Navigating Hype vs. Reality for Effective Deployment

Agentic AI has the potential to redefine call center operations by automating routine tasks, enhancing customer experience, and improving agent performance. However, as with all emerging technologies introduced into the call center, its successful deployment and overall effectiveness are constrained by data silos—fragmented customer information dispersed across disparate systems. This article clarifies the concept of agentic AI, differentiates it from generative AI, examines the implementation challenges it faces, and presents a structured framework for evaluating vendors and advancing credible, enterprise-ready solutions for the call center environment (Gartner, 2024).

Understanding the Term “Agentic AI”

The term “agentic AI” is often misunderstood in call centers, where some assume it refers to AI mimicking human agents. Instead, it derives from agency—the ability to act independently, make decisions, and pursue goals autonomously. Agentic AI combines reasoning, planning, and execution to handle complex tasks with minimal human input, unlike rule-based automation or chatbots. [McKinsey, 2025]

Example: In a call center, agentic AI doesn’t just respond to a customer query about a delayed order; it retrieves data from an ERP, evaluates options (e.g., offering a discount), and resolves the issue proactively, aligning with business objectives. This autonomy makes agentic AI ideal for dynamic call center workflows.

Agentic AI vs. Generative AI: A Key Distinction

Also understanding the difference between agentic AI and generative AI is critical to avoid misaligned expectations. Generative AI, powered by large language models (LLMs), creates content like emails or summaries but lacks decision-making autonomy. Agentic AI, however, autonomously executes multi-step tasks by reasoning and accessing data. [Forbes, 2025]

Example in Call Centers: For a delayed order query, generative AI might draft a response summarizing the issue, requiring agent approval. Agentic AI, like Verint’s Smart Transfer Bot, retrieves order status from an ERP, checks CRM inventory, offers a discount, and escalates only if needed—all without human intervention.

Challenges of Agentic AI Deployment in Call Centers

Agentic AI’s potential is hindered by data silos and integration issues in call centers. Key challenges include:

• Data Fragmentation: Disparate systems (CRMs, ticketing platforms, legacy telephony) create incomplete customer data. For example, a retail call center’s AI failed to upsell due to unlinked ERP purchase data and call logs.
• Integration Complexity: Connecting AI to legacy systems like on-premise PBX or ERPs is complex. A healthcare call center faced delays integrating AI with an EHR for symptom-based routing.
• Data Quality Issues: Inconsistent records (e.g., mismatched addresses) cause AI errors, like misrouting calls. Enterprises spend 80% of AI prep time cleaning data. [Deloitte, 2024]
• Security and Compliance Risks: Accessing sensitive data across silos risks GDPR or CCPA violations. A good example is a financial call center facing issues when AI accesses unencrypted data.
• Governance Needs: Autonomous AI requires oversight, but siloed data complicates monitoring. For example, a telecom company using AI to offer unauthorized discounts due to poor governance.
• Scalability Barriers: Standardizing AI across global call centers with varied data structures is costly. A retailer could experience inconsistent AI performance due to regional CRM differences.

These challenges highlight the need for robust data integration and governance for successful agentic AI deployment.

Hype vs. Reality: What Agentic AI Can Deliver

Agentic AI in 2025 isn’t fully autonomous but delivers significant value when deployed strategically:

• Routine Task Automation: AI handles repetitive queries like account updates, reducing agent workload. Aisera’s platform automated 80% of Adobe’s support queries by unifying siloed data. [Aisera Case Study, 2024]
• Personalized Customer Experiences: AI uses unified data to tailor responses, boosting satisfaction. Sprinklr’s AI resolved delayed orders by detecting supply chain issues. [Sprinklr, 2025]
• Real-Time Agent Support: IBM’s watsonx.ai suggests responses or escalates issues, improving efficiency in healthcare call centers. [IBM, 2024]
• Proactive Issue Resolution: Verint’s CX/EX Scoring Bot anticipates delays, notifying customers to enhance retention.

Limitations: Agentic AI cannot handle complex emotional interactions or operate without clean, integrated data. Claims of “self-driving” call centers are exaggerated, and scalability requires standardization.

FAQ on Agentic AI Capabilities:

• Can agentic AI replace human agents? No, it augments agents for routine tasks but struggles with nuanced emotional interactions.
• What’s the ROI of agentic AI? Studies show 20-30% cost savings in call centers with proper data integration. [McKinsey, 2025]
• How long does deployment take? Pilots take 3-6 months, with full deployment requiring 12-18 months for complex systems.

Evaluating Vendors: A Framework for Enterprise Call Center Technology Buyers

To choose effective agentic AI vendors, leaders should follow this structured evaluation process:

1. Demand Relevant Case Studies: Require proof of success in call centers with data silos. Aisera’s 80% query resolution for Adobe is a strong example. Avoid generic claims.
2. Assess Integration Capabilities: Request demos showing real-time data unification with your tech stack (e.g., Salesforce, Zendesk). IBM’s watsonx.ai excels with legacy systems. [IBM, 2024]
3. Prioritize Data Management: Choose vendors with tools for cleaning siloed data. Salesforce’s Data Cloud bridges structured and unstructured data. [Salesforce, 2025]
4. Ensure Transparent Testing: Ask for testing frameworks handling edge cases like incomplete data. Microsoft Copilot Studio’s validation is considered exemplary. [Microsoft, 2025]
5. Verify Security and Compliance: Confirm GDPR/HIPAA compliance with encryption and access controls. Sprinklr’s protocols are a pretty good model. [Sprinklr, 2025]
6. Run Pilot Programs: Test AI in small-scale pilots, measuring KPIs like first-call resolution. Eneco’s pilot with Microsoft Copilot Studio improved resolution by 70%. [Microsoft Case Study, 2024]
7. Seek Independent ROI: Demand third-party audits or testimonials to verify ROI. Avoid vendors with unverified claims.
8. Engage Experts: Use consultants to map data silos and recommend tailored vendors.

Recommendations for Vendors: Delivering Credible Agentic AI

Vendors must address call center challenges and build trust through practical solutions:

1. Invest in Data Integration: Offer pre-built connectors for CRMs, ERPs, and legacy telephony. NVIDIA’s NeMo microservices support custom integrations. [NVIDIA, 2025]
2. Enhance Data Preprocessing: Provide tools like data lakes to clean and unify data. Salesforce’s Data Cloud is effective. [Salesforce, 2025]
3. Embed Security and Compliance: Use encryption and access controls for GDPR/HIPAA compliance. Verint’s protocols are robust. [Verint, 2024]
4. Offer Scalable Testing: Provide testing environments for real-world scenarios. Microsoft Copilot Studio sets a standard. [Microsoft, 2025]
5. Support Human Oversight: Include dashboards and escalation triggers. IBM’s watsonx.ai offers strong human-in-the-loop tools. [IBM, 2024]
6. Prove ROI: Share third-party-validated case studies. Sprinklr’s proactive order resolution case is a good model. [Sprinklr, 2025]
7. Enable Flexible Deployment: Offer turnkey (e.g., Microsoft Copilot) and customizable (e.g., NVIDIA NeMo) solutions with clear trade-offs.
8. Educate on Data Foundations: Guide clients on auditing data silos, as Aisera does during onboarding. [Aisera, 2024]
9. Avoid Overpromising: Market AI as an augmentation tool, focusing on automation and personalization, like Verint’s Smart Transfer Bot. [Verint, 2024]
10. Foster Partnerships: Provide ongoing support for data and compliance needs, as IBM does with customer success teams. [IBM, 2024]

Conclusion

Agentic AI offers transformative potential for call centers, automating routine tasks, personalizing interactions, and supporting agents. However, data silos and integration challenges require strategic deployment. By understanding agentic AI’s agency-driven capabilities, rigorously evaluating vendors, and partnering with providers who prioritize data integration and transparency, leaders can achieve sustainable outcomes. Vendors must deliver robust, compliant solutions and communicate realistic value to bridge the hype-reality gap in 2025. [Gartner, 2024]