Three days at Vivatech 2025 confirmed something we see in practice every day; the hardest part of managing sustainability data is not the strategy. It’s the data itself. Disconnected systems, inconsistent formats, hundreds of suppliers across multiple tiers: AI is the tool that’s finally making this manageable at scale.

Sweep’s CEO Rachel Delacour and CTO Yannick Chaze joined on-stage conversations at Vivatech this year, speaking with business leaders and practitioners about where AI is creating real, measurable value for sustainability teams. Here are three practical takeaways from those discussions.

1. AI is solving a data complexity problem, not just a speed problem

Sustainability data is structurally harder to manage than financial data. It comes from dozens of disconnected systems across multiple tiers of a supply chain. It mixes structured and unstructured formats. It shifts depending on which reporting framework you’re working against. For years, the limiting factor wasn’t the willingness to act. It was the sheer difficulty of connecting all of that information reliably.

AI changes that equation. At Sweep, AI collects and normalizes data across tier one, two, three, and four suppliers, finds patterns across large datasets, and surfaces the specific hotspots where emissions reductions are both possible and material. Sustainability teams no longer need to manually map where their biggest exposures sit.

2. Sustainability reporting is the starting point, not the destination

For many organizations, sustainability has historically meant an annual reporting exercise. That framing is changing fast. The companies getting the most value from their sustainability data are using it continuously, the same way they use financial data, to inform decisions across operations, procurement, and investment.

Yannick framed this as a two-step process. Step one: get the reporting right. Build an accurate, auditable picture of your footprint. Step two: use that baseline to take action, identifying where to reduce emissions, which suppliers to engage, and where the highest-ROI interventions are. AI supports both steps, but its real value sits in enabling the second.

3. Boards are spending real time on non-financial data

One of the most concrete signals from Rachel’s conversation was a direct example: a major global retailer now dedicates a third of its board time to reviewing sustainability data. That’s not a sustainability team reporting upward. That’s executive leadership treating non-financial performance as a core business input, on par with revenue and cost.

The expectation that comes with that level of scrutiny is also higher. Data reaching the boardroom needs to be auditable, consistent, and held to the same standard as financial statements. That’s where the quality of the underlying data infrastructure starts to matter significantly.

What this means in practice

The organizations already ahead have moved past data collection and into continuous analysis, using AI to turn large, messy datasets into decisions. For businesses still building that foundation, the priority is clear: get the data infrastructure right, and the insight follows.