Solving your business’ problems with the AI you (might) need, not the AI you want

Generative AI is one of the latest types of technology to fall victim to a hype cycle – with an astounding amount of media coverage seeing it become the ‘must have’ for every business, and indeed lots of individuals too.

With headlines abundant, social media trends galore, and a whole host of famous and influential faces lining up to extol the virtues of text and image based generative AI systems, it’s no wonder they’ve turned so many heads – with everyone from the largest of corporations to the newest of start-ups looking to somehow incorporate these tools into the workings of their business.

Yet, we’re already seeing the next stage of the hype cycle emerge: the ‘trough of disillusionment’ when early implementers begin realising it doesn’t necessarily match up to everything they were promised by the media, tech creators, and influencers alike.

Despite generative AI being among the most well-known tools of their kind, making their way into homes as well as businesses, and on to personal mobile phones and tablets as well as company systems, they are not the be-all and end-all of AI technology, and that’s why we’re slowly seeing the narrative around them beginning to change.

The alternatives

There are so many AI options out there which aren’t text or image generators, and which haven’t received anywhere near the same amount of hype and media coverage as the chatbots of the world.

These specialised models have never attracted the same sort of attention and have remained comparatively under the mainstream radar, despite the fact they can deliver exactly the sort of productivity, safety enhancements, accuracy, and decision-making support that would genuinely be useful within many businesses. But yet the world has largely ignored them because they need plumbing, interpretation and understanding; they’re not chat based.

Conversations around generative AI have been seemingly endless in recent years, to the point where the most well-known tools have been hailed as a life-changing development akin to the invention of the wheel or the discovery that humans could create fire.

Goldman Sachs asserted in 2023 that generative AI would increase global GDP by 7%, while in the same year Forbes shared an article entitled ‘How Generative AI will change all of our jobs in 2024’. Fast forward to this year, and University of Oxford researchers published a paper on the theory that it would ‘transform the labour market’ and the Alan Turing Institute recently claimed that these tools would free up 40% of public sector workers’ time.

Chatbots and the like are seen as a ‘must have’ –the AI world’s equivalent of the Rolls Royce. By contrast, these specialised models are more like owning a mountain bike: yes, there are many people who realise the advantages associated with them, but given the choice between the two, it’s a Rolls Royce to go please.

But what happens when you’re faced with a mountain to climb? Then your decision to opt for the shiny luxury car which draws admiring looks from everyone around – that decision doesn’t look quite so smart.

Define the problem before buying the solution

The problems many organisations are now facing have stemmed from the rush to acquire generative AI, without a thorough investigation into and understanding of the problems within existing systems. In the race to be among the first to implement these tools, to be seen as forward thinking, industry leading, and on-trend, their usefulness within specific organisations has seemingly been an afterthought.

Leaders have simply bought into the hype around this type of tool and decided they’re essential in every business, including theirs. And then once they’ve spent money on acquiring the tools, they’re now faced with the realisation that they’re actually not solving the problems they needed them to solve.

So, that leaves them with two options: either completely abandon the project and reverse the implementation of these tools in their business, or keep on spending money on adaptations and enhancements until the tools actually provide some value to them. Either way, it’s a case of (a lot of) money, time and effort spent for sometimes very little or no gain.

Rather than rushing to acquire the ‘must have’, pausing to look past what was wanted and identify what was needed could have not only saved this money, time and effort, but would also likely have led them to a different solution. This may have been simply fixing issues within their existing systems to improve efficiency and effectiveness, or it may have involved looking at the more specialised AI models.

Either way, those who haven’t yet invested in generative AI may well have saved themselves a costly exercise by not rushing full steam ahead. Much like the well known fable of the tortoise and the hare, a ‘slow and steady’ approach to implementing new technology, pausing first to define the problems which need to be overcome and looking at all of the options will pay off for these companies. Essentially, they are more likely to end up with exactly the tools they need in order to enhance their procedures, simplify their systems, and maximise productivity.

When it comes to business, and particularly when it comes to costly hyped-up technologies, there may well be a gap between what we need and what we want. And it’s up to leaders to identify whether a mountain bike might be exactly what’s needed, even if it doesn’t draw the same looks of envy from their competitors as a Rolls Royce might.