Why AI automation starts with understanding your customers

AI deployment and adoption have quickly become boardroom priorities.

Across industries, organizations are investing in AI to improve customer experiences, reduce operational costs, and help employees focus on higher-value work. In the contact center, much of that excitement has centered on Agentic AI capable of answering questions, resolving common issues, and handling routine customer interactions.

The possibilities are exciting—but they also raise an important question:

Where should organizations begin?

For many businesses, the instinct is to start by building. What AI agent should we create first? How quickly can we get something into production?

These are reasonable questions, but they often skip the most important step.

Before deciding how to automate, organizations need to understand what should be automated.

That idea sits at the center of Omdia's report, Dialpad's Skill Mining: Prioritizing Contact Center Automation Using Real Customer Conversations. Rather than treating AI development as the starting point, Omdia outlines a lifecycle that begins with discovery—understanding why customers contact your organization, how agents resolve those issues, and which interactions are best suited for automation.

Technology isn't the bottleneck

The rapid evolution of AI has made building conversational experiences easier than ever. But easier technology doesn't automatically lead to better outcomes.

Many AI initiatives struggle because organizations automate the wrong work. They focus on low-volume requests, complex interactions that still require human judgment, or use cases based on assumptions rather than customer behavior.

The challenge isn't building AI.

It's knowing where AI will create the greatest value.

That requires a shift in mindset. Instead of starting with technology, organizations should start with the customer experience they're trying to improve.

Your customer conversations already contain the answers

Every interaction tells you something about your business. By looking at your conversation data, you can instantly see:

• Intent & resolution: Why the customer contacted you, and exactly how the agent resolved the issue.

• Volume & frequency: How often the issue occurs, and whether the same workflow is happening again and again.

Viewed individually, these are simply customer support conversations. Viewed together, they become a blueprint for how your organization serves customers. Patterns begin to emerge. Some requests are rare and highly specialized. Others happen hundreds or thousands of times, following nearly identical workflows. Those repetitive interactions are often the strongest candidates for automation because they create the greatest opportunity to improve efficiency while maintaining a consistent customer experience.

The challenge is scale. Even the most experienced quality assurance teams can only review a small percentage of customer interactions. That means valuable insights often remain hidden within thousands of conversations.

Discovery comes before development

This is where Omdia's perspective is particularly valuable.

The report emphasizes that successful AI automation starts with discovery and prioritization before moving into development. Organizations first need to understand customer intent and identify which interactions are suitable for automation. Only then should they build, validate, deploy, and continuously improve AI agents.

That sequence matters.

Instead of asking, "What AI should we build?" organizations can ask, "What customer work happens often enough—and consistently enough—to benefit from automation?"

It's a subtle shift, but it leads to smarter investment decisions, faster time to value, and AI solutions that solve real customer problems instead of hypothetical ones.

Turning conversations into insight

Traditionally, organizations relied on quality reviews, surveys, and dashboards to understand customer needs. Those approaches remain valuable, but they only capture part of the picture.

Conversation data provides something richer: direct evidence of what customers need and how agents solve those problems.

According to Omdia, Dialpad Skill Mining analyzes transcripts and digital conversations, extracts customer intents, evaluates which interactions are strong candidates for automation, and accelerates the creation of purpose-built AI agents. 

Rather than relying on assumptions, organizations can make decisions based on the work already happening every day.

Instead of debating which AI use case should come first, leaders can identify where customers consistently need help and where automation can have the greatest operational impact.

The right AI starts with the right problem

As AI becomes more accessible, competitive advantage won't come from building the most AI agents. It will come from building the right ones.

That starts with understanding customers.

Every conversation contains insight into how your business operates and where automation can create meaningful value. Organizations that learn from those conversations before investing in AI will be far better positioned to improve customer experience, increase efficiency, and deploy automation with confidence.

Omdia makes a compelling case that discovery is the foundation of successful AI automation.

And once you've identified the right opportunities, the next challenge becomes much more practical:

How do you uncover those opportunities across thousands—or even millions—of customer conversations?

That's exactly what we'll explore in Part 2.