AI is elusive – working its way through hype cycles. It’s turbocharging forensics, cataloguing smells and giving voice to those recovering from throat cancer. It is transformative, but it’s not miraculous – nor is it more than a tool to augment human capabilities. But it doesn’t stop businesses fantasising about it, or jumping on the bandwagon. So, what is AI really and how can it make a true impact for businesses today?
The latest Gartner research on the Hype Cycle of AI provides the perfect moment for reflection here.
It highlights the demand from users, who want more than AI can currently deliver – but also the pace with which that is changing. Zoning in on 34 types of AI, no less, we see that the speed of innovation is picking up with an above-average number of innovations reaching mainstream adoption within two to five years.
Most emerging technologies will follow a hype cycle – let’s call it a rollercoaster of expectations vs reality. To gauge the perception of AI today and understand it’s true reality, getting to grips with these hype cycles and the various iterations of AI helps.
Gartner maps these out in five clear cycles. These are kick started with ‘innovation trigger’, when early proof of concept raises awareness. ‘Peak of inflated expectations’ is the next phase with a flurry of early success stories following publicity – but also failures. This is followed by ‘the trough of disillusionment’ when interest wanes following failures and only improvements of surviving providers ensure the tech’s survival.
All going well, it’s the heady ascent of ‘the slope of enlightenment’, when second and third generations provide a better view of the benefits. Finally, it hits the ‘plateau of productivity’ when the tech goes mainstream with widespread adoption. It’s a fair journey and the majority of AI technologies fall within the first three cycles – from innovation trigger to the tough of disillusionment.
Alas, AI is in a state of flux. But momentum is growing – and the possibilities becoming undeniably more exciting. But there are a few characteristic mistakes that businesses make when indulging in the hype, that encourages the inevitable fallout as they ride the AI rollercoaster. Firstly, one size does not fit all – it’s a tired old phrase, but ever relevant.
It may still be the cool new tech on the block, but its true impact will only be felt when it is applied to real, specific and appropriate business challenges. AI is not a band-aid for general business problems. It requires strategic application. So it goes without saying, AI will not solve all your problems and getting your business’ AI fix for the sake of it will not boost competitiveness – but it will feed into the failures that drive the sharp descent of those hype loops.
Equally, onboarding the next AI iteration alone will not be enough to ensure success. To give AI the best chance of turbocharging business requires the right human talent, too. Ensuring the best data scientists and experts are on hand to govern a bespoke rollout that takes business needs into account is critical.
Moreover, just as human learning is a process of failure on repeat until a task is understood, so expectations of immediate success should be leveled. A process of trial and error should be anticipated as businesses and AI technologies evolve alongside one another – this should be built into any AI strategy. Any other approach risks inevitable disillusionment, which as we know can slow adoption and limit innovation.
However, despite slicing through the hype – progress is picking up. As businesses prepare for the ensuing data deluge as the digital era progresses exponentially off the back of the pandemic, the demand for automation and AI capabilities grows. IDC’s latest figures highlight 64.2ZB of data created or replicated in 2020 alone. It states that “the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage.”
IoT data is the fastest growing segment and data created at the edge is growing almost as fast as that in the cloud. As emerging technologies converge so the acceleration will accumulate to G-force speeds – so keep your arms and legs inside the ride. This is all ample fodder for data hungry AI applications and will certainly increase the need for greater automation and AI solutions across data strategies.
The UK government is keen to nurture this potential hotbed of innovation, with ongoing talk of loosening AI regulations. However, as the same discussions play out around the world we know there are a variety of approaches to be taken here – with China tightening its grip on the algorithm and the US taking a more balanced approach.
The Gartner Hype Cycle of AI report spotlights Responsible AI as an area that will reach the ‘plateau of productivity’ in the next 5-10 years. As trust and privacy become hot topics accountability for the risks that come with using AI will form a key element in the technology’s ongoing progress. For example, Japan, Canada and the US are setting guard rails for AI to help reduce risk, boost accountability and ultimately ensure its ongoing success.
In the same way, businesses looking to take part in the AI revolution must consider the transparency, trust, ethics, safety and diversity of their data and AI applications to ensure success. It’s a reputation issue and it’s a moral obligation to customers and society at large to ensure the foundations of a fair digital future.
While governments define their own approaches, businesses must safeguard themselves in the meantime with good AI governance.
This strikes a heavy note, but it is testament to AI progress – slowly but surely it is becoming embedded within our everyday lives. While progress is not always visible, it is real and its fate lays in the hands of those who take hold of the opportunity.
