When customers factor sustainability into a purchasing decision, they're not comparing your intentions but your evidence. A CO2 figure per product, a supply chain analysis, a certificate that aligns with the customer's standard, an answer to a sustainability questionnaire that matches the rest of your file. The company that wins on this dimension is not necessarily the one with the best underlying performance. It's often the company that puts the evidence on the table faster and more consistently at the moment the customer asks for it.
That is precisely where the work lies: gathering data from procurement, production and logistics, aggregating it into a figure per product or service, and keeping that figure traceable when an auditor or customer asks further questions. Until recently, this was work that required people spending weeks on spreadsheets, supplier statements and manual checks. That determined how quickly you could respond to a tender with sustainability requirements.
The work behind sustainability as a sales argument broadly consists of three layers. First, gathering base data: energy consumption, material flows, transport kilometers, waste figures. Second, translating that data into a standard or norm that the customer recognizes, such as a CO2 footprint per unit or a score according to a sector framework. Third, the substantiation: can you show where a figure comes from when someone checks it.
The first layer is largely a matter of unlocking data and structuring it consistently, something for which AI is increasingly deployed to automatically recognize and merge data from invoices, supplier portals and internal systems. The second layer, the translation into a standard, is shifting to a model in which AI makes an initial classification and a human approves or rejects that classification with reason, because standards differ per customer and per sector and a wrong application has consequences for the credibility of the figure. The third layer, the substantiation during an audit, remains human work for now: someone must be able to explain why a figure is correct, not just that a system calculated it.
As soon as the gathering and aggregating of sustainability data is largely automated, what is distinctive changes. Previously, the company with the largest dedicated staff for sustainability reporting won: more people, more capacity to answer requests. If that capacity is freed up because a large part of the gathering work happens by itself, the distinction shifts to the quality of the underlying data and to the speed with which a company can answer a new customer question without starting a new project.
This does not happen everywhere at the same pace. A company with organized source data, standardized procurement processes and a clear supply chain structure can have this work taken over faster than a company where the same data is spread across separate systems and employees' personal knowledge. So the difference lies not in the willingness to automate, but in how accessible the underlying data already is. Whoever has that in order can answer a sustainability question in a tender faster and in more detail than a competitor who is still manually gathering figures. That difference is measurable in turnaround time and in how much of the requested substantiation is actually delivered, not in a marketing claim.
Where personnel decisions arise as a result of this shift, they are subject to their own legal requirements; that is a matter for the employer, not for a comparison of competitive positions.
Sustainability rarely stands on its own in a purchasing decision. It is often weighed together with other dimensions, and the same shift plays out there too: how automation of audit work is changing the comparability of certifications, what it means for competitive position when warranty and risk assessments can be substantiated faster, and why sustainability claims in marketing copy often sound interchangeable between competitors. Anyone looking at several of these dimensions at once will see that companies leading on one front are usually leading on more than one, because the underlying data skill is transferable between dimensions.
The question of which part of this work can actually be taken over by AI in your own company, and which part remains oversight or human work, cannot be answered in general terms. That depends on how your data is currently organized and which standards your customers use. That question is answered per task with FTE TO AI's work scan.
To know whether sustainability is a dimension on which you are winning or losing, it must first be clear what you claim to be winning on and whether that can be substantiated with evidence. In the free dimension check, you identify what you think you're winning on, and see which of those claims can be defended with evidence. The full benchmark, with your position and that of your peer group on all deal-deciding dimensions, is under construction.