It’s science fiction today, but there may come a time when jobs as we know them today will be done by machines with the future of artificial intelligence. Here's our A.I. & automation comparison for expense reporting.
In fact, the stirrings of what we can accurately say is A.I. are appearing little by little. Siri can learn from her mistakes (if you manually edit your input, she’ll remember it the next time you give the same command by voice). Google Translate uses an artificial neural network to understand the world through trial and error, making corrections based on those errors. But, even Google Translate might not deliver an accurate rendering of what those words “artificial intelligence” actually mean.
Artificial Intelligence vs. Automation
Perhaps it’s self-evident, but maybe some clarification would be helpful: artificial intelligence might look like automation, but it’s not the same thing. The word intelligence is important here. Automation is when the system does the work. Artificial intelligence is when the system does the doing and thinking (and even learning).
While A.I. is a bit farther away than scientists and engineers at Google would like, automation is already here. We already see automation in how historical data is gathered, how a system can predict future trends based on the past, and how a system can look at the past to decide how an expense report will be categorized based on historical data.
The Gift of Automation
In specific, automation is a true gift when it comes to expense reporting, saving administrators the time of manually approving each report. But, automation can also make us overly reliant on a system that does but doesn’t think. Assuming that the system is doing the right thing can be risky, especially in terms of compliance. It’s good to let the system do the work (freeing up your brain), but it’s also important to use a system with auditability.
For example, an administrator might assume that because OCR is reading receipt data, it means that the receipt satisfies policy requirements. However, OCR is gathering the data, not necessarily understanding what that data means.
Related Article: Why We Think Artificial Intelligence Is “Next-Gen” And Not “This-Gen”
The Future of Artificial Intelligence
The lesson behind this example is that even with automation, the world still needs thinkers and learners: the world needs you! A system can do the heavy lifting for you, but you and your fellow humans are needed to understand why the work needs to be done in the first place.
If you’re still not using an automated system for your time and expense, imagine the solutions to problems you can create if a system is doing the monotonous work for you. Just make sure to prioritize compliance alongside automation.
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