Eric Mason is the CFO of the city of Quincy, Massachusetts. He is one of the experts who will speak at the upcoming CFO Brew live event, “Next-Gen Finance: Future-Proofing Your Business Operations.”

Ahead of the event, we talked to Mason via email about how he’s approaching his existing (and future) tech stack.

This interview has been lightly edited for length and clarity.

Compare your current tech stack with the one you had five years ago. What would you tell your former self, knowing the tech capabilities available to you now?

Our tech stack has greatly evolved, which it had to do. Covid-19 really forced us to take an “all-in” approach to innovating our tech stack to allow us to decentralize, [and encourage] remote work. As we came back into the office, the momentum carried through this tech revolution allowed us to keep introducing more technology, especially on the software side, because the end users were much more open to new tech.

I would tell myself to start pushing earlier. The efficiency gains were so great that it would be worth the inevitable difficulties of staff retraining.

Generative AI unlocked a new era of tech. Within the last year, what’s been your personal motto as you prioritize AI versus other tech investments?

I’m a big believer that AI isn’t going to take anyone’s job, but someone using AI will take the job of the person not using it. Let’s use AI to give us more time to address complicated problems.

What’s the best application of generative AI that you’ve seen in the world of finance so far?

I have seen two great use cases so far. The first being the use of AI to simplify complex financial language. AI has worked great as a bridge between the often complicated and verbose world of large-scale statistical modeling and non-quantitative operations. Personally, I like using LLM to explain complicated processes and results in a digestible manner. Previously, it would take hours to write reports like this, but with AI the language can be democratized much faster with more time being devoted to complicated financial processes.

The second use case is generating more efficient coding, particularly in open-architect analysis. Being able to leverage syntax generation in AI to refine modeling code has proven to be both a valuable time saver and a pronounced expansion of ability.

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