The contact center leader on my screen looked exhausted.

Her center was losing nearly 90% of its new hires during training. Not after six months. Not after a year. During training.

And no, that’s not a typo.

Behind that statistic were real people who had accepted a job, invested their time in training, and wanted to succeed. Yet before they ever reached the floor, something had convinced them that success would be difficult, unlikely, or simply not worth the cost.

For any leader, a loss rate that high creates urgency. It creates pressure to find answers, and quickly.

Like many contact center leaders today, she was looking closely at AI.

The very things that improve the operation today are often the same things that determine whether a new technology succeeds tomorrow.

She was convinced that if she could just get the right AI solution in place, things would improve. Maybe it was:

• An agent assist application feeding agents answers in real time.
• AI-powered quality monitoring that could identify coaching opportunities.
• Automated call summaries that would reduce after call work.
• Conversational AI that could handle customers’ questions before they reached the agents.

On paper, each of those solutions offered a compelling promise.

• Faster answers.
• Better support.
• Improved productivity.
• Better retention.

Better yet, each came with a business case and projected ROI that was far easier to present to senior leadership than a proposal focused on training, coaching, knowledge management, and/or process improvement.

The Repeating Contact Center Script

As I listened to that contact center leader, I found myself thinking:

I’ve seen this movie before.

When it comes to technology, the contact center industry is like “Groundhog Day,” where actor Bill Murray wakes up each morning to find out that day is exactly like the day before, and no matter what he did, that day turns out to be the same (until he broke that loop).

Over the years, our industry has fallen in love with one “next big thing” after another.

• DTMF IVR
• Speech-enabled IVR
• CRM
• Omnichannel customer service
• Cloud contact centers
• Chatbots
• And now, AI

The technologies are different. But the pattern is remarkably similar. And as the FIGURE shows, it becomes an endless loop.

The newest solution captures our attention because it offers something every leader wants: hope. Hope that a persistent problem can finally be solved. Hope that improvement can come faster than the hard work of operational change.

The challenge is that technology rarely eliminates the need for strong hiring, effective training, quality coaching, trusted knowledge, and sound processes. More often, it amplifies the strengths and weaknesses already present in the operation.

The very things that improve the operation today are often the same things that determine whether a new technology succeeds tomorrow. As also shown in the FIGURE, which I will discuss later, they form the foundation of success.

The Customer Always Gets a Vote

There is another reason these technology cycles feel so familiar. Every implementation plan is built on assumptions about customer behavior. And customers have a long history of surprising us.

Which is why the current AI conversation feels less like a revolution and more like “Groundhog Day.”

One of the most consistent lessons from decades of contact center technology implementations is that customers rarely use new tools exactly as designers expect. That has happened with almost every major contact center technology wave.

• With IVR, customers learned to pound “0” repeatedly to escape the system.
• With speech-enabled IVR, customers learned which phrases would get them to an agent faster.
• With email support, customers learned that certain wording generated quicker responses.
• With web chat, customers discovered they could multitask and disappear in the middle of conversations.
• With omnichannel, customers started channel hopping, expecting the company to remember everything from the previous interaction.
• With knowledge bases, customers often became more informed than frontline agents.

Now, with AI, customers are already learning how to prompt, manipulate, challenge, and test the system.

Some customers are finding ways to get better answers than designers anticipated. Others are discovering weaknesses we never imagined.

Agents have a habit of doing the same thing, finding shortcuts, workarounds, and entirely new ways to use systems once the realities of the work set in.

Every implementation plan contains assumptions about user behavior. Then users arrive. They ask different questions, take unexpected paths, find shortcuts, expose gaps in knowledge and process, and teach us what we failed to anticipate.

In many ways, the customer becomes the final designer of the solution.

That is why technology implementation is never a finish line. It is, instead, the beginning of a learning cycle.

Now, with AI, customers are already learning how to prompt, manipulate, challenge, and test the system.

The organizations that succeed are not necessarily the ones that launch first. They are the ones that listen, adapt, and learn fastest after launch.

The customer always gets a vote. History suggests they usually get the last one too.

The CRM Lesson I Never Forgot

Years ago, a client asked me to review a CRM request for proposal (RFP). CRM was still relatively new to the industry, and the organization had done what many do when a new technology appears.

The client researched every available feature and function they could find and included all of them in the RFP.

As we reviewed the document together, I began asking a simple question: “How will this feature improve the contact center?”

The response was almost always the same. “Why wouldn’t we want it if it’s available?”

So, I changed the question:

“Assume this feature adds hundreds of thousands of dollars to the project. How will you recover that investment?”

The room became very quiet.

What followed was one of the most valuable conversations the organization ever had.

• Instead of asking what technology could do, we started asking what business problems needed solving.
• Instead of asking what was available, we started asking what created value.

We discussed implementation costs, support requirements, customization needs, upgrade cycles, and governance responsibilities.

We also discussed something almost nobody was talking about at the time.

Knowledge.

• Where would it live?
• Who would maintain it?
• How would agents know which information to trust?

The technology itself was never the problem. The assumption that every available feature automatically created value was.

Looking back, I see the same conversations happening today around AI. The technology is different. The thinking is remarkably similar.

Another Groundhog Day Mistake

One lesson I am learning firsthand from organizations implementing AI today is that the technology itself is rarely the biggest challenge. The challenge is everything surrounding it.

Organizations routinely underestimate the resources required to design, develop, launch, monitor, and continuously improve these solutions.

The software may be purchased in a matter of weeks. But building an effective solution often takes months of operational effort. Sometimes much longer.

Many organizations are learning how to use AI at the same time they are trying to implement it. They are building the airplane while learning to fly it.

Another reality executives should consider is the difference between buying technology and buying expertise.

Many vendors are excellent at providing technology. Some also provide experienced consultants who understand contact center operations, knowledge architecture, change management, and implementation strategy.

But other vendors do not. In those situations, the customer becomes the implementation consultant, solution designer, knowledge architect, tester, trainer, and change manager while still trying to run the business.

So, what happens?

• Timelines stretch.
• Unexpected work emerges.
• Requirements evolve.
• And leaders become frustrated.

Eventually, someone concludes that the technology failed. But in many cases, it did not. The technology simply exposed problems that were already there.

• Weak knowledge.
• Unclear processes.
• Unrealistic expectations.

The technology wasn’t the problem. It revealed where the organization was unprepared.

The difficult truth is that technology cannot compensate for knowledge that has not been organized, processes that have not been defined, or expectations that have not been aligned.

…technology cannot replace the need to prepare people, support them, and understand the challenges they face.

No AI platform arrives with knowledge of your customers, your policies, your processes, or your culture. Someone must bridge that gap. The software is often the smallest part of the project.

In reality, the work starts long before the contract is signed. Defining the problem, preparing the knowledge, aligning the process, establishing ownership, and building organizational readiness often determine whether a technology should be considered in the first place.

The Lesson We Keep Forgetting

After decades of watching technology cycles come and go, I have become convinced that successful contact centers are built on four foundations:

People. Process. Knowledge. Technology.

In that order.

When I say people, I am not talking about headcount. What I am talking about is:

• The new hire trying to make sense of six different systems during their first week on the job.
• The supervisor balancing coaching, performance management, and customer escalations.
• The trainer preparing employees for situations they have not yet experienced while the business continues to change around them.
• The experienced agent whose knowledge quietly holds the operation together.

Technology can support these people. It can make their jobs easier, faster, and more consistent.

But technology cannot replace the need to prepare people, support them, and understand the challenges they face. Nor can it compensate for weak processes, fragmented knowledge, or unclear expectations.

Yet this is where the “Groundhog Day” cycle often begins.

We invest in the technology. Then we redesign the process around it. Then we organize the knowledge. Then we train people to work within the new environment. But that approach rarely ends well.

Technology should support people, process, and knowledge, not the other way around.

Knowledge deserves particular attention because AI is only as good as the information it can access. If policies conflict, procedures are outdated, or information is scattered across systems, AI will reflect that confusion. Yes, the many decades-old proven true acronym GIGO. Garbage In. Garbage Out.

When organizations feed incomplete, contradictory, or poorly governed knowledge into AI systems, those systems do exactly what they are designed to do. They fill in the gaps. Often confidently.

But sometimes that means combining conflicting policies into a single answer, surfacing outdated procedures, applying rules outside their intended context, or drawing conclusions from incomplete information.

The AI is not necessarily malfunctioning. More often, it is faithfully reflecting weaknesses that already existed in the underlying knowledge.

Before asking whether your organization is ready for AI, ask whether your knowledge is ready for AI.

If you would hesitate to let your knowledge speak directly to a customer, it is not ready to power AI.

People. Process. Knowledge. Technology.

In that order.

The fundamentals have not changed. Only the tools have. The organizations that remember that will be far more likely to realize the promise of AI than those chasing it.

And that may be the biggest “Groundhog Day” lesson of all.

AI is reshaping and in many cases shrinking entry-level employment opportunities, particularly for younger workers. And that includes, notably, in the contact center both now and in the future.

• Research by Anthropic revealed that customer service positions are the second most likely type to be exposed to potential AI replacement, trailing computer programmers and followed by data entry keyers.
• A report by the Burning Glass Institute, “No Country for Young Grads,” revealed that entry-level work was being replaced with automation.
• In Canada, the trend is similar. Last year, postings for early-career positions fell by almost 40% according to the Labour Market Information Council.

The impacts of eliminating early career experience will most certainly be felt all around, including the corporations that are investing in AI.

It makes sense that frontline roles are ripe for AI disruption…

This article examines why frontline roles are so susceptible to AI displacement and how enterprises and contact centers are finding a balance between AI enhancements and customer experience (CX).

Why AI Is – and Isn’t – Taking Over Entry-Level Roles

When enterprises and organizations adopt technology of any kind, including AI, it’s typically for some kind of return on investment (ROI).

Frontline roles offer the best bang for your buck for corporate AI adoption, according to research from The Josh Bersin Company, published in The People Space. “…[F]rontline, not back-office, work represents AI’s highest potential return on investment and organisational value.”

It makes sense that frontline roles are ripe for AI disruption because they often involve the types of functions that are repetitive or administrative in nature.

AI is most effective when it can tackle items that benefit from greater efficiency, quality, or increased productivity. Two examples (that can also reduce customer contact volume by preventing issues) include:

• Quality control with greater precision and consistency.
• Warehouse operations and inventory management with real-time updates.

Jack Kelly, in “These Jobs Will Fall First As AI Takes Over the Workplace” (Forbes), said economic incentives, cost pressures, and shrinking timelines are accelerating AI adoption. He said this momentum is impacting the frontline disproportionately:

“AI’s impact will not be uniform. Jobs like data entry, scheduling, and customer service are already being overtaken by AI tools like chatbots and robotic process automation.”

The author also shared key data, like a 2024 study from the Institute of Public Policy Research that found as many as 60% of administrative tasks are automatable, which helps organizations cut costs.

At the same time, a recent report from Gartner, cited in a CX Today article, “AI’s Broken Promise: Customer Service Automation Costs Set to Soar,” reported that AI adoption may not be taking over their frontline at the same rate.

Said the article: “Only 20% of customer service leaders have actually reduced agent staffing because of AI, with most reporting that their headcount has remained steady.”

Frontline AI Adoption is Limited

There are limitations to AI, and new adoption projects are being scrutinized after recent reports that as many as 95% of AI pilots are failing.

Similarly, AI adoption has inherent risks associated with it and in the hiring space. And recent lawsuits have made the C-suite nervous.

…there is some indication that AI use will deck-shuffle rather than discard the human agents’ “cards.”

Beyond risk exposure, there are technical issues that are holding the widespread AI takeover back. These can include barriers related to the following:

• The high cost of AI adoption, especially at the enterprise level.
• The value that humans can deliver may, in some cases, be more cost-effective than automation.
• AI is limited by access to high-speed internet, cloud infrastructure, and computing power.

AI adoption, then, isn’t necessarily going according to plan. While some industries can benefit from AI more than others, as we are seeing there are limitations and risks involved.

The Increasing Cost of Replacing Agents

As automation trends rise within frontline roles, the cost of doing business with AI is increasing as well, particularly for contact centers.

According to data from Gartner, the cost-per-resolution using generative AI solutions will rise to $3 by 2030, which is more than it would cost for a B2C offshore human agent to resolve the same task.

Senior Director Analyst at Gartner’s Customer Service and Support practice, Patrick Quinlan, warned: “Customer service leaders are determined to use AI to reduce costs, but return on those investments is far from guaranteed.”

The rise in costs, Gartner explained, is predicted to stem from increased data center fees, the lack of available subsidies for AI companies that will now have to show profitability, and the need for new talent that can implement and manage complex AI use cases.

Moreover, there is some indication that AI use will deck-shuffle rather than discard the human agents’ “cards.” Gartner is now predicting that by next year, “50% of companies that attributed headcount reduction to AI will rehire staff to perform similar functions, but under different job titles…”

Balancing The Contact Center’s Frontline

Despite rising costs, AI-powered chatbots and virtual assistants currently handle large volumes of predictable queries and simple tasks, reducing the headcount historically needed for these frontline functions in contact centers.

But while automation of customer service may have reduced the number of agents on the floor, it hasn’t increased customer satisfaction.

A study by HubSpot and SurveyMonkey, “In AI We Trust? How Brands Are Earning Loyalty in an Automated World,” revealed that a whopping 82% of customers prefer human service: even if the outcome of their call was the same as if they’d spoken to a chatbot. 52% even said they hated the use of AI in service interactions.

This is supported by a UJET study, “Critical State of Automation in Customer Experience,” which found that 80% of customers had increased levels of frustration after an interaction with a chatbot.

As I’ve stated before:

“Customers still crave human interaction, particularly when dealing with emotionally charged issues or situations that fall outside predictable parameters. (…) Human agents also serve as the moral compass of customer service.”

“They can make judgment calls, navigate gray areas, and build trust in ways that machines cannot. In industries like healthcare, finance, and legal services, this human touch is indispensable.”

Even beyond CX, contact centers benefit from entry-level roles that build a talent pipeline and support ongoing workforce mobility. To remain competitive, contact centers will have to reimagine their frontline positions so they add even more value.

A hybrid approach is recommended. Entry-level positions can be enhanced by AI without replacing humans altogether, a Forbes article explained. Tedious admin tasks can be automated by technology, for example, freeing up humans to do what they do best: be human.

Reimagine Entry-Level Roles from Recruitment

In a recent interview, I said: “As automation and self-service systems handle simpler interactions, organizations need agents with stronger interpersonal and problem-solving skills to handle complex or emotionally charged situations.”

Agents that have the right skills are able to solve multifaceted problems and apply critical reasoning or empathy when needed. These are the types of interactions that can help a brand build loyalty.

Contact centers need to reimagine what the role of their frontline agents looks like and consider hiring based on the right skills.

Hiring agents based on the strong presence of proven CX soft skills, like acknowledgement and empathy, will help contact centers select the right agents and set them up for success before day one.

To remain competitive, contact centers will have to reimagine their frontline positions so they add even more value.

Aside from soft skills, agents and other entry-level positions will be required to have AI skills. A recent report from the World Economic Forum predicts that in just five years’ time, AI will have created 170 million new jobs.

And so even though it will have replaced about 78 million jobs, there will actually be a 7% increase in employment compared to the workforce today.

In a hybrid frontline role, where humans work with AI, the technology has the potential to enhance the employee experience. Imagine the following scenario:

“Imagine an AI HR assistant in the pocket of every frontline manager or supervisor, guiding them in the flow of work. It provides instant policy guidance, supports staffing decisions, offers coaching prompts, auto-generates recognition messages, and answers HR questions in seconds. Weekly pulse checks feed insights on morale, recognition gaps, fairness, and burnout, with the AI suggesting the next best actions.”

When AI is put to good use, organizations can get the efficiency gains they crave, while keeping humans in the loop. But whether every organization and contact center will be able to afford to do so in the near future is another story.

The Future of the Frontline

AI is undeniably reshaping entry-level employment for the time being by taking over routine tasks. But this shouldn’t be a reason to eliminate human roles, at least for customer service.

Human agents are essential for building trust, handling nuanced situations, and serving as the empathetic face of the brand.

In contact centers, fully replacing humans at the frontline risks harming customer satisfaction, long-term retention, and increasing operational costs in the long run.

Organizations and contact centers must rethink and elevate their frontline jobs, revamp how they recruit and screen talent, and reorient roles toward complex problem solving and CX excellence.

The customer service frontline can still become more efficient with AI and keep their jobs. Done right, this evolution not only preserves human involvement but strengthens it, ensuring that humans and AI complement each other to deliver superior service.

I could be the nicest person in the world, but when the interaction drops into my headset, the customer is already yelling.

• They have been through the website FAQ, IVR, agentic voice AI, and now I am the fourth layer they are facing.
• They are frustrated, angry, and they want the company, meaning me since I answered the phone, to know how angry they are.

That is what agents are facing now. That is what makes emotional intelligence, de-escalation skills, and the ability for agents to emotionally reset after a call crucial for both customer retention and agent retention.

The Broken Training Model

With agentic AI voice and chat, every contact that moves to a human agent is an escalation. Those require more advanced skills and better training. However, many contact center training models were built in the 1990s and early 2000s. They desperately require updating for the age of agentic AI.

New hire agent training used to be about training people to handle the most common interactions, such as password resets and account balance inquiries.

So, after the new agents graduate, they can handle 80% of their interactions by themselves. Then with additional coaching, they can learn the rest on the job and handle rarer or more advanced interactions.

The old training goal was bare minimum at graduation, competency within six months, and mastery within one year.

AI throws all of that out the window.

Since AI is deflecting an increasing percentage of straightforward interactions, harder ones are coming to the agents. So, what might have been a once a week or once a day difficult interaction now becomes several times a day.

You cannot just train agents to 80% anymore. Instead, new hire training is going to have to cover:

• Emotionally intelligent customer service skills.
• Negotiation skills.
• How to deal with complexity.
• How to take escalated “handoffs” from AI, review the details AI has already gathered (to avoid making customers repeat themselves), and then start their own conversations with the customers.

Otherwise, when new hire training graduates hit the queues, they will feel like they were never properly trained in the first place because most interactions are those they were not trained to handle.

…many contact center training models were built in the 1990s and early 2000s. They desperately require updating for the age of agentic AI…

That frustrated feeling can breed resentment, a loss of confidence in the organization, and a feeling of “this is not what I signed up for.” These then lead to increased new hire attrition, further stressing your service levels.

Watch out for this by monitoring:

• Has the percentage of new hires quitting in the first two or three months increased versus pre-AI?
• Is your turnover rate higher at the six-month mark than it was?

Those are indicators that agents feel they were never properly trained in the first place.

Revamp Your New Hire Training

When I was the training manager for a national telco’s 900-agent contact center, one of the challenges was how to train new hires to do their jobs without making training take forever.

We had a six-week training program followed by two weeks of nesting, where new hires could sit together, take interactions, and get immediate help from veteran coaches.

However, feedback from past new hire classes showed they could benefit from additional customer service training.

We also needed to slow down the course somewhat, to give new hires a chance to digest the information without becoming overwhelmed.

There was just too much material compressed into too little time. This resulted in agents who could pass each test, but who could not necessarily apply that knowledge immediately to actual customer situations.

The course also put too much pressure on the nesting coaches. They ended up doing remedial training on concepts new hires did not have time to fully understand within the tight course timeframe.

That gap between passing a test in training and applying it under pressure is now the daily reality, as new hires face escalations from agentic AI as soon as they hit the contact center queues.

Yet every time I proposed making training longer, I got pushback from senior leaders. It was more important, they told me, to get bodies into the queue than to train for an extra week.

Their argument was training time costs money because new hires are in the classroom instead of being productive on the floor. But with today’s AI deflection making every human agent interaction an escalation, having better trained new hires is not a luxury. It is an essential.

If you do not do that, you are going to have to spend more time having a senior agent, team leader, or coach help agents deal with tough one-off interactions.

As I noted earlier, under the old new hire training paradigm, only the most common 80% of interaction types were covered in training. But if agentic AI is containing more of those, that number drops to perhaps 40% of a human agent’s daily interactions.

Based on this logic, the remaining 20% of rare interactions triples to 60% of a human agent’s interactions today. That means three times the volume of weird, complex, and emotional interactions. And most new hires are not receiving training for that.

The obvious solution is to increase the length of new hire training to accommodate more customer service and negotiation skills training, as well as training on how to solve more complex issues.

Of course, the key word is “productive.” If new hires cannot handle a heavy dose of agentic AI escalated interactions today, they are not productive. Instead, lack of training on tough issues makes them a potential liability. They could give out wrong information, credits, or aggravate a customer into leaving.

There are ways to make new hire training more effective though, without adding weeks to the course.

First, and ironically, AI is one way to drive training efficiency. Trainers should work with the QA (quality assurance) team and use AI-driven speech analytics to find the top interaction drivers escalated to human agents. As a trainer:

• Is your new hire course even designed around interaction drivers?
• Can you name the most common interactions AI escalates to your human agents?
• Is there an established workflow between your training team, QA team, and operations management to keep that curriculum current?

You need those feedback loops to make curriculum changes and keep your new hire training aligned with what is happening on the floor.

A second way to increase learning and save time is to make your training scenario based. For example, instead of just showing new hires how to add an address in your CRM, frame your training with the scenario of a customer calling in to change their address.

Scenario-based training is faster, more meaningful, and has better learner retention, because human agents first learn WHY they need to do something, then HOW to do it. That prepares them to handle actual customer scenarios versus learning facts or processes in a vacuum.

Add Training for Veteran Agents

It is not just new hires that need these advanced skills. Your existing veteran human agents may also need training on:

• Customer service skills.
• Emotional intelligence, empathy, and negotiation skills.
• How to solve more complex problems.
• The same training new hires receive about how to take escalated “handoffs” from AI and begin their conversations with customers.

The way to look at it is that the unusual, complex, or emotional situations that may have happened once or twice a day may now be happening a half dozen to a dozen times a day. This can increase your existing agents’ stress level.

Have you had an existing agent quit because they feel customer interactions are much harder than they used to be?

They are feeling pressure now from angrier customers and more complicated issues escalated to them by AI. That forces the agent(s) to cope with tougher interactions and more of them. They may have never been trained to do this.

Bottom line: it is more cost-effective to train existing agents on the skills they need to handle what agentic AI cannot do.

A recent Verint survey found “46% of agents aged 18–34 were likely to leave within six months.” To help retain agents instead, provide additional training on how to deal with these escalations, rather than train brand new hires on everything.

Back to “productivity.” Which is more cost-effective, no, or more additional training?

Consider this: McKinsey and Company research puts the cost of replacing a contact center agent at $10,000 to $20,000, a figure still widely cited across the industry today.

Bottom line: it is more cost-effective to train existing agents on the skills they need to handle what agentic AI cannot do.

Check the Effectiveness of Your Training

For new hires, check with the post-training “nesting team” to see if they are having to do more work now than they did before to fill in new hire skills gaps.

Also check with your QA team to see if the kinds of interactions they are scoring agents on match what is coming to human agents. The point is that new hire human agents need to know more to handle the escalations that are coming to them.

For both new hires and existing agents, one way to check for rising interaction complexity is to look at average talk time rather than average handle time (AHT).

The reason I mention talk time as opposed to AHT is AI agent assist, automatic note-taking, and faster backend processing could reduce after-call work (ACW). Reduction in ACW can mask an increase in talk time, because AHT may look the same or even shorter, even as your actual talk time is rising.

View increased talk time or increased chat handling time as indicators these escalated interactions are more complex and more emotional now. That means veteran agents and new hires need more training and coaching to handle those issues.

And here is the irony. The better your agentic AI is at containment, the better your human agent’s emotional intelligence and problem-solving skills will need to be.

I once had a colleague, who when walking into and through the office, would often say “Never enough time…”

He was right, and on so many levels.

Particularly so in the contact center, where agents are under constant and too often growing pressure to provide even better service to increasingly demanding customers. While at the same time having to meet – or if possible, preferably exceed – exacting performance and productivity metrics.

A critical component of ensuring high – and higher – standards of performance and productivity is having agents well-trained. Also, to have coaching available to help them immediately and successfully address any issues that crop up in customer interactions.

But here’s the rub: coaching and training consumes time. Time that would need to be found in the agents’ schedules. Time that would have to be spent off-hook and offline and away from engaging with customers. Time that costs money.

So, how can contact centers balance these issues: finding the time and justifying its allocation while ensuring excellent, timely customer experiences (CXs)? Can the ever-developing AI-based solutions help manage this issue?

To find out, we had a conversation with Dan Smitley, a leading workforce management (WFM) authority and founder of 2:Three Consulting, whose mission is “to optimize workforce management while prioritizing the value of every individual.”

Q. How have coaching and training sessions been typically scheduled?

There are probably three ways this tends to happen, and most organizations are using some mix of all three whether they realize it or not.

The first is what everyone is aiming for. Ops identifies who needs coaching or training and why, and the WFM team works with Ops to place it into their schedule(s) in a way that doesn’t create unnecessary service level risk. It’s planned out, or at least as planned as it can be.

Then you have the more real-time version of that, where something comes up and Ops just needs to address it. WFM gets pulled in, or sometimes WFM is just informed, and everyone kind of accepts that the service level might take a hit, but the issue is worth it.

And then there’s the version that’s really just friction between teams. Ops pushes coaching into the schedule regardless of impact, or WFM supervisors push back because they’re trying to protect service level. At that point, it’s less about scheduling and more about a lack of alignment.

Ideally, coaching and training are planned and you’re using available time without really feeling it from a service level perspective. In reality, most teams are bouncing between all three depending on the situation.

Q. Have you been seeing – and do you expect to see – changes to both coaching/training and in setting aside time (and how much) for them?

I think there’s a version of the future where coaching and training increase, but it’s not automatic.

As more of the simpler interactions get handled through self-service, what’s left for agents to resolve or complete (like an involved sale) is just harder to accomplish. That should create more need for coaching and training, and in theory it should also create some space for it.

“If agents are viewed more as a cost to manage, then time away from the queue is always going to be questioned.” —Dan Smitley

The part that complicates that is the cost is very easy to see. If I pull 20 agents into a training session, I can do the math on that immediately. The benefits are a lot less obvious. You’re trying to improve conversations, reduce churn, build better skills, and a lot of that shows up indirectly if it shows up at all.

At the same time, even small reductions in volume start to put pressure on headcount. If you’re deflecting 5% of contacts, it’s hard for a leadership team to ignore that and not at least ask questions about staffing.

So, you’ve got this tension where the work is getting harder and arguably requires more investment, while the math is pushing you to do the opposite.

Where I have seen clear change is in how the time gets scheduled. Tools like Intradiem and QStory have taken a lot of the manual effort out of placing coaching and training into the day. That part is just getting easier and more precise.

Whether organizations actually use that to invest more in development is still very much up in the air.

Q. What challenges are you seeing in the ability and the justification for scheduling coaching and training, including time spent away from customer interactions?

A lot of this comes back to how the organization views the role of the agent, and that shows up pretty quickly in how easy or hard these conversations are.

If agents are viewed more as a cost to manage, then time away from the queue is always going to be questioned. If performance is acceptable, there’s not a lot of urgency to invest more into development. And if anything, the pressure is usually in the other direction.

But if agents are viewed as part of how the company delivers value to customers, then coaching and training are a lot easier to justify. Not because the cost goes away, but because the expectation is that it leads to better outcomes.

Even then, it’s not simple. The cost is immediate and visible. The benefits are often preventing something from going wrong later or improving something that’s hard to tie directly back to a single training session.

That’s where a lot of teams struggle. They know coaching and training matter, but it’s hard to prove in a clean way. And if you can’t articulate the upside clearly, the conversation usually defaults back to cost.

Q. Is the infusion of AI into the contact center, both to deflect but also to shorten and improve the outcomes of customer contacts, impacting coaching/training and in scheduling agents for these sessions? In agent forecasting?

It is, but probably more in terms of pressure than clean solutions.

The scheduling side is improving through AI and automation. It’s now easier to place coaching and training into the schedules without someone manually trying to protect service levels all day.

The bigger shift is in the type of work agents are doing. As more gets handled through self-service, the interactions that make it to the agents tend to be more complex. You see some of that in handle time, but that doesn’t really capture how mentally or emotionally taxing those conversations can be.

From there, organizations tend to go one of two directions.

1. Some lean into the efficiency side and reduce headcount as volume drops. That makes everything tighter, including the ability to schedule coaching and training.
2. Others look at the same shift and decide they need to invest more in their agents because the job itself is getting harder.

Where it gets messy is in how we talk about upskilling. It can mean a lot of different things depending on who you ask. Like better soft skills, better sales outcomes, or preparing someone for a different role entirely. If that’s not clearly defined, it’s hard to build a real case for it, and cost savings usually win out.

Q. What are your recommendations to contact center leaders and managers who seek to ensure their agents are well-trained, performing to the best of their abilities, and continue to work for their employers?

A lot of this comes down to being honest about what you expect coaching and training to actually do.

If it’s just something that’s an option, and that you try to fit in when there’s extra time, it’s always going to get squeezed out. There’s always something more immediate pulling agents back to the queue. And in a lot of organizations, that’s exactly how coaching and training are treated.

That’s where coaching and training really splits, and with this your ability to justify the agent and supervisor time and resource allocation to the C-suite.

If they’re viewed as optional or just a way to use idle time, they’ll never be consistent and they won’t drive much impact.

But if they’re viewed as part of how you build better agents, better customer outcomes, and even future talent for the organization, then they start to look a lot more like a requirement than a nice-to-have.

That also means being clearer about what you’re developing:

• Upskilling sounds good, but it’s vague, as I noted earlier. Are you trying to improve how agents handle complex conversations? Increase sales effectiveness?
• Prepare people to move into other roles? Is marketing a real path for your agents? Maybe IT or Accounting?

All of those skills and roles are valid, but they’re different, and they should drive different types of coaching and training.

Without that clarity, it’s really hard to connect the investment to an outcome, and when that happens, cost tends to win the conversation.

There’s also just a reality that the return doesn’t show up immediately. You’re investing time now to improve something that plays out over weeks or months. Some organizations are fine with that but others aren’t.

But if the goal is better performance and people who stick around, coaching and training can’t just be something you do when you have time. They have to be something you make time for, even when it’s inconvenient.

Making Time for Training

Table of Contents, August 2026

FEATURE ARTICLE
Making Time for Training
By Brendan Read; Q&A with Dan Smitley
How to justify having your agents off-hook/offline.

TRAINING
Every AI Handoff Is an Escalation
By Mike Aoki
Is your training ready?

AGENT PERFORMANCE
The Illusion of Competence
By Dina Vance
Why knowledge alone does not drive performance.

CONTACT CENTER EMPLOYMENT
Could AI Eliminate Entry-Level Jobs?
By Stephane Rivard
Why and how human agents can work with AI.

LEADERSHIP
“Groundhog Day” in the Contact Center
By Kathryn E. Jackson
The technology changes. The lessons don’t.

COACHING
Focus on the Basics!
By Brendan Read; Q&A with Laura Sikorski
Look beyond AI at the real needs and methods.

TECHNOLOGY DEPLOYMENT
Stop Deploying AI in Your Contact Center!
By Charlie Adams
Until you’ve read this article.

ARTIFICIAL INTELLIGENCE
Leading Through the AI Shift
By Andy Freed
How coaches and supervisors can help agents.

LEARNING
Scaling Tacit Knowledge in Contact Centers
By Todd Moran
How social learning cultivates lifelong learners.

For more than a decade, contact centers have invested heavily in chatbots and conversational automation. Their promise has always been the same: deflect volume, reduce costs, and resolve customer issues faster.

Yet despite years of tuning, tooling, and AI upgrades, a familiar pattern persists. Customers still escalate to live agents far more often than leaders expect. Resolution rates plateau. Frustration rises. Automation teams work harder, but results remain stubbornly uneven.

The uncomfortable truth is this: most chatbot failures are not caused by weak AI models or poor intent recognition. They are architectural failures.

Many contact centers are running modern language models on top of systems designed for a much earlier era of automation. But as customer interactions grow more complex, emotional, and unpredictable, those foundations begin to crack.

Across the industry, self-service programs still see a large share of customer conversations escalate to agents, especially for billing, eligibility, and exception-handling scenarios.

This article examines why traditional chatbot architectures collapse and outlines a new software design approach, micro-GPTs, that offers a more resilient, governed, and leader-friendly path forward.

Why Legacy Bots Break at Scale

Most production chatbots today are built on some combination of three familiar patterns:

• Intent trees that route customers through predefined paths.
• Keyword matching layered onto structured flows.
• Rigid dialog orchestration optimized for predictable requests.

These approaches worked reasonably well when customer needs were narrow and transactional: checking order status, resetting a password, or updating an address.

But modern contact centers deal with something very different.

Customers arrive with partial information, emotional context, and multi-step problems. They expect systems to remember what they said five turns ago, adapt when plans change mid-conversation, and recognize when self-service is no longer appropriate.

Legacy bots struggle – and break – because they were designed around classification, not reasoning.

At scale, contact center leaders see the symptoms clearly:

• Intent libraries explode as teams try to model every variation.
• Conversation flows become brittle and hard to maintain.
• Escalation rates rise despite constant tuning.
• Automation teams spend more time maintaining bots than improving outcomes.

These are not tuning problems. They are structural limitations.

Why Better AI Doesn’t Fix A Broken Design

To address these issues, many organizations have embedded more advanced large language models (LLMs) into existing bot platforms. But while surface-level understanding improves, sustained resolution does not.

Why?

Because the surrounding architecture still assumes:

• One centralized decision engine.
• Static intent definitions.
• One conversational flow that is responsible for everything.

LLMs excel at flexible reasoning. But when constrained by brittle orchestration layers, their intelligence is throttled.

The legacy orchestration layer—specifically the centralized dialog manager and the intent-routing engine—forces the model to behave like a smarter classifier rather than a reasoning assistant. Leaders often interpret this as an AI maturity problem. In reality, it’s a design mismatch.

The technology has evolved. The architecture has not.

The Micro-GPT Model: Smaller Scope, Bigger Results

Micro-GPTs represent a fundamentally different approach to conversational automation.

Instead of deploying one general-purpose chatbot responsible for everything, this model breaks automation into purpose-specific agents.

In this article, “micro-GPTs” refers to a software architectural pattern for building governed, retrieval-grounded conversational assistants, not a specific product or vendor solution.

A micro-GPT is not a smaller model. It is a bounded system, defined by these four core principles.

1. Domain-bounded. Each micro-GPT operates within a clearly defined problem space: billing disputes, shipping issues, service eligibility, plan changes. Narrow scope reduces ambiguity and improves accuracy.
2. Retrieval-grounded. Responses are generated using approved knowledge sources—policies, procedures, FAQs, and structured data—not free-form guessing.
3. Policy-guarded. Business rules, compliance constraints, and escalation thresholds are enforced explicitly, not inferred probabilistically.
4. Composable. Multiple micro-GPTs can collaborate or hand off context, allowing conversations to evolve without collapsing into a single, monolithic flow.

This architecture mirrors how contact centers already operate: specialized teams, governed processes, and clear accountability.

How Micro-GPTs Improve Resolution While Keeping Control

For contact center leaders, the appeal of micro-GPTs is not novelty. It is control.

Loss of control is one of the most common executive concerns surrounding generative AI.

Traditional bots force organizations to choose between flexibility and governance. Micro-GPT architectures eliminate that tradeoff by embedding control at the system level rather than the dialog level.

This FIGURE provides a high-level comparison of legacy chatbot architectures and micro-GPT–based systems.

With micro-GPTs leaders gain:

• Higher first contact resolution (FCR) through constrained reasoning.
• Lower maintenance overhead by updating knowledge instead of retraining intents.
• Predictable behavior under stress, with safe failure and clean escalation.
• Clear ownership tied to operational domains.

For leaders accountable for both customer experience (CX) and operational risk, these attributes matter far more than raw model sophistication.

If your bot needs constant tuning, you don’t have an AI problem: you have a design problem.

Governance is the Difference

Loss of control is one of the most common executive concerns surrounding generative AI. Ironically, micro-GPT architectures improve governance rather than weaken it.

Because each micro-GPT is policy-guarded and retrieval-grounded, organizations gain:

• Transparent decision boundaries.
• Auditable response logic.
• Explicit escalation triggers.
• Consistent compliance enforcement.

Instead of asking, “Why did the model say this?” leaders can ask, “Which policy, source, or boundary was applied?”

That shift becomes critical as regulatory scrutiny increases and boards demand clearer accountability for automated decisions.

A Practical Migration Plan

For organizations with deep investment in legacy bot platforms, replacing everything at once is neither realistic nor necessary. Micro-GPTs can be introduced incrementally.

A pragmatic migration approach looks like this:

1. Identify high-friction interactions. Focus on use cases with high escalation rates or persistent customer dissatisfaction.
2. Define clear domain boundaries. Be explicit about what each micro-GPT application can and cannot handle.
3. Ground responses in authoritative knowledge. Connect agents to approved policies, procedures, and data sources.
4. Wrap automation with governance. Define escalation rules, confidence thresholds, and handoff logic up front.
5. Integrate alongside existing systems. Measure impact before expanding scope.

This approach reduces risk, preserves prior investment, and delivers visible wins that build executive confidence.

The Path Forward

The next phase of contact center automation will not be defined by larger models or more aggressive deflection targets. It will be defined by architectural maturity: systems designed to reason within constraints, collaborate across domains, and operate transparently at scale.

Traditional bots force organizations to choose between flexibility and governance. Micro-GPT architectures eliminate that tradeoff…

Micro-GPTs are not a silver bullet. But they represent a decisive shift away from brittle, monolithic bot designs toward automation that aligns with how contact centers actually work.

For leaders planning their inbound and automation strategies, that shift may be the difference between incremental improvement and meaningful transformation.

I recently spoke with the head of a contact center at a very large company that’s been around since before mobile phones.

When I asked what his key performance indicators (KPIs) for the year were, the answer was a roundabout “none.” This executive didn’t track any metric consistently on his contact center spend. A quick scan of online reviews of his company made me believe him.

Being unorganized breaks when scale and complexity rise or when the operations go through a platform shift. And we’re in the middle of just that.

Voice AI: The Platform Shift

Voice AI is triggering an enormous operating model change inside both existing and new contact centers alike:

• Established teams are redesigning workflows.
• In some organizations, new stakeholders are now accountable for customer conversations (Product, Engineering, Ops, Finance).
• Day-to-day decisions are happening faster and at higher volume.

The bar for measurement is rising because small failures can now scale instantly. In this environment, intuition isn’t enough. You need a measurable operating system.

Pick one KPI you’d bet your job on. Don’t worry, you can change it as your priorities shift. But you need focus now.

There’s a saying in Silicon Valley: startups = growth. In other words, startups are containers around growth.

Similarly, I think, contact centers are containers around customer success. We should borrow operating principles from Silicon Valley and apply them to contact centers, especially during this voice AI platform shift.

Pick one metric that defines winning, add a few guardrails that keep you honest, and run weekly goals like the best AI teams to drive compounding improvement. I will now explore these in detail.

Set a Primary KPI

A. Redefine KPIs

KPIs are a small set of quantitative metrics that tell you how “healthy” your operation is. The “health” part matters because it’s easy to confuse activity with progress.

KPIs force you to confront whether performance is improving or if you’re just keeping busy.

Setting KPIs is important for two reasons:

1. Obtaining objective truth

Numbers don’t lie: assuming you’re measuring the right thing(s). They keep you realistic about where you are.

2. Creating a feedback loop and prioritization

These tell you whether changes you make (policy tweaks, routing, training, tooling, voice AI iterations) are working and what to do next week. If you set them wrong, you can steer into circles.

B. Know what makes a primary KPI “good”

A good primary KPI should do these two things:

1. Quantify value delivered (to customers and to the business).

2. Be a usable feedback mechanism (moves frequently enough that you can act on it).

If a metric is easy to move but doesn’t represent value, it’s a vanity metric.

C. Know what are bad KPIs (and why they fail)

Before you pick your KPI “North Star,” it helps to see common pitfalls, especially during voice AI rollouts, such as these.

1. “% automated” or “# of AI calls handled”

Why it’s tempting: it’s easy to measure and looks like progress.

Why it’s bad: it measures activity, not outcomes. You can increase automation while customer success declines.

How it breaks: teams over-optimize containment; escalations become more frustrated; repeat contacts rise.

2. CSAT/NPS as the steering wheel

Why it’s tempting: executives recognize it.

Why it’s bad: it’s lagging, noisy, and biased. It’s great as a check engine light, not as a steering wheel.

How it breaks: teams argue about survey methodology instead of fixing the underlying operation.

Rule of thumb: if the metric can go up while customers get worse outcomes, it cannot be your primary KPI.

D. Select better KPIs

Pick one KPI you’d bet your job on. Don’t worry, you can change it as your priorities shift. But you need focus now. Too many KPIs cause fatigue, debates, and eventually non-use of the system.

Your primary KPI should:

• Move often (weekly is ideal).
• Tie directly to your business outcome.
• Capture what success looks like to you for the near future.

If you pick right, it shouldn’t feel reductive; it should feel focused.

Install Guardrails

Once you’ve chosen a primary KPI, pick two-three secondary health metrics. These are your operational guardrails: metrics that prevent you from “gaming” the primary number. Think of them as your contact center version of product “retention” and “quality” metrics.

Here is an example of a strong default set:

1. Repeat Contact Rate (7–14 days)

If this rises, your “resolution” is probably brittle.

2. Handoff Success Rate (a.k.a. escalation friction)

Measure whether escalations land cleanly: the human receives full context, the customer doesn’t have to repeat themselves, and time-to-first-human stays within your SLA.

3. Critical Defect Rate (compliance errors/over-refunding/etc.)

For refunds, cancellations, identity verification, regulated disclosures, or anything that can create liability.

Create an Operating Loop

A. Set weekly goals

There’s another Silicon Valley saying: do things that don’t scale.

In contact centers, that might look like:

• Listening to 20 calls yourself this week.
• Spending an hour rewriting the escalation policy yourself for the top two intents.

These aren’t long, glamorous projects with names, but they move the needle.

B. Aim for 1% – 2% weekly improvement

Set weekly goals for your primary KPI and target 1%-2% improvements. Those improvements compound (see CHART).

For example, a sustained 2% weekly improvement compounds dramatically over a year. Compare this to a large capstone project: an academic term that in business means a long, multi-team, big corporate effort that absorbs a lot of time: which doesn’t show any meaningful KPI movement until late, if at all.

That means you end the year at about 280% of baseline, without relying on a single giant capstone project. A small team (or even one operator) can often find a 2% win this week.

C. Draw the loop

1. Make the target visible

Draw a forward-looking chart for the next ~12 weeks. Put it somewhere the team sees weekly. Update it every week. Airbnb founders are famous for drawing it on the bathroom mirror of their offices, but you don’t have to go that far.

2. Set weekly prioritization

Every week, list the changes that could move the primary metric. Pick the one or two bets that are most likely to hit the weekly target.

3. Ship, measure, learn

Voice AI makes iteration fast if you have instrumentation in place to analyze calls and outcomes (intent tags, outcomes, recontacts, escalation reasons, and failure clusters).

Voice AI is bringing about not just a staffing change but a new operating model.

Early course correction and six months of focused engagement can pay dividends for years. You’ll build operational muscle for all members in your organization. You should also review your primary and secondary KPIs monthly to make sure they reflect the stage of business you’re in now.

When You Miss…

A. Constantly address the limiting factor

Missing a week (or two) is fine if you know why. Use “5 Whys” until you reach something actionable. 5 Whys is a simple root-cause method where you ask: “Why did this happen?” repeatedly (usually 5 times) until you get from the symptom to a specific, fixable cause. For example:

Problem: First Contact Success dropped from 62% to 54% this week.

1. Why did First Contact Success drop?

Because more calls were escalated to humans mid-flow.

2. Why were more calls escalated?

Because the voice AI couldn’t complete identity verification reliably.

3. Why couldn’t it complete identity verification?

Because the verification timed out so the agent started failing.

4. Why did the API time out?

Because traffic spikes caused rate-limiting and higher latency.

5. Why did rate limiting hurt us so much?

Because we had no retry mechanism and no fallback path (e.g., SMS verification).

Early course correction and six months of focused engagement can pay dividends for years.

So, the actionable fix is to ask the engineering team to add retries and a fallback verification path. Having proper instrumentation and call analytics in place helps you diagnose issues at startup speeds.

B. Understand if it is a metric problem versus an execution problem

When a KPI doesn’t move, it’s usually one of two things:

1. Metric problem: the definition is wrong or doesn’t reflect value.

2. Execution problem: you’re measuring correctly but not addressing the constraint.

It’s important to know what to fix.

C. Realize that instrumentation matters more in the voice AI era

Call analytics are no longer optional. Modern large language model (LLM)-based systems make it feasible to:

• Auto-tag intents and outcomes.
• Cluster failure patterns.
• Quantify policy violations.
• Surface the 20 calls that best explain why KPIs moved.

Finally…

Voice AI is bringing about not just a staffing change but a new operating model. The teams that win will treat their contact center like a modern growth organization:

• One metric that defines winning.
• A few guardrails that keep optimization honest.
• Weekly targets and a repeatable operating loop.
• Fast diagnosis powered by call analytics.

We can take lessons from the Silicon Valley playbook to improve our organizations and get compounding gains in customer success.

For years, many organizations treated customer service as a break-fix function. A customer had a problem, the contact center solved it, and efficiency meant speed, volume, and cost control.

However, with the advancement and incorporation of AI, that definition is much too small. World-class organizations are instead empowering teams to act as customer success advocates, focusing their efforts on proactive contact to develop more robust relationships between customers and brands.

In a generative model, every interaction contributes to future value…improving the entire customer lifecycle.

This work extends past fixing immediate concerns and leans into strengthening relationships, protecting account health, encouraging repeat business, and identifying opportunities for long-term growth.

Technology will accelerate this change, but the great differentiator is the human element, such as insight, empathy, and judgment.

The Four Stages of Service Evolution

This shift begins with culture and a firm understanding of CRM management. Service, and the CRM systems that support it, evolves through four distinct stages that map to when and how customer engagement occurs, which are before, during, and after contact.

1. Reactive Service: The Starting Point

Reactive service fixes what breaks. Customers reach out when something goes wrong, and agents work through queues that are focused on responsiveness and throughput. The problem is that these environments often reward closing the interactions rather than closing the loops.

Reactive service, by definition, starts after the customer has already experienced friction. Even when the agent resolves the issue correctly, the customer has still paid a tax in time, uncertainty, and emotional energy.

2. Predictive Service: Anticipating Needs

Predictive service operates before and during customer contact; it anticipates what is likely to break. Leaders treat service demand as something that can be understood and forecasted, not merely endured.

Before contact, organizations use data, AI models, and historical patterns to forecast likely issues, identify at-risk customers, and prepare the right responses.

During inbound or outbound interactions, predictive systems surface context in real time, helping agents understand intent, next-best actions, and potential outcomes.

AI is changing what CRM intelligence looks like before a customer interaction ever happens. Modern systems can synthesize account data, usage patterns, and prior interactions to give teams a deeper understanding of customer context before the customers ever experience friction.

3. Proactive Service: Preventing Disruption

Proactive service prevents issues from becoming disruptions. Instead of waiting for customers to report problems, proactive organizations intervene earlier. They correct errors before they trigger callbacks and deploy fixes before failures become outages.

The metrics that matter are the ones that measure avoided friction, such as repeat contacts, reopens, transfers, and customer effort.

4. Generative Service: Creating Future Value and Deepening Relationships

Generative service operates after and across interactions, using insights from every touchpoint to create future value and opportunities to strengthen the bonds with the customers.

It transforms the service from a moment of resolution to a continuous feedback engine that informs product design and policy decisions and improves digital journeys. This requires leaders to treat every interaction as data and an emotional touchpoint, rather than just a ticket.

In a generative model, every interaction contributes to future value, ensuring the organization focuses on high- context, relationship-building work and improving the entire customer lifecycle.

These stages are not isolated; they build on one another. Organizations typically operate across multiple stages at once. But maturity is defined by how far upstream they can move: from reacting to issues to predicting, preventing, and ultimately learning from them.

Overcoming the Barriers to Growth

Several common barriers can stall progress:

• Service Delivery versus Service Learning. Many contact centers produce insights but have no clear paths to operationalizing them or have owners accountable for translating what service learns into changes in product or policy.
• The Cost Center Mindset. As long as service is framed primarily as a cost center, investments in prevention and insight generation will always compete against near-term efficiency mandates.
• Metric Mismatch. If metrics prioritize speed over ease, and volume over value, the organization will optimize the wrong things.
• The Human Barrier. Service evolution asks leaders to protect employee capacity and emotional energy, not just productivity. You cannot scale proactive or generative service on a burned-out workforce.

The Path Forward

A practical way to lead this journey is to establish a roadmap and treat each stage as a set of explicit requirements.

• In the reactive stage, the cultural need is clarity. Customers want competence when something goes wrong.
• In the predictive stage, the cultural need is foresight and preparedness. Leaders must trust data enough to act on it, equipping teams with the tools and context to anticipate needs rather than simply responding to them.
• As you move to the proactive stage, the cultural need shifts to curiosity and cross-functional collaboration. There is also the need to build customers’ trust, like ensuring that the contacts are not seen by them as fraudulent (see BOX below).
• In the generative stage, success requires trust in service insights and trust in frontline judgment.

Proactive and predictive customer service, enabled by disciplined CRM management, is how the promise of experience-led growth becomes operational.

CRM is the mechanism that connects proactive service to business outcomes by linking customer context to predictive insight and orchestrating timely intervention.

Experience-led growth does not begin with selling more. It begins with listening, anticipating, and acting before customers are forced to ask, accomplished by integrating humans with technology. For this is how you build successful and valuable customer relationships.

Over the past decade, texting has become an essential support channel for modern contact centers. For good reason: it’s fast, convenient, asynchronous, and allows you to connect with customers via a device that’s rarely out of their reach.

But today, audiences expect a richer, more immersive, and more trustworthy texting experience. Fortunately, there is a newer protocol called rich communication services (RCS) that promises to deliver just that.

Here’s why contact center leaders should familiarize themselves with RCS as soon as possible.

What Is RCS?

RCS is a communications protocol that powers more interactive and modern texting experiences directly within a contact’s native messaging app. It supports high-resolution images and videos, customizable buttons, typing indicators, read receipts, and more.

RCS rivals the look and feel of conversations in over-the-top (OTT) messaging applications like WhatsApp without requiring users to download anything new. And since you can communicate more information (and even share files), it can help increase agent efficiency while slashing resolution times.

Beyond those engagement and productivity-boosting features, RCS also helps tackle one of text messaging’s biggest weaknesses: earning a recipient’s trust.

Thanks to verified sender profiles, where senders have been verified and approved by the individual telecom carriers and Google, messages arrive via a branded RCS agent, which displays a brand’s logo, colors, and a verified checkmark (rather than a random, faceless phone number).

In an era of rising impersonation scams and text-based fraud, having this visual assurance can give contact center agents instant credibility.

RCS…supports high-resolution images and videos, customizable buttons, typing indicators, read receipts, and more.

Plus, RCS lets you track more performance metrics than traditional texting. In addition to delivery stats, you’ll have insight into behavioral analytics like opens, clicks, and conversion rates. This can be incredibly useful when you want to determine which types of message content drive the best results.

Why Is RCS Suddenly So Important?

We say RCS is newer because, although it’s been around for a while (mobile users in the U.K. and EU have been using RCS for almost a decade); the North American rollout (U.S. and Canada) has been much slower.

Fortunately, now that major carriers have aligned on standards and Apple has given the green light to RCS with the release of iOS 18, we’ve seen a massive wave of adoption.

Additionally, as businesses clamor to adopt the new protocol and beat out their competition, it has recently become a popular topic of conversation in marketing circles.

In other words, if it seems like everyone is suddenly talking about RCS, you’re not imagining it. And we expect interest to grow even more in the second half of 2026 and into next year.

How RCS Compares to Other Channels

As a contact center pro, you’re probably wondering why you need RCS when you already have SMS texting, email, voice calls, and in-app messaging at your disposal. Do you really need to train your agents on yet another communication tool?

The truth is that RCS isn’t a new channel to master, nor is it a replacement for any of the channels you’re already using. Instead, it’s a complementary method and an essential part of any modern omnichannel strategy.

Here’s how it compares to other channels.

SMS

SMS is the universal texting protocol, meaning it’s supported by nearly every carrier and mobile device worldwide. But while SMS supports only text-based messages, as I noted earlier, RCS also supports high-quality visual content, more interactive features, and verified brand profiles (also see FIGURE).

Email

Email can be useful for communicating a large amount of information at once, but texting drives significantly higher open rates. And because RCS also allows you to include attachments, it’s an excellent tool for real-time conversations and more actionable follow-ups.

Voice

There’s no denying voice calls are still essential for those more emotion-driven, high-stakes moments. But in situations where customers are looking for a more asynchronous experience (without hold times), RCS offers contact centers a low-cost, high-engagement way to meet customer needs.

In-app messaging

Apps like WhatsApp and Facebook Messenger can be great for supporting customers who use those tools regularly. But RCS delivers equally immersive experiences for contacts who prefer their native texting apps.

SMS + RCS: The Future Is Hybrid

As RCS gains traction in the business world, more of our customers have been asking whether it will replace SMS. And our constant refrain is loud and clear: no. Not anytime soon.

Although RCS coverage has grown substantially over the past couple of years, it’s still not supported by every carrier and device.

So, if you were to rely entirely on RCS for texting, you’d inadvertently limit your reach. (We imagine the customers who still depend on SMS wouldn’t be too happy to discover they could no longer engage with support teams via text.)

Since SMS is universal, it’s important that you don’t count it out. Instead, we always recommend a hybrid approach where you set up SMS as your fallback.

Here’s what that looks like in action:

Suppose an agent supports a customer via a voice call and promises to follow up with some additional documents sent via text. They message the customer via RCS with an image carousel, and each image links to a separate PDF.

If the customer can’t receive RCS messages, the system will fall back to SMS and send the message in text-only format.

Depending on the texting platform your contact center uses, you may be able to set up a fallback version of the message. For example, since an SMS recipient wouldn’t be able to receive the carousel linking to PDFs, you might include a link to a page that contains all of the files.

SMS fallback then serves as a safety net to ensure everyone can receive your message, regardless of their device or carrier.

RCS Challenges

RCS offers some incredible features and benefits, but it isn’t perfect. Just like all communication methods, it has a couple of limitations and drawbacks.

Availability and reach

RCS adoption is growing fast. But, because it’s not yet universally adopted, you will likely always have at least a small portion of your audience that can’t receive these messages. (Which is why it’s critical you use SMS as a fallback.)

Operational complexity

The visual richness you enjoy with RCS also takes additional effort and tech. Creating these assets often requires careful coordination with other departments, and you’ll also need to make sure you have the right texting platform in place.

Although RCS coverage has grown substantially over the past couple of years, it’s still not supported by every carrier and device.

Ideally, you’ll want to work with a provider that has expertise in both SMS and RCS and offers a user-friendly interface with a shorter learning curve (so you can get agents up to speed more quickly).

RCS Success Best Practices

Since RCS is still relatively new, contact centers are often unsure how to get started. Should you dive in headfirst or take a slower, more methodical approach?

The answer depends on your resources, immediate goals, and the clients you serve. But, in general, here are three things I always recommend.

1. Get your verified sender status ASAP

Generally, it takes eight to 10 weeks to get approval from the major telecom carriers and Google for your RCS agent; some carriers may take longer than others.

We always recommend that our customers begin the registration process as soon as possible, even if they aren’t quite prepared to launch an RCS campaign. This way, once they’re ready to begin using RCS in earnest, they can hit the ground running with a verified, branded presence.

2. Plan for a progressive rollout

As with any new communication method or channel, it’s a good idea to test your effort with one or two use cases first, such as order tracking, customer onboarding, or a short-term campaign.

This allows you to familiarize yourself with RCS and work out any kinks in your process before you commit to it across the board.

3. Invest in the right platform and infrastructure

A lot of a company’s success with RCS comes down to having the right tools and technology in place.

Choosing a reliable platform will make a tremendous difference in how quickly and easily you can spin up your RCS program. And a great RCS partner will help you handle the registration process and support you in addressing security and compliance, too.

For contact center leaders, the message is clear: RCS adoption is accelerating, and customers are hungry for richer, more engaging, and more trustworthy experiences. Organizations that invest in the right expertise and technology today will be best positioned to take the lead in the months ahead.