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Debunking the 5 biggest myths of AI  

A lot of assumptions get made when it comes to Artificial Intelligence (AI). Whether it’s the sci-fi portrayal it receives in TV & film, or people’s general fear of the unknown. The truth is, there really isn’t much to fear at all, and it’s important that organisations understand how helpful AI can really be in achieving business objectives.

 

1. AI is just for big complex problems

In simple terms, artificial intelligence refers to situations where computers carry out work that would have previously been done by actual people. This is something that doesn’t sit well with some, with many fearing that AI is going to negatively impact the world we live in.

But if you look beyond the hype and rumours, the truth is that AI is not here to create robots and overthrow the human race!

The majority of the jobs carried out by AI are in fact pretty simple ones – for example, following an expert-defined set of rules to decide whether or not to approve a credit card application.

Once in place, basic procedures such as these can offer numerous benefits and make a great starting point for moving onto more complex AI problems and solutions.

 

2.  AI is going to take people’s jobs

If anything, intelligent automation actually frees up employees to do far more value-adding work – focusing on tasks AI can’t carry out, which often have a higher level of complexity and importance associated with them.

It’s true that AI software is designed to replicate human intelligence, so there is a possibility that it will replace humans in the completion of some business activities. To some extent, this has already been happening for some time in certain business sectors, particularly manufacturing.

The reality is that most jobs will only be partially impacted, with only certain aspects of a role being replaced with automation. The tasks being automated are likely to be those that are transactional, repeatable, predictable and high volume. However, most jobs involve some aspect of subjective reasoning, so it’s unlikely that AI can replace that bit.

 

3.  AI is a black box

AI can come across as a bit of a ‘black box’. By that we mean it can carry out tasks without any explanation or human understanding of how it has got to that point.

AI systems look for patterns in data by following a set of rules given to it by the end user. This process often involves complex intricacies that would be far too difficult for a human to understand or replicate. If the AI platform in question doesn’t provide explanations for its decisions, then it is impossible to know whether the right result has been achieved.

Instead, AI solutions can be built in a way that allows them to be interrogated for total transparency. Tools can be used to analyse datasets and identify every indicator used, and subsequently modify the rules to follow the desired direction of the client more closely. This means that there is scope for humans to influence AI practices, and we don’t have to blindly trust robots to do all the work.

The logic behind every automated decision can also be captured in data form, for audit and reporting purposes.

 

4.  You can only do AI if you have lots of data

To some extent, data is key to AI implementation. Data is needed in order for AI systems to be able to spot patterns and train algorithms that can subsequently be used as part of business processes, insights and decisions.

However, it’s important to remember that a lot of this ‘learning’ has already been conducted by big AI companies such as AWS, Google and Microsoft, using their own vast banks of data. This includes things like image recognition, fraud detection and document analysis, which can feed AI platforms that are used by numerous client organisations, saving them the hassle and expense of gathering data themselves.

This just shows that there are other options available to businesses with a perceived lack of data, and they should certainly not rule out implementing AI systems on this basis.

 

5.  AI requires skills most business don’t have

In the past it was always likely that there would be a need for data scientists, technology developers and AI specialists to be involved in order to take advantage of AI.

However, with the advent of low-code platforms, cloud-based AI services and ready-to-go business solutions, this is no longer the case. AI has become far more accessible to businesses, and thanks to the development of intuitive AI tools, little or no training is now required to start using intelligent automation platforms successfully.

 As far as businesses are concerned, it’s important not to get too concerned about the complex inner workings of AI and instead stay focused on the results the technology can achieve. By having a little bit more trust in data and automation methods, there is significant reward to be had in terms of efficiency, accuracy and work capacity.

 

Authored by: Cleo Chaisty, copywriter for Xpert Rule , who are a UK-based software development and licensing company on a mission to simplify intelligent automation and empower businesses to use AI for growth. 

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