Are businesses bullish on AI in 2026? New research says yes, but security is a top concern

A Storyblok survey of 200 enterprise IT and procurement professionals shows persistent bullishness on AI despite headlines about failing pilots: 57% expect AI budgets to significantly increase in 2026, 33% a slight increase, and only 2% expect cuts. Respondents report concrete ROI—54% cite operational efficiency gains, 48% faster time to value and 41% streamlined decision-making—and 91% say generative AI will improve content management, but adoption of Generative Engine Optimization is limited (23% fully implemented; 41% not started/early). Key adoption barriers are data privacy/regulatory concerns (61%), security risks (58%) and legacy systems (43%), with firms prioritizing data governance, integration and vendor transparency, signaling both continued spend and investor opportunities in vendors that can demonstrate robust governance and GEO capabilities.

Analysis

Storyblok's survey of 200 enterprise IT and procurement professionals shows continued budgetary commitment to AI: 57% expect AI spend to "significantly increase" in 2026, 33% a "slight increase," 9% flat and only 2% expecting cuts. Respondents cite measurable ROI from existing pilots, with 54% reporting operational efficiency gains, 48% faster time-to-value, 43% improved employee productivity and 41% streamlined decision-making, supporting the bullish outlook despite headlines about failing pilots.

Generative AI is viewed as transformational for content management—91% expect improvement—yet adoption gaps are material: only 23% report a full Generative Engine Optimization (GEO) strategy while 41% are not started or in early stages. Gartner’s projection of a 25% drop in traditional search volume underscores urgency for GEO and CMS vendors to deliver commercialization levers tied to changing consumer search behavior.

Key adoption risks center on governance and security: 61% flagged data privacy and regulatory concerns, 58% cited security risks and 43% pointed to legacy-system constraints. The article cites structural failure modes (MIT’s 95% pilot failure headline reframed as reliance on brittle tools lacking memory/learning loops) and real incidents (agentic manipulation of Claude Code; an OpenAI–Mixpanel security episode), with respondents prioritizing improvements in data governance (62%), integration (54%) and vendor transparency (42%).

Implication for market positioning is binary: vendors that can demonstrate enterprise-grade data governance, security, seamless integration and GEO capabilities should capture disproportionate spend, while those perceived as generic or brittle face adoption headwinds and valuation risk if regulatory and security incidents accelerate.

Key Decisions for Investors

• Consider increasing selective exposure to enterprise-focused CMS and GEO enablers that can demonstrate documented ROI and enterprise-grade data governance and security,
• Prioritize vendors with clear integration capabilities and vendor transparency roadmaps given 54% and 42% of respondents flagging these as priority improvements,
• Avoid broad bets on undifferentiated, "generic" AI tooling lacking persistent memory/learning loops; favor companies with defensible technical moats and productized governance,
• Monitor regulatory developments, reported security incidents and adoption KPIs (AI budget intentions and GEO rollout rates) as primary triggers to scale positions or hedge exposure,