Artificial Intelligence (AI) and Machine Learning (ML) arent 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 theyre untrustworthy and will take everyones jobs remains the same today.

For years, businesses have been trying to outdo one another with revolutionary promises and yet theres still a lack of understanding of what AI and ML represents and how it can and cant drive an organisation forward.

Thats because theres still a lack of trust and its become apparent that many people are still reluctant to hand over the reins to a machine when confidence is low. Its something that isnt new for those who occupy the world of business intelligence because the industry knows how AI promises everything but doesnt always deliver. Uncertainty has ensued because automation hasnt 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, its 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 isnt 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?

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 sports Hawk-Eye technology system could provide some food for thought when analysing AIs 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 umpires call.

And the reason why many players and the crowd accept those crucial outcomes is because tennis hasnt 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 wasnt there before.

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

However, where mechanical AI hasnt fared as strongly is the example of VAR recently introduced into the English Premiership. If anything, its riled footballing crowds up even more to voice their displeasure, all because the machines decisions arent being shared with them. What it has determined is a lack of trust from supporters and onlookers because they havent played any part in the final decision.

For human centred AI to have a true impact on an enterprises 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 isnt 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, thats 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 isnt prepared for that, the results could be catastrophic. However, where this manufacturer prevails is by utilising a smart screen which shows how its 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 isnt magical, but can instil trust, transparency and longevity for organisations.

Ken Miller, co-founder and CTO, Panintelligence

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