Building the Future of Sustainability Through AI
We’re entering a new era, where humans and agentic AI work side by side to redefine sustainability and energy impact together. The technology revolution is not just on the horizon; it’s here. And it’s a transformation that will fundamentally change the way we help teams address today’s most pressing sustainability challenges while unlocking lasting impact for the future.
At Schneider Electric, we’ve long embraced the transformative power of AI, tripling our operational capacity and establishing three global AI hubs to accelerate development and deployment. Our deep expertise in machine learning enables us to analyze interval data, detect anomalies, and forecast trends—empowering enterprises to predict equipment failures, reduce energy waste, and unlock new efficiencies. With the launch of our agentic AI-driven ecosystem, we’re harnessing collaborative intelligence, pairing world-class experts with cutting-edge technology, to solve sustainability’s toughest challenges through predictive insights and scalable innovation.
As Microsoft’s 2025 Work Index reveals, the birth of ‘Frontier Firms’ – organizations structured around on-demand intelligence and human-agent teams signals a new organizational blueprint for functions of all kinds. Sustainability teams who adopt collaborative intelligence won’t just improve their operations; they will be positioning their companies at the frontier of both environmental leadership and organizational innovation.
In this article, we’ll explore how collaborative intelligence: the powerful synergy of human expertise and agentic AI – can accelerate innovation, drive greater efficiency, and empower businesses to deliver measurable sustainability at scale.
The Evolution of AI From Traditional to Agentic
For decades, traditional AI technology like rules-based expert systems, statistical machine learning models, and deep neural networks has delivered business value in narrow domains such as fraud detection, recommendation engines, and predictive maintenance. These systems excel at pattern recognition within structured data but require explicit programming or training for each specific function.
Generative AI represents a significant evolution, capable of creating new content rather than merely analyzing existing data. Large language models like ChatGPT and its successors, and image generators like DALL-E, learn from massive datasets to generate text, images, code, and more, making AI accessible to non-technical users. However, these systems typically operate within a single session and rely on prompting from their users.
Agentic AI takes this further by incorporating planning, memory, and goal-directed behavior. These systems persist over time, can learn from interactions, plan multi-step processes, and take autonomous actions to achieve objectives. They represent the frontier of AI development, capable of breaking complex problems into logical steps and maintaining context across extended operations.
Deploying AI to Solve Enterprise Sustainability’s Biggest Challenges
In an era where sustainability is imperative as a strategic driver for resilience, businesses face growing pressure to measure, report, and reduce their environmental impact, while also utilizing it as a corporate strategy. Navigating the many nuanced complexities of decarbonization, emissions tracking, resource efficiency, and supply chain transparency can be overwhelming. Enter Collaborative Intelligence, the fusion of human expertise and advanced AI systems. Today, this approach transforms how businesses tackle sustainability challenges, offering actionable insights and autonomous solutions that align with both environmental and economic goals.
Defining Collaborative Intelligence for Sustainability Success
Collaborative intelligence serves as the concept of combining human expertise and agentic AI to confront sophisticated sustainability obstacles. This concept creates a seamless partnership between people and machines, leveraging their unique strengths to achieve outcomes that neither could accomplish alone. For sustainability professionals, this approach integrates their deep industry knowledge combined with AI systems’ advanced capabilities, enabling precise actions such as improving data quality, conducting interactive scenario modeling, and quickly calculating emissions for regulatory requirements.
In research involving 1,500 companies, it was found that firms achieve the most significant performance improvements when humans and machines work together. This collaboration actively enhances each other’s complementary strengths: the leadership, teamwork, creativity, and social skills of humans, and the speed, scalability, and quantitative capabilities of AI (Harvard Business Review).
The Critical Role of Experts in AI Development
Subject matter experts play a pivotal role in transforming AI systems into dynamic collaborators, capable of driving meaningful progress toward sustainability objectives. In large-scale consulting engagements, necessary subject matter expertise may include data management, sustainability, emissions strategy, reporting, and decarbonization. Deeply trained subject matter experts who can range from software developers, data scientists, AI prompt engineers, architects, product owners, climate scientists, and sustainability experts, can provide the necessary foundational knowledge required to ensure AI systems interpret complex, industry-specific data correctly and make autonomous decisions closely aligned with business targets. This iterative approach ensures AI systems stay relevant and grow increasingly smarter over time, improving their ability to make informed decisions. Furthermore, for AI to move beyond being a general tool and become a truly powerful, reliable, and transformative partner in the specialized fields of energy and sustainability, it needs to learn from the best – the human experts who live and breathe these challenges every day. They provide the critical intelligence, context, and oversight that turns potential into practical, impactful solutions. Without their deep involvement, AI tools risk being superficial, inaccurate, or even counterproductive.
Real-World Applications of Collaborative Intelligence
Agentic AI, or digital agents, leverage large language models and a set of tools to execute tasks or achieve objectives on behalf of a user. These agents develop plans using reasoning capabilities and solve problems within their environment. Simple agentic systems follow pre-defined workflows, while advanced systems operate with complete autonomy, iteratively adjusting their plans as they gain more information.
For example, digital agents can automate processes such as selecting emissions factors across Scope 1, 2, and 3, and analyzing vast records spread across multiple systems. Soon, agents will assist in understanding travel records and capital goods purchases to identify appropriate emission factors.
Agents also enhance data quality by automating audits, allowing greater coverage than manual efforts. Beyond reporting, agents support developing decarbonization roadmaps, helping achieve sustainability goals quickly. They review data, analyze constraints, identify measures, and assist in gaining stakeholder support and project initiation.
To simplify sustainability reporting, emissions measurement, and decarbonization efforts, businesses can leverage AI agents that provide traceability, offering rationale for every decision, building confidence and trust among their users.
There is a critical ‘capacity gap’ in today’s workplace, where the pace of business demands exceeds what human teams can sustainably deliver. According to Microsoft’s 2025 Work Trend Index, 80% of employees report lacking the time or energy to complete their work effectively. This challenge is especially pronounced for sustainability teams, who must navigate increasing reporting requirements, stakeholder demands, and regulatory scrutiny. Collaborative intelligence addresses this challenge by providing “intelligence on tap”—a solution that augments human capabilities with AI-driven support. This approach empowers sustainability professionals to meet growing demands more efficiently and effectively.
Figure 1. Intelligence on tap fills the capacity gap. Source: Microsoft 2025 Work Trend Index
Integrating Mind and Machine for Sustainable Innovation
Collaborative intelligence also serves as a transformative partnership between human expertise and advanced AI systems, redefining how industries tackle sustainability. By combining the nuanced insights of subject matter experts with the dynamic capabilities of agentic AI, organizations can achieve a level of precision and adaptability that was previously unattainable. This synergy ensures AI interprets data meaningfully, enabling autonomous actions that align with both operational and environmental goals.
Schneider Electric’s commitment to developing agentic AI ecosystems highlights the strategic importance of this approach. Since 2021, our significant investments in AI have tripled our team size and established global innovation hubs, enabling breakthroughs like our Reinforcement Learning (RL) agents that examine commodity market data and our client portfolios to set daily price triggers, reviewed by our highly skilled expert risk managers, and support decarbonization planning. Together, these advancements underscore our leadership in driving real-world impact. By empowering businesses to bridge the gap between human creativity and machine efficiency, collaborative intelligence offers a pathway to enhanced performance and sustainable innovation. As industries face mounting pressure to meet ambitious environmental targets and comply with mandatory reporting regulations like the Carbon Disclosure Project (CDP) or the Corporate Sustainability Reporting Directive (CSRD), the integration of human expertise and AI will become increasingly essential.
Final Thoughts
Collaborative intelligence is poised to revolutionize sustainability efforts across industries in the next several years. It’s well understood that this innovation will transform how clients automate tasks surrounding their sustainability reporting, enabling more precise carbon tracking, accelerating clean technology development, and helping design adaptive climate mitigation strategies. Agentic AI is like adding a team of colleagues who can take the initiative and work independently toward broader objectives, enabling humans to focus on what matters – taking action. Schneider Electric’s strategic investment in data scientists, engineers, and product management, along with the development of agentic AI ecosystems, positions us as a leader in this transformative approach, driving operational excellence and environmental stewardship.
References
Collaborative Intelligence: Humans and AI Are Joining Forces
Work Trend Index: Microsoft’s latest research on the ways we work.