Contact centres generate more operational and customer insight than almost any other part of the business, as every call, chat, and email contains valuable information about customer needs, agent performance, and operational friction.
Yet despite this abundance of data, many contact centres still struggle to act on it effectively.
To find out more, we asked Chris Mounce, Product Training & Enablement Specialist at evaluagent, to explain how with AI transforming how interactions are analysed, the opportunity now is not just to collect more data, but to finally connect it.
Video: AI Connecting Dots: Does Your Workflow Connect Insight to Action?
Watch the video below to hear Chris explore how to use AI to connect the dots (not create more silos) and whether your workflows connect insight to action:
With thanks to Chris Mounce, Product Training & Enablement Specialist at evaluagent, for contributing to this video.
This video was originally published in our article ‘How to Use AI to Connect the Dots – Not Create More Silos’
4 Steps to Break Down the Data Silos Holding Contact Centres Back
Contact centres are not facing a problem with a lack of information, it’s the fragmentation as data often sits across disconnected systems, teams work from different views of reality, and valuable insights fail to translate into action.
To help get you break down these data silos, here are four steps to get your journey started:
1. Understand That Limited Visibility Creates Incomplete Decisions
Most quality assurance (QA) programmes still rely on interaction sampling, typically reviewing only 2–5% of conversations.
At first glance, that may seem reasonable, but in practice, it means:
- Different teams are often analysing different interactions
- Operations, QA, and customer experience teams work from separate datasets
- Decisions are made from incomplete or inconsistent views
“Contact centres are sitting on more data than almost any other part of the business. Every call, every chat, every email, all of it recorded. But some teams are still working in silos.
And that means still making decisions from an incomplete picture, still finding out about problems after they’ve already cost something. But it’s not an issue with the data, it’s connecting it.”
No single team is necessarily wrong, they simply aren’t seeing the same picture, which creates fragmentation across the organization and limits the ability to identify broader trends or root causes.
2. Implement AI to Enable 100% Interaction Evaluation
AI is changing this dynamic by making full-scale interaction analysis possible, as Chris explained:
“The first place to look is sampling. Most quality assurance programmes review somewhere between 2 and 5% of interactions, which sounds reasonable until you realize that every team is potentially working from a different 2 to 5%.
Operations has one picture, quality has another. Neither is wrong. They’re just not looking at the same thing. AI makes 100% evaluation possible. Every conversation, every channel scored against the same criteria. You can’t connect dots that were never fully captured.”
Instead of reviewing small samples, contact centres can now evaluate every call, every chat, and every email across every channel
And critically, all interactions can be assessed against the same criteria.
This creates a single, consistent source of truth across teams, where patterns become clearer, blind spots disappear, and leaders gain far greater confidence in the insights they use to make decisions.
You can’t connect dots that were never fully captured, and AI finally makes complete visibility achievable.
3. Keep in Mind That Full Coverage Alone Isn’t Enough
Even with 100% evaluation, silos persist if systems remain disconnected, and in many contact centres:
- QA data lives in one platform
- Conversation intelligence lives in another
- Customer experience insights sit elsewhere entirely
These systems often operate without shared logic or integrated workflows.
Yet each serves a complementary purpose as QA evaluates whether standards are being followed, and conversation intelligence reveals whether those standards reflect real customer needs
When connected, these functions strengthen one another, as Chris continued:
“But full coverage alone doesn’t break down silos. The second issue is that quality data and customer intelligence, they usually live in separate systems with no shared logic between them.
Quality assurance tells you whether your standards are being met. Conversation intelligence tells you whether your standards are measuring the right things.
When those two functions talk to each other, each one sharpens the other. QA frameworks get updated based on what customers are actually saying, and customer themes get validated against what’s happening on the floor.
Without that connection, you get compliance without context, and insight without an anchor.”
4. Turn Insight Into Action
The biggest challenge often comes after the insight is discovered.
AI can surface patterns, trends, and risks in seconds. But if those findings remain buried in dashboards, they still exist in a silo.
“And then there’s the final gap. AI can surface a pattern in seconds. What most programs haven’t solved is what happens next. Inside it lives in a dashboard and never reaches the people who could do something with it.
Well, that’s still a silo.
The contact centre typically has the right people closest to the data. The question is whether the workflow connects what you find to what you actually do about it – a coaching action, a process change – something that means the next interaction is better than the last one.”
Real value only emerges when insight leads to action, which could include:
- A coaching intervention for agents
- A process improvement initiative
- A change to customer communications or workflows
The contact centre already has the people closest to the data, but the question is whether they have workflows that connect discovery to execution.
From Fragmented Data to Continuous Improvement
Modern contact centres don’t need more data; instead they need better-connected data.
And by combining full interaction analysis, integrated intelligence systems, and operational workflows that drive action, contact centres can move from reactive problem-solving to continuous improvement.
The goal isn’t simply visibility, it’s ensuring that every insight leads to a better experience in the next interaction.
If you are looking for more great insights from the experts, check out these next:
- How to Capture Customer Preferences From Conversations
- 3 Principles for AI Pilots That Actually Deliver
- How AI Helps Move From Transcription to Customer Insight
Author: Robyn Coppell
Reviewed by: Jo Robinson
Published On: 3rd Jul 2026
Read more about - Video, Analytics, Artificial Intelligence (AI), Chris Mounce, Customer Experience (CX), Customer Service, EvaluAgent, Personalization, Quality, Service Strategy, Technology Enablement Strategy, Videos
