How CIOs use AI to better manage data lifecycles

Data is the fuel for AI since generative, agentic, and ML systems are only as effective as the information they consume. Across all stages of the data lifecycle, including creation, storage, usage, archival, and destruction, CIOs and their business peers must consider how information feeds AI services.

Almost two-thirds of organizations are unsure whether they have the right data management practices for AI, says Gartner. The tech analyst predicts organizations will abandon 60% of AI projects by the end of the year due to a lack of data readiness. In such circumstances, a potential competitive advantage quickly becomes an innovation cul-de-sac.

CIOs who want to exploit AI will need a way to improve data management across the lifecycle, and it’s here where AI itself can play a crucial role. Gartner recommends organizations build on their existing practices by iteratively adding AI-specific services that extend and improve data management techniques.

Stephen Wood, COO at Rathbones Asset Management recognizes this opportunity, and he and his firm’s staff are eager to avoid lifting and shifting information from one place to another in its data management efforts. AI could help.

“Our people want to run models to look at the data, whether that’s the shape, structure, value, or whatever it happens to be,” he says. “If that task becomes something AI can do, I’m not sure yet. But given its power, it seems obvious you’ll be able to manage elements of the data lifecycle.”

Digital leaders suggest AI can improve information management processes, but there are limitations. First, they must recognize that emerging technology needs high-quality data, and then they need to identify key challenges, particularly around governance and trust. Then they must carefully explore where AI can play a key role.

Establishing data quality

For some digital leaders, a clear direction for data lifecycle management is already part of the operating process, including using AI.

Alwin Bakkenes, head of software engineering at Volvo Cars, says his company’s driver assistance systems (ADAS) rely on high-quality information. With a strong heritage in vehicle safety, Volvo has collected millions of data points since 2020, all with customer consent, to improve its collision-avoidance systems.

“In our ADAS stack, data curation is one of the most important elements,” he says. “There’s a lot of data coming in from a large fleet, and we curate it and figure out what information we need to make model training more efficient.”

Bakkenes gives the example of collecting data from vehicles to help understand traffic hotspots, such as large Parisian roundabouts. His team sends triggers to sensors to collect more data, which is then used to train AI models and test whether they behave as expected.

“It’s quite an open loop but that’s our data management strategy,” he says. “And this insight must be maintained and curated over time. Crucially, more data isn’t always the best data. Instead, data quality matters, and we’ve had good data for a long time.”

This strategic approach to information collection chimes with Ankur Anand, group CIO at recruiter Harvey Nash. He says successful data lifecycle management in the age of AI involves four underlying processes. “First, the data has to be findable, which means AI can locate the relevant information without relying on a professional’s knowledge,” he says.

Second, data must be understandable. He gives the example of multinational companies using different definitions across the business. “If there’s no common language or consistent terms, the context will vary, the AI will interpret the data differently, and that can have a big impact on the decision-making processes,” he says.

The third core element is data must be trustworthy. “The cleanup stage is very important,” Anand says. “The data must be permissioned appropriately so you don’t have an issue with people seeing information they shouldn’t be seeing. Confidentiality is crucial, because if the trust is broken, people won’t use AI.”

Finally, data must be usable at speed, which relies on the three previous processes. “It shouldn’t take people long to get a proper summarization, comparison, or reasoning from AI.”

Recognizing the potential, with limitations

Once data quality is established, digital leaders can start to consider how emerging technology can be inserted into stages of the data lifecycle.

Take Emmanuel Frenehard, chief digital officer at biopharmaceutical giant Sanofi. “I think AI can help surface gaps in data,” he says, referring to emerging technology, such as Snowflake’s agents, to scan information and identify areas where decision makers lack detailed insights.

Frenehard adds that AI can also help correct data. Most of his firm’s clinical trial data involves some element of human correction, and emerging technology can spot anomalies. “That’s very useful,” he says. “We use AI as a data governance methodology for clinical trials, where we review everything that comes in and self-heal the data.”

However, while these capabilities exist, Frenehard says there are limits. In a heavily governed industry like biopharmaceuticals, a probabilistic AI model that uses statistics to predict likelihoods and manage uncertainty can be helpful, but it can also create risks. His firm isn’t prepared to use AI to manage data without human input.

“The potential is there, but the trust would have to be there, too,” he says. “I always have concerns with probabilistic models. Probabilities are never 100%. There may be areas of business where it’s okay not to be 100% certain, yet there are also areas where it’s not, such as patient data and treatment recommendations.”

That sentiment resonates with Bernhard Seiser, VP of digital, data, and IT at AOP Health, who says his firm isn’t using AI to manage the full data lifecycle. He says discussions about data often cover similar concerns in big, heavily governed sectors, especially when considering whether AI is ready for information management.

“In our industry, 99% certainly doesn’t help me in a data space that needs to be 100% accurate,” he says. “But of course there are areas where you can leverage AI. For example, if you bring in new data sources, having somebody from the business available to describe each field is tough. For that task, gen AI tools can do a fantastic job.”

Seiser says his organization will continue to explore emerging technologies, but temper expectations. More AI might mean a degree of uncertainty that’s unpalatable, and right now, pioneering developments that could push AI-enabled data lifecycle management to new heights seem a long way off.

“For the more sophisticated stuff, like creating data products, writing queries, and so on, you always need to live with the fact that it’s not 100% certain,” he says. “I’ve spoken with technology experts, and people are unsure the technology will be ready for managing the full data lifecycle in five or even 10 years.”

Exploring solid use cases

While digital leaders aren’t eager to automate the data lifecycle in its entirety, pioneers recognize AI brings new efficiencies to specific stages of the process.

Michael Cole, chief technology officer at the DP World Tour, has spent the past two years working with key partners, such as HCLTech and AWS, to digitize his organization’s historical archive of over 50 years of data, covering 20,000 tapes, 27,000 hours, and 1.2 petabytes of content. This process has helped highlight the value of AI-enabled data management.

“That’s only the start of the journey,” he says, referring to the transformation. “We’re about to announce a new platform for media asset management, which means we’ll not only have all archived content in a digital format, but it’ll be easily accessible for internal resources, media, broadcasters, partners, and ultimately fans as well.”

Cole says this work illustrates the organization’s focus on digitizing information and making it available to younger fans who want to consume sports content in myriad ways. As this process gathers pace, emerging technology will play an important role in data management.

“We already use AI to automate the clipping of player shots, and we’ll continue to use AI for metadata tagging because that process is critical to creating an efficient way to source content and data,” he says. “Everything must be tagged accurately to enable that efficiency. We think gen AI and agentic technology will evolve to boost the efficiency of not only producing content but making it accessible to our fans.”

Freshworks CIO Ashwin Ballal is another digital leader who says AI can help manage stages of the lifecycle, such as the manually intensive data-cleansing process. An AI agent should be able to speed up that laborious process exponentially.

However, Ballal also suggests the key point isn’t whether AI can do data management work quickly, but whether tools work effectively to improve data integrity and quality. The good news is model training can help boost effectiveness. Now his firm is looking to insert agentic AI into the data management lifecycle.

“We’re just beginning that process,” he says. “We have a Databricks platform and have built LLMs and agents on top of the data. So if you’re a salesperson, you’ve got a sales agent, a marketing agent, and a product agent. This technology taps into the power of the data.”