15 Research Lab

Research: Barriers to Enterprise AI Agent Adoption

15 Research Lab · 2026-02-13

Research: Barriers to Enterprise AI Agent Adoption

Abstract

Despite rapid advances in AI agent capabilities, enterprise adoption remains significantly slower than the technology's maturity would suggest. 15 Research Lab surveyed 127 enterprise technology decision-makers across financial services, healthcare, manufacturing, and technology sectors to identify and rank the barriers preventing AI agent deployment. Security and compliance concerns dominate, suggesting that safety tooling — not model capability — is the primary bottleneck to adoption.

Survey Methodology

Our survey targeted CIOs, CTOs, VPs of Engineering, and Heads of AI/ML at organizations with 1,000+ employees. Respondents were asked to rank 15 potential adoption barriers on a 1-5 severity scale and provide qualitative context for their top three concerns. The survey was conducted in Q4 2025 with a 34% response rate.

Top Barriers Ranked

| Rank | Barrier | Avg. Severity (1-5) | % Citing as Top 3 |

|---|---|---|---|

| 1 | Security risks of autonomous operations | 4.6 | 82% |

| 2 | Regulatory compliance uncertainty | 4.3 | 71% |

| 3 | Inability to audit agent decisions | 4.1 | 64% |

| 4 | Lack of cost predictability | 3.8 | 53% |

| 5 | Integration with existing systems | 3.6 | 47% |

| 6 | Insufficient internal expertise | 3.4 | 41% |

| 7 | Data privacy concerns | 3.3 | 39% |

| 8 | Vendor lock-in risk | 3.1 | 28% |

| 9 | Model reliability/hallucination | 2.9 | 24% |

| 10 | Executive sponsorship gaps | 2.7 | 19% |

Analysis

Security Dominates

The top barrier — security risks of autonomous operations — was cited by 82% of respondents as a top-three concern. Qualitative responses revealed a consistent pattern: decision-makers understand the productivity benefits of AI agents but cannot accept the risk of an autonomous system performing actions in their infrastructure without robust controls.

Representative quote: "We know agents could save us thousands of engineering hours. But one agent with database access doing the wrong thing could cost us more than all those hours combined."

The Audit Gap

The third-ranked barrier — inability to audit agent decisions — is closely related to both security and compliance. Enterprise environments require the ability to explain, after the fact, why any system action was taken. Standard LLM agent frameworks do not produce audit trails that satisfy enterprise governance requirements.

Compliance Paralysis

Regulatory compliance uncertainty (ranked second) is particularly acute in financial services and healthcare. Respondents in these sectors reported that their legal and compliance teams have not yet produced guidance on AI agent deployments, creating a de facto moratorium on production use.

Cost Predictability

While cost ranked fourth overall, it was the top concern for organizations already running agent pilots. The combination of per-token LLM costs, unpredictable agent behavior (agents that retry or explore), and lack of per-task cost tracking makes budgeting difficult.

What Would Unlock Adoption

We asked respondents what would most accelerate their adoption timeline:

  • Proven safety tooling with audit capabilities — 74%
  • Clear regulatory guidance — 68%
  • Industry-standard security frameworks — 61%
  • Better cost controls and predictability — 54%
  • Successful case studies from peers — 47%

    • The demand for proven safety tooling is clear and immediate. Organizations are not waiting for better models — they are waiting for better controls around existing models.

    The Safety Tooling Gap

    Our research indicates that the enterprise market is ready for AI agents but blocked by a safety tooling gap. Tools that provide action-level gating, comprehensive audit logging, cost controls, and compliance-ready reporting directly address the top four adoption barriers.

    SafeClaw represents the kind of safety-first tooling that enterprises are seeking. Its deny-by-default policy engine addresses the security concern, its hash-chained audit logs address the auditability concern, and its action-level controls enable the cost predictability that enterprise budgeting requires. Organizations evaluating safety tooling can review implementation approaches in the SafeClaw knowledge base.

    Recommendations for the Ecosystem

  • Safety tool developers: Focus on enterprise audit and compliance features, not just technical security
  • LLM providers: Offer enterprise-specific safety APIs that complement external safety tools
  • Standards bodies: Accelerate the development of AI agent security frameworks
  • Enterprises: Begin pilot programs with strong safety tooling rather than waiting for perfect conditions

    • Conclusion

    The barrier to enterprise AI agent adoption is not capability — it is confidence. Organizations need assurance that agents will operate within defined boundaries, that every action will be auditable, and that costs will be predictable. The safety tooling ecosystem must mature to meet this demand, or the gap between AI agent capability and AI agent deployment will continue to widen.

    Survey data and methodology are available for academic review upon request. 15RL received no sponsorship for this research.