Data quality & availability top list of AI adoption barriers

Data quality and availability are the greatest barriers to artificial intelligence (AI) adoption, according to the PEX Report 2025/26.

The report, based on the results of a PEX Network survey of more than 200 professionals, paints a comprehensive picture of the modern business transformation landscape. It found that while businesses are investing in various types of AI to support transformation, data issues are a persistent obstacle to success.

More than half of respondents (52 percent) cited data quality and availability as the biggest AI adoption challenges, followed by lack of internal expertise (49 percent), regulatory or legal concerns (31 percent) and resistance to change (30 percent).

Meanwhile, the PEX Report also revealed that less than half of businesses currently have an AI governance policy in place.

Data inadequacy or unreliability are major issues in AI adoption

Organizations are forging ahead with AI adoption, the report found. Almost two-thirds (63 percent) use generative AI, with 62 percent using standard/traditional AI and 40 percent using AI agents/agentic AI. Only 10 percent of those polled say their business uses none of these.

Almost half (48 percent) of surveyed businesses are prioritizing AI adoption in the coming year, while almost three-quarters (70 percent) state that AI is either “critical” or “very important” to their organization’s strategic goals. More than half (59 percent) of businesses are planning to invest in agentic AI/AI agents in the next 12 months, followed by generative AI (58 percent) and standard/traditional AI (43 percent).

However, just 43 percent of surveyed organizations have an AI governance policy, with a quarter (25 percent) still in the process of implementing one. Almost a third of organizations (29 percent) have no AI governance policy.

“Sadly, data inadequacy or unreliability is a major issue today,” commented Rahul Zende, senior principal data scientist – enterprise AI strategy at Navy Federal Credit Union. He also highlighted the complexity of replacing or integrating with existing systems or processes.

“The third one on my mind is an (often scary) gap in AI skills and/or understanding of AI amongst leaders as well as senior individual contributors. I don’t think solving these issues is rocket science but it needs to be done in a case-by-case manner.”

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What are businesses missing when it comes to AI?

Only a third (34 percent) of respondents consider their AI initiatives to be fully aligned with overall business goals, increasing to 48 percent for somewhat aligned. Nearly a fifth (18 percent) say their AI initiatives are not aligned with overall businesses goals.

Respondents believe their organizations would be in a stronger position to leverage AI if they had better training programs for employees (49 percent), data infrastructures (48 percent), strategic guidance (45 percent) and access to talent (32 percent). These are ahead of governance strategies (31 percent), funding or investment (30 percent) and clearer regulations (20 percent).

“Without alignment, clarity and transparency, the three pillars of my ACT model, companies risk undermining their own initiatives,” commented Doug Shannon, AI and intelligent automation thought leader. “Businesses need to embed context, which still comes from humans, and ensure AI systems support and coordinate with employees rather than disrupt them.”

As maturity grows, businesses can aim to shift from human-in-the-loop to human-on-the-loop oversight, delivering greater autonomy while preserving accountability, he added. “Value emerges when employees see that AI is there to help them succeed rather than replace them.”

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