AI: from experimentation to adoption

There is no doubt that Artificial Intelligence (AI) has captured global imagination and attention. But in the business world, the rate of adoption of artificial intelligence has lagged behind the level of interest through 2019. Even though we hear that most business leaders believe AI provides a competitive advantage, up until recently, some industry watchers have pegged enterprise adoption at between 4% and 14%.

But as we enter 2020, we are seeing an uptick, not only in interest but in AI adoption. And that uptick is reflected in the results of a new survey commissioned by IBM in late 2019. From Roadblock to Scale: The Global Sprint Towards AI polled more than 4,500 technology decision makers – including over 500 from the UK. We wanted to gauge the current and future states of AI deployment to better understand the landscape and the challenges. As you’ll see, it’s about to change dramatically.

AI Adoption on the Rise

UK results from the survey indicate that while there is still work to be done, advances in data discovery and IT management, skills training and new innovations in AI explainability are driving the rate of AI adoption faster than many predicted.

Slightly over one in five (21%) of the UK audience surveyed report that their company has actively deployed AI as part of business operations, while nearly half (46%) say that their company is exploring but have not yet deployed AI. These numbers are significantly higher than some industry watchers have estimated to date. Some of the more telling data points from the UK survey include:

• Skills. Major roadblocks are still holding companies back from the benefits of AI. Among respondents, 38% cite limited AI expertise or knowledge as a hindrance from successful AI adoption at their business, with increasing data complexities and siloed data (26%) and lack of tools for developing AI models (21%) following close behind.

• Trust. Trust is part of the bedrock of AI’s deployment - almost 70% of respondents in the UK say it is very or critically important that they can trust that their AI’s output is fair, safe, and reliable. It is also clear that ‘breaking open the black box’ of AI is key – with 74% of respondents saying that being able to explain how AI arrived at a decision is important.

• Business Need. Over half of respondents (55%) cited that business needs are driving AI adoption within their organisations, followed by competitive pressure (38%) and directives from leadership (30%).
  

The AI Journey Streams Across the Business World

Based on our interactions and the results of this study, we expect to see organisations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work in a multitude of ways. Specifically, the UK audience cites data security (34%), process automation (25%), and customer care (22%) as the top three ways that AI is being used by their organisations.

I see the excitement building with clients every day as they realize the potential of AI. Just last year we announced that SPF Private Clients, one of the UK’s leading financial services firms, has adopted IBM Watson and IBM Cloud to develop Ava, a new AI virtual Help-to-Buy mortgage adviser. Ava helps first time home buyers onto the property ladder by offering round the clock support for queries and a mortgage indication in just three minutes.

When I look at insights from the report, which was conducted by the firm Morning Consult, and think about my interactions with clients, the roadblocks to AI adoption have been a prime concern. They’re the reason we’ve worked to lower the barriers of entry and make AI more accessible to businesses.

It’s why we have invested in building capability in our services teams across Europe and launched the Data Science Elite Team in 2018, to build a global group of experienced technical professionals who help companies solve and scale AI solutions to real problems. It’s what drove us to introduce innovations like Watson OpenScale, to help mitigate bias in AI models; Watson AutoAI, which literally uses AI to build AI models; and it’s what led us to create the first-of-its-kind container-based data analytics platform, Cloud Pak for Data, that lets people run Watson with any cloud services. 

We’ve also, taken skills training and support to whole new levels, with robust data science work with several open source standards bodies, such as The Open Group and The Linux Foundation. And in response to the need for transparency and explainability in AI, IBM has been directly involved in working with The European Commission to shape its Ethics Guidelines for Trustworthy AI designed to set a global standard in advancing AI ethically and responsibly.

There is no doubt that 2019 was a productive year for AI, but 2020 is shaping up to bring an exciting new level of commitment and with it, exciting new outcomes for business and society.

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