#243 How Can You Leverage AI for ESG and Sustainability Reporting

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Annual sustainability and ESG reporting is now becoming a necessity for many businesses, whether driven by region specific regulations and legislation, industry expectations or client demand.

However, doing so is definitely easier said than done. It requires a complex network of data being gathered from multiple sources which then needs to be collated, analysed and summarised in a cohesive report for leadership and possible public publication.

Thankfully, there have been developments in new AI driven technology that can help ease this annual burden, allowing you to focus on utilising the results to make meaningful sustainability impacts.

In this episode Mel Blackmore is joined by Darayush Mistry, Head of Product at Pulsora, to discuss how AI can make a difference in ESG and sustainability reporting, including its benefits, pitfalls and the balance of utilising AI while considering its environmental impact.

You’ll learn

• Who is Darayush?
• Who are Pulsora?
• When did Darayush realise how AI could be utilised for ESG and sustainability reporting?
• What are the positives of AI in this space?
• Why is AI for ESG and sustainability reporting becoming more necessary?
• What are the risks involved in using AI for ESG and sustainability reporting?
• Where is AI making a real difference in reporting?
• What parts of ESG and sustainability reporting need human judgement?
• How does AI help collate data from multiple sources?
• How might regulators react to AI being utilised in reporting?
• How can businesses utilise AI while still considering it’s environmental impact?
• Darayush’s advise to sustainability leaders looking to explore AI solutions

Resources

• Pulsora
• Darayush Mistry
• Carbonology

In this episode, we talk about:

[00:25] Episode Summary – Mel is joined by Darayush Mistry, Head of Product at Pulsora to discuss the use of AI tools in ESG and Sustainability reporting, how you can leverage this technology and what risks you need to be aware of before doing so.

[02:40] Who is Darayush Mistry? Darayush has been working with enterpirise software for the past 2 decades. This technology is used by companies to help operationalise their business.

He began his career at a company called Siebel Systems, which operated in the CRM space, spending 10 years there before moving onto the world of sustainability.

Darayush recalls how everyone was so used to working from a set of spreadsheets just 20 years ago, whereas now most will use a central CRM for business operations.

This is an area that sustainbilty reporting seems to have lagged behind, with many still trying to collate their data from multiple spreadsheets and other external sources rather than having a dedicated central system. This is why he was eager to work with Pulsora, to bring similar solutions to businesses as he once had with CRM’s in the past.

[05:25] Who are Pulsora? Pulsora are an AI-forward SaaS (software as a service) platform.

The Pulsora platform helps businesses to operationalise their sustainability initiatives, which includes data collation, calculation and reporting features. This is set up for scope 1, 2 and 3 level reporting, with considerations for climate related goals, waste water monitoring, biodiversity and policy oriented information.

Darayush’s role as Head of Product means he sits at the intersection between customers and Pulsora’s engineering and design teams. His job is to ensure that whatever Pulsora created ultimately provides value to their customers in the form of successful sustainability outputs.

[07:50] When did Darayush realise how AI could be utilised for ESG and sustainability reporting? Darayush can pinpoint a time four years prior when he first stepped into a more sustainability focused role, speaking to the co-founders of Pulsora back in 2021 they were sharing experiences of using the then early versions of AI tools such as ChatGPT and Gemini.

It clicked for them then that they could do something similar for sustainability reporting, making it as easy as possible while still being accurate. It wasn’t until 2 years later that they had a product to launch with Pulsora AI in late 2024.

This initial product allowed users to write long from narrative responses for carbon disclosures. Regulations like CSRD require a comprehensive disclosure, but not everyone is an expert in parsing the data to write that, so Pulsora AI helped get past that writers block, to give people the building blocks for that professional disclosure.

[11:55] What are the positives and negatives of AI in this space? The biggest benefits include:

• Giving professionals and sustainability teams more time back to achieve their desired outcomes.
• Cutting down on spending time in spreadsheets and on calculations on an annual basis.
• Reduction of repetitive tasks
• Ease of data collection from multiple sources and locations
• Ease of data calculation
• Allowing for pre-audit of data using AI tools
• Highlighting data gaps when rationalizing the data

[17:20] Why is AI for ESG and sustainability reporting becoming more necessary? People are starting to move on from the mindset of ‘Let’s try AI’ to ‘Let’s use AI’.

Time is one of the most precious resources we have, and any tool that can help accelerate more mundane tasks so that people can focus on making results happen should be a priority.

Sustainability teams are under increasing pressure to produce tangible results, something that can be made easier with the help of AI tools.

[20:06] What are the risks of using AI in ESG and Sustainability reporting? Don’t treat AI as this magic wand, it’s a tool you can leverage. At the moment, it’s good at certain tasks, but it cannot act on its own.   

In order to progress, sustainability teams need to push on the initiatives to produce results. People know their business best, and though AI can infer certain information and produce a result, it may not always be the best solution for you. You still need that human input into areas such as strategy and action planning.

Darayush reminds us of Amara’s Law: “We as humans severely overestimate technology outcomes in the short-term, and severely underestimate that in the long-term”

Don’t fall into the trap of thinking AI can do everything.

[22:30] Where is AI making a real difference in reporting? Data collection, ad-hoc sustainability reporting and providing insights into the data provided. It can also help with providing a starting point for carbon disclosures or options for various strategies that you could explore.

Currently, the biggest one is data collection, as it can help do this efficiently and consistently, allowing for improved accuracy in your overall sustainability data.

[25:20] What parts of ESG and sustainability reporting need human judgement? Darayush states that these are complementary to each other, it should never be all of one and none of the other.

There will be elements that need more human in the loop and areas where it’s required less. It’s applicable in degrees.

One example of where the human input will be higher is in completing a materiality assessment and figuring out how to execute your decarbonisation strategy, which will require your knowledge and experience of how the business operates, it’s core values and what your ultimate goals are.

AI can do the heavy lifting in areas such as sustainability reporting, as it can collate all the data and create initial reports very fast. But, at the end of the day, humans still need to understand these outputs and provide their own judgement.

‘AI’ today isn’t true AI, they’re LLM’s with a great capacity to collect data, analyse it and provide outputs that can be starting points. It cannot replace human judgement, as we provide the nuance in context and experience needed to apply those results effectively.

AI responses operate in a perfect world where everything is an easy step by step process, which we all know does not reflect reality.

[29:40] How does AI help collate data from multiple sources? Older technologies like OCR (optical Character Recognition) was the go to years ago when scanning various different documents like spreadsheets, PDF’s, receipts etc. This required specific code to be written to read these docs accurately, this would then feed into pipelines to bring this data together. This code was quite rigid, so any changes to document layouts would cause things to break.

AI in comparison is much more adaptable, it’s capable of reading much more natural language and extracting what’s required for its designated task. It also provides a much more friendly UI (user interface), meaning you don’t need an IT specialist to utilise the technology.

[33:15] How might regulators react to AI being utilised in reporting? Based on Darayush’s previous experience in the finance sector when people were using dedicated platforms for financial reporting, the regulators didn’t care where the data came from or how it was collated, they just card if it was accurate.

 Regulators want transparency, accuracy and a big part of this is providing an audit trail so they can see where the data came from. They simply want businesses to follow their guidelines, the how you get from A to B is of little importance so long as the result is accurate.

If anything, the existence of these tools will raise the bar of expectations from regulators, as businesses should be able to provide the required information with these tools readily available.

[36:30] How can businesses utilise AI while still considering it’s environmental impact? – AI can certainly aid the sustainability industry in certain areas, such as reporting, but it’s a resource intensive tool.

It consumes a lot of energy and water. Like with most emerging technology, the sustainability impact usually isn’t addressed until much later. Much like with mobile phones, which create tonnes of E-waste every year, not to mention the mined material required to make them. It’s factors like this which eventually get regulators involved to help reduce the overall harm caused.

AI is yet to go through this evolution, but both regulator and consumer pressure is building to reduce the impact of AI. This will inevitably lead to innovation as companies seek to find more sustainable ways to cool data centres and reduce the resource burden.

On the flip side, AI can help save energy in other ways, such as time taken to complete the tasks for a human, which will include travelling to an office and amount of time they use a device for the task. This also has its own carbon footprint, which can comparatively be reduced by using AI to complete the tasks in minutes as opposed to hours or days.

The bottom line as of the start of 2026 is, we know there is a resource issue when it comes to AI, and companies are looking at better ways to address it as the technology develops.

[42:20] Darayush’s advise to sustainability leaders looking to explore AI solutions – Identify a problem space where you can apply AI in a measured way an start using it. The only way you can find out how it impacts you is to use the technology.  

Currently, AI shines is areas such as collating data from multiple sources and locations, so if that’s an issue you’re tackling where sustainability reporting is concerned, that’s a good place to start with utilising AI.  

If you’d like to learn more about Pulsora, check out their website.  

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