What are the potential challenges of using AI to aid decision-making?

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AI is seemingly everywhere at the moment. Everyone, from college students to finance professionals are using it, for tasks varying from summarising study notes to predicting business trends. Even if you haven’t purposefully used AI, you’ve probably interacted with it via a smart speaker or a chatbot on a website.

Whilst there’s some natural hesitancy around this new tech, there’s no denying that it can be useful. One area where AI really excels is decision-making – it can process huge amounts of information in rapid time, and pull out key insights that would otherwise take hours to find. Why then are some people reluctant to steam ahead and use it in daily operations? Well, the technology isn’t perfect yet, and there are some potential risks to using it that must be taken into account. In this post, we explore some of these challenges that AI implementers may face.

Lack of trust in the technology

Like any new thing, there are some people who just aren’t sure that AI is up to scratch. Concerns about regulation and duplicate data mean that many people feel more comfortable completing tasks manually, or relying on technology that has several years of proven reliability.

This means that even if you use AI for decision-making in your company, there may be colleagues who won’t feel comfortable relying on the results. Additionally, if you use it to drive your decisions for your clients, they may feel uneasy and request that you do additional manual checks. Whilst research shows that 65% of businesses are likely to trust a company that uses AI, there are still some people that need convincing.

Lack of data or poor data quality

AI relies on data, and data is a bone of contention for many companies. Some have too little data, some struggle to ensure quality, some data is biassed, and some isn’t secure enough. Experts suggest that for AI to be the best it can, organisations need to get a handle on better cleaning and labelling their data, to ensure that AI decisions aren’t skewed by duplicates, missing information or outliers.

On the other end of the scale, too much data can overwhelm AI and give it too much to consider. It’s therefore important to ensure that any programmes that you choose to use are adequate for the job at hand.

Integration with existing systems

As AI pushes the boundaries of innovation, it equally tests the limits of compatibility, demanding often intricate and costly reconfiguration of legacy systems. If you already heavily rely on data management platforms, it can be a challenge to make sure that your AI programmes can get access to the information they need to work effectively. There’s no doubt that data security is very important, but it can be an issue if security authorisation means data can’t flow between systems where needed.

If you can’t connect your AI and legacy systems, you may have to download your data and then reupload it to your AI software. Not only is this time-consuming, but there’s also a potential security risk to consider. Data could lose levels of protection, or even be altered without you realising, putting your data integrity at risk. You could also lose sole ownership of the data.

Don’t just see the challenges, see the opportunities

It’s clear from this post that integrating AI into your working environment isn’t always going to be seamless. However, the insights that it can give you once it’s up and running are invaluable. By keeping feedback and communication lines open within your organisation, you can catch any issues early and make sure that everyone is on board.

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