While nearly all IT executives are optimistic about artificial intelligence, most organizations are struggling to translate that enthusiasm into concrete business outcomes. At Info-Tech LIVE 2026, a major conference for chief information officers (CIOs) held in Las Vegas from June 9 to 11, research presented to thousands of IT leaders exposed a critical disconnect: 91% of IT executives are bullish on AI and 96% expect AI budgets to increase over the next 12 months, yet only 42% of organizations report cross-departmental AI adoption with measurable impact, and just 50% have a board-approved dedicated AI strategy.

The conference, themed "Agentic IT: From Hype to Value," centered on a central message for technology leaders navigating the current landscape: AI value within organizations depends on disciplined execution, strong governance, reliable data foundations, and the ability to scale the right work. Agentic AI refers to AI systems designed to operate autonomously, making decisions and taking actions with minimal human intervention, often by using tools and functions to accomplish specific tasks.

What's Causing the Gap Between AI Enthusiasm and Real Results?

The research findings underscore that organizations are entering a new phase of AI maturity. The challenge is no longer convincing executives that AI matters; it's proving where it creates measurable value. According to Info-Tech Research Group's Chief Research Officer, the shift is fundamental.

"CIOs are no longer trying to prove whether AI matters; they're now being asked to prove where it creates measurable value," stated Gord Harrison, Chief Research Officer at Info-Tech Research Group. "Agentic IT requires a different operating discipline that connects value creation and control through stronger strategy, governance, data readiness, security, and measurement."

Gord Harrison, Chief Research Officer at Info-Tech Research Group

The conference featured over 200 sessions exploring how technology leaders can prepare their organizations for more autonomous systems while maintaining control, resilience, and business alignment. The underlying theme across keynotes, panels, and breakout sessions was consistent: execution, not adoption, is now the differentiator.

How to Build an Organization Ready for Agentic AI?

Info-Tech's research and speaker sessions outlined a framework for moving from AI ambition to measurable enterprise value. The framework emphasizes four core priorities that organizations must address to succeed with agentic systems:

• Own the AI Mandate: Establish clear ownership and accountability for AI initiatives across the organization, ensuring that someone is responsible for strategy, execution, and outcomes.
• Pick the Right AI Bets: Make deliberate investment choices about which workflows and processes are best suited for agentic automation, rather than attempting to automate everything at once.
• Enforce AI Discipline: Implement governance structures, security controls, and operational guardrails before deploying agents into production environments to manage risk and ensure compliance.
• Prove and Scale AI Value: Measure the impact of AI initiatives rigorously, demonstrate measurable outcomes to stakeholders, and only scale work that delivers real business results.

One of the conference's keynote speakers, Martin Bufi, Principal Research Director at Info-Tech Research Group, shared implementation evidence from a year of agentic AI development work. His team built 63 agents across 13 workflows and five domains, creating 123 tools in the process. Bufi emphasized a critical principle: autonomy without architecture creates risk.

"Successful agentic systems must be mapped, engineered, and governed before they can scale," explained Martin Bufi, Principal Research Director at Info-Tech Research Group. "Every agent can be measured, stopped, and improved."

Martin Bufi, Principal Research Director at Info-Tech Research Group

Bufi's practical lessons from building agentic systems included the need to standardize workflows before automation, design agents around specific jobs rather than broad generalist tasks, build tool integrations deliberately, and ensure every agent can be measured, stopped, and improved.

What Role Does Security Play in Agentic AI Deployment?

As organizations embed agentic capabilities into workflows, cybersecurity becomes increasingly critical. AI systems that operate autonomously introduce new attack surfaces and require new defensive approaches. During the conference, security leaders were challenged to rethink their discipline, which was traditionally built for human-scale operations.

Technical experts at the conference framed threat detection, response, vulnerability management, and compliance as engineering challenges rather than just security problems. As AI increases both the speed of attacks and the complexity of defenses, security leaders must audit data and workflows, define guardrails before deploying agents, and prepare teams for new roles such as CyberAI agent supervisors and agent engineers.

The shift toward agentic systems also requires organizations to rethink their operating models. One keynote speaker, Carlene McCubbin, AVP of Agentic AI Implementation, explored what it takes to build an organization that runs on AI, not just one that uses it. The transition moves from assisted and augmented work to agentic workflows, orchestrated systems, and enterprise-wide compounding value.

To achieve this transformation, McCubbin explained that leaders must start with the right workflows, architect agents for governed execution, and ensure production-ready systems have clear ownership, bounded permissions, defined incident paths, and observable trace logs before scaling.

Why Data Readiness Matters More Than You Think?

The conference reinforced that AI value depends on more than just technology. Organizations need strong data foundations, clear governance structures, and measurable execution models. The research presented showed that while IT executives are broadly bullish on AI, organizations still need stronger strategy, governance, data readiness, and execution models to realize impact.

The message from Info-Tech LIVE 2026 was clear: the leaders who succeed will be the ones who know which AI bets to make, which to stop, and how to scale the work that delivers real outcomes. As organizations move beyond AI experimentation and begin embedding agentic capabilities into workflows, the focus shifts from proving that AI matters to proving where it creates measurable value.