But on a more sobering note, research commissioned by ServiceNow - The AI Maturity Index - reveals that only 18% of UK businesses warrant the description of ‘pacesetters’ when it comes to their use of AI. The majority remain at the experimental phase. Of course, the hype around AI is still intense, and it’s still relatively early days for a technology that is going to have long-term impacts that are still far from clear or easy predict.
Nevertheless, that’s no reason to postpone more rapid adoption. Those that are pushing ahead with the use of AI at scale are already reaping benefits and creating a gap in competitive advantage that other, slower starters may find it increasingly hard to close. PwC, for example, found that jobs exposed to AI recorded a rise in productivity nearly five times that of other workers. As Phil Baker, who leads a team of strategists at ServiceNow, explains “Those early adopters and innovators are finding the use cases for AI that are helping their people to focus on the things which make them money.”
Many businesses and their leaders, however, still find it challenging to turn their optimism and enthusiasm for AI into practical reality on the ground. Nick Rutherford, a consultant who has worked with many different organisations across industries to shape and implement their digital strategies, explains the importance of acting as soon as possible.
How, then, should organisations make sure that they are, at the very least, on the AI train? There are innumerable ‘AI readiness’ assessment tools and services available on the market as part of the AI advice gold rush that has inevitably accompanied the arrival of such a game-changing technology. But for Nick Rutherford, before they stand up new AI solutions, many organisations will need to take a more fundamental step. First of all, he says, it’s essential to start the AI journey from the right place. That means “It’s all about getting the technology and data foundations right that actually allow you then to start evolving and innovating or continuously imporving."
His advice is echoed by business leaders’ sentiment in the UK. Nearly nine out of 10 (86%) say that to take advantage of AI they will have to modernise their technology architecture. This will be less of a leap for some organisations than others. But, as Nick Rutherford asserts, starting with the right platform is non-negotiable, and for many there’s still a significant gap to bridge: “What still surprises me a little is the low ebb that some companies start to operate from in terms of their technical capabilities. They do need to make a step change to establish those good foundations.”
One of the common challenges that many will face is the considerable systems complexity that has built up in organisations over time. As Nick Rutherford explains: “Many organisations with legacy technology have effectively engineered complexity into their existing architectures over many years. So engineering that out is not always going to be easy. And it's making it even harder for them to step away into platform plays and get their foundations correct.”
For many, making that transition is still challenging. Many tech businesses, for example, have been able to adopt AI extensively, their characteristics – staffed by programmers and developers with access to extensive user-generated data – predispose them for success in this area. The path to AI for businesses with considerable technical debt such as utilities or manufacturing businesses, for example, is less straightforward and likely to be rather steeper. Others, such as those in highly regulated industries like financial services, also face additional complexity in implementing AI in customer-facing applications.
For all organisations, laying the necessary technology foundations to get ready for AI is key. But the human angle is just as critical. After all, it’s people that will be using the powerful new capabilities that AI offers. They need to be prepared and ready to work in new ways. A survey by McKinsey & Co makes this point emphatically.
Those organisations that McKinsey designates ‘early adopters’, with six or more AI use cases already fully implemented, are much more likely than their less-advanced counterparts to focus on the human dimensions of AI adoption. They also invest heavily in supporting their employees to develop the new skills they’ll need to work with AI. And 40% of early adopters provide extensive support to encourage their employees to use AI, compared with just 9% of those organisations that are still just experimenting with AI.
Inspiring people to take up the technology requires empathy for every worker in a specific role and with their own unique concerns. ServiceNow’s Phil Baker believes that understanding what every individual wants to achieve with AI is fundamental to driving adoption.
Research shows that for many organisations, employees are often further up the AI adoption curve than the organisations they work for. McKinsey, for example, found that even in organisations that were still only experimenting with AI, 88% of employees said they used AI tools as part of their work. Of course, the independent use of those applications by individuals can create some real risks in terms of security and more besides. But preventing their use altogether is likely to be very hard to enforce.
Just think back a few years. The case for bring-your-own tech became unanswerable when consumer devices in the early 2000s offered a far superior user-experience compared to the corporate tools that many employees had to use. It’s easy to see the same thing happening with the proliferation of powerful AI tools that are easily accessible to anyone who wants to use them.
Nick Rutherford suggest that leaders need to get in front of the coming wave of AI tools and capabilities, and do it fast: “Business leaders have no choice. If they're not going to invest their time, they're not going to understand these capabilities. Then their level of service and their efficiency could all take a negative turn, be it customer satisfaction, staff retention, or whatever else matters most to them. All facets of every organization are going to start to be affected by AI.”