The catch with Artificial Intelligence

There is no doubt that Artificial Intelligence has made life more convenient over the past couple of years. Technology and innovation are making tremendous progress with AI - speech recognition, image recognition, self-driving cars - AI is all around us. And going by the acquisitions being made by tech giants like Google and Apple, these innovations are only going to increase.

But, like any new fad or hype, it becomes easy for companies to get in trouble when trying to take advantage of these technology trends. Here are a few catches to watch out for when deploying an AI-driven service for your organization.

1. Lacking a good technology partner

A major problem companies face when implementing an AI solution is the lack of an experienced technology team. Artificial Intelligence provides major advantages when implemented right, but without the right team to get maximum value out of the technology, the organization will likely not use it to its full advantage.

While your algorithms don't necessarily have to be complex, the technology team should be able to manage the AI systems in place, get the right data and analyze the information gathered well.

2. Trying to use AI where it can't be

A lot of organizations get on the bandwagon just to join the hype. While Artificial Intelligence does help, it is not the solution for all problems. There are no off-the-shelf solutions which would make your business better immediately. An AI solution needs to be custom-built for your organization, your needs and the automation or analytics you need.

An AI solution might not even be the right solution for you - make sure you try and use the correct technologies applicable to your business. If you are unsure, ask a technology expert what solution would work best for your business objectives.

3. Keeping up with the big guns

A lot of organizations try to keep up with larger organizations - think Google, Twitter or the likes. Google might use neural networks to make their data centers more efficient, Twitter might use AI and machine learning to keep their thumbnail generation game on-point. But effectively using such solutions requires extensive training and large data sets to validate the algorithms before they are put into practice. Developing these solutions requires expertise and large computing capacity - which these organizations have. But that does not hold true for anyone looking at AI implementations. It might be a good idea to start small and then expand on the idea when it starts taking shape.

While adopting any new technology in your organization, make sure you remove yourself from the hype and then judge whether going ahead is the right step for you or not. Underestimating the knowledge, time or cost for effective implementation of an AI solution can be very easy and lead to major roadblocks. If you are not certain whether an AI solution is right for you or how to use AI in your business, get in touch with a technology expert and let them help you through the process.