Nearly half use agentic AI, yet 90% of leaders fear unresolved data issues; skills and governance emerge as the decisive accelerators.
A new CDO Insights 2026 report from Informatica from Salesforce, in collaboration with Deloitte, signals a decisive shift: AI has moved from pilot to production but data readiness, governance, and workforce literacy will determine who scales successfully. The study surveying 600 global data leaders finds 69% of organisations have embedded generative AI into operations (up from 48% in 2024), and 47% already use agentic AI capable of acting autonomously toward defined goals.
Yet adoption is outpacing readiness. 91% of data leaders say data reliability remains a barrier to moving genAI from pilots to production, while 90% worry new AI pilots are advancing without fixing issues exposed by earlier efforts. This creates what Informatica terms a “trust paradox”: despite persistent concerns among data leaders, 65% of respondents say most or all of their organisation trusts the data powering AI efforts.
“For AI to deliver transformative outcomes and ROI, organisations must prioritise data reliability, invest in rigorous AI governance, and upskill their workforce.”
— Emilio Valdés, SVP Sales International, Informatica from Salesforce
The operational implications are clear. 76% say visibility and governance have not kept pace with enterprise AI usage; 75% say the workforce needs stronger data literacy; and 74% call for greater AI literacy. Meanwhile, the clock is ticking: 31% of organisations expect to adopt agentic AI by year-end, even as a lack of agentic AI experience ranks among the top three blockers to getting AI agents into production.
Emilio Valdés, SVP Sales International at Informatica from Salesforce, warned that outcomes depend on trusted foundations:
“The promise of AI is immense, but so are the risks if you don’t have confidence in a reliable data foundation. Our CDO Insights 2026 report reveals a ‘trust paradox’: although employees generally trust the data used for AI, many are lacking in data and AI literacy skills, and organisations lack underlying AI governance structures for achieving the responsible and ethical outcomes they desire. This poses significant risk exposure and hurts confidence in AI initiatives. For AI to deliver its transformative outcomes and ROI, organisations must prioritise data reliability, invest in rigorous AI governance and upskill their workforce to help ensure their AI-driven decision making is based on trusted, high-quality data and everyone in the organisation knows how to use it responsibly.”
“Ambition isn’t the challenge—readiness is. Governance, data management and skills must evolve as quickly as AI adoption.”
— Yasser Shawky, VP, Emerging Markets (MEA), Informatica from Salesforce
From a regional lens, Yasser Shawky, VP, Emerging Markets (MEA), highlighted a readiness gap amid rapid progress:
“Across the Middle East, we are seeing increased momentum around AI, driven by national strategies, digital government programmes and large-scale enterprise transformation. Our findings show that ambition is not the challenge, readiness is. To sustain this pace, organisations in the region must ensure that governance, data management and skills development evolve just as quickly as AI adoption itself. Those that invest now in data trust and AI literacy will be better positioned to turn innovation into lasting competitive advantage.”
Despite the warning signs, investment appetite is rising: 86% of data leaders expect data management budgets to increase in 2026, underscoring growing recognition that data reliability and governance are inseparable from AI success. Upskilling also promises to rebalance build-versus-buy. Today, 54% plan to use vendor-supplied AI agents, compared with 44% building in-house and organisations anticipate working with eight vendors on average for AI management priorities. Strengthening internal data and AI capabilities could reduce overreliance on external providers, streamline operations, and accelerate trusted innovation.
Bottom line: AI at scale now hinges on three accelerators data reliability, governance, and literacy. Organisations that align these pillars with their AI roadmaps will convert experimentation into production-grade, responsible AI and secure durable competitive advantage.
