This is the greatest most informed article, assuming today is sometime in early 1993. Sadly terms like artificial intelligence and machine learning have become powerful buzzwords so they’re being used in situations where they are far from appropriate.
One particular flaw is where the author makes a point about difference in file formats, such as PDF docs vs text files. Extracting data from these kind of situations has long since been addressed by non-AI software such as ACL and IDEA. In fact many Audit functions have been preformed by software for years.
Another example, the author talks about how AI is better at aligning data, this is so vague, an Excel formula can easily show if a PO is related to an Invoice and again – software in particular Audit software has been doing this for a long time and never under the guise of ML or AI.
Where I do feel it has some value is that he recognizes that
Only human beings, such as the auditor, can tell the true story behind the data.
All in all, I think this is 20 years too late.
Source: Artificial intelligence comes to financial statement audits.
The world suddenly starts to look brighter as the larger hand hovers over the 6. 4:30pm you think only 30 minutes until I can go home for the weekend. That dreaded popup ends your jubilation as you start to read the email from your boss. Was the Fredricks order competed? You wince in anger and some regret as you start to key your reply. “No, I thought the priority was the Johnson order, I’ll take care of the Fredricks order Monday morning – we’re close, it won’t be problem” Moments later your boss replies with a terse “Very well, however this lateness will be noted in your review.” Wishing you could protest, but you know better, you know there’s no arguing with an AI.
More and more companies are embracing AI workers, not just in task oriented roles, but also in decision making ones. Some companies have gone as far as to implement monitoring systems to ensure compliance with the AI’s directives. HBR’s article discusses several such examples. And shows us a world where data-scientists are our ambassadors to our computerized coworkers.
While this sounds like Skynet it’s not all doom and gloom or shouldn’t be Schrage notes
CEOs should worry less about bringing autonomy to heel than making it a powerful source and force for competitive advantage.
Learning to trust the algorithm takes “humility and faith” two qualities that leaders should have in abundance.
Will you able to trust the machine?
Source: 4 Models for Using AI to Make Decisions