Why scaling AI is the key to sustainable business transformation

Why scaling AI is the key to sustainable business transformation

Aurelie Lustenberger

Sep 24, 2025

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AI is set to transform organizations, including in terms of sustainability. Scaling sustainable AI across the enterprise - instead of simply adding isolated use cases - can unlock long-term business value and impact.

AI is fast emerging as a game-changer for productivity. It's also opening the door to smarter decision-making and greater agility - particularly when dealing with complex, cross-functional challenges. In sustainability, AI - and especially agentic AI (with sustainability-focused agents) - is already being leveraged for a variety of goals. Organizations are using agentic AI to advance climate strategy, energy efficiency, waste management, compliance reporting, and more. They're addressing social topics too, such as diversity and inclusion, employee training, and employee mental health.

Technology made for flexibility

We know that adopting AI at scale is more operationally efficient, sustainable, and cost-effective than using it only for isolated cases. Scaled AI can drive greater impact and increased value across the company. But large-scale implementation represents a major investment. How can businesses ensure they reap the benefits and avoid the pitfalls?

Organizations adopting AI most effectively start by identifying the problems at a strategic level, then working with an expert partner like Capgemini to design, deliver, and deploy a scaled solution. These cloud-based technologies are scalable by nature, tailored in response to business needs.

Scaling brings increased efficiency

While there are benefits to running AI agents in isolated use cases, scaling can ensure efficiency and therefore increased sustainability. Starting with a clear plan to launch at scale can help companies avoid issues resulting from uneven implementation. Defining an organization-wide strategy can help establish clear applications, avoid duplicated efforts, and prevent the kinds of inefficiencies typically caused by using multiple different systems in parallel.

Scaling successfully also means process-wide implementation. AI is most effective when built into a process from end to end, instead of only in one stage. Take a multinational company's process for the month-end financial close as an example: From subsidiaries submitting their reports to the global accounting team consolidating them, then calculating liabilities, and submitting for CFO approval, there are multiple sequential steps that can drag out the timeline. However, if an AI agent is placed at every step - validating subsidiary data, consolidating figures in real-time, flagging issues continuously, and so on - the company can avoid a frantic scramble at month-end. A fully 'agentified' process will have smoother step-to-step transitions and fewer bottlenecks.

No matter the use case, AI needs data. With data from across an organization, AI can conduct a richer, deeper, and more critical analysis. Unlike siloed AI solutions, which may miss vital pieces of information, a fully connected approach can manage processes and predict outcomes for a whole company. Equipped with robust data, AI agents can also cross-check data and analysis with other agents for a deeper, more accurate output.

Scaled AI also allows for standardization. Data chains and AI that are connected by design ensure operations run smoothly. They ensure accurate data collection at every stage of the value chain and provide outputs that are aligned across the board.

External disclosures: scaled AI in action

To improve external disclosures processes such as CDP questionnaires, investors or shareholders questionnaires or regulatory reporting such as CSRD in Europe, employees must obtain information from both internal teams and suppliers, synthesize the inputs and then analyze the results.

However, the work required to collect and organize this information is time-consuming and resource-intensive for human employees. AI can do it much more efficiently - freeing up the humans to focus their time and efforts on the next level of optimization and strategy. Specialized agents can be set up depending on the type of data and focus to serve multiple reporting needs instead of parallel data collection and computation.

Doing it right the first time

With strategic, organization-wide AI adoption, businesses can achieve transformational results. Deploying agentic AI at scale can make processes more efficient, unlocking real business value and creating lasting impact across entire organizations. Agentic AI enables companies to not only more easily meet their compliance requirements (like CDP reporting) - it also liberates humans to focus on higher-value work.

By scaling agentic AI with standardized systems and integrated data flows, its benefits can be fully realized. At Capgemini and Microsoft, we know that when AI is implemented with a clear purpose and on a large scale, it acts as a catalyst for sustainable transformation across the organization and beyond. Develop your company's catalyst today: