Companies may be eager to harness artificial intelligence (AI), but research shows that making the most of the emerging technology is easier said than done.
About 87% of data leaders say AI is either being used by only a small minority of employees in their organizations or not at all, according to Carruthers and Jackson’s Data Maturity Index.
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The consultancy’s Data Leaders annual poll suggests that many organizations suffer from “AI-induced paralysis”, with only 5% of businesses boasting a high level of AI maturity, established AI departments or clear AI processes.
However, it’s important that data leaders who feel their organization lacks maturity don’t get too frustrated.
Carolyn Carruthers, CEO of Carruthers & Jackson, told ZDNET that every new technology goes through a period of justification, governance and acceptance.
“We’re all on a journey,” he said. “We have more data than ever before. Data is fundamental to our business.”
As a starting point for building AI momentum in slow-moving organizations, Carruthers suggests four priorities for data leaders looking to move beyond current AI paralysis:
- Start with purpose — “I can’t stress enough. What do you want to do? What problem are you trying to solve? What keeps you up at night? What opportunities do you have? What excites you? You need to have some reason to go. And without that, We look like a bunch of kids playing sports on Sunday. We’re scattered all over the place. So first and foremost, focus on the objective.”
- Focus on targeted results — “What’s the smallest part of that objective that you can start to make a difference? When you go down this road, and as you mention things like AI, everybody goes to ‘bigger is better.’ It’s like, ‘Most What is the big problem? Can we solve world peace?’ Instead, focus on the smallest problem where you can make a difference and use that as your model.”
- Shout about your success — “The data people aren’t very good at telling everyone about the good work they’re doing. We’re very good at thinking about how much work we have left. And we’re a very good staff at running and doing a lot. But we’re not very good at going, ‘Look at this cool thing we have,’ and encouraging people to come on the journey with us.”
- Use data to prove your case — “Show people the results of your project. Did it work? Did the AI do the things we all told it would do? Could we have done the project better or faster? Understand the metrics, so you can shop for more projects.” do.”
Focusing on these four priorities will help your organization build AI momentum.
But given all the hype and excitement for generative tools like OpenAI’s ChatGPT and Microsoft Copilot, why is AI at such an early stage of development?
Carruthers says the explanation is simple – embracing AI involves the ability to overcome two major barriers: people and regulation.
Barrier 1: The human problem
When it comes to people, all types of employees in businesses — from the boardroom to the shop floor — need to be convinced of the value of AI.
And Carruthers, who is a former chief data officer (CDO) at UK infrastructure giant Network Rail, says convincing people is not an easy task despite all the excitement surrounding the rapid growth of generative technology.
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“As soon as you mention the word AI, people imagine Skynet and start thinking they’re going to lose their jobs,” he says, referring to both Fantastic AI system in The Terminator And many have real fears about the potential impact of emerging technologies on workforce numbers.
“While many data leaders feel they need to do something with AI, they also face an inherent level of built-in resistance before they start doing anything.”
Barrier 2: Regulatory constraints
When it comes to regulation, Carruthers and Jackson research suggests that executives are rightly concerned about the prospect of stricter data laws focusing on data ethics and data use.
However, as the form of these rules and laws is still unclear, many companies are choosing to bide their time before pushing headfirst into AI.
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“It’s a bit like smoke and mirrors. Laws are coming — we know a lot of people are talking about it, but we don’t know what these laws mean yet,” Carruthers said.
“So, I think people are hedging their bets a little bit because they don’t know what’s going to happen.”
Momentum needs solid foundations
The research suggests that the complex combination of an intimidating workforce and the unpredictability of the current regulatory environment means many organizations are still stuck at the AI starting gate.
As a result, not only are pilot projects thin on the ground, but the fundamental foundations—in terms of both data frameworks and strategies—on which these initiatives are built.
Nearly two-fifths (41%) of data leaders say they have little or no data governance framework, up from just one percent. Maturity index of previous yearWhile 40% of data leaders said they do not have or have a data governance framework, which is a set of standards and guidelines that enable organizations to manage their data effectively.
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Just over a quarter of data leaders (27%) say their organization has no data strategy, which is only a slight improvement from the previous year’s figure (29%).
“I understand why everyone isn’t there yet,” says Carruthers, who, as a former CDO, understands the complexities involved in strategy and governance.
Moving towards your data-led goals
Carruthers and Jackson’s research suggests that the key role of governance means companies that want to be ready to exploit AI must focus on developing a data strategy and a supporting data framework.
“We have to put things in place that we didn’t have before to understand what AI can do and the implications of how good it can be,” Carruthers said.
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The good news is that some digital leaders are making progress. Andy Moore, CDO of Bentley Motors, focuses on building the foundation for exploiting emerging technologies such as AI.
He recently explained to ZDNET how he built an enterprise-wide data strategy around four key pillars: governance; Data Cloud, which is Bentley’s technology stack; Data Dojo, its internal data literacy program; and enablement, which focuses on helping the data team work with the rest of the business.
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“My ongoing challenge as a data leader is to set the possibilities for these technologies without saying you can have it now — because, of course, everybody wants AI now,” he says.
“I have to say, ‘I can’t give you AI right now because I have to lay the groundwork first.’ So, my role is about balancing expectations, while still moving at the pace of business needs.”