for instance, those changes will require a financial and risk assessment. Legal leaders should be integrating ESG into those assessments, aiming for energy-neutral processes wherever possible.
Finally, to provide proper advice, professionals will need to learn how AI not only impacts their own work, but how it impacts the business at large. For example, our research also shows that less than one-third of professionals have received training specifically focused on GenAI. However, to be able to be proper partners on AI projects, professionals will need to at least learn the basics of how AI can be used to streamline projects, rather than simply consuming energy.
In the near future, these actions will become central to energy goals rather than simply helpful additions. According to the Reuters Events/Siemens report, 70% of respondents agreed that future innovation in sustainability will be driven by industrial AI applications and solutions. Thus, planning across all parts of an organization must begin now to better design AI usage in a way that aligns with achieving future sustainability goals.
“The meta challenge of businesses is how to balance productivity and commercial outcomes with the imperatives of social and environmental outcomes,” added Cole. “Via big data and AI, there’s the opportunity to not just optimize operations but drive symbiosis across entire industries where there’s historically been a disconnect, to align increasingly digital systems and make sense of a broader ‘system of systems’.”
You can find more about how organizations are handling the challenge of sustainability here
