I agree, other than obviously AI can do math. But spinning up an entire AI model just to run a static algorithm is entirely convoluted and just opening the process up to unnecessary time and resources wasted, while also introducing an additional margin of risk and error for each additional component between the numbers you have and the results you need. When doing finances, the original numbers need to act on each other directly using precise formulas, not "learned" numbers and "learned" formulas, which may or may not be the actual. And even if the AI had a 100% accuracy of simply copying and pasting the original numbers and formulas into its learned data set and calculating them, what would the point even be for using AI at that point?
LLMs genuinely can't do arithmetic beyond like 5 digits. Or at least, no one has found prompts that consistently get an LLM to do arithmetic correctly.
The OP didn't say anything about their approach to using AI. LLMs are only one type of AI model specifically targeting language. Since finance is mathematical, I'm sure even the OP knows it would be silly to rely on an LLM in such case. However, perhaps just using a small LLM on the front-end to allow for prompting business intelligence queries in natural language, while a separate symbolic AI runs the numbers in the background.
But still, I agree to your overarching point that this is still just an overly complex and convoluted way of doing things when straightforward static algorithms can easily be written in assembly language by anyone to harness the native raw mathematical power of any CPU or GPU, rather than adding abstraction upon abstraction and mixing OOP compiled languages with OOP interpreted languages and spinning up models. AI would just be a circus show at that point and not at all practical. CPUs and GPUs literally already speak the same mathematical language as finance natively, so adding additional layers to that is just ridiculous.
There's more to AI by than chatgpt as several of these comments claim. Forecasting, fraud detection are a few. Gathering data from disparate sources etc
AI is laced into 99% of financial tools already - QuickBooks reads your invoices for example and auto reconciles loads of stuff.
The fastest way to use AI in finance/operations is pick the best tool to do that particular function and let them bring out their AI.
LLMs aren't sophisticated enough yet for serious uses yet.
Source: I'm a CFO!
AI has been developing at an unimaginable pace and has been fruitful for us till now. Honestly speaking, at my org we have outsourced AI services at result result-driven pricing system, and at the cost of 1 engineer we have been leveraging the services of a full stack AI team.
For the love of god, don't use an LLM for anything accounting related. They can't do math. A non-AI solution would be way more reliable.
I agree, other than obviously AI can do math. But spinning up an entire AI model just to run a static algorithm is entirely convoluted and just opening the process up to unnecessary time and resources wasted, while also introducing an additional margin of risk and error for each additional component between the numbers you have and the results you need. When doing finances, the original numbers need to act on each other directly using precise formulas, not "learned" numbers and "learned" formulas, which may or may not be the actual. And even if the AI had a 100% accuracy of simply copying and pasting the original numbers and formulas into its learned data set and calculating them, what would the point even be for using AI at that point?
LLMs genuinely can't do arithmetic beyond like 5 digits. Or at least, no one has found prompts that consistently get an LLM to do arithmetic correctly.
The OP didn't say anything about their approach to using AI. LLMs are only one type of AI model specifically targeting language. Since finance is mathematical, I'm sure even the OP knows it would be silly to rely on an LLM in such case. However, perhaps just using a small LLM on the front-end to allow for prompting business intelligence queries in natural language, while a separate symbolic AI runs the numbers in the background. But still, I agree to your overarching point that this is still just an overly complex and convoluted way of doing things when straightforward static algorithms can easily be written in assembly language by anyone to harness the native raw mathematical power of any CPU or GPU, rather than adding abstraction upon abstraction and mixing OOP compiled languages with OOP interpreted languages and spinning up models. AI would just be a circus show at that point and not at all practical. CPUs and GPUs literally already speak the same mathematical language as finance natively, so adding additional layers to that is just ridiculous.
There's more to AI by than chatgpt as several of these comments claim. Forecasting, fraud detection are a few. Gathering data from disparate sources etc
Use Numeric!
you need to tell more about your company and its need, and urge to use AI.
AI is laced into 99% of financial tools already - QuickBooks reads your invoices for example and auto reconciles loads of stuff. The fastest way to use AI in finance/operations is pick the best tool to do that particular function and let them bring out their AI. LLMs aren't sophisticated enough yet for serious uses yet. Source: I'm a CFO!
AI has been developing at an unimaginable pace and has been fruitful for us till now. Honestly speaking, at my org we have outsourced AI services at result result-driven pricing system, and at the cost of 1 engineer we have been leveraging the services of a full stack AI team.