Is your business AI-ready? 5 ways to avoid falling behind

The AI transformation is gathering steam. Consultancy Carruthers and Jackson’s recently released Data Maturity Index found that just 7% of organizations don’t use any form of AI, a significant drop from 26% last year.

Caroline Carruthers, CEO at Carruthers and Jackson, told ZDNET that the pace of adoption is proof that generative AI and machine learning are now business-as-usual technologies.

“A lot more organizations are openly admitting they’re using AI,” she says. “I would say we’ve gone over the tipping point. AI has moved into the space where it’s proved itself useful.”

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However, it’s not all good news. Carruthers and Jackson’s report shows that the rapid adoption of AI complicates the data landscape.

While more businesses are embracing AI, they are not necessarily ready for the rise in automation and won’t be able to make the most of the emerging technology they deploy.

For business leaders who want to overcome this challenge, here are five ways to prepare your organization for an AI transformation.

1. Create a formal data strategy

Experts suggest an effective data strategy is crucial to helping organizations use AI safely and successfully.

However, the report found that over a quarter (26%) of organizations still lack a formal data strategy.

Carruthers said business leaders don’t have to create a weighty tome. A good data strategy can be a section in the broader business strategy that communicates how the organization will manage and value information.

Yet whether long or short, this strategy must focus on the right areas — and getting that correct focus is often a sticking point.

“The mistake I see a lot of organizations make is they talk about having a data strategy, and then when I look at it, it’s a technology ecosystem strategy. So, it’s about CRMs, data lakehouses, and it’s purely based on IT,” she said.

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Carruthers said effective data strategies blend people, process, and technology.

“The wording about how you look after data in your organization is important,” she said.

“The strategy should encompass the purpose of why you’re looking after data, the people elements, the data literacy side, how you make decisions on data, how you bring people together, how you keep data out of silos, and then it should also cover the tools, the architecture, the metrics, the risks, and those kinds of things as well.”

2. Establish a tailored governance framework

Over a third (39%) of organizations report they have little or no governance framework. While these figures show a marginal improvement over previous years, the report suggests they underscore persistent gaps in foundational data management practices.

Carruthers said her organization investigated this finding in detail and discovered that, despite the headline statistics, there are reasons to be positive.

“We did some investigation and found there’s an awakening about the fact that one size doesn’t fit all when it comes to data governance,” she said.

“So, some organizations don’t have a data governance framework across the piece. But they know the crown jewels of their data and treat that information appropriately.”

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Carruthers said the report highlights a growing shift toward tailored, department-specific data governance approaches — and that trend should be welcomed.

“I would much rather organizations focus on important stuff and not waste resources looking after the equivalent of yesterday’s newspaper,” she said.

“The key message is identify the crown jewels of your data and figure out what you need to do to look after that.”

3. Get tough on ethical practices

There’s wide recognition among industry experts about the dangers of using AI without a consideration of the risks.

Yet the report highlights that while 44% of organizations have seen a moderate rise in ethical discussions around AI, only 13% have formalized these conversations into structured policies.

Carruthers said the focus now must be on turning ethical considerations into practical measures.

“Too often, ethics is one of those areas that people talk about forever and never reach a conclusion,” she said.

“Success is about time-boxing your ethical considerations and going, ‘Right, we will debate until a set point and then we have to make a decision and go forward.'”

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Carruthers advised business leaders to put humans in the loop and ensure people are questioning the outputs of AI services.

“The art of dealing with ethics is understanding the questions you should be asking in the first place regarding the use case and what areas are concerning.”

4. Train the right people

The report also stresses that while 53% of respondents reported more AI usage, over half (57%) said most employees still lack data literacy.

Carruthers said business leaders have talked about boosting data literacy for over a decade, and the perceived lack of progress is concerning.

She advised executives to take a more targeted approach to training and development.

“Too many organizations try to make everyone data literate,” she said.

“For example, many companies have data apprenticeships, which can be fantastic. But you have to ask, ‘What do you do with those people when they’re through the apprenticeships?'”

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Carruthers encouraged business leaders to train the right people to use data more effectively and focus on how those skills will benefit the individuals and the organization in the longer term.

“It’s horses for courses,” she said. “For some people, effective data literacy is about filling in a form properly. That level of training would be good enough for them and your business.”

5. Focus on decision-making processes

As data complexity increases, organizations must address foundational issues such as inefficient or insecure data flow, reported by 40% of respondents in the report.

Carruthers said there are two sides to data flow concerns. First, data teams in some organizations can’t access the data they need because it’s held in legacy systems or hasn’t been collected in the first place.

“The water pipes aren’t there to get information to the people who use data to make decisions,” she said.

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Second, some organizations fail to grasp how much data they feed professionals.

“Data scientists can be a bit like the plant in *The Little Shop of Horrors*,” she said. “There’s never enough data you can give them, so they’ll always say, ‘I want more,’ because that’s the nature of the beast.”

Carruthers encouraged business leaders to think more carefully about information flows around the organization.

“Focus on the data you need to make decisions,” she said. “Identify the data you need to make or validate the decision and then work backwards from that point.”