What We’ve Learned by Talking to Prospects and Customers
Agentic AI has moved from hype to prototype remarkably quickly. Across industries, organizations are actively piloting AI agents to automate workflows, make better use of internal data, and interact with a variety of systems. The intent to adopt is clear. What’s less clear, at least until you start talking directly to buyers, is how hard it is to move from experimentation to production.
Over the past several months, we’ve had conversations with security and technology leaders evaluating or actively working on agentic AI initiatives. While these leaders come from different verticals and company sizes, the roadblocks they describe are strikingly consistent. The challenges aren’t about whether AI is valuable, they’re about whether it can be deployed safely and at enterprise scale. Based on analysis of inbound buyer conversations with senior enterprise leaders, several themes come up consistently. Together, they paint a picture of enterprises who are ready for agentic AI but constrained by structural barriers that cut across sectors and org charts.
Security Is the First (and Most Important) Concern
If there’s one takeaway that dominates every conversation, it’s this: security risk is the biggest blocker to agentic AI adoption. Nearly every buyer we spoke with raised concerns about unauthorized access, over-permissioned agents, or AI systems interacting with sensitive applications in ways they couldn’t fully control. This concern shows up across industries. Financial services leaders worry about regulatory exposure. Technology companies worry about internal abuse or data leakage. Enterprise IT teams worry about AI becoming a new, unmanaged identity layer. What’s important is that these concerns aren’t hypothetical. Buyers aren’t asking “what if AI is risky?” they’re saying, “we already know this could go wrong, and we probably haven’t thought of all the potential risks.”
Governance and Compliance Are Non-Negotiable
Leaders consistently emphasize the need for auditability, traceability, and explainability in AI systems. For regulated industries, this is an obvious requirement, but even in less regulated environments, governance is seen as essential to long-term adoption.
Agentic AI introduces new questions:
- Who approved an agent’s access?
- What actions did it take?
- Why did it make a particular decision?
Without clear answers, organizations can’t defend AI-driven outcomes to auditors, regulators, or even internal stakeholders. What’s notable is that governance concerns aren’t slowing interest in AI; they’re shaping buying criteria. Teams want to move fast, but not at the expense of control. In many cases, governance maturity is the deciding factor between staying in pilot mode and moving to production.
The Current State Is Ad Hoc Almost Everywhere
Another consistent theme is how fragmented today’s AI implementations are. Most organizations describe their current state as experimental, manual, or stitched together from one-off solutions. Organizations often rely on a handful of engineers maintaining fragile integrations or have multiple teams building agents independently, with little shared infrastructure or visibility.
The result is the same: AI adoption is happening, but without standardization. That lack of consistency makes it difficult to enforce security policies, apply governance controls, or even understand what’s running in production. Many leaders told us they suspect agentic AI projects already exist in their environment, but they don’t have a clear inventory or oversight model.
Custom Integrations Don’t Scale
Closely related to the ad hoc problem is over-reliance on custom code and point integrations. While custom engineering can get an AI pilot off the ground quickly, it becomes a liability as systems scale. Buyers repeatedly mentioned brittle connectors, high maintenance costs, and slow iteration cycles. Over time, this slows time-to-value and increases operational risk, exactly the opposite of what agentic AI promises. This is one of the clearest signals that the market is shifting. Organizations no longer want clever prototypes, they want enterprise-ready foundations that reduce engineering burden while increasing consistency and control.
Everyone Wants to Move from Pilots to Production
Perhaps the most encouraging insight is that buyers are eager to move beyond experimentation. Many explicitly stated a desire to modernize existing systems and deploy production-grade AI agents. What’s stopping them isn’t lack of budget or executive support. It’s the absence of trusted infrastructure: visibility into agent behavior, monitoring and audit trails, integration with existing workflows, guardrails and assurance that AI won’t undermine established security posture.
A Systemic, Cross-Industry Challenge
The most important lesson from these conversations is how universal these roadblocks are. Whether talking to a CISO at a financial institution or a CTO at a technology company, the themes repeat with remarkable consistency. This isn’t about one vertical being “behind” or one company size being more cautious. These are systemic challenges inherent to deploying autonomous systems inside large, complex enterprises.
Agentic AI is powerful precisely because it can analyze and act. But action without guardrails is unacceptable in production environments. Until security, governance, standardization, and visibility are addressed together, many organizations will remain stuck between pilots and production. The good news is that buyers know what they need. They’re not asking for magic; they’re asking for enterprise-ready tools that let them confidently adopt agentic AI at scale, without sacrificing the controls they’ve spent years building.
Enable Agentic AI Safely with Cequence AI Gateway
Most of these conversations have been very positive, as the Cequence AI Gateway is designed to solve many, if not all, of the concerns that were raised. Some of the key differentiators that make the AI Gateway such a great agentic AI enablement tool include:
- Private cloud or SaaS deployment options
- Built-in enterprise-grade authentication and authorization that integrates with existing OAuth 2.0-compliant IdP solutions
- Built-in registry of trusted MCP servers
- Real-time application and user monitoring
- Agentic AI guardrails to prevent agents from going rogue
- Additional application and data protection via the Cequence UAP platform
Ready to eliminate the roadblocks to agentic AI adoption? Contact us to learn how we can help.
