By Bobby Brown, founder and CEO of Nucleo. 

In a bid to boost market appeal and appease stakeholders, I’ve noticed that more and more companies are falling into the trap of ‘AI-washing’, rebranding old and familiar technologies as AI. 

And confusion about what AI actually is – and how it differs from technologies like intelligent automation (IA) – is only compounding the issue. 

But without understanding both AI and IA, businesses risk applying the wrong tool to their problems – leading to overspend, wasted resources, and a heavy reliance on technology for tasks it just doesn’t have the ability to perform. 

What’s the danger? 

Businesses are investing heavily in AI, yet that investment isn’t always yielding results. 

A recent survey by BCG found that around three-quarters (74%) of businesses are struggling to achieve and scale AI value. That means that a significant proportion of businesses globally are paying for AI that isn’t used, doesn’t work, or both. 

A lot of the issues stem from the adoption process of AI. Many projects struggle to make it successfully out of the pilot phase, with MIT research finding that a staggering 95% of GenAI pilots fail. And if the foundations – like the company’s existing data systems – aren’t right, or there isn’t enough buy-in from employees, the risk of failure only increases. 

But it can also be because the company deployed the wrong tool to start with, often because they lack the understanding of exactly what they’re buying. AI-washing is a core part of this problem. 

AI or imposter?

AI-washing has become so prominent because AI itself is so difficult to define. I could ask a room full of AI experts and probably get different answers to what AI actually is. That makes it really easy for businesses to get away with claiming they are using or selling AI without having to back it up. 

The issue with AI-washing is that without knowing what you’re deploying, you don’t know if the technology will solve your problem. That wastes time and money, and erodes trust in genuine AI solutions. 

So, how can you tell whether what you’re buying is AI, IA, or neither? 

Understanding the difference between AI and IA 

Artificial intelligence is rooted in machine learning. It’s about systems that are capable of mimicking human cognitive functions – such as reasoning and problem-solving – to perform tasks autonomously. It includes things like generative text models like ChatGPT and facial recognition. 

Intelligent automation is more about augmenting human abilities. Typically, this looks like automating specific, routine tasks – like data entry or report generation – to cut back time spent on mundane work and allow human employees to focus on more high-level tasks. One example of this could be a sales professional using IA to qualify sales leads, allowing the employee to spend more of their time on outreach and building the customer relationship. 

Which technology is right for me? 

Aligning the right technology with the right business problem can be tricky. 

My biggest piece of advice is rather than thinking about which technology to use, focus on your use case instead. That will steer you to the answer. 

For example, if you have massive data sets that you want to analyse or have processes that require contextual judgement, AI might be more appropriate. 

Whereas if your processes are quite repetitive and rules-based – or you’re looking to just eliminate some of the more tedious, manual tasks – IA is likely the way to go. 

When to ignore the hype 

Avoid implementing a solution just because it’s what your peers are doing. There’s no point investing in a solution that doesn’t bring any advantages to your business. 

With the pace technology is evolving at, there’s always going to be something new right round the corner. 

If you want to protect your investments from being swallowed by the hype, the most important thing to do is to stop thinking about buzzwords and instead ask if the technology you’re deploying is actually solving the problem.

The organisations seeing the greatest return from AI aren’t necessarily those investing the most. They’re the ones that start with a business problem and then choose the technology that solves it.