Artificial intelligence (AI) has entered a new era, moving away from experimentation to rapid expansion. Governments and corporations are investing billions into the development and deployment of AI, with the market size tripling over the past year alone and expected to quadruple by 2030.
Along with its benefits and opportunities, AI brings with it a new wave of environmental, ethical, and operational challenges that organizations, particularly their boards, must confront with urgency.
To balance AI’s rapid evolution while sustaining progress on long-term sustainability goals, businesses require a governance approach that is both agile and strategic. Boards must be capable of enacting quick decisions, without compromising ethical considerations. But to achieve this, there must be closer alignment between technology, ESG targets, and long-term value creation.
Opportunity and Risk
AI is already testing the boundaries of many companies’ ESG strategies. We’ve already seen big corporations like the famous GAFAM (Google, Amazon, Meta, Apple, Microsoft) struggling to manage the environmental impact of AI expansion, with some AI investments consuming millions of liters of water or gigawatts of power.
That said, organizations are also exploring how AI can actively support in reducing emissions through the additional capabilities it brings to the table.
Optimizing energy & asset utilization in data centers through AI driven workload and capacity distribution is one of the promising use cases AI bring to the table.
Greater visibility on current status and data-driven insights on sustainability projects with direct prioritization based on ROI and impact, is another one. For example, we created our EcoStruxure Resource Advisor Copilot, a conversational AI tool designed to help business leaders see and interact with their energy and sustainability data. The tool allows companies to ask natural language questions about their energy use and sustainability projects, instantly generating prioritized recommendations to maximize carbon reduction and financial return.
These initiatives, among others, show that while AI presents new sustainability challenges, it can also be part of the solution.
The central question for companies of all sizes is how to understand how AI can apply to their business model and operating models in a way that bring tangible business value, but also how that usage is meaningful with regards to the resource consumed for it, which are not yet part of the economic equation of AI business models for the time being, without losing eye on the longer term implications of AI.
Indeed, responsibly embracing its potential without compromising environmental targets or stakeholder trust is key. Maximizing its impact on workforce future proofing & ethical usage with regards to customers are other aspects to look into.
Boards have a central role to play in this balancing act. As stewards of long-term strategy, they are uniquely positioned to oversee the development of frameworks that can mitigate AI’s risks- while unlocking its potential to support sustainability goals.
However, this requires foresight, subject matter expertise, and adaptability to the sheer speed of evolution we see in the AI space, and hence an updated and adapted governance structures that is adapted to the pace of change.
Aligning boards with AI governance strategies
To manage AI’s risks and opportunities effectively, organizations need to evolve their own internal governance structures.
One-way companies are doing this is by including dedicated AI or technology oversight committees. These may be supported by panels of external advisors, futurists, ethicists, data scientists who help translate emerging risks into actionable insight.
Equally important is the distribution of responsibility across the board, ensuring that AI oversight doesn’t rest with a single function or team. Adapted executive compensation and well defined leading and lagging KPIs, tied to the outcomes and progress sought after are a good way to ensure the above.
Education is also a critical enabler here. Many board members (but also executives!) today are not technologists, and that’s entirely reasonable. However, in the context of AI’s growing influence on strategy, risk, and reputation, a baseline understanding of its capabilities and implications is now essential. Ongoing board & executive education, training programs, and engagement with research institutions or industry conferences will help bridge this gap and ensure AI oversight is both informed and effective, beyond the current hype seen in the market.
Another practical mechanism for improving oversight is the use of real-time metrics. Much like ESG dashboards that track emissions or water usage, similar systems should be developed for AI-related performance, ethics, and environmental impact. These tools can give board members a clearer view of how AI is evolving inside the business, whether it’s aligning with sustainability commitments or where interventions may be needed.
Engaging with the whole ecosystem
Importantly, boards must also consider how AI’s effects ripple through their entire value chain, especially for large corporations aiming to reduce Scope 3 emissions.
Be it the need for resources the investment in AI will require; to the issues (including Sustainability related ones) companies will be enabled to solve with AI tools. Implications certainly do not limit themselves to the very company’s very own operations but go across.
In this context, a company’s business ecosystem is a strategic resource giving access to expertise, applied knowledge, access to markets and customers, as well a common drive for mutual success, and should not be overlooked, but rather engaged and if possible embedded when making plans in order to reach objectives.
Building transparent, structured frameworks that boards & executives can look to when shaping responsible AI strategies inclusive of the whole value chain will help maximize outcomes and turn sustainability and AI from siloed priorities into shared ecosystem goals that can drive impact at scale.
Ensuring long-term governance
Adopting AI into a corporation is not an option anymore as the potential benefits are numerous, regardless of the challenges to extract tangible value.
Beyond creating value for the company, it is also important to do so in the long term and do so responsibly by measuring the benefits you to their underlying cost (energy, carbon, resource usage, but also data used to train the model and its outputs) and how those can potentially pose a reputational risk to the company.
By thinking beyond financial & operational benefits to take into account other factors will ensure maximal outcomes while minimizing risks and even potentially new use cases.
The safe way to do so still lies in investing in education of employee and leaderships & governance to ensure the right decisions with the right considerations are taken at the relevant levels of the company, which also enables better agility and speed.
Doing so properly is the best way to drive long-term value by maximizing outcomes while minimizing risks.
Ultimately, effective AI governance is a critical foundation for building trust, achieving climate goals, and ensuring innovation serves the broader good.
