Some 98% of global executives agree that foundation AI models will play an important role in their organizational strategies over the next three to five years, according to Accenture. You’ve likely heard of foundation AI models in the past few months, but under different names: ChatGPT or Google Bard.
They are all large language models — a powerful type of generative AI. We’ve talked a lot about LLMs and how they can help accountants do their jobs. LLMs and generative AI are all the buzz right now, but much of the media coverage focuses on the potential for this technology to replace people rather than to enable them and enhance their working lives.
If you are an accounting leader evaluating LLMs as a solution for workflow automation, there are three common limitations of LLMs to be aware of before you adopt this emerging technology in your revenue operations:
- Hallucinations and reliability;
- Prompt sensitivity; and,
- Context window limits.
These issues represent gaps in contextual knowledge and strategic ability that only humans can fill. We see this technology not as a replacement for the accounting pros we work with, but as their best new team member. And as with any other team member, you have to know where their strengths and weaknesses lie.
What are they? How do they manifest, and why? Read on to learn more about these limitations and how they will impact the way B2B accounting professionals work in the next three to five years.
Hallucinations and reliability
“Hallucinations” occur when an AI model fabricates a confident but inaccurate response. This issue can be caused by a number of factors, including divergences in the source content when the data set is incredibly vast, or flaws with how the model is trained. The latter can even cause a model to reinforce an inaccurate conclusion with its own previous responses. It’s not hard to see why that might be a problem for finance and accounting teams. Your work involves mission-critical workflows that demand certainty and repeatability, and a hallucinating AI model represents an unacceptable risk when it comes time to recognize revenue on-time or reconcile POs with factual data.
Prompt sensitivity
When working with LLMs there are also significant limitations surrounding prompt engineering, which in its current form can be challenging and inefficient. A prompt is the user input to a GenAI model, based on which it creates its output. LLMs are highly sensitive to the way prompts are framed. The same idea phrased in three different forms could generate three vastly different responses. OpenAI is actively working to mitigate this issue, and GPT-4 suffers far less than its predecessors. However, it is still not entirely resilient to the problem. It’s for this very reason that the role of “prompt engineer” has been popping up on many companies’ hiring pages!
Context window limits
The last limitation involves context window size. Expanding the input parameters associated with context windows in LLMs is a significant technical hurdle to overcome. As the amount of text to be considered goes up, as does the computational complexity of the task. GPT-4 has expanded its context window to an astonishing 32,000 tokens — far ahead of the competition — but this limit still puts constraints on the larger, more complex tasks common to document review and accounting workflows. Even the most advanced models can only ingest and analyze a finite amount of information while considering an answer. And a 250-page MSA is beyond the scope of even the most powerful LLMs!
It’s critically important for users to have accurate search functionality, whether it’s identifying nonstandard termination for convenience within their documents or confirming the correct billing address within a purchase order. This requires semantic search built on top of LLM capabilities to address the gap. Users need a system designed to be easily used and understood by accounting pros, to speed through document and contract review with ease.
What does this all mean for you?
The growth and adoption of LLMs creates a new reality accounting professionals must contend with. There is potential for AI to be inherently good, while its influences do need to be explored with consideration to reap the rewards of it without stumbling over its potential drawbacks. The potential benefits to employing LLMs are such that it will be hard for anyone to opt out of using them entirely, so knowing their limitations will be as critical as understanding where they can help.
GenAI will not replace human accountants, but accountants using AI in their daily work will accomplish vastly more and enjoy a better quality of life. To make the latter possible, evaluate areas where you want to use AI to automate lower-level manual efforts in your workflows. Use that time you earn back from AI to enable the higher-level skills unique to financial accountants that will always be critical to do the job.
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