A global survey of 505 data and analytics leaders finds that technological infrastructure (54%) and skills (51%) are the top two barriers to artificial intelligence (AI) adoption that organizations are encountering.
Conducted by LeBow College of Business at Drexel University in collaboration with Precisely, a provider of data management and integrity software, the survey identifies financial investment (45%), directive from leadership (40%) and time (36%) as the next three major obstacles.
Specific skills that are lacking include an ability to deploy AI at scale in a business environment (30%), expertise in responsible AI and compliance (29%), skills in translating business needs into AI solutions (28%) and AI model development and basic AI literacy (27%), the survey finds.
Once those issues are addressed, the top business problems respondents are hoping to address using AI planned over the next one to three years are fraud and threat detection (34%), supply chain optimization (33%), risk management and compliance (33%), software development (32%), customer service and support chatbots (31%), research assistance (30%) and employee hiring and onboarding (29%).
However, the survey also finds that only 31% of respondents are measuring the success of AI initiatives in relation to business outcomes that are tracked using key performance indicators (KPIs).
That suggests most organizations have yet to tie investments in AI to specific key performance indicators, says Dave Shuman, chief data officer for Precisely.
Nevertheless, more than half of respondents (52%) said AI is now exercising the most influence over their organization’s overall data program, followed by advanced analytics (42%).
Additionally, more than half (51%) identified data quality as their organization’s top priority for 2026, followed closely by data privacy and security (50%). Only 36%, however, said data governance is a priority.
Finally, the top data challenges respondents said are impacting their organizations are lack of effective data management tools (35%), data literacy (34%), complexity of the data ecosystem (33%), awareness and adoption (30%), cost (30%) operational support (29%) and executive support (29%).
Those data management issues are collectively going to slow the pace at which organizations can operationalize AI, an issue that is likely to be only further aggravated as AI creates more data faster, notes Shuman. “The data gap is going to become severe,” he says. “Bad information only leads to the wrong action.”
Fortunately, a recent Futurum Group survey suggests that organizations are now starting to have a new appreciation for the value of their data. The global data intelligence, analytics, and infrastructure (DIAI) market is projected to grow at a 17% compound annual growth rate through 2028 off a base of $541.1 billion in 2026 to exceed $1.2 trillion by 2031.
Arguably, no matter how advanced AI becomes the ability to derive business value from an IT investment once again comes back the quality of the data being used to drive it. Unfortunately, most organizations have not historically paid a lot of attention to data quality issues, many of which are now coming home to roost in the age of AI. The challenge and the opportunity now is to resolve those issues as quickly as possible to ensure that AI projects succeed. Otherwise, organizations will find it difficult to justify investments in AI that, because of bad data, result in suboptimal outcomes that no one on the business side really trusts.
