6 AI Implementation Challenges And How To Overcome Them

6 AI Implementation Challenges And How To Overcome Them
Summary: Artificial Intelligence is the future of online learning. But what are some AI implementation challenges you'll have to overcome first?

What Are The Top Obstacles When Implementing AI?

Artificial Intelligence (AI) is changing our lives with each passing day. From writing tools to self-driving cars, we are slowly learning to incorporate the various uses of AI into multiple aspects of our lives. Another field where AI can be used with great success is online learning. However, companies and institutions looking to update their learning systems with Artificial Intelligence might find themselves having to deal with unexpected hurdles. In this article, we will look at 6 AI implementation challenges as well as ways to overcome them.

6 AI Implementation Challenges To Keep In Mind

1. Insufficient Or Low-Quality Data

AI systems function by being trained on a set of data relevant to the topic they are tackling. However, companies often struggle to “feed” their AI algorithms with the right quality or volume of data necessary, either because they don’t have access to it or because that quantity doesn’t yet exist. This imbalance can lead to discrepant or even discriminatory results when operating your AI system. This issue, otherwise known as the bias problem, can be prevented if you make sure to use representative and high-quality data. In addition, it would be best to start your AI journey with simpler algorithms that you can easily comprehend, control for bias, and modify accordingly.

2. Outdated Infrastructure

For Artificial Intelligence systems to give us the expected results, they need to process large amounts of information in fractions of a second. The only way to achieve that is by operating on devices with suitable infrastructure and processing capabilities. However, many businesses are still using outdated equipment that is in no way capable of taking on the challenge of AI implementation. Therefore, it goes without saying that businesses that want to revolutionize their Learning and Development methods with machine learning must be prepared to invest in infrastructure, tools, and applications that are technologically advanced.

3. Integration Into Existing Systems

Incorporating AI in your training program is much more than downloading a few plugins on your LMS. As we have already discussed, you need to take extra time to consider whether you have the storage, processors, and infrastructure necessary for the system to function properly. At the same time, your employees must be trained to use their new tools, troubleshoot simple problems, and recognize when the AI algorithm is underperforming. Collaborating with a provider who has the necessary AI experience and expertise can help you overcome all these issues and guarantee the smoothest transition to machine learning possible.

4. Lack Of AI Talent

While we’re on the subject of expertise, considering how new the concept of AI in learning and education is, it’s safe to say that finding people with the necessary knowledge and skills is a considerable challenge. In fact, lack of internal knowledge keeps many businesses from trying their hand at AI. Although searching for a provider who can transition your company to machine learning is a viable solution, forward-thinking companies are coming to the conclusion that it’s more beneficial in the long run to invest in your internal knowledge base. In other words, they suggest training your employees on AI development and implementation, hiring AI talent, and even licensing capabilities from other IT companies so that you can develop your learning prototypes internally.

5. Overestimating Your AI System

The technological advancements we have witnessed sometimes lead us to believe that technology can do no wrong. But AI relies on the data it’s given, and if that isn’t correct, neither will the decisions it makes. A great AI implementation challenge is that the process of learning is rather complex, especially when trying to formulate it into a set of data we can import into a system. For this reason, AI explainability is crucial for a successful transition into machine learning. Breaking down algorithms and training users on the decision-making process of Artificial Intelligence provides transparency and helps prevent faulty operation.

6. Cost Requirements

Based on everything we’ve discussed so far, it’s easy to understand that developing, implementing, and integrating Artificial Intelligence into your training strategy won’t be cheap. To get it right, you’re going to have to collaborate with AI experts that have the necessary knowledge and skills, launch an ongoing AI training program for your employees, and probably update your IT equipment to be able to handle the requirements of your machine learning tools. Although it’s impossible to avoid some of these costs, you can definitely minimize them by looking into budget-friendly training programs or free applications. There are various options available that can help you figure out which AI capabilities your training program would benefit from before spending money on acquiring them.

Other AI Challenges

In addition to the AI implementation challenges we discussed in this article, we could also mention the discrepancies in AI availability around the world. Specifically, while some countries are already making leaps in AI technology, others are struggling to conquer much simpler technological advancements. Moreover, there are many legal and ethical concerns surrounding Artificial Intelligence, as the data it needs are sometimes subject to data protection laws. There are already many talks in place to set regulations which will ensure transparency and security.

Despite the number of challenges AI implementation poses for businesses, governments, and institutions, it’s essential that they overcome them in order to enjoy its advantages and become part of the future of machine learning. Hopefully, as more research is done on AI, the mystery surrounding it will slowly dissolve.