The news: Excitement about AI and its enterprise possibilities aren’t translating into meaningful adoption as corporate strategies lag.

• 38% of tech leaders say their organizations are piloting agentic AI projects, but only 11% have agents in production, per Deloitte’s Tech Trends 2026 report.
• 42% of organizations are still developing their strategy road map for agentic AI, and 35% having no formal strategy at all.

Although most tech leaders (78%) think agentic AI will be integrated into architecture work flows over the next five years, foundational challenges—such as data quality and security governance—are blocking the path to clear ROI. Without mature strategies, companies may struggle to move past proof of concept projects and into production-grade deployments.

The challenge: The gap between interest and implementation could indicate a few overarching issues.

• Data limitations. A lack of clean, structured data or reliance on outdated infrastructure could undermine the reliability of agentic systems, which needs significant time and resources to correct.
• Skills shortages. Not having enough AI-skilled talent on staff—or failing to upskill existing employees—could reduce the efficacy of agentic AI projects and slow down progress.
• Workslop. Poorly designed applications of AI or a lack of training could cause the tech to actually add to employees’ workloads and result in low-quality outputs, reducing productivity and dampening worker enthusiasm.
• Lack of clear use cases. While AI hype is high, many companies may not have identified opportunities with clear, measurable ROI, making it hard to prioritize which AI solutions to pilot first.

What CMOs should do: Balance excitement about agentic AI’s potential by first applying the technology to larger pain points rather than scattering resources on small, disconnected projects that may not prove ROI.

Use cases could include automating routine marketing tasks like campaign updates, helping teams clean and organize data to streamline insight generation, or surfacing customer trends from reviews and feedback.

Establish clear KPIs before deployment, and monitor the quality of initiatives to ensure that initial goals are met.

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