By Beate Rosenthal and Hugo Carreira
The world is at a crossroads: digitalization, AI, and sustainability are no longer optional—they are imperative. At Roland Berger, we have found that organizations that act on AI and sustainability transformations simultaneously can generate strong synergies and drive impactful change. This strategic integration is known as Twin Transformation.
"Twin Transformation offers immense potential for competitive advantage and positive sustainability impact."
The integration of AI and sustainability is more than just a theoretical concept—it presents a practical and strategic opportunity. In environmental sustainability, AI enables resource optimization, climate risk mitigation, and the scaling of sustainable practices. In the social sphere, AI enhances healthcare diagnostics, accessibility, and misinformation detection. For governance, AI enables stronger compliance, transparency, and fraud prevention.
Our new study explores the synergies between AI and sustainability, identifying shared success factors, real-world applications, and practical guidance for businesses looking to lead in this space.
The convergence of external pressures compels businesses to pursue Twin Transformation. Consumers increasingly favor brands with strong sustainability commitments and ethical AI applications. Investors prioritize ESG-aligned investments, and expect technological advancementas as drivers of productivity and growth. Meanwhile, governments enforce stricter regulations on both sustainability practices and AI governance, requiring organizations to comply with evolving standards. Additionally, employees seek purpose-driven work environments and AI tools and adoption as priorities.
The integration of AI and sustainability has already proven to bring significant business benefits. Five key outcomes illustrate the value proposition:
During separate assessments of the success factors for transformations in AI, digital, and sustainability, we discovered a surprising convergence. Shared success factors include a commitment to change, leadership buy-in, robust governance, data readiness, employee engagement and culture and a balance between efficiency and innovation.
While many success factors overlap between AI and sustainability transformations, some are unique to each domain.
"AI is not just transforming industries; it’s redefining sustainability. From optimizing energy consumption to reducing waste, AI is the catalyst for a greener future. The question is no longer if AI can drive sustainability, but how fast we can scale its impact."
Our analysis of social media and news from 2024 shows largely positive or neutral sentiment toward AI and sustainability. Social media discussions tend to be more positive about AI’s role in sustainability than traditional news outlets. The social dimension of ESG receives the most engagement, supported by Health care opportunities with AI, while environmental concerns, particularly AI’s energy consumption and sustainability challenges, have gained traction. Discussions around AI governance and regulatory frameworks are both a source of optimism and skepticism.
Results vary across languages. English content contains the most positive sentiment, for instance, followed by German and then French. Further nuances highlight the need for tailored strategies that address regional sensitivities.
While AI and sustainability present vast opportunities, organizations must overcome several hurdles. We have identified three critical complexities that must be addressed.
Despite its many benefits, the energy consumption of AI models, particularly in data centers, remains a concern. Indeed, according to the sentiment analysis, this “blind spot” already presents a risk for investors and the public. Companies must prioritize energy-efficient AI systems and accurate emissions tracking and reporting.
Secondly, AI’s transformative potential must be matched with robust ethical oversight, without which it can reinforce biases and widen inequalities. Strong governance frameworks are essential to ensure responsible AI deployment.
The third challenge lies in aligning AI and sustainability goals. AI’s rapid adoption often prioritizes efficiency and profitability, sometimes at odds with sustainability objectives. Businesses must integrate sustainability considerations into AI development to ensure a balanced approach.
Twin Transformation offers immense potential for organizations to achieve competitive advantage while fostering positive societal and environmental impacts. However, this journey is complex and requires a structured approach, strategic alignment, and a commitment to cultural and operational change.
Download our study to find out more and how organizations can begin their Twin Transformation journey!
We would like to thank Daria Mikheeva for her contribution to this study.
Our new study reveals how AI and sustainability intersect, offering key strategies for successful Twin Transformation.
