From hype to reality: why the C-suite are getting serious about AI

How much progress have organisations made with implementing artificial intelligence, and what do tech leaders hope to see in the near future?

Artificial intelligence (AI) is already having a huge impact on the workplace. In the UK, one in six organisations (432,000) had, by last year, embraced at least one AI technology, according to a government report. More than two thirds of large companies have already deployed AI tools, a separate study shows.

For Chief Technology Officers and Chief Information Officers (CTOs and CIOs), this means focusing on getting the best out of the tools already available, as well as preparing for further innovation.

Simon Morris, Vice President, Solution Consulting at ServiceNow, the AI platform for business transformation, divides AI implementations into two categories. “We have the embedded use cases, where AI tools are increasingly available through the platforms you use to run your business, improving the cognitive ability inside your existing workflows,” he says. “Then you have ‘line of business’ applications – the bespoke use cases that organisations are developing for themselves, using tools such as generative AI to build new solutions based on their data.”

Examples in the first category continue to proliferate, ranging from automation tools that optimise workflows to analytics programmes that parse information such as sales and marketing data to identify insights that could drive growth. Line of business AI, by contrast, requires greater thought and investment, but many organisations are making rapid progress. One manufacturer, for example, has built a tool that enables its service engineers to instantly identify which one of thousands of components is needed to fix a particular problem and also tells them how to do it.

Organisations must develop both types of use cases, says Jenny Rae, Chief Information Officer at Imperial College London, who is responsible for the technology the university depends on for its teaching, research and broader operations. “There are two main areas of focus for us right now,” she explains. “One is around productivity – how we do things faster and quicker – where we’ve launched tools such as a chatbot to support our staff and students. The other is around customer experience. For instance, our academics have published a wealth of research throughout their careers. Can AI be leveraged to make this research more accessible to our students, thereby enhancing their educational experience?”

Rae agrees with Morris that while AI tools focused on productivity are at a more advanced stage of implementation, it is the bespoke solutions that offer greater potential. “We’re getting really positive results from our chatbots, but this doesn’t feel revolutionary; by contrast, the pilots we’ve done around our avatars suggest this could be transformational.”

There’s also a multiplier effect to consider, argues Darren Martin, Chief Information Officer at Davies, a professional services and technology firm that works with insurers, financial services companies and other regulated businesses. AI tools can drive growth without driving up costs, he points out, underpinning a virtuous circle of expansion and improving profitability. “This is about how we grow organically while we stay in control of our cost base,” he says. “As we grow through acquisition, it is about how we use our technology strategically to drive efficiency in those businesses, too.”

Davies has implemented AI tools that enable it to better support clients across its various divisions. In insurance, for example, it uses AI to automate vehicle fraud and related checks to reduce case-handling times, as well as to analyse medical reports in casualty cases and to triage business-interruption claims as they arise. “It’s good for us in that it reduces our operational costs,” says Martin. “But it’s also good for the client, because it reduces turnaround times and therefore supports their customer retention and renewal numbers.”

The key, he suggests, is to build the business case for future investments, potentially in even more far-reaching areas, on the basis of success generated from existing products. “We’ve got lots of ideas, but we want to focus on doing the right things first, in terms of implementing technologies that really engage our clients,” he says. “That hopefully gives them the confidence to support our innovation as it evolves in the years ahead.”

It's a challenge many organisations are wrestling with. They must build on gains from existing AI deployments to achieve something even more transformational.

ServiceNow’s Simon Morris suggests a dual strategy. “Be prepared to explore and exploit simultaneously,” he says. “Be ready to get every penny of value out of the use cases you have already implemented, but also start thinking about those future use cases, and the organisational adjustments that may be required to enable them – including preparing your people for change.”

Inevitably, that will carry risk and, given the sums at stake, many CIOs and CTOs are nervous about committing to investments that might take their organisations down blind alleys. However, as businesses are discovering, those that aren’t investing in AI are increasingly losing out to those that are. The actual cost of doing nothing and seeing what happens will far outweigh the cost of adopting new technologies.