Sustainability expectations are changing rapidly across industries. Companies now have greater visibility into their emissions spanning products, processes, supply chains, and their overall business carbon footprint. This is increasingly supported by more mature, granular, and structured data, which enables greater transparency into the environmental impact of business activities.

Turning this increasing amount of information into practical action remains difficult. Sustainability teams often work with data spread across systems, presented in static reports, and dependent on manual analysis. Even when data is available, it can still be difficult to answer practical questions such as:

• Where should we focus our sustainability efforts first?
• Which changes will have the greatest environmental impact—and the greatest impact on our business?
• How do our sustainability goals reduce costs, improve business performance, minimise risk, and meet our operational needs?

Companies are balancing environmental responsibility with day-to-day decisions, planning cycles, and collaboration across teams. Their sustainability efforts need to support their decision-making, not slow it down.

As a result, many companies are shifting their focus from understanding their footprint to determining where action will have real impact. These companies know that sustainability insights need to directly support everyday decisions that improve environmental outcomes while reducing costs and boosting business performance.

How AI helps you act on sustainability data

Organisations are facing increasing pressure to consider corporate carbon footprint and product carbon footprint data in their business decisions. Regulations continue to evolve, standards are not always aligned, and requests for footprint data extend to individual products, suppliers, and activities. At the same time, sustainability information is used throughout multiple business functions to mitigate risk and inform planning and next steps.

AI can help you manage this complexity while reducing manual effort. By reviewing sustainability data across products, processes, and scenarios, AI helps reveal patterns that are difficult to identify through traditional analysis. This makes it easier to understand what contributes to your business carbon footprint and identify the most effective opportunities for change.

The Footprint Optimisation Agent* in SAP Sustainability Footprint Management—an ERP-centric carbon management solution—enables you to explore alternatives more quickly. By using this AI agent, you can transform sustainability insights into operational decisions by:

• Identifying footprint hotspots and reduction levers.
• Simulating the impact of operational choices.
• Comparing emission-reduction scenarios against your baseline.

Instead of rebuilding analyses when assumptions or priorities change, you can use the Footprint Optimisation Agent to evaluate different options using consistent underlying data. This helps you prioritise environmental actions with confidence, even as business conditions change.

The Footprint Optimisation Agent also makes sustainability insights easier to explore, explain, and report on. This supports planning, investment, operational discussions, and tracking of sustainability initiatives by helping teams understand drivers, trade‑offs, and outcomes.

Making sustainability data usable for compliance and real-world decisions

Many organisations are discovering that insights into sustainability do not necessarily reduce carbon footprint. Even when using AI tools, results depend on the quality, structure, and context of the underlying data. Footprint data is often shaped by different assumptions and influenced by evolving regulatory requirements. Without a consistent foundation, insights can be difficult to validate, explain, and apply.

Regulatory change adds pressure, too. Requirements such as the EU Corporate Sustainability Reporting Directive (CSRD) increase expectations regarding data depth, traceability, and consistency. Likewise, the EU Carbon Border Adjustment Mechanism (CBAM) requires carbon emissions data for certain imported goods. This connects sustainability data more directly to trade and cost considerations.

In addition, calculation approaches can vary by region, sector, and methodology. AI tools can certainly analyse massive volumes of information quickly. However, they require clear definitions of what data are included and consistent methods that support meaningful comparisons across products, locations, and scenarios.

There is also a practical challenge: sustainability insights must align with how companies actually make decisions. If analyses remain isolated from financial planning, operational trade-offs, and resource constraints, they are unlikely to influence outcomes.

In short, using an AI sustainability tool should reduce manual effort and friction, not introduce another layer of complexity disconnected from day‑to‑day work.

The Footprint Optimisation Agent is designed to address this gap, giving you:

• Quicker validation of potential carbon reductions.
• More effective decarbonisation decisions using scenario-based insights.
• Clearer visibility into business impact, including financial and operational analysis.
• A faster transition from footprint insight to prioritised carbon-reduction planning.

Let’s look at four ways in which the Footprint Optimisation Agent helps you move from footprint insights to informed action with real impact.

1. Identify hotspots and reduction levers across your footprint

You cannot reduce what you cannot clearly see. Many organisations track greenhouse gas emissions, energy consumption, and waste, but the data is often difficult to use for decision-making. Information may exist at corporate, plant, or product levels, but not always in a way that helps you see what is driving impact or where to focus first.

The Footprint Optimisation Agent helps you review your corporate carbon footprint and product carbon footprint more quickly and in greater detail. You can quickly analyse emission sources across multiple dimensions—including products, processes, plants, and value‑chain stages—using a consistent calculation foundation. This allows you to see where impact is greatest and which activities contribute the most to your overall footprint.

With this clarity, you can move beyond high-level summaries and identify hotspots linked to everyday business activity. You can also identify practical options for reducing impact that relate directly to operational choices such as production methods, energy inputs, and sourcing patterns. This provides sustainability, operations, and finance teams with a shared starting point for prioritisation.

Use case: Focus improvement efforts where impact is greatest

A manufacturing company uses the Footprint Optimisation Agent to review its carbon footprint data at both the product and plant level. This review finds that a small number of high-volume products is responsible for a large proportion of emissions. Additional analysis shows that energy consumption in specific production stages accounts for most of the impact. With this insight, the team can focus its efforts on refining those production steps instead of launching broad sustainability initiatives across the company’s entire product portfolio.

2. Simulate the impact of potential operational changes

Once you’ve identified your hotspots, the next challenge is deciding which changes to implement first. Reduction ideas often compete for attention, and without clear insight into their predicted effects, discussions can stall or rely on rough estimates.

Simulation helps you explore potential changes before making them. By using identified reduction levers as inputs, you can run what-if scenarios to examine how adjusting operational processes and sourcing decisions could affect your corporate carbon footprint and product carbon footprint. This supports what-if analysis by allowing you to test assumptions consistently, rather than building separate analyses for each idea.

Because simulations rely on the same underlying carbon footprint data, teams can review different scenarios using shared assumptions.

Use case: Evaluate supply chain options in response to CBAM requirements

To comply with CBAM requirements, a consumer goods manufacturer needs greater visibility into the carbon emissions of their imported raw materials. Using the Footprint Optimisation Agent, the company’s supply chain team simulates what‑if scenarios involving alternative suppliers and transport methods for a high‑volume material used across multiple product lines. Through what-if analysis, the team identifies sourcing options that reduce reported emissions under CBAM—balancing cost, availability, and operational constraints.

3. Compare scenarios to focus on what delivers the greatest impact

Once you’ve run simulations, the next step is understanding which options actually matter. Without clear comparison, teams can struggle to assess trade-offs or explain why one course of action makes more sense than another.

By comparing simulated scenarios against a current baseline, you can see how potential changes could affect your emissions, energy use, or waste. The Footprint Optimisation Agent supports side‑by‑side comparison so that you can evaluate results using consistent data and assumptions. This helps you understand whether a change reduces impact—and by how much.

These comparisons enable you to make more informed decisions across the company. Instead of debating assumptions, teams can focus on the relative impact of operational changes such as substituting materials, adjusting processes, and updating energy sources and strategies. You can also more easily identify which actions will yield the greatest reduction potential and which offer only limited benefits.

Use case: Prioritise initiatives with the greatest potential for reduction

A global industrial manufacturer with energy-intensive operations is evaluating potential improvement initiatives across its production sites. The company uses simulation to compare material substitutions, process changes, and energy sourcing adjustments against its current footprint. Although multiple initiatives show incremental reductions, only a few deliver significant emissions reductions at scale. With clear, comparable results, the team prioritises those high-impact initiatives for detailed planning and investment and rules out the rest.

4. Report results and turn insights into concrete action

Insights only create value when they lead to follow-up. Sustainability teams often encounter a gap between analysis and implementation, where results exist but actions remain unclear or disconnected from business processes.

The Footprint Optimisation Agent helps you present report results in a way that supports concrete decision-making and next steps. Using the scenarios and comparisons created with this AI agent, you will be able to generate clear, executive-ready views of footprint calculations, drivers, scenarios, and comparisons. These views help teams agree on priorities and move discussions from analysis to action.

From there, teams can use report results to support their decarbonisation efforts, track progress against reduction goals and initiatives, map the financial impact of these changes, and engage other parts of the business in sustainability planning and follow-up. This may include improving operations internally, discussing sustainability considerations during procurement, and directly engaging suppliers to address emissions and sustainability targets. By connecting insights with follow-up tasks, sustainability work becomes part of how the business plans and acts.

Use case: Align leadership and teams regarding next steps

A multinational electronics manufacturer consolidates product-level footprint results into an executive-ready report for its quarterly business review. The report highlights a set of approved reduction initiatives, expected emissions impacts, and responsible teams. Sustainability teams use the report to track progress, while operations and procurement leaders incorporate the agreed-upon actions into production planning and supplier engagement. This shared view helps keep leadership aligned and sustainability commitments moving forward.

A clearer path from insights to action

Businesses face increasing pressure to reduce their environmental impact while managing complex data, constantly evolving requirements, and time constraints. Knowing where to take action can often seem more difficult than measuring your corporate carbon footprint and product carbon footprint. The Footprint Optimisation Agent in SAP Sustainability Footprint Management helps overcome these challenges.

Using the Footprint Optimisation Agent to integrate your sustainability data into everyday business processes helps you make decisions with greater clarity and confidence. You gain enhanced visibility into emissions hotspots and reduction levers, quicker insights into which changes matter most, and stronger support for collaboration across sustainability, operations, and finance teams.

With this AI agent, you can simulate the impact of operational choices, explore options, compare potential outcomes against your baseline, and focus sustainability efforts on areas with the greatest potential impact.

When your carbon footprint data is tied to business context, your sustainability efforts become easier to explain, align, and act upon. This reduces uncertainty, improves decision-making, and helps translate sustainability goals into measurable progress.

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