Artificial annoyance: Is AI really everything that it seems?

For finance teams across the country, artificial intelligence is fast becoming a Mary Poppins’ bag of possibilities. From spotting fraudulent activity to identifying trends that save a company tonnes of money, businesses are being promised that IT solutions can independently gather, analyse and report on data, unlocking all kinds of wonders.

It sounds amazing, right? Well unfortunately, it’s not quite as simple as it seems because businesses not just in the UK, but globally, are currently weathering the latest deluge of industry terminology which is making reporting a little foggy.

Artificial intelligence. Machine learning. Augmented intelligence. Chatbots. Business intelligence. The last few years has seen a surge of interest in programmes and solutions that can think and adapt themselves, with many – if not all – of these terms being used interchangeably.

As more and more information is captured, the notion that an intelligent tool will be able to do the heavy lifting – and get more useful over-time is hard to resist. Unfortunately, there is little, if any true artificial intelligence available for businesses to use – we’re still closer to Clippy from Microsoft Word than we are to HAL 9000.

Take chatbots for example. By relaying information and questions to you in real-time, similar to a conversation with a person, chatbots are often lauded as being intelligent, aware pieces of technology. In reality, they are a web form that can identify pieces of information, which are presented in a dynamic format.

That’s not to discredit AI, which is an area I have a specific interest and faith in myself. It’s just that it’s not developed enough to truly be of benefit to companies. As we move forward it will be useful, but will start by aiding the individual, not entire organisations. For truly intelligent solutions to be able to independently offer council to an entire business, a conservative estimate would be that they’d need to be planted and analysing a company’s data for a number of years.

For now, what does need clarity is exactly how autonomous these technologies are – and how much work is needed from employees analysing the data alongside them, allowing businesses to make smart decisions about how they approach reporting and insights. The entire idea of automation and machine learning is to lessen the burden on put-upon staff and free up more of their time – not add to it.

We haven’t quite reached peak intelligence, and as it stands, businesses would be wise to keep the stabilisers firmly on any machine learning tools they adopt. In fact, the best approach still remains a combination of an advanced data reporting tool with a dedicated human specialist – or consultative intelligence as it is known.

This approach helps ends users get to the point and make the right decisions by utilising both the power of technology and the expertise of an expert. After all, we shouldn’t let AI run before it can crawl.