Wanting is not being able. But being able necessarily means wanting. Businesses may be eager to harness artificial intelligence (AI), but recent studies suggest it’s easier to want than to do.
According to Carruthers and Jackson’s Data Maturity Index, 87% of enterprise data leaders say AI is only used by a small minority of employees within their organization or not at all. is not at all.
The survey suggests that many organizations are suffering from “AI paralysis”, with only 5% of companies boasting a high level of AI maturity, with AI teams, and clear AI processes.
However, data managers who feel that their organization lacks maturity should not be discouraged just yet.
Caroline Carruthers, CEO of Carruthers and Jackson, tells ZDNET that any new technology goes through a period of justification, governance and acceptance.
As a starting point for building AI momentum in organizations, Caroline Carruthers suggests four priorities for data leaders who want to break out of the current AI paralysis:
1. Start with a goal
“I can’t stress this enough,” says Caroline Carruthers. “What do you want to do? What problem are you trying to solve? What keeps you up at night? What opportunities are there for you? you? What are you passionate about?”
“You have to have a reason to move forward. Without that, we’re like a bunch of kids playing sports on a Sunday. We’re all over the place. So first and foremost we have to focus on the objective”.
2. Focus on targeted results
“What is the smallest part of this goal that you can start to make a difference on? When we start down this path, and as soon as we mention AI, everyone is like ‘more ‘The bigger the better.’ We say, ‘What’s the biggest problem? Can we solve the problem of world peace?”
“Instead, focus on the smallest issue where you can make a difference and use that as a model for moving forward.”
3. Communicate your successes
“Data scientists aren’t very good at talking about the good things they’re doing. We’re very good at thinking about how much we still need to do.”
“But we’re not very good at saying, ‘Look what’s great about us,’ and encouraging people to come along with us.”
4. Use data to prove your point
“Show people the results of your project. Did it work? Did the AI do what we said it would do? Could we have done the project better or faster? Understand indicators, in order to be able to obtain support for other projects.”
By focusing on these four priorities, your organization can begin to build momentum around AI.
But given the popularity of generative tools, such as OpenAI’s ChatGPT and Microsoft’s Copilot, why does AI remain at such an early stage of development?
According to Caroline Carruthers, the explanation is simple: the adoption of AI involves the ability to overcome two major obstacles: people and regulations.
Obstacle 1: the people problem
When it comes to people, everyone in the company – from the board of directors to the shop floor – must be convinced of the value of AI.
Mr Carruthers, who was chief data officer (CDO) at UK infrastructure giant Network Rail, says it is not easy to convince people, despite the enthusiasm around the rapid growth of generative technologies .
“As soon as you say the word ‘AI,’ people imagine Skynet and start thinking they’re going to lose their jobs,” she says.
“While many data leaders feel they need to do something with AI, they also face a level of built-in resistance before they can even begin to do anything.”
Obstacle 2: Regulatory constraints
When it comes to regulations, Carruthers and Jackson’s study suggests that executives are rightly concerned about data ethics and the potential for stricter data laws focused on information use.
However, because the form of these rules and laws is not yet clear, many companies are choosing to wait before diving headlong into AI.
“It’s a bit of smoke and mirrors. Legislation is coming – we know a lot of people are talking about it, but we don’t know yet what these laws mean,” Carruthers says. “So I think people are protecting themselves a little bit because they don’t really know what’s going to happen.”
Momentum towards generative AI needs solid foundations
The study suggests that the tricky combination of a fearful workforce and the unpredictability of the current regulatory environment means many organizations are still stuck at the AI starting point.
As a result, not only are pilot projects few in number, but so are the basic foundations – in terms of data framework and strategies – on which these initiatives are built.
41% of data leaders reported having little or no data governance framework, which is one percent more than the previous year’s maturity index.
Just over a quarter of data leaders (27%) said their organization had no data strategy, which was only a slight improvement on the previous year’s figure (29 %).
“I understand why not everyone is there yet,” says Carruthers, who, as a former CDO, understands the complexities of strategy and governance.
Moving towards data-driven goals
Carruthers and Jackson’s study suggests that companies that want to be ready to leverage AI need to focus on creating a data strategy.
“We need to put something in place that we didn’t have before to understand the implications of what AI can do,” Carruthers says.
The good news is that some digital leaders are making progress. Andy Moore, CDO of Bentley Motors, is focused on laying the foundation for leveraging emerging technologies, such as AI.
He told ZDNET how he created a company-wide data strategy around four core pillars:
- The governance
- Data in the cloud, which is Bentley’s technology stack
- A data warehouse (infrastructure and training), which is its internal data training program
- Enablement, which focuses on helping the data team work with the rest of the business
“My ongoing challenge as a chief data officer is to define the possibilities of these technologies without saying you can have them right away – because, of course, everyone wants AI right away,” he says .