You’ve likely heard the buzz around agentic AI, but is it worth the hype? As AI continues to evolve, leading companies are no longer just experimenting; they are embracing it. They’re utilizing AI to enhance efficiency and transform the customer experience, particularly in post-sales support.

With 56% of support services organizations already piloting AI, it’s clear that we're past the early experimentation phase. However, understanding the next wave—agentic AI—requires cutting through the noise and focusing on real business value.

This blog outlines a practical look at how AI agents are used in real enterprise environments, what’s technically possible (and what’s not), and how your business can approach AI investments with a clear, ROI-driven strategy.

Here’s what you’ll learn:

• Where AI agents are making the most significant impact in customer support.
• What technical and organizational challenges you’ll need to overcome.
• How to decide whether to build or buy your agentic AI solutions—and how to maximize ROI.

If you're ready to move past the hype and build a smarter, more sustainable AI strategy for post-sales customer experience (CX), you're in the right place.

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Why Agentic AI Is Changing the Way You Work

At the heart of agentic AI is a shift in how work gets done. Right now, most companies fall somewhere along this workflow spectrum:

• Traditional/AI Light: Employees handle everything using enterprise software.
• AI Augmented: Workers are supported by tools like GenAI.
• Agentic: Digital agents interact directly with customers and processes.
• AI Automated: Sophisticated background AI handles tasks with little human involvement.

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Your business likely operates across a mix of these. The question is: where do you sit today, and where should you aim to be?

To find out, look at:

• Your spend on technology vs. labor.
• How many workflows AI influences.
• How data-driven your business decisions are.

These markers help guide your AI maturity model and determine how agentic AI fits.

Related: Hype, Myths, and Realities of Agentic AI

What Is Agentic AI?

Most people think of AI as tools like ChatGPT—ask a question, get an answer. That’s session-based AI. Agentic AI takes things further. Agentic AI refers to models that run continuously in the background, working on your behalf without being prompted.

These agents:

• Monitor signals like customer sentiment and escalation risk.
• Work 24/7 without needing to be manually activated.
• Provide proactive recommendations based on live data.

Think of it like a health tracker that alerts you when something’s off, not just when you check it. This ambient intelligence seamlessly integrates into your operations, quietly enhancing outcomes without requiring your attention.

For agentic AI to work, you need more than raw data; you need context. Enterprise support is complex. Customer interactions span email, chat, phone calls, internal tickets, and engineering systems, often involving multiple people and products over extended periods.

Agentic AI connects these dots:

• It builds a comprehensive view of the customer journey.
• It detects sentiment and intent across every channel.
• It understands when a customer is confused, frustrated, or at risk and acts accordingly.

This signal stitching enables AI to drive meaningful outcomes rather than providing generic responses. While full automation remains a longer-term goal, agentic AI agents deliver real-world results today. These background agents specialize in solving specific problems.

Types of AI Agents in Use:

• Sentiment Agents: Monitor emotional signals across interactions.
• Escalation Agents: Predict and warn of potential escalations.
• Routing Agents: Identify the right person to handle a case.
• Coaching Agents: Provide feedback on soft skills and communication.
• Prioritization Agents: Assist in triaging issues based on their level of urgency.
• Knowledge Agents: Deliver relevant content to agents in real time.
• Account Health Agents: Flag accounts at risk before renewal cycles.

Each tool supports both human and digital agents, enhancing performance and efficiency behind the scenes.

Common AI Deployment Pitfalls To Avoid

Despite the promise, many companies get their AI deployments wrong. The biggest mistakes include:

• Applying the wrong tech to the problem.
• Underestimating data fragmentation.
• Lacking historical context for interactions.

To overcome these, you need a strong foundation:

• Centralized and accessible data across platforms.
• Signal extraction that understands nuance and technical jargon.
• Compliance with enterprise security standards (HIPAA, GDPR, etc.).

It’s not just about choosing an AI engine. It’s about building the whole system around it, like a high-performance car needs more than a powerful motor.

Related: The Power of Ambient AI in Customer Support

How Agentic AI Transforms the User Experience

We’re at a turning point in how users interact with enterprise software. The traditional graphical user interface (GUI) gives way to natural language interfaces, where you can speak or type your needs and receive actionable results.

Agentic AI takes that even further. You don’t need to engage directly—your tools work silently in the background, surfacing insights only when required. This evolution unlocks significant gains in support workflows:

• Faster resolution times through smarter prioritization.
• Better coaching and development for your agents.
• Fewer escalations—and more efficient escalations when they do happen.

Related: Leveraging AI to Enhance Customer Experience and Efficiency

Measuring ROI From Agentic AI

AI investments only stick when they deliver measurable business value. The good news? Agentic AI provides ROI in four distinct stages:

1. Day-Zero ROI: Immediate Cost Savings

• License consolidation (e.g., retiring sentiment tools, outdated analytics platforms).
• Budget freed up to reinvest in AI deployment.

2. Near-Term ROI: Efficiency Gains

• Escalation volume drops (20–80% reduction).
• Fewer support hires are needed even as volume grows.
• Faster mean time to resolution (MTTR).

3. Mid-Term ROI: Financial Outcomes

• Improved customer retention.
• Reduced risk of renewal churn.
• Visibility into expansion opportunities and competitive threats.

4. Long-Term ROI: Strategic Impact

• Enhanced brand experience.
• Lower customer acquisition cost (CAC) and higher lifetime value (LTV).
• Proactive risk mitigation through buried signal detection.

Agentic AI is already delivering value across customer support organizations like yours—improving resolution times, preventing escalations, and surfacing insights from unstructured data you’ve long overlooked. But success doesn’t come from simply installing a model. It comes from investing in the systems, context, and architecture around it.

If you want to make AI work in support, start small, stay focused on business impact, and build the foundation needed to scale.

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Your Key Takeaways

• Agentic AI goes beyond chatbots. Unlike session-based GenAI, agentic AI operates in the background, continuously monitoring, analyzing, and acting on data without requiring manual prompts—thereby helping your support teams become more proactive and efficient.
• Context is everything. AI must connect fragmented customer interactions across channels and time to deliver real business value. Without context stitching, even the most intelligent AI won't provide the necessary insights.‍
• ROI starts on day one. From license consolidation to reduced escalations and smarter prioritization, agentic AI can deliver immediate and long-term returns—if you invest in the proper infrastructure and strategy.