Advancements in AI have created an incredible opportunity for businesses to change the way they interact with customers through automation. Not just to reduce costs, but to resolve customer needs faster and improve the overall experience. Deploying voice and chat AI agents through CallMiner OmniAgent is a key part of a broader opportunity we call CX automation.

The common challenge: Most automation projects still start from guesswork and intuition about what to automate, limited data samples, and lengthy trial-and-error cycles.

The risk: Automating the wrong conversations, or automating conversations wrong, wastes budget, slows time-to-value, and can erode customer trust.

The smarter approach: Start with your own customer interaction data to drive automation decisions, answering the what, why, and how. Your data is your in the drive toward implementing AI and automation strategies.

The problem with current automation approaches

Too often, voice and chat automation is measured primarily by , meaning, how many interactions are kept away from live agents. The goal of automation shouldn’t be to prevent customers from talking to you; it is to solve their problems efficiently.

Automation approaches that aren’t informed by conversation intelligence operate essentially in the dark. Without the deep analysis of your actual customer conversations, you cannot know with confidence:

• Which requests are recurring and predictable enough for automation
• Where hand-offs to human agents occur, and why
• Which resolution paths truly drive strong customer satisfaction and revenue outcomes

These gaps don’t just impact KPIs. They frustrate customers, waste agent time, and make automation investments less likely to succeed.

AI classification: The conversation intelligence blueprint

Automation initiatives start with knowing exactly what happens in every inbound and outbound customer interaction.

This is one of the fundamental capabilities of CallMiner conversation intelligence, achieved through the AI classification of every interaction, at scale.

Classification is the systematic breakdown of conversations into their fundamental building blocks, identifying core reasons behind each interaction, resulting outcomes to help map the desired path to resolution, understanding behaviors of agents and customers, and other referenced entities such as products, programs, locations, etc.

The result is a continuously updated, near real-time blueprint of every customer conversation — the foundation for smart automation decisions that deliver measurable impact.

AI Assist: Insights that drive action

AI Assist, CallMiner’s copilot, is more than a natural language interface to insights for analysts and business users alike. It has evolved into the intelligence engine powering CX automation – acting as an analyst, strategist, and builder in one. AI Assist leverages proprietary agentic AI to do everything from surfacing insights and patterns hidden in large volumes of data to generating charts and trackers for ongoing valuation and monitoring.

As announced at LISTEN 2025 in early October, AI Assist has taken another leap forward — becoming your automation guru. It not only answers “What could we automate?” but also, “Why should we automate these interactions?” and “How can we build the automation workflow to make it happen?”

This approach ensures success with your automation efforts, delivering cost savings, faster resolution, less customer effort, and better CX.

From insight to automation: In clicks, not months

With classification providing structure, and AI Assist acting as your automation guru, CallMiner enables organizations to move from raw conversation data to deployed AI agents in just four steps:

1. Discover – Identify the highest-impact opportunities for automation, across any targeted set of conversations.

2. Validate – Quantify relative impact across automation opportunities, and qualify with examples matching the recommendations.

3. Design – Generate workflow blueprints for the recommended automation, including conversation flow, personalization points, and escalation triggers, giving you a structured plan ready for refinement.

4. Deploy – Transfer to OmniAgent Designer to tailor the AI agent’s brand voice and personality, and integrate to CRM and other systems for data access and process automation.

This approach compresses time-to-value dramatically, eliminating months of manual blind building and reducing deployment to days.

Beyond automation: Process improvement insights

Automation isn’t just about replicating what happens today. The insights from conversation intelligence and assessing automation opportunities can reveal entirely new ways to engage customers or improve operations.

Inbound improvements:

• Capture interactions currently missed due to after-hours closures
• Use automation for surge overflow during seasonal spikes or unexpected events
• Reduce repeat contacts driven by customers seeking updates they aren’t proactively getting

Outbound improvements:

• Proactively contact customers to prevent attrition, cancellations, or missed appointments
• Send reminders and updates to reduce unnecessary inbound calls, improving transparency, trust and experience
• Secure revenue by ensuring follow-ups that human agents might overlook

Process improvements:

• Identify and correct undesirable agent behaviors with the consistency of automation
• Reduce lengthy hold times by automating status checks across multiple systems

By uncovering these hidden gaps and opportunities, conversation intelligence ensures automation isn’t just about replacing tasks. It’s about reimagining how you serve customers and optimizing operations across every channel.

Why it matters: The business outcomes

When automation starts with real customer conversation data, the business impact is immediate and measurable:

• Higher resolution rates and reduced handling costs
• Revenue lift from upsell or cross-sell paths in AI-assisted interactions
• Consistent customer experience without variability or fatigue
• Infinite scalability, from one AI agent or thousands, deployed across voice and chat (with one flow supporting multiple channels)
• Continuous improvement loop where every automated interaction feeds back into conversation intelligence for evaluation and optimization

From guessing to knowing

The question isn’t if you’ll automate – it’s whether you’ll automate with clarity and precision or operate in the dark. Effective automation isn’t about racing to deploy; it’s about deploying the right automation first.

CallMiner closes the loop: Your data → AI insights → automation blueprint → live AI agent → more data

For leaders ready to move from partial visibility to clear, confident automation decisions, there’s no faster path from discovery to deployment.

Try CallMiner’s AI agent for yourself. Explore our self-serve demo of CallMiner OmniAgent to see first-hand how intelligent automation can power better customer experiences.