How CIOs use AI to better manage data lifecycles
Almost two-thirds of organizations are unsure if they have the right data practices for AI—and 60% will abandon AI projects by year-end due to data readiness failures.
Why it matters
CIOs are discovering that AI's effectiveness depends entirely on data quality and lifecycle management. The article profiles how digital leaders are using AI itself to improve data curation, governance, and accessibility—but with hard limits in high-stakes industries where probabilistic models can't replace human judgment.
The key facts
16 to knowGartner: 66% of organizations unsure about data management practices for AI
Gartner predicts 60% of AI projects will be abandoned by year-end due to lack of data readiness
Volvo Cars uses AI-driven data curation for ADAS training; collects millions of data points since 2020
Harvey Nash CIO outlines 4 data lifecycle requirements: findable, understandable, trustworthy, usable at speed
Sanofi uses AI for data governance in clinical trials but stops short of full automation due to trust/regulatory concerns
AOP Health requires 100% accuracy in regulated data spaces; sees AI as useful for metadata tagging, not end-to-end lifecycle management
DP World Tour digitized 50+ years of archive (20,000 tapes, 1.2 petabytes); using AI for metadata tagging and content clipping
Freshworks building agentic AI for data cleansing and sales/marketing/product agents on Databricks platform
Gartner: 64% of organizations unsure of data management practices for AI
Gartner prediction: 60% of AI projects abandoned by end of year due to lack of data readiness
Volvo Cars uses AI-driven data curation for ADAS; collects millions of data points since 2020 with customer consent
Harvey Nash CIO Ankur Anand: four core data processes needed: findable, understandable, trustworthy, usable at speed
Sanofi uses AI for data governance in clinical trials; AI surfaces gaps and corrects anomalies but requires human input for regulatory compliance
AOP Health: AI unsuitable for 100% accuracy-required healthcare data; useful for descriptive tasks like field documentation
DP World Tour digitized 50+ years of archive: 20,000 tapes, 27,000 hours, 1.2 petabytes; using AI for metadata tagging and content clipping
Freshworks building agents on Databricks for sales/marketing/product data workflows; agentic AI inserted into data management lifecycle
Go to the source
CIOcio.com
Publisher excerpt: 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…