FEATURE
They can handle narrow tasks but when conversations stray from scripts or require coordination across systems, they quickly fall short.
SP: In contact centers, one of the most challenging hurdles to agentic AI adoption that CX leaders must overcome is consumer trust and reluctance.
Despite the promise and advancements of AI, consumers remain cautious. We found in our paper,“ 2025 Customer Experience Report – Consumer Edition,” that nearly a quarter( 23 %) of consumers still report they are uninterested in using AI for customer service.
NV: What I’ m hearing from conversations with contact center leaders and integrators is that the challenges with agentic AI aren’ t about the technology being overhyped. [ Instead ], they’ re about the realities of bringing it into complex environments.
The promise of AI that can reason, act, and learn on its own is exciting. But many organizations are quickly discovering that the foundation underneath( data, integrations, governance) just isn’ t ready yet. First, the data and context infrastructure is often underbuilt.
Agentic systems need seamless access to fresh, unified knowledge across CRM, workflows, support systems, and historical interactions. When those sources are stale, siloed, or disconnected, the AI hits dead end or hallucinates.
[ Second ], we also see challenges around error compounding and governance.
In a multi-step task, one misinterpreted step early can cascade into costly mistakes downstream. Leaders want to innovate but are cautious about compliance, auditability, and reputational risk. Human oversight isn’ t optional; it is core to maintaining confidence as AI takes on more responsibility.
The challenges we’ re seeing aren’ t failures of AI; they’ re lessons in execution. Agentic systems can absolutely deliver value, but only when organizations treat them as part of an evolving ecosystem that blends people, process, and technology.
RECOMMENDATIONS
WHAT ARE YOUR RECOMMENDATIONS TO OBTAIN THE OPTIMAL RESULTS FROM AGENTIC AI, INCLUD- ING AVOIDING AND RESPONDING TO ITS ISSUES?
KM: The success or failure of agentic AI comes down to data: how it’ s organized, shared, and continually refreshed. Too many AI systems are built on brittle integrations and siloed data, which leads to hallucination and drift.
The answer is a living data lake architecture that unifies structured and unstructured data: voice transcripts, chats, CRM records, and knowledge articles into a single accessible fabric.
On top of that, a knowledge mesh ensures AI agents can find the most current and authoritative information, regardless of where it resides. This turns data into a dynamic asset rather than a static warehouse.
Organizations that design for discoverability, traceability, and continuous learning get AI that evolves with the business. Those that don’ t end up with digital self-checkouts: technically functional, but easily abandoned.
" AGENTIC AI ISN ' T A ' SET IT AND FORGET IT ' SOLUTION. SUCCESS REQUIRES ONGOING EVALUATION AND IMPROVEMENT. THIS INCLUDES SOURCING AND APPLYING FEEDBACK..."
-- SARIKA PRASAD
SP: To address consumer hesitancies, companies have to increase [ their ] focus on deploying agentic AI tools that are trustworthy, intuitive, and efficient. Additionally, to win over reluctant users, transparent deployment, coupled with compliance and accuracy, is essential.
Agentic AI isn’ t a“ set it and forget it” solution. Success requires ongoing evaluation and improvement. This includes sourcing and applying feedback from customers and agents to better understand what is working, what isn’ t, and what can be improved.
Companies that regularly update and optimize their AI agents can address small issues before they become major problems, [ thus ] ensuring the technology meets its promise rather than its hype.
NV: To achieve optimal outcomes with agentic AI, organizations should adopt a strategic approach that emphasizes purposeful design, robust training, and continuous oversight.
It starts with purpose-driven design, defining clear objectives and leveraging historical and operational data to train AI agents so they can deliver meaningful impact.
Equally important is human oversight: robust governance frameworks, continuous monitoring, and audit trails ensure AI operates safely and transparently within enterprise standards.
Agentic AI should augment, not replace, human expertise. Seamless collaboration between AI and frontline teams improves both efficiency and CX.
Finally, continuous learning is critical, as AI models must evolve based on real-world feedback and changing business contexts to stay relevant and effective.
Brendan Read is Editor of Contact Center Pipeline. He has been covering and working in customer service and sales and for contact center companies for most of his career. Brendan has edited and written for leading industry publications and has been an industry analyst. He also has authored and co-authored books on contact center design, customer support, and working from home. Brendan can be reached at brendan @ contactcenterpipeline. com.
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