If everything worked, customers would rarely get in touch. The contact center catches what other processes and departments drop.
Even the simplest contacts, such as “When is trash collection day?” or “How do I reset my password?”, often trace back to a failure in how the customer is informed elsewhere. Or to a process that is breaking.
Complex calls are often caused by failures in more niche processes. Some are easy fixes; some will always require human intervention.
Contact center leaders have known where things are failing in the business for years. But it has been too nebulous to analyze cost-effectively and too internally politically contentious to drive change. Here are several examples:
- Sampling missed the scale.
- Manual QA could not keep up with the sheer volume of interactions.
- Taxonomies went stale.
- Search was brittle.
As a result, contact centers managed the symptoms of these broken processes, but the root causes lived on.
Even when a problem is deeply damaging to the business, there are too few credible data points to make a compelling case for resource prioritization. And too little understanding of what goes on behind the scenes to link problems to a common cause.
Contact center leaders have known where things are failing in the business for years.
This leaves the contact center stuck as a cost center: the price the business pays for all the messy, broken processes it has not fixed elsewhere.
The contact center leaders know the problems, but they haven’t been able to solve the root causes.
The Challenge, Reframed
This has always been a challenge, and lately it is getting harder. Interaction volumes rise relentlessly, while budgets do not. Customers expect more, product and policy change is constant, and channel sprawl makes the picture more complex. The net is carrying more weight with fewer stitches.
Here is the good news. The conditions that kept the contact center out of the root cause conversation are shifting. Leaders now have the tools, data, and mandate to do more than “handle the calls.” They can fix the system that creates the calls.
Customer service and the contact center are now C-suite discussions. Boards are paying attention. AI is forcing change, but it is also giving leaders an opportunity to guide that change and reframe the conversation.
Change is coming. But how the leaders manage it will determine whether the contact center remains a torn safety net.
Data Availability Has Changed
For years, the story from the front lines was compelling but anecdotal. Contact center leaders had transcripts in pockets, flagged calls, sample QA scores, and embedded operators with rich experience.
What they lacked, though, was total visibility across interactions, with enough precision to quantify the impacts and enough currency to survive a budget meeting.
That is now available. The unstructured mass of conversations, messages, and case notes can be captured, searched, clustered, and trended at scale.
- Modern speech and text analytics unlock who said what, where, and why.
- Generative AI tools summarize long interactions into crisp, comparable records.
- Topic detection groups emergent issues that would never have been named in static taxonomies.
- Continuous QA gives a fuller picture than a 1% sample ever could.
Beyond this, the visibility outside the contact center is far greater. It’s not just “our” systems that can be interrogated and aggregated, it’s all of them, from the CRM through ticketing, even internal communications.
Data alone, however, does not change minds. It must be presented in such a way that it is clearly understood by others.
Packaged well, data gives leaders what they have lacked: evidence, timing, and a clear “so what.” It converts “we think” into “we know” to justify ongoing and new investments in the contact center. And it makes inaction harder to defend.
The conversation is happening, that C-level mandate is forcing them. Leaders like yourself have the data to guide the conversations: and with the data we have the soft power to drive real change. Other areas of the business can’t refuse the conversation or label the problem anecdotal.
That gives contact center leaders the mandate to drive change, to fix the net, so they can bring the customers along.
AI Makes it Economically Feasible
There is so much buzz, hype, and excitement around AI that leadership teams are predisposed to invest in it. Customer service has become a C-suite priority because of the perceived potential with AI.
Ironically, those who are most ardently calling for AI transformation are often the least familiar with the workflows, constraints, and risks of production operations.
This opens the door to failed implementations that wreak havoc on all departments, not just customer service.
Worse still, it alienates customers and the agents who engage with them. It also wastes investment resources, both time and money. And it squanders the opportunity for positive transformation.
Boards are paying attention. AI is forcing change, but it is also giving leaders an opportunity to guide that change and reframe the conversation.
Contact centers are extremely complex, mission-critical operations. AI capabilities are new, powerful, and rapidly evolving. But the two elements don’t easily mix. Effective deployment requires:
- A workflow-first view of how service actually gets done, end-to-end.
- Ready access to the right data, governed and reliable.
- Pragmatic, iterative change, where you automate what should be automated, assist where judgment matters, and redesign where the system itself is wrong.
When leaders internalize that reality, the conversation shifts. It becomes acceptable, even necessary, to make broader changes around policy, data quality, and ownership to harvest the ROI that AI can offer.
It’s not about reducing the contact center workforce. Instead, it is about building a system that handles growth and complexity with less friction, one that frees human talent for the work only humans should do.
The right approach is to build a collaborative system where AI enhances the contact center human workforce so that they can keep pace with rising volumes while changing the underlying mechanics of service for the better.
Embedding Customers in the Workflow
Contact centers have spent decades optimizing interaction handling. Now you must optimize workflow orchestration with the customer inside it.
Customers increasingly arrive already identified and contextualized. They’re in an app, logged into a web portal, or conversing with a bot that can pass the customer’s state forward. That context is an asset that allows you to move from reactive triage to guided progression.
Here’s what embedding the customer in the workflow really means:
- From tickets to tracks. Instead of treating each contact as a fresh transaction, recognize it as a segment on a defined track, such as onboarding, billing inquiry, service outage, cancelation risk, and claims fulfilment.
- Tracks expose milestones, owners, and dependencies. They allow you to identify what breaks within the system, to cause the interaction in the first place.
- Co-navigation, not mystery. People are calmer and more trusting when they can see the steps, status, and ETA.
- Surface the work to the customer (what’s been done, what’s next, and what’s needed from them) and let the customer provide their feedback in the process.
- It’s a conversation that you can mine for insight, so wherever possible, capture their reactions and thoughts. Let them tell you if the process isn’t getting them where they need to go. Silence breeds repeat contacts; transparency reduces it.
- Customer-led triage. With context, you can ask smarter questions and let customers route themselves safely. “It looks like you updated your address yesterday; are you calling about the new delivery?” You’re leveraging what you already know to shrink discovery.
- Use your UIs. Historically the only interface we have with a customer is the interaction itself. If they’re on the phone, we have to talk them through it. If they’re on a webchat, we type.
- However, if the customer is authenticated and, in a portal, you can surface information in whatever way makes sense to inform the workflow and the conversation.
- State that persists. Whether the customer is in a bot, a portal, or on a call, the workflow state should travel with them. Repetition is the enemy of trust. Persistence is table stakes.
- Signals that teach the system. Every pause, backtrack, and upload is a signal. When captured and fed back, these signals reveal friction points and broken policies faster than any post-mortem.
Embedding customers in workflows creates a shared truth. It reduces effort, cuts repeat contacts, and exposes root causes in real time. It also surfaces to the customer all the work being done for them.
The right approach is to build a collaborative system where AI enhances the contact center human workforce...
When you know where you stand, can see that someone’s working to fix our issue, and feel part of the solution, you’re just happier about it. Most importantly, it reframes service as progress through a system, not a series of disconnected conversations.
Bringing it All Together
The critical cognitive shift that must occur is to move from considering the immediate interaction to understanding and enhancing the workflow behind it.
- Interactions are transactional; they’re about managing the fallout from the broken process.
- Workflows are foundational; they’re an understanding of how things really work for the customer.
When you instrument those workflows with data, amplify them with AI, and place the customer inside them, three things happen:
- Root causes light up. You can see where the system breaks and what that break costs.
- Change becomes fundable. Clear evidence plus credible ROI makes investment easier to approve.
- Experience compounds. Transparency and continuity reduce effort for customers and agents, and each cycle makes the next one better.
Why It Matters for Your People
The frustration of being the front line is real. It is not just about broken processes, taking the hit has a real human cost.
Customer service professionals are wired to help. Giving them a way to fix causes, not only symptoms, improves morale, reduces burnout, and makes the job worth staying in.
When agents see that their notes and tagged calls lead to real change, you get better data, ideas, and retention. It doesn’t just help your customers; it helps your teams too.
Beyond this, many in the business are predisposed towards eliminating agent positions. “Why can’t we just automate that cost center away?” is damaging thinking that must be countered. That requires a clear plan, with leaders articulating why automation for cost reduction only further rips the net.
Repairing the Net
Customer service and AI are now board-level priorities. That opens the conversation about what is preventing the experience you want to deliver.
The data you need is available, and if the leadership is predisposed to believe there’s a business case, the technology makes it possible.
Crucially, customers can now be involved in the overall workflow in ways that future-proof the investment. Involving them sets up the constant iteration and enhancement needed to keep pace.
When agents see that their notes and tagged calls lead to real change, you get better data, ideas, and retention.
The contact center will always be a safety net: and it will still catch what slips through the system. If you build and run the loop, the net stops tearing. It becomes a signal that makes the whole organization stronger, and it aligns the business towards the value you deliver to customers.
Fail to make the shift, and the contact center remains the punching bag. We in the industry continue paying for everyone else’s mistakes on a fraction of the budget.
But make the shift, and the contact center becomes the engine that drives customers on their journeys, and which guides how the organization designs, delivers, and learns.