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December 14, 2023

AI-induced ‘paralysis’ stalls tech leaders’ data strategy plans

Though the AI revolution has raised the salience of data governance, progress on implementing strategies has been slow.

By Matthew Gooding

More than 40% of businesses still have no data governance strategy in place, according to new research. The authors of the report put this lack of progress down to AI-induced “paralysis”, with the tech leaders fearing they will approach the task of controlling the use of artificial intelligence tools in the wrong manner.

Data leaders are facing the challenge of putting governance in place around AI tools and services. (Photo by Gorenkodoff/Shutterstock)

The Data Maturity Index, published today by data consultancy Carruthers and Jackson, polled 268 chief data officers and other executives with data responsibilities from businesses around the world, covering a variety of sectors including financial services. Of those surveyed, 41% said their organisation has implemented little or no data governance framework, up 1% from last year.

AI poses data governance questions

While the hype around AI tools has accelerated in the last year, with major enterprise software vendors such as Microsoft and Salesforce infusing their tools with AI functions, business leaders remain reticent to deploy a technology that is still in its infancy.

Some 87% of data leaders surveyed report few or no employees are using AI in the workplace, with nearly two-thirds (61%) concerned that most employees lack the basic data literacy required to use AI in a safe and compliant way.

And though 38% said that the use of AI had led to a significant increase in discussion of the ethical use of data, this has not translated into more businesses introducing governance frameworks.

Caroline Carruthers, CEO, Carruthers and Jackson, said that trepidation among tech leaders as to the ethical implications of deploying AI remains high. “The rise of AI has added an additional layer of complexity to data operations for almost all organisations, as leaders scramble to prepare their businesses to integrate new tools and capabilities whilst pre-empting what the future might look like,” Carruthers said.

She added that the findings demonstrate that the focus on trying to “future-proof data operations”, together with the AI revolution, has “created a sort of paralysis amongst data leaders, with data strategy and literacy rates showing few signs of progress over the past 12 months.”

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Carruthers continued: “It is clear that the much-heralded AI transformation in the workplace seems to have met a roadblock: insufficient data maturity.”

Will political leaders help businesses embrace AI?

Businesses are attempting to come up with their own AI governance policies against a backdrop of regulatory uncertainty. Last week, the EU approved its AI act, the first attempt at creating overarching legislation to police large language models and AI tools. The UK is taking a more hands-off approach, with a white paper released earlier this year detailing what it calls a “pro-innovation”, with control of AI being left up to individual sector watchdogs.

Carruthers argues there is cause for optimism in the report, as it shows data leaders are successfully laying the technical groundwork for data-powered AI tools. In 2022, 55% of those surveyed believed that their data does not flow efficiently through their organisation, with that number falling to below half (49%) in this year’s Index. A further 68% report that tools including data warehouses and catalogues mostly support their use of data.

“It is unclear what the ethical and regulatory implications of AI will be for the data world, and data leaders have concerns about creating and implementing a strategy without knowing the whole picture,” Carruthers said. “Simply put, data-driven strategies have to stretch beyond the implications of AI alone, as demonstrated by the way in which existing data tools are shown to be helping organisations to tackle common data challenges.

It is “crucial”, she added, that organisations “build a data foundation robust enough to support any future tech transformation, but this cannot come at the cost of neglecting their overall data maturity.”

Read more: How real is the threat of data poisoning to generative AI?

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