5 Common AI Adoption Mistakes You Should Never Make

AI Adoption

Did you know that the number of businesses that have adopted artificial intelligence grew by 270% in the last four years? Yes, you read that right. According to artificial intelligence statistics, the global artificial intelligence industry will surpass the $641.3 billion mark by 2028. In addition to this, a vast majority of businesses (91%) have already invested in artificial intelligence.

Not only businesses, employees and customers are also in favour of implementing artificial intelligence. 61% of employees say that artificial intelligence has helped them improve their productivity while 62% of customers are willing to share their data with artificial intelligence so they can have a great experience with a business. 

According to a survey conducted by PwC, 86% of participants are expecting artificial intelligence will become a mainstream technology in their organization by the end of 2021. All these statistics clearly highlight the exponential growth in AI adoption as well as acceptance amongst businesses and customers.

Despite this, most businesses fail to implement artificial intelligence the right way because they end up making mistakes along the way. As a result, they end up paying a hefty price instead of benefiting from AI adoption.  If you don’t want to make the same mistakes that businesses make when adopting artificial intelligence then, you are at the right place.

In this article, you will learn about five common mistakes businesses make when adopting artificial intelligence.

5 Common AI Adoption Mistakes You Should Avoid

Here are some of the most common mistakes businesses make when implementing artificial intelligence but should not.

1. Ignoring the use case

Most businesses understand the benefits of implementing artificial intelligence and a vast majority of them have already implemented some kind of automation. The problem arises when they start to implement artificial intelligence based on their gut feelings or whim instead of logic or use case.

Just because others are implementing artificial intelligence in mobile apps or businesses does not mean that you should too. You need to develop the right use case first to achieve the desired results from your artificial intelligence initiatives just like mobile app development companies do. When evaluating use cases, ask yourself whether you are implementing the right type of AI according to the situation or not.

Make sure your artificial intelligence initiatives align with business goals otherwise, all the resources and time spent on implementing artificial intelligence will go to waste. As a business, you don’t want that. That is why it is important to develop your artificial intelligence use case with goals in mind. Try to be as specific as possible when developing a use case instead of taking a generic approach. The more specific questions you can ask yourself, the easier it will be for you to develop a highly targeted AI use case for your business.

2. Hiring The Wrong People

The ever-widening talent gap in the IT industry and great resignation have put businesses in hot waters. According to a survey, 50% of technology recruiters are finding it difficult to fill in open positions. Retaining the existing talent or hiring new talent will continue to be a daunting challenge for businesses in the near future as the great resignation continues to make things worse. This is even more obvious when you are looking to hire AI talent.

Candidates with specialized AI skills are not only hard to find but they are very expensive too. The demand for these professionals has skyrocketed in recent years. Yes, you can find data scientists but finding the right candidates who have hands-on experience with artificial intelligence is not easy.

If you want to succeed with AI projects, you need candidates that specialize in modelling.  This will help you deliver tailor-made AI solutions for your business. Combine that with data engineering skills and you can get a perfect combo. If you want to attract AI talent, you will have to provide them with career growth opportunities as well as great perks and a wonderful environment where they can showcase their talent.

3. Not Paying Attention To Data

Data is the new oil. You might have heard and read this but did you know that this is actually true in the case of artificial intelligence. Data is to artificial intelligence what fuel is to your car engine. Your AI system is as good as the data you feed it. Instead of making data a sole responsibility of your IT department, you should make it a shared responsibility. This means that every functional unit has to contribute when it comes to collecting, creating, evaluating or maintaining data. As your data volume grows, your data management and governance processes need to evolve with it.

4. Lack of Tracking and Optimization

Irrespective of how good your AI system might be, it still requires tracking and optimization to work effectively. This is where humans can come into play. Let’s say, market conditions change or your business goals change, your AI processes need to change with it. Sadly, it can not do this automatically and needs human interaction.

Track key metrics related to the effectiveness of AI systems. This will help you identify shortcomings in your AI systems and plug-in those gaps to increase the efficiency of your AI systems. Even if you are doing a human intervention, make sure they are scalable and repeatable so it does not burden the AI users.

5. Neglecting Data Bias

Just like humans, AI also has bias. This is because the data you feed AI models contain bias, which leads to biased outcomes from AI systems. If you feed data that contain racial, gender or class bias to AI models, the final output will also reflect that. 

You can eliminate data bias by choosing the right model to solve a particular problem or selecting a representative data set. Make sure to track the performance of your AI models using real data. Identify factors that introduce bias in your data in the first place and create a plan to prevent them.

Which is the biggest mistake you have ever made when implementing artificial intelligence in your business? Share it with us in the comments section below.


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