AI in Action is a Talentedge leadership interview series exploring how artificial intelligence is reshaping business. These are conversations with senior leaders and founders about how they’re adopting, applying or building AI within their organisation.
We hope these perspectives offer a rounded view of how AI is transforming work, strategy and leadership across industries.
I sat down with Jon Hale, CFA , the General Manager of Climate X , to discuss the business, how they are utilising AI internally and how he has supported the integration of such tools to help the business scale efficiently.
Jon initially joined the business as Director of Finance. The focus of his role evolved to include operational responsibilities, focusing on using finance to improve operational efficiency and reduce bottlenecks across the organisation. Recently, he moved into the General Manager role, leading global financial strategy and UK operations.
He has a background in finance and technology, including experience in hedge fund investing, venture capital, and roles combining finance and operations in tech companies. Initially studying Business, followed by a Masters degree in mechanical engineering with a focus on robotics. This led to a career in venture capital as an investor before transitioning to the operating side of innovation with leadership roles in tech and climate-related businesses.
Can you tell me about Climate X?
Climate X is a leading climate risk data provider focused on the impacts of climate change on society and economics. The company has developed 14 different climate models assessing hazards such as sea level rise, tropical cyclones, subsidence, and flooding, far into the future. We combine climate data with internal loss modelling to estimate financial and business risks to real assets and companies globally.
Our customers include large global banks and real estate and asset managers who use Climate X data for assessing new business opportunities and stress testing existing portfolios against climate risks. We offer either data via API or a SaaS platform with a Google Maps-like interface allowing users to visualize climate hazards at granular levels and understand financial implications over time.
What areas of the business are you utilising AI the most?
Internally I like to divide AI utilisation into operations and product areas. For example, tools such as Carta, uses AI to map company asset bases globally by analysing publicly available data, enabling our clients to assess climate impact on businesses. Additionally, we use AI in product development for quick A/B testing with tools like Lovable and to accelerate engineering through automated code development with Cursor.
When looking at our commercial operations, AI supports business development with agentic BDR tools like Clay to enhance out outreach. Our customer support is also being augmented with AI by integrating internal knowledge hubs (predominantly Notion) with ticketing systems (Zendesk) to automate first-line support.
In regard to our finance operations, my team’s focus is on automating traditionally manual processes such as monthly close and bookkeeping using platforms like Light to reduce time and cost significantly. On top of this we are automate invoicing and expense reimbursements by using OCR to read contracts and bills and auto-generate invoices. It’s imperative we reduce manual workload since we run a lean finance team.
We aren’t necessarily trying to reinvent the wheel here, they are low-hanging fruit areas where AI can replace manual, repetitive tasks to improve efficiency.
How do you go about assessing what’s on the market for different AI solutions? Do you have a specific business issue and then try to find something that will match that?
When we are assessing AI solutions it typically involves two parts: initial scoping and then the practical implementation. Every team at Climate X has a performance pillar to increase efficiency by 20%, and we use this to push AI adoption strategies by encouraging teams to find or build AI solutions that contribute to this goal. We as a company also hold biweekly AI hackathons where R&D teams present AI tools or solutions to improve productivity.
There is also the resource conversation. Before hiring any role, we evaluate whether AI can optimise the current team or replace the need for additional headcount entirely. We ask individual team members to do a look back at the end of the week to reflect on inefficiencies and opportunities for AI automation to help the team succeed as much as possible.
How do you assess the ROI of any investment into AI tools? How do you fight through the noise of a lot of AI solutions that have been thrown your way?
Whilst we are always keen to optimise and make things more efficient through AI, we don’t just throw a blank check at teams for tools and we conduct formal business cases for new AI tools, focusing on tangible cost savings (consolidating software or reducing future headcount etc.) and efficiency gains (reduction in time per task/function etc.). We try avoid herd behaviour and chasing hype and instead set a clear ROI bar that must be met to justify cost & adoption.
For example, on the finance side, we are replacing our accounting system with an agentic ledger, which we expect to create significant cost saving by cutting expensive bookkeeping costs, accounts payable processing and time to close at end of month. Our business case ROI is clear and high-confidence, supported by conservative assumptions rather than hype.
How were you prepared for the trend of AI? How have you found the attitudes from other senior leadership of implementing AI?
Fortunately, as a senior leadership team and Board, we are highly supportive and engaged with AI adoption, which is crucial for successful implementation. Our full management team fully recognises AI as both an opportunity and a competitive necessity. Without this buy-in, initiatives and new processes stand no chance of sticking and becoming business-as-usual.
Also, the fact we are backed by Google, naturally fosters a culture of innovation and AI optimism. Our investors are themselves at the cutting edge of AI development, which ultimately strengthens our internal knowledge and adoption at senior leadership level.
The main challenge, which I believe is the case in many businesses currently, is ramping all employees up to the same level of AI competency and acceptance. Given AI’s nascency, our employees have varying levels of familiarity and comfort with AI tools. Not everyone has grown up with AI technologies. However, this is very manageable – we need to spend more time educating employees on the benefits, building AI adoption into performance expectations, and centring our culture around a drive for operating excellence.
Has the AI first mindset shifted any hiring strategies or the need for different skills when bringing in a role that relates to AI?
As mentioned before, our AI-first mindset has changed our approach to hiring and we now think from first-principles; we evaluate whether a planned future role can be replaced by AI or restructured with AI augmentation.
For our R&D recruitment, there is significant focus on candidates’ experience with AI tools like Cursor, and we do have an internal Machine Learning team that are well versed in latest technologies and innovation. However, in terms of hiring strategies for specific skillsets and experience using AI, especially in G&A and commercial roles, AI experience is not yet a formal part of the process. I do see that changing as we grow and more elements of certain roles are increasingly automated.
What has been the biggest challenge of adopting AI internally?
Limited bandwidth to fully assess solutions and the difficulty in balancing complex AI change management with the need to not disrupt our high growth operation. Bandwidth can be especially challenging at senior levels given demands on an Exec team. Even with my technical background in robotics, I still need to spend considerable time upskilling and dedicate time to strategically consider AI at scale. From an organisational standpoint, mapping out which workflows to fully or partially replace with AI is complex and I now have a dedicated operations team to help us get it right.
The rapid pace of AI tool development in itself is a blessing and curse. Though innovation is also wanted, the speed right now creates difficulty in testing and selecting the best solutions, as we find new tools emerging frequently. It also can be hard reaching consensus on preferred tools as by the time we move through testing, decision and implementation, there is considerable risk the tool becomes obsolete or deprecated by a newer and better product.
I also note the not insignificant Cybersecurity risks, particularly regarding data storage and information flow between systems and machines, especially when using AI notetakers. It’s crucial we are managing these risks carefully.
What excites you the most about where AIs are heading or what they can do?
Internally, I’m excited about the speed and reduced cost to market enabled by AI across the company. The fact we can quickly A/B test products using AI tools like Lovable, is a game changer. AI hasn’t replaced our core scientific aspects of our products, but we can see the potential for AI to lower barriers and costs in product development and speed to market.
From a finance perspective, AI is helping reduce our cost base by enabling smaller, more strategic teams supported by automation and offshore resources. Looking forward, I want to build in house finance teams that add strategic value rather than focusing on transactional tasks. There is enormous room for AI adoption in finance & operations. I could see a future where companies plug and play a fully automated back-office function off the shelf; Finance & Operations “as-a-Service”.
Do you have any advice for other leaders who are kicking on with their AI journey?
The main advice I can give to leaders, is to not to dismiss AI as hype or be sceptics without proper consideration, as this would result in missed opportunities and end up with them falling behind. I’d always recommended upskilling, learning, and forming informed opinions about AI’s role within their business and how it can just make things more efficient.
Whilst I am definitely pro AI usage, don’t go overcommitting or investing in AI solutions without clear ROI or value, as that is just using AI for its own sake. AI adoption should be driven by tangible benefits and not external pressure or narratives. We have already demonstrated value from AI-enabled tools, reinforcing the importance of practical adoption.
