November 29, 2023

What are the main obstacles to implementing AI?

Artificial Intelligence (AI) is changing our lives every day. From writing instruments to self-driving cars, we are slowly learning to incorporate different uses of artificial intelligence into different aspects of our lives. Another area where AI can be used with great success is online education. However, companies and institutions looking to update their learning systems with artificial intelligence may find themselves facing unexpected obstacles. In this article, we’ll look at 6 AI implementation challenges, as well as ways to overcome them.

6 AI implementation challenges to keep in mind

1. Insufficient or poor quality data

Artificial intelligence systems work by being trained on data sets relevant to the topic they are solving. However, companies often struggle to “feed” their AI algorithms the quality or volume of data they need, either because they don’t have access to it or because the amount doesn’t yet exist. This imbalance can lead to inconsistent or even discriminatory results in the operation of your AI system. This problem, otherwise known as the bias problem, can be avoided if you use representative and high-quality data. Besides, it would be best to start your AI journey with simpler algorithms that you can easily understand, check for biases and adjust accordingly.

2. Outdated infrastructure

In order to give us the expected results, artificial intelligence systems need to process large amounts of information in fractions of a second. The only way to achieve this is to run on devices with the appropriate infrastructure and processing capabilities. However, many businesses are still using outdated equipment that is in no way capable of taking on the challenge of implementing AI. Therefore, it goes without saying that businesses looking to transform 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 into your training program is much more than downloading a few plugins to your LMS. As we’ve already discussed, you need to spend more time considering 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. Working with a provider that has the necessary AI experience and expertise can help you overcome all of these challenges and ensure the smoothest possible transition to machine learning.

4. Lack of AI talent

Speaking of expertise, given how new the concept of artificial intelligence is in teaching and learning, it’s safe to say that finding people with the necessary knowledge and skills is a significant challenge. In fact, a lack of internal knowledge prevents many businesses from trying AI. While finding a provider that can transition your company to machine learning is a viable solution, forward-thinking companies are concluding that investing in your internal knowledge base is more beneficial in the long run. In other words, they recommend training your staff in AI development and implementation, hiring AI talent, and even licensing capabilities from other IT companies to develop your learning prototypes in-house.

5. Overvaluing 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 provides, and if it’s not right, the decisions it makes won’t be right either. A big problem in implementing artificial intelligence is that the learning process is quite complex, especially when we try to formulate it into a set of data that we can import into the system. For this reason, the explainability of AI is key to a successful transition to machine learning. Breaking down algorithms and training users on the AI ​​decision-making process provides transparency and helps prevent wrongdoing.

6. Cost Requirements

Based on everything we’ve covered so far, it’s easy to see that developing, implementing, and integrating AI into your training strategy won’t be cheap. To do it right, you’ll need to work with AI experts who have the necessary knowledge and skills, run an ongoing AI training program for your staff, and likely update your IT equipment to handle the demands of your machine. teaching aids. While some of these costs are unavoidable, you can definitely minimize them by looking into affordable training programs or free apps. There are a variety of options available that can help you figure out which AI capabilities your training program could use before you spend the money to acquire them.

Other AI challenges

In addition to the AI ​​implementation issues discussed in this article, we could also mention the inconsistencies in the availability of AI around the world. Specifically, while some countries are already making leaps in AI technology, others are trying to conquer much simpler technological advances. Additionally, there are many legal and ethical concerns surrounding AI, as the data it needs is sometimes subject to data protection laws. Many negotiations are already underway to establish regulations that will ensure transparency and security.

Despite the many challenges that AI implementation presents to businesses, governments and institutions, it is imperative that they overcome them in order to reap its benefits and become part of the future of machine learning. Hopefully, with further research into AI, the mystery surrounding it will slowly dissipate.

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