You Ask, I Answer: Untapped Use Cases for AI in Business?

In today's episode, Ashley asks, in your opinion, what are some of the untapped use cases for AI in businesses today? So there's a couple of different use cases that people aren't paying enough attention to because they're being distracted by the shiny objects. Um, particularly with larger context models now. So, for example, the GPT series from OpenAI can handle a context window of anywhere from 16,000 to 32,000 tokens. A token is essentially a three or four-letter fragment of a word.

So uh 16,000 tokens, well, any number of tokens is basically uh 66% of that is words. So if you have 16,000 tokens, you have you know about 11,000 words. If you have uh 32,000 tokens, you have about 20-ish thousand words to work with. Most of our business documents don't clock in much higher than that. Uh if you look at Claude 2 from Anthropic, that has a hundred thousand uh token context window, which boils down to about sixty-ish thousand words.

Most books don't have that, uh, at least in business books. And that gives you the ability to prompt these artificial intelligence uh pieces of software just to do both transformative work and I think the one that's really overlooked is auditing work. Now we've all done some basic auditing tasks with these large language models. We've had them do things like fix spelling or fix your grammar or uh reformat the text to be more aesthetically uh pleasing, to be more readable. But we don't really think of these tools as auditing tools in the sense of let's do have a large language model do analysis on it.

Again, a tool like Claude 2 or GPT 4 can do extensive analysis on large amounts of of data. And it doesn't just have to be plain text, it can be PDFs, it can be spreadsheets, it can be, you know, any any machine readable text format. Think about this. If you were to put in, say, all of your income tax forms into a large language model and say, here is the known tax code out of the time. Maybe you could even feed in the tax coders, the one sections that apply to you.

Find irregularities, find anomalies, find opportunities to save money. That auditing capability is something that large language models are capable of doing, but most people don't think to do that. You can take, for example, your checkbook register from your bank. You can turn that into a CSV file, hand it to a large language model. You want to make sure the privacy settings are set so that they're not recording your data.

Um, and then say, identify where I'm wasting money every single month. And it can look at your accounting data and say, okay, here are some possible candidates for things that don't seem to make a whole lot of sense. Uh these tools are very good at auditing in the sense of looking for fraud. Hey, here's again, uh a list of customer purchases. And you can say, here are the ones that seem a little anomalous, you know, validate your sense of probability that this is a fraudulent transaction.

Auditing tools that are that are based in large language models are probably the most uh untapped opportunity that these tools have to offer because everyone's so focused on them being generative, and generative AI, it's cool, right? You can make blog posts and news articles and newsletters and things, and that it's great, you should. But the mathematics underneath these models make them better at comparison than generation. So if you say, here is my transactional data, compare it to known best practices for uh double entry bookkeeping. These tools can do that.

These tools can do that and say, here are the anomalies. Here are the things that don't make sense. Um, here is my website's privacy policy. Tell me, is this GDPR compliant? And if it's not, what do I need to fix?

Here is an e-book I just wrote. Read it as a sensitivity reader. Tell me where I've said things or written things that would be culturally insensitive or problematic or biased. All of these auditing capabilities are things people are just not looking at nearly enough, and there's tremendous value in it, in helping us refine the work that we've already done, helping us identify problems, helping us elevate the quality of our work. You know, these are essentially editors and proofreaders and inspectors and auditors who can look at our work independently and offer data-driven opinions.

Now, will they always be right? No. If it's something that's mission critical, please have a qualified professional, you know, do so. Uh look at it. But these are some of the use cases I think people are really missing out on.

They're really just not paying enough attention and missing the benefits of some of these large language models. It's a really good question. There's a lot to explore. There's a lot of different ways we can use these tools to for to our benefit in a comparative sense rather than a generative sense, even though these models are capable of both. So thanks for the question.

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