AI focus: how artificial intelligence can help accounting students thrive

As use of artificial intelligence (AI) becomes more commonplace, it’s vital to understand how to get the best out of it while you study.

Whether working in industry or practice, more and more accountants and finance learners are using AI technology to save time, reduce the likelihood of errors and provide real-time financial insights by analysing huge swathes of data at the push of a button.

It goes without saying, however, that AI tools are only as good as the data they are given to work with; if there are mistakes in the underlying data provided then the AI analysis of that data will inevitably be wrong too.

As ChatGPT says: “The more specific you are, the better the AI can tailor its response to your needs. A vague request will likely result in a broad and possibly less useful answer.”

Tips for AI success

Here are five top tips on how to harness the power of AI tools and make them work for you by asking suitable and effective prompts:

Be clear and specific: The more detail you can give AI tools regarding the outcome you are seeking, the more closely the result of its analysis is likely to fit your requirements.
Use context or background information: explaining why you want the information can help AI to give suggestions that are relevant to your situation.
Request specific formats: if you want a series of five bullet points for a presentation then say so. Asking for examples is also a useful tip.
Refine your prompts if necessary: you can ask for clarification in a follow-up question if required.
Provide constraints or limits: if you need to explain a concept without using financial jargon, specify this in your request.

AI increases speed, but always be accurate

Accuracy is essential in accounting as even tiny mistakes can have huge consequences. That’s even more true when you harness the power of AI tools to forecast financial outcomes or flag issues such as fraudulent activity.

Accountants are used to preparing highly accurate datasets, so in this way they are well placed to make good use of AI tools.

However, many AI tools require substantial amounts of data to provide a useful solution. If they don’t have enough, they simply can’t perform the mathematical calculations required to produce an outcome.

Some AI tools also need external as well as internal data to do the job, which is not always possible to access at an appropriate cost.

Last, but certainly not least, when using AI-generated concepts and solutions, it’s also vital to check the results against your own interpretations – just to be sure.

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