Technology

The Three Biggest Mistakes Companies Make When Utilizing AI

Here are the three biggest mistakes that companies make when trying to add AI into their business processes.

Even though it’s no longer just a thing of the future, you might think of artificial intelligence as a component of a badly ending science fiction movie. While this new technology can make us antsy and fearful in fiction, it has many practical applications in real life. 

In business, artificial intelligence (AI) can optimize customer experiences, streamline processes for employees, and save time and money for companies. However, companies shouldn’t and can’t implement AI without doing their homework.

Here are the three biggest mistakes that companies make when trying to add AI into their business processes.

Mistake #1: Not Properly Matching the Solution to the Problem

This issue stems from the biggest underlying root problem companies face: not doing enough homework. Often, because AI is the cool new thing to implement, companies can perform the business equivalent of closing their eyes, pointing at a particular technology, and utilizing it.

This approach seldom works in real life, and it doesn’t work in business, either. Not all AI software is equal, and even when the software is good, it may not be tailor-made to the task it’s chosen to do. Companies can easily fall into this problem when they see AI as the end goal when it is really the means to the end. 

For example, some types of AI are developed for customer service-oriented tasks. At the same time, some are better suited to running analysis integrated with a company’s customer relationship management system or CRM. 

Purchasing software from a reputable source will involve some time and effort. Making a quick, easy, and cheap purchase will often result in AI software being mismatched to the problem it was purchased to solve.

However, many good software developers will allow companies to test drive the products to find the best fit, ensuring that the solution is a proper match to the problem.

Mistake #2: Keeping AI Solutions Siloed

The business world has borrowed this farm-structure term to refer to boundaries that keep teams and departments from cooperating and communicating.

Hence, even if a company chooses a sound, well-matched AI software for their business concerns, it can still make a critical mistake by keeping the software siloed

If a company implements AI to, for example, collect and analyze data for sales forecasting but then doesn’t circulate the data and resulting information for use throughout different departments, the software is, effectively, hobbled.

It’s pretty standard for companies to relegate their new, unresearched software to the IT department’s realm. Without bridges and channels to integrate AI fully into the inner workings of all their departments, companies don’t get their money’s worth out of their AI investments.

Mistake #2.5: Lack of Skill Investment

One reason why companies may fall into unintentional siloing is the failure to invest time and effort in reskilling current employees and hiring new talent. Therefore, this can be a sort of mistake within a mistake.

Without employees who know how to utilize the software to its fullest potential, companies can easily fall into the trap of keeping AI efforts firmly planted in the IT department. Many companies also don’t correctly estimate the skills needed to upkeep, update, and integrate their new software well. 

Mistake #3: AI-Washing

This huge mistake can happen when companies fall prey to the errors listed above. It can also take root before any other mistakes have been committed, right at the start of the process.

AI-washing occurs when a company claims to have integrated AI when it really hasn’t. This sounds like a serious infraction, but it can often be hard for some inexperienced companies to draw the line truthfully.

Because technology has made data and its analysis so quick and easy, sometimes companies can implement solutions that border on AI; they look, smell, and sound like AI, but in truth, they’re simply good pieces of technology suited to the job they’re doing.

Artificial intelligence, at its core, progressively learns. It takes in information and remembers contexts, data, and details and uses it all to improve itself. When technology doesn’t utilize this process, it can’t be claimed as AI.

If the problem is solved and the technology works, why does it matter what you call it? Customer trust is a crucial part of any business. Correctly and truthfully labeling business practices with full transparency is a key, but often overlooked, component of customer loyalty.

Final Thoughts

At the end of the day, companies can avoid pretty much all of these mistakes and their ramifications by doing their due diligence.

 By analyzing needs, goals, and budgets on the front end before shopping for AI, companies can find a good software fit for their concerns and integrate it fully for optimal use throughout the workplace.

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