CONTACT CENTER STRATEGY
This incident happened years ago, not as a pilot or proof of concept, but as normal work for a company that was simply paying attention.
REMOVING BARRIERS TO OBTAINING INTELLIGENCE
I started my career in a contact center supporting customers for a software company before moving into product management, so I knew from both sides what product managers were missing and what support conversations already contained. The barrier was never technical feasibility as much as the effort, specialization, and imagination required to determine what the data could answer.
As those barriers fall, the question for contact center leaders shifts as well. It’ s becoming less about how efficiently the contact center is staffed and more about whether technology investments are delivering better customer and business outcomes. The pattern also goes back decades. Roughly 20 years ago, a cable operator used speech analytics not for quality monitoring( QM), but to detect signal degradation in specific service areas and quantify what that degradation was doing to customer satisfaction.
That was network intelligence derived from conversations.
• The case manufacturer I mentioned earlier turned them into product design intelligence.
• More recently, a direct-to-consumer pet supply company traced a cluster of low-star Amazon reviews back to a root cause identified in support calls and changed the product.
In each case, the organization wasn ' t simply asking how efficiently the contact center handled an interaction; it was asking what those interactions could tell the rest of the business.
HOW AI MAKES A DIFFERENCE
What ' s actually new isn ' t the capability. Organizations have been doing this for years. The value of that intelligence has been demonstrable for a long time.
What ' s changed recently is that AI has made it dramatically easier and faster
WHAT ' S CHANGED RECENTLY IS THAT AI HAS MADE IT DRAMATICALLY EASIER AND FASTER TO UNDERSTAND WHAT ' S INSIDE THE DATA...
to understand what ' s inside the data, while raising general awareness that this understanding is even possible.
Historically, extracting meaningful insight from large volumes of customer conversations required setup time, specialized expertise, and a fairly clear idea of what you were looking for in advance.
AI changes that equation, reducing the specialized skills and time to surface insights. Through faster analysis, organizations gain a broader ability to understand why customers are contacting the business, where recurring issues may be taking shape, and more.
Conversation data also becomes more accessible beyond the contact center. Product, Marketing, Operations, and other teams can use AI to easily interact with conversational data to identify themes and trends in customer feedback that would have only been accessible to specialized teams.
The underlying customer signals were always there; AI is lowering the effort required to find them, connect them, and put them to use across the business, truly democratizing intelligence.
THE CONTACT CENTER IS ONE OF SEVERAL INTELLIGENCE SOURCES
The key to delivering those outcomes is ensuring that you are tapping all possible sources of data from which you extract intelligence and actionable insights.
The contact center is one of the richest sources of voice of the customer( VOC) insight, but it isn ' t the only source.
Organizations should instead be aggregating contact center interactions with every other stream of customer interaction and feedback, including digital, survey, review, chat, field, and sales data.
This is especially important in large enterprises with multiple contact centers, multiple BPOs, and dozens of customer touchpoints, each holding only a partial view.
Every channel holds valuable customer signals, but the insights become even more powerful when those signals are connected across channels and feedback sources. Fragmentation, not scarcity, is the real obstacle.
There is a competitive dimension to this as well. Algorithms are purchasable, and models are licensable, but your customer interaction data can ' t be replicated by a competitor. That ' s the durable asset.
When an organization genuinely makes this shift, ownership of conversation intelligence begins to migrate beyond the contact center and becomes a capability serving Product, Marketing, Billing, Compliance, and Operations.
If the intelligence still begins and ends with contact center operations, there is considerably more value to unlock, regardless of how much the organization has invested in technology. OCTOBER 2026 41