Apply AI To Accelerate Decarbonization

This summary distills insights from a three‑part webinar series co‑hosted by Accenture and WBCSD, designed to help companies understand how AI can accelerate credible decarbonization and enable sustainability teams to work faster, smarter, and with greater impact.

Sustainability teams are expected to deliver credible emissions reductions across complex operations and value chains, often with fragmented information, limited capacity, and increasing expectations on transparency and decision quality. As AI tools become more accessible, many organisations are looking for practical guidance on where AI helps, what good use looks like, and how to adopt it responsibly.

Potential of AI for sustainability

AI can support sustainability work in 4 areas:

• Operational efficiency: Improve resource and energy performance through optimisation and predictive insights.

• Sustainable value chains: Strengthen supplier and product data collection, screening, and traceability, particularly for Scope 3.

• Communication and reporting: Speed up drafting and structuring recurring sustainability outputs, with final review retained by human-in-the-loop.

• Strategic innovation: Support development of products, services, and operating models that embed sustainability outcomes.

Figure 1: There are several use cases of AI for sustainability which can be categorized across 4 key themes

A key insight shared was handprint vs. footprint: AI should be applied where its enabled impact outweighs the resources required to run it, reinforcing fit-for-purpose use and efficiency.

Figure 2: Environmental Impact of AI - As AI scales exponentially, managing its energy, water, and carbon intensity becomes a strategic imperative

Practical usage and emerging patterns

Across company examples, AI is being applied to:

• Convert unstructured information into usable inputs by extracting, organising and checking information from documents and datasets.

• Support decisions that involve trade-offs such as cost, performance and emissions considerations in materials, circularity and operations.

• Enable monitoring and verification workflows by improving evidence collection and documentation, including approaches that combine digital and remote sensing inputs.

Scaling these use cases is typically constrained by data availability and comparability, interoperability across systems, and collaboration across value chains, especially for Scope 3.

Implementation guidance and enablement

Moving beyond pilots requires building practical capability, not just access to tools:

• Build capability in stages: Start with AI fluency for day-to-day tasks, then enable workflows through low/no-code automation, and progress to repeatable agents embedded in sustainability processes.

• Use structured prompting to improve quality: Apply consistent prompt practices (clear persona, goal, context, constraints, and sources) and iterate, so outputs are reliable and decision ready.

Figure 3: Productivity hack - The structure of a good prompt

• Use agents to automate recurring workflows: Configure focused agents around specific sustainability workflows, with curated reference materials and clear boundaries, to reduce repetition and improve consistency across teams.

• Define ownership and review points: Assign responsibility for how outputs are used, and keep human review where it matters, especially for reporting, disclosures, and external-facing claims.

This series was for WBCSD members looking to build clarity and confidence on how AI can support sustainability and decarbonisation, and to learn how leading companies are applying it in practice.

It was useful for organisations that wanted to:

• Understand where AI is already being used today, including:

  • materials and product design (comparing options across carbon, performance and cost)

  • agriculture and nature (monitoring regenerative practices, satellite insights, soil and yield optimisation)

  • built environment and operations (energy optimisation, predictive maintenance, real-time efficiency insights)

  • data interoperability (digital product information, data spaces and product carbon footprint exchange mechanisms)

• Anticipate what it takes to scale, with common barriers discussed across industries:

  • data fragmentation and limited comparability

  • uneven supplier maturity

  • lack of interoperable systems

• Apply the learning within their organisations, by improving speed and consistency in document- and data-intensive work while aligning sustainability, IT and business owners on data readiness and responsible use.

Climate Impact:

• Faster progress from targets to actions: AI can help teams identify where emissions are coming from, compare reduction options, and prioritise initiatives - especially when information is spread across many documents, systems and partners.

• Stronger monitoring and verification: Examples shared showed AI supporting evidence collection and verification (including remote sensing + document-based evidence workflows), which can reduce the effort and cost of tracking climate actions - particularly in value chains (Scope 3).

• Net climate benefit depends on efficient use: A consistent theme was that AI should be applied where it clearly adds value, and used efficiently, so its climate benefits outweigh its own operational footprint.

Business Impact:

Benefits:

• Less manual effort and faster turnaround: AI can speed up document- and data-heavy work such as extracting information, drafting structured outputs, and maintaining consistency across recurring sustainability processes.

• Better-informed trade-offs: Examples discussed showed AI helping teams compare options that involve cost, performance and carbon impacts (e.g., materials choices, circularity opportunities, operational optimisation).

• Efficiency reduces cost: Efficiency was framed as a practical business lever - better use of data and automation can reduce both time spent and operating costs.

Costs:

• Tool setup and enablement: Setting up AI tools, configuring access, and helping teams start using them in their day-to-day work.

• Governance and assurance effort: Defining ownership, review steps, and documentation so AI-supported outputs remain credible - particularly for reporting, disclosures, and external-facing claims.

• Capability building and change management: Training teams, updating ways of working, and supporting adoption so AI augments work consistently rather than remaining ad-hoc experimentation.

• Compute and sustainability footprint costs: Ongoing cost of running AI (and its energy/water/emissions footprint), reinforcing the need for efficient, fit-for-purpose use and transparency from providers.

Approach:

• Start small, then scale what works. Begin with a small number of meaningful sustainability use cases (often document- and data-heavy workflows), prove value, and only then expand to additional processes.

• Embed AI into ways of working (not only one-off use). Standardise how teams use AI for recurring tasks (e.g., structured extraction, drafting, summarisation, evidence workflows) so outputs become consistent and reusable.

• Use “fit-for-purpose” AI. Apply the right type of tool and model to the task, rather than defaulting to one specific LLM and most sophisticated model for everything.

• Keep humans accountable. Maintain clear review points and decision ownership.

Stakeholders involved:

• Sustainability leads and practitioners (use-case owners; responsible for quality of sustainability outputs).

• IT / data / architecture teams to enable secure access to systems and data, and to support scalable deployment choices.

• HR / learning to support skills building and role evolution as AI changes workflows.

• Risk / legal / compliance (as relevant) to define acceptable use, confidentiality boundaries, and review controls (particularly for external-facing outputs).

Key parameters to consider:

• Data readiness: completeness, consistency, comparability, and where critical inputs sit outside the organisation (especially Scope 3).

• Governance & accountability: who owns the output, what requires human review, and how decisions/assumptions are documented.

• Confidentiality & policy: what content can be shared with which tools; controls for sensitive documents and supplier data.

• Environmental footprint: energy, water, and emissions impacts vary by model and deployment; ask providers for transparency and prioritise efficiency and fit-for-purpose solutions.

• People & change management: adoption is about augmenting teams and evolving workflows (not “replacing jobs”); invest in skills and manager support to avoid unmanaged change.

Implementation and operations tips:

Top 10 Lessons for Business Leaders

• Anchor on a business decision or outcome. Be clear what will improve (speed, quality, cost, emissions impact).

• Choose one high-value workflow to start. Prioritise a repeatable, document- or data-heavy task.

• Match the tool to the task. Use fit-for-purpose AI, avoid “AI by default” (reaching for the same AI tool for everything instead of selecting the right method and model for each job).

• Use structured prompting. Set role, goal, context, constraints, and sources, then iterate.

• Verify before you rely. Check facts, assumptions, and calculations,

• Keep human ownership clear. Define who reviews and signs off, particularly for reporting and claims.

• Standardise what works. Convert learning into reusable prompts, templates, and checklists. And agents?

• Scale with agents and automation. Use focused agents with curated sources and clear boundaries for repeatable workflows

• Manage barriers proactively. Where data quality, fragmentation, or comparability are not fully in place, plan for constraints early (including scope 3 data and supplier maturity).

• Measure benefit and footprint. Prioritise efficient use, measure benefits of use cases and ask providers for transparency on energy/emissions impacts