Where is the future of finance heading with generative AI & GPT technology?

Eyes ahead! Allow us to virtually strap a pair of future-viewing binoculars (imagine they look cool like something like Apple’s Vision Pro goggles) to your head, and give you some glimpses into where the future of finance jobs is heading.

Coming from an AI company that has been passionate about AI long before it was technically cool, we have a strong understanding of what AI technology is capable and not capable of, and able to provide some good insights into where it is heading. As a caveat, we aren’t professional fortune tellers, or registered time travellers — this is us sharing our opinions based on our 7 years of knowledge and experience.

You are probably reading this because you have heard about and have likely tried out ChatGPT or Dall-E, and caught an inkling of the subsequent impact this technology can have on the way finance teams work. This feeling you are experiencing is a little bit of fear, a little bit of excitement and a little bit of intrigue.

There is still a lot of unknown in the exact way that AI is going to impact jobs and the way people in finance teams work, but what we do know is that AI is incredible at doing the tasks that finance teams tend to hate most (or at least tend to be time consuming and repetitive). Before you start thinking too creatively about what that is; this is manual data entry, writing effective reconciliation emails, generating insights and writing reports.

Before concerns arise about AI taking over jobs, let’s recognise the areas where AI currently faces limitations. You may have picked up on these already while using publicly available open AI tools (like ChatGPT or Dall E). but these are things like complex problem solving, ethical decision making, adaptability, innovation, as well as creative and strategic thinking.

These limitations stem from the fact that generative AI models including large language models are primarily trained on existing data sources. These sources could be internal knowledge base documents, historical financial reports, or website pages. However, the models are constrained to what already exists and the lack of capability to generate entirely new ideas or envision previously non-existent concepts.

In the near future, finance teams will be spending far less time on repetitive, mundane tasks as they will be about 80% automated. These include tasks like processing new invoices and adding them into accounting software, generating AP reconciliation reports from lists of invoices and PO’s and writing personalized emails to vendors, as well as analysing data to generate insights and financial reports.

Currently, these AI assistants or tools need a ‘human in the loop’ checking their performance and outputs for quality control purposes, and to add additional ‘human’ touches or edits. The technology and tools will undoubtedly get better as time goes on, but it is highly likely there will need to be an experienced human to approve the final outputs.

What this means for finance professionals is that they will still be completing mundane tasks, but being able to get them done much faster (with AI automating 80% of the work), and only needing to input the additional 20% of work reviewing and editing.

This leaves much more time to use the outputs from AI to do the creative, strategic and critical thinking including providing and honing those skills that take human expertise. New positions will also begin to emerge specialising in integrating, managing, and monitoring these new softwares and systems.

We would love to answer any questions that you may have about how AI will impact finance teams, so please comment with any questions that are lurking in your mind (there are no stupid questions!)