How AI helps sustainability teams track progress on decarbonisation goals

Published 6 November 2026

How do AI-driven platforms help sustainability teams track progress on decarbonisation goals?

DitchCarbon keeps the progress picture current without a reporting project. AI reads what organisations publish and a team of 15 analysts checks it, so every supplier and holding gets a projection against each organisation's own target trajectory, rolled up to category, portfolio or account level. The team sees who is off course and spends its time where a conversation moves the number.

In DitchCarbon, the tracking runs on verified emissions data for over 2 million organisations, built on primary emissions data wherever it exists. That includes published GHG inventories and product carbon footprints, CDP responses, EcoVadis scorecards, SBTi targets and data that suppliers declare on their own profiles.

For a company with a Scope 3 target, tracking has two halves: its own inventory, and the suppliers whose reductions move it. AI does most of its work in the second half, because that is where the volume is. A large supply chain holds thousands of organisations, each publishing on its own schedule, in its own format and language. Our guide to tracking progress against an SBTi target covers the first half: the base year, the recalculation policy and the record an assurer checks.

How does AI turn published reports into figures a team can track?

Language models read sustainability reports and disclosures and turn them into structured, comparable data. That is the hard part of tracking at scale: organisations write the same thing in hundreds of thousands of ways, across languages and formats, while the GHG Protocol has fewer than 30 scopes and categories. DitchCarbon trained a model to resolve those variations, so "SCOPE 3 Emissions from Investments" and "Emissioni Scope III, investments" land in the same category.

Rules-based validations then route anything anomalous to a team of 15 analysts before it reaches a customer's data. As organisations publish and restate, the figures refresh continuously, with documented sources, and restated years are reconciled so a trend compares like with like.

How does AI match those figures to your suppliers and holdings?

Entity resolution attaches each figure to the organisation on your list. DitchCarbon matches against DUNS, LEI, ISIN and VAT identifiers where your list carries them, and from a company name alone where that is all it holds. Where an operating entity has no data of its own, DitchCarbon rolls it up to its parent, so the parent's disclosures and commitments apply; where the operating entity has published its own figures, those are used.

Requests and replies run from the platform, so the method is the same for 100 suppliers as for 10,000. That is what lets a team track a large supply chain or portfolio with the same staff it has today.

How does the platform project each supplier against its own target?

DitchCarbon draws a projection against each organisation's own target trajectory, rolled up to category, portfolio or account level. Actuals run forward to 2050 on a two-year and a five-year trend, next to the line the organisation's published target requires. The gap between the two is the answer to "will this supplier get there?"

The target comes from a register of every reduction target the organisation has published, with its scope, type, base year, target year, source and original wording, and SBTi-validated targets marked as such. Two companies can both claim net zero by 2030 and mean very different things, so the register keeps them apart. Scenario planning sits alongside it.

How does the platform flag who is off course?

It compares each trend with each trajectory and surfaces the gaps that matter most. Every target carries an on-track status, and every supplier and category is ranked by how much of your total footprint acting on it would move, so the list of conversations starts where the tonnes are.

Two trend lengths give the team two readings: a five-year trend smooths out one good year; a two-year trend catches a recent change of direction, and a supplier whose two-year trend has turned upward is worth a call whatever its longer record says. At portfolio level, SBTi progress can be weighted by count, spend or embodied emissions, because the three give different answers. DitchCarbon shows all three, so the team chooses the basis it reports on.

How can a team check what the AI has done?

Open any figure and follow it to the document it came from. In DitchCarbon every figure carries its source and change history, and the source documents list their assurance level for each scope: reasonable, limited, unspecified or none, with the assurer the document names. A reviewer sees what was read, where, and what changed since, which is the record an auditor asks for when a progress number moves.

AI reads public disclosures only. External models see snippets of text from public reports and no customer data, and a customer's supplier, spend or investment data stays inside DitchCarbon's environment. The trust centre sets out how the models are used and holds the security documents.

For where AI agents help in carbon work and where they need an audit trail, see AI agents in carbon management in 2026. The calculation behind the baseline is verified to ISO 14064-3, limited assurance, by UL Solutions, renewed annually, so the starting figures a team tracks from are audit-ready.

What does a sustainability team do with the time AI gives back?

It runs the conversations that change the trajectory. Each supplier and category where action moves the footprint most comes with a reduction plan drawn from a named peer that has already made the cut: a goal, an action plan, four questions to ask, and a contract clause with a date and a shortfall remedy, ready for the next meeting or RFP. Suppliers already on a credible path are left to carry on, so the effort goes where a gap is opening.

Where a figure needs more detail to track progress properly, a request goes from the platform, prepopulated with what the supplier has already published, asking for what the buyer needs: a site's energy use, the activity data behind a purchase, a product carbon footprint. A recent deployment gained additional primary data from 30% of over 40,000 recipients. The data that comes back joins the same record, and the projection updates with it.

More on the route from collection to reduction is in AI for Scope 3. That is how a Scope 3 target becomes something a team can steer during the year, with progress it can show to the board, customers and lenders.

Being asked to show your own progress?

If customers or investors are asking how your company is tracking against its target, claim your company profile. Your published figures and targets are already there; add the detail they need once, and every customer and investor reads the same answer.