When You Don’t Need AI

Reading Time: 2 mins

Artificial Intelligence (AI) is a buzzword today. Business executives want to transform their organizations by tapping into the power of AI. However, AI is not a fit for every situation. There are some situations where it pays to NOT invest in the technology.

But, how do you figure out if AI is the right fit for your needs?

I recently published an article in Entrepreneur that throws light on the 5 situations where you should not invest in AI. Here’s a summary and a few key takeaways from the article.

5 Reasons You Need Humans in AI Solutions

When You Have Simpler Alternatives

Not every business problem is complex enough to demand powerful algorithms. In many instances, businesses can choose simpler solutions that will get the job done effectively.

When You Don’t Have Enough Data

Today, AI algorithms require a high volume of data to spot patterns and gain intelligence. Unfortunately, large volumes of data is not a luxury every organization can afford. If you have low volumes of data, you must choose simpler techniques.

When AI is Still in Experimentation

AI research is progressing rapidly. However, there are several areas where AI works well only in carefully controlled scenarios. By all means, you must experiment with AI. However, check if the technology is ready for primetime, for your area of application.

When Costs Exceed the Business Benefits

You must balance the costs of building and maintaining AI with the business ROI you are likely to get from it. If the total cost of ownership of AI isn’t favorable, don’t make the investment.

When You Need Understanding and Empathy

When it comes to user interactions, empathy and care are critical. AI is great at pattern spotting and making predictions, but it doesn’t really ‘understand’. When you need emotional intelligence, you need greater human involvement.

Check out the article published on Entrepreneur to find real-time examples that detail the points mentioned above.

Ganes Kesari

Ganes is the Co-founder and Chief Decision Scientist at Gramener. He is an entrepreneur, AI thought leader, author, and TEDx speaker. Ganes advises executives of large organizations on data-driven decision making. His expertise lies in the application of data science to solve business challenges, and in building teams to promote a culture of data.

Next SaaS Customer Retention Strategies [Best Practices to Improve Customer Experience] »

Previous « The Best of Gramener Blogs 2020 - Data Science, Storytelling, Analytics, and More

View Comments

Amy Jackson says:

19/04/2023 at 1:03 pm

In situations where ethical or legal concerns arise, such as in the case of biased or discriminatory algorithms, it may be appropriate to avoid using AI altogether. Just an add-on suggested. Thanks, Ganesh for writing on a such a unique topic.