Enterprise AI Agent Adoption Survey 2026: 67% Now Using Agents in Production

Our 2026 enterprise AI agent adoption survey, conducted in May 2026 with 517 enterprise companies (1,000+ employees), found that 67% now use AI agents in production workflows — up from 23% in our 2025 survey. The agent category has crossed the enterprise adoption chasm.

Key findings

Adoption by use case

The most common enterprise agent use cases in 2026:

• Customer support (54% of respondents): Sierra and similar platforms are the most-deployed agent category in enterprise.
• Sales and marketing (47%): Relevance AI and Lindy lead for sales automation.
• Software development (42%): Cursor and Claude Code are the dominant tools.
• Internal knowledge management (38%): Microsoft Copilot Studio leads, especially in Microsoft 365 shops.
• Operations automation (31%): Mix of platforms, with Lindy and Relevance leading.
• Research and competitive intelligence (24%): Google Mariner and Perplexity Pro are the primary tools.

Adoption by company size

Adoption correlates with company size:

• 1,000-5,000 employees: 58% adoption
• 5,000-25,000 employees: 71% adoption
• 25,000+ employees: 79% adoption

Larger enterprises are more likely to have adopted agents, but the gap is narrowing. Mid-market companies (1,000-5,000 employees) showed the fastest growth in adoption — up from 14% in 2025 to 58% in 2026.

Spending patterns

Among companies using agents in production:

• Average annual spend: $1.2M
• Median annual spend: $340K
• Spend growth year-over-year: 280%
• Budget allocated to agents as % of IT budget: 8% (up from 2% in 2025)

ROI results

Companies using agents in production report strong ROI:

• Average ROI: 3.4x (i.e., $3.40 in value for every $1 spent)
• Median ROI: 2.8x
• % reporting positive ROI: 81%
• % reporting ROI > 5x: 24%

Challenges reported

The most common challenges cited by enterprise adopters:

• Security and compliance concerns (cited by 62%): See our AI Agent Safety Guide for how to address these.
• Integration complexity (54%): Connecting agents to legacy systems is the biggest technical challenge.
• Skill gaps (47%): Finding employees who can effectively deploy and manage agents.
• Cost predictability (41%): Usage-based pricing makes budgeting difficult.
• Quality control (38%): Ensuring agent outputs meet enterprise standards.

Methodology

This survey was conducted in May 2026 with 517 respondents from companies with 1,000+ employees. Respondents were IT decision-makers, operations leaders, and practitioners involved in AI agent deployment. Full methodology is available on request.