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What is human centred AI and how can it impact business performance?

(Image credit: Image source: Shutterstock/PHOTOCREO Michal Bednarek)

Artificial Intelligence (AI) and Machine Learning (ML) aren’t new, in fact mechanical automation has been around for decades – so why is there still an incredibly slow adoption rate across modern-day workforces?

Although it might not have been called AI or ML until recent times, the world has been surrounded by examples of this technology – from having an x-ray to taking out a mortgage. Its primary purpose is to exist to try and make things simpler, quicker and easier for people.

And yet, despite being present for nearly a century, the concerns that machines are ‘going to take over the world’ – or that they’re untrustworthy and will take everyone’s jobs – remains the same today.

For years, businesses have been trying to outdo one another with revolutionary promises and yet there’s still a lack of understanding of what AI and ML represents and how it can – and can’t – drive an organisation forward.

That’s because there’s still a lack of trust – and it’s become apparent that many people are still reluctant to hand over the reins to a machine when confidence is low. It’s something that isn’t new for those who occupy the world of business intelligence because the industry knows how AI promises everything but doesn’t always deliver. Uncertainty has ensued because automation hasn’t quite solved initial problems, but at the same time no-one is being ‘automated out of a job’ either.

A great example to truly highlight how this translates into business performance is by looking at online delivery brand, Amazon. This is an organisation that deploys hundreds of robots which have been simplistically coded to collect products off the shelf and not bump into one another – which is the AI part.

And the same concept can be applied in business. For example, if a person has applied for a bank loan within the last 20-25 years, it’s unlikely they will have had their initial query dealt with by a human. It will have been digested by a machine – an algorithm – that categorises individuals alongside others who have similar attributes.

AI isn’t ‘magical’ – but it can collate large volumes of data by the second, which is an obvious advantage for the fast-paced and heavily evolving economic climate.

So, it does pose the question of ‘exactly how does human centred AI truly impact business performance?’

Enhanced business performance using AI begins with trust

For larger scale AI adoption, organisations must think outside the box – and perhaps onto the tennis court. Yes, this maybe a strange path to take, but the sport’s ‘Hawk-Eye’ technology system could provide some food for thought when analysing AI’s success through a transparent lens.

Looking back to the days when John McEnroe would infamously contest huge calls with showmanship and bravado, Hawk-Eye today often limits those moments – instead shutting down any questionable decision with critical evidence to back up the umpire’s call.

And the reason why many players and the crowd accept those crucial outcomes is because tennis hasn’t ‘black boxed’ its automation. Instead, every single person watching can see exactly how the decision has been made by the machine, which introduces a greater level of fairness and trust that wasn’t there before.

Line judges provide the human element, but it’s the machine that augments their decisions. Now because it’s done so well, it’s become a part of the game – alongside other sports.

However, where mechanical AI hasn’t fared as strongly is the example of VAR recently introduced into the English Premiership. If anything, it’s riled footballing crowds up even more to voice their displeasure, all because the machine’s decisions aren’t being shared with them. What it has determined is a lack of trust from supporters and onlookers because they haven’t played any part in the final decision.

Openness and transparency are key in customer adoption of AI

For human centred AI to have a true impact on an enterprise’s bottom line and adoption level, there must be observation points included – something which business intelligence is built upon. Having machinery that allows customers to understand and see how decisions are being made can instil a vast amount of trust and willingness to have confidence in the detail it forecasts.

There isn’t a crystal ball when it comes to knowing exactly how the business landscape is going to evolve day-to-day but having smart platforms that can consume key trends and marketplace developments can help to balance business risk – and determine decisions more swiftly.

And when wrong conclusions are met, that’s where humans – or rather the ‘observers’ – come in to tweak and adjust the model so that the path is corrected, and danger is subsequently averted.

Think of Tesla and how its self-drive capabilities are truly innovative. But, it can still get things wrong – and if the driver isn’t prepared for that, the results could be catastrophic. However, where this manufacturer prevails is by utilising a smart screen which shows how it’s making decisions. As a result, the person behind the wheel understands when to trust the vehicle to take control, and when they need to intervene.

Again, the same can be said for organisations and their employees who become the ‘observers’. To begin to achieve success, the team needs the desired information from AI, in order to elevate their jobs, watch the machine in action and work side-by-side – all of which comes via business intelligence.

Examining real-time activity, enterprises are in fact creating ‘cockpits’ where humans can step in where needed and trust the machine to effectively understand the detail and make commercially savvy decisions – all of which the customer sees too.

Everybody has a part to play in ensuring what technology works best for their business – and automation should ultimately be embraced rather than feared. However, for there to a positive impact made on business performance, firms must firstly get their business intelligence right and continuously study its ML as a pre-requisite.

Simplify the tools, demystify the black box and provide smart intelligence that isn’t magical, but can instil trust, transparency and longevity for organisations.

Ken Miller, co-founder and CTO, Panintelligence