Preparing Your Business for AI Integration: What You Need to Know About Your Data in 2023 and 2024

Picture this: After months of hearing about how A.I. can transform your business, you find yourself willing to make the investment to integrate artificial intelligence into your business.

Maybe it was the ads for watsonx on Monday Night Football, maybe it was the GO AI blogs keeping you informed about A.I. in the business world. Maybe it was Maybelline…’s A.I. photo filters that were released this past summer.

Whatever it was, you finally decide to take the plunge. But an A.I. vendor tells you that your business cannot implement A.I., leaving you surprised.

The reason?

It’s not you. It’s your data.

Data Problems Mean AI Integration Problems

A.I. can work wonders for businesses, but the ugly truth is that only a small percentage of businesses are actually able to finalize their AI integration.

Don’t worry, the problem is fixable. Much of the unfitness has to do with the business’ organization of their data.

If you are a business owner, you may not even realize just how valuable the data your operations naturally produce really is.

And even if you do know that, you still may not realize that your data may not be useful if it is not organized or “cleaned” correctly.

Right now, one of the things that makes your data so valuable is that it is a dietary necessity for most A.I. that you may wish to integrate into your operations.

This means that if you don’t prepare your data correctly, A.I. won’t perform well.

You may need to do several things to ensure that you prepare your data well. We will cover them below.

Cleaning Your Data for AI Integration

Several things can dirty A.I.

For one, there may be misspellings.

Of course, you do not need to run through your massive treasure trove of data and spruce up the gold with your own handiwork. No, an automated spell-checker can do that for you.

The reason you need to do all this in the first place is that A.I., for all its sophistication, is still lacking in the common sense department. And it never will gain common sense, because at the end of the day A.I. is an algorithm rather than a conscious being that can develop common sense.

For this reason, even the simplest errors or inconsistencies that human common sense can naturally overlook can confuse A.I.

An example: Consistently misspelled customer names can make it difficult for an A.I. agent to put two-and-two together and realize that a correctly spelled name and a misspelled name could signify the same person.

When you make the correction, suddenly A.I. can offer more complete and dependable insights into the behaviors in your customer base.

People link dirty data with impurity for a reason: In the world of A.I., the principle is clear – When you feed it garbage, you’ll receive garbage as the result. When you clean your data, the A.I. solutions will be clean as well.

Organizing Your Data for AI Integration

Unorganized data is like a giant burlap sack of mixed goodies that you unload at the feet of an A.I. agent. “Go on and sort it”, you may say. But many A.I. agents are incapable of sorting it, or at least sorting it well.

For this reason, you’ll want to make sure you organize your data well before attempting to integrate it with an A.I. platform.

Here is an instance of how data can become disorganized: You may have single lines crammed with the purchase amount and date of purchase in your records of customer purchases.

Something as simple as putting that data into a spreadsheet where you separate the purchase amount and date of purchase into separate columns can be an easy solution for your organization problem.

Again, this is something that you do not need to roll up your sleeves and accomplish yourself. In fact, even your interns do not need to do it. Using data management software can be a simple way to organize your data—and ensure that data produced from thereon out is organized and A.I.-ready.

Summary

If you want A.I. to enhance your operations, the first step is to ensure that you can integrate your data with an A.I. platform.

The most common problems with data include “dirty data”, where data may have errors that make it either unreadable or misleading for A.I.

Another issue is a lack of organization for your data. Although some A.I. platforms are quite skilled in making sense of “unstructured” data, many require data to be organized in a sensible manner.