Agentic AI may dominate boardroom conversations, but most enterprises aren’t yet running it at scale.
That’s the headline from a new global report by GLG, which surveyed 110 senior leaders responsible for implementing agentic AI solutions within their organizations. The findings reveal a widening gap between ambition and execution—and a growing urgency to close it.
Despite mounting competitive pressure to deploy AI agents capable of autonomous decision-making and workflow execution, only 46% of respondents say their organizations have at least one agentic AI solution in production.
For a technology widely framed as the next leap beyond generative AI, that’s a telling statistic.
The Hype vs. the Hard Reality
Agentic AI refers to systems that go beyond generating content or responding to prompts. These AI agents can take actions—navigating workflows, triggering processes, coordinating tasks, and making decisions within defined parameters.
In theory, that means:
- Automated procurement negotiations
- Self-directed customer service workflows
- AI-managed compliance monitoring
- Multi-step operational coordination across departments
In practice, deployment remains uneven.
According to GLG’s report, organizations face both technical and strategic roadblocks:
- 71% of respondents worry about erroneous outputs or flawed decisions by AI agents.
- 65% cite concerns about agents misusing or inappropriately accessing sensitive data.
- More than half report that leadership cannot clearly articulate a concrete need for AI agents—despite public enthusiasm about the technology’s potential.
That last point may be the most revealing. Enterprises are eager to “do something” with agentic AI, but many haven’t defined the business case tightly enough to justify large-scale rollout.
Data Risk and Governance Loom Large
If generative AI raised concerns about hallucinations, agentic AI raises the stakes further. An AI chatbot that produces flawed text can be corrected. An AI agent that autonomously executes a flawed action—approving a transaction, modifying a database entry, triggering a compliance workflow—introduces operational risk.
The survey results underscore that anxiety.
Data access governance is particularly sensitive. AI agents often require integration across enterprise systems: ERP, CRM, HRIS, data warehouses, and external APIs. That interconnectedness increases both power and exposure.
In regulated sectors—financial services, healthcare, energy—the tolerance for autonomous missteps is low. Enterprises are demanding stronger guardrails, auditability, and explainability before trusting AI agents with mission-critical tasks.
The Strategic Clarity Gap
Perhaps the most surprising insight: more than half of respondents say leaders struggle to articulate a clear need for AI agents.
That suggests many organizations are still experimenting at the edge rather than redesigning workflows around autonomous systems.
Historically, transformative enterprise technologies—from cloud computing to DevOps automation—succeeded when tied to defined operational bottlenecks. Agentic AI may require the same discipline.
Without a sharp use case—reducing cycle time in procurement by X%, cutting compliance review hours by Y%, automating Z% of tier-one support tickets—agentic AI risks becoming another exploratory sandbox initiative.
GLG’s Playbook: From Experiment to Operationalization
The report doesn’t stop at diagnosis. GLG outlines a tactical playbook aimed at helping enterprises transition from pilot programs to production-scale deployments.
Core themes include:
Process Discipline:
Define clear workflow boundaries and escalation paths. Avoid handing agents ambiguous mandates.
Data Integration and Governance:
Ensure clean, well-governed data pipelines before layering agentic systems on top. Poor data quality compounds quickly when automation scales.
Cross-Functional Collaboration:
Agentic AI touches IT, legal, compliance, operations, and business units. Silos can stall deployment.
Operationalization:
Move beyond proof-of-concept environments and build monitoring, audit, and feedback loops into production systems.
The emphasis on operational rigor echoes lessons learned from early generative AI rollouts, where many companies rushed to deploy chatbots without integrating them into structured governance frameworks.
Why This Matters Now
The AI market is shifting rapidly from content generation to workflow automation. Major enterprise software vendors are embedding AI agents directly into productivity suites, CRM systems, and ERP platforms.
But scaling autonomous systems requires more than model performance. It demands cultural readiness, governance maturity, and a clearly articulated business rationale.
GLG’s findings suggest many enterprises are still in the early innings.
The pressure to adopt agentic AI is intensifying as competitors experiment with automation-driven cost savings and speed advantages. Yet the data shows a measured approach: fewer than half of surveyed organizations have moved beyond pilots into production.
That cautious pace may reflect a healthy recalibration after the generative AI hype cycle. Enterprises appear to be asking harder questions about risk, accountability, and measurable value before granting AI agents operational authority.
The Bottom Line
Agentic AI promises to redefine how work gets done—automating multi-step processes rather than single tasks. But ambition alone isn’t enough.
GLG’s report paints a picture of organizations caught between urgency and uncertainty. The opportunity is historic, but so are the risks.
For enterprises aiming to turn AI agents into a competitive advantage, the next phase will hinge on clarity: clear use cases, clear governance models, and clear accountability structures.
Until then, agentic AI may remain more strategic aspiration than operational reality.
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