The contact center industry has always been defined by change: shifting budgets, evolving leadership priorities, expanding product portfolios, new technologies, and regulatory pressure.

These complex changes, gathered from conversations and research, will not only continue throughout 2026, but they will also likely accelerate.

At the same time, the role of the contact center itself is being fundamentally redefined. Long viewed as a cost center optimized for efficiency, today’s contact center is becoming a strategic hub for high-value customer interactions.

As routine inquiries migrate to self-service channels and AI-powered agents, human representatives are left to navigate the most complex, emotionally charged, and critical customer moments. But a higher-skilled function requires a more innovative approach to employee development.

The traditional onboarding and learning management system (LMS) model, where agents are front-loaded with information and then reactively coached or mentored, is no longer sufficient. To thrive, organizations must move from episodic training to a culture of continuous, social, and lifelong learning.

The Challenge of Tacit Knowledge

The difference between a “good” agent and an exceptional one isn’t limited to their mastery of product knowledge and available tools. Often, it lies in tacit knowledge: the intuitive judgment, empathy, and situational awareness developed primarily through experience.

Tacit knowledge shows up in subtle ways:

• Knowing when a frustrated customer needs reassurance before resolution.
• Recognizing vocal cues that signal an impending escalation.
• Understanding when to bend processes in order to preserve trust.

But these skills are not easily documented, standardized, or delivered through periodic coaching sessions or quarterly slide decks.

And that’s the challenge. Tacit knowledge is notoriously difficult to codify. An agent cannot learn the right tone for a distressed VIP or the timing of a strategic concession from an LMS module or on-the-fly mentoring.

Sustaining a culture of lifelong learning requires a parallel shift in leadership.

Instead, these capabilities are learned through socialization: observing experienced peers, receiving feedback on live interactions, and reflecting on real customer moments.

In an environment where AI increasingly handles more rudimentary tasks, human judgment becomes one of the contact center’s most valuable (but least scalable) assets.

Scaling Human Judgment

One of the most effective ways to accelerate the development of tacit knowledge is through mentorship and structured peer learning.

Consider a tier-two support agent handling a high-risk “save” for a long-tenured customer threatening to churn over a billing issue.

A traditional LMS can teach the refund policy. But it cannot teach the agent how to read the customer’s emotional subtext to decide whether to lead with empathy, a technical explanation, or immediate remediation.

But when organizations intentionally connect newer agents with experienced “super agents” through social learning, they are doing more than transferring knowledge; they are scaling judgment. The instincts of top performers become accessible to the broader workforce.

Research from Brandon Hall Group supports this approach, showing that organizations embedding mentoring into their learning strategies see a 41% improvement in time-to-productivity and significantly higher readiness for leadership roles.

In contact centers, this translates to shorter “nesting” periods, fewer supervisor escalations, and a more robust pipeline for future floor managers.

Mentorship also addresses the “quiet quitting” epidemic. When a new agent is thrown into a complex queue with only a search bar for help, they feel isolated and disposable.

Conversely, when development is continuous and social, it creates a sense of belonging. When agents feel they are being invested in as professionals rather than monitored as units of production, they are far more likely to take ownership of customer outcomes.

The Manager’s New Role: From Monitor to Coach

Sustaining a culture of lifelong learning requires a parallel shift in leadership. In the traditional model, supervisors functioned primarily as monitors, tracking average handle time (AHT), adherence, and ticket volume. But in the modern contact center, their role evolves into that of a “Learning Architect.”

Rather than policing performance, these leaders design the environment where learning happens every day. This can take many forms:

• Collaborative call reviews replace isolated scorecard discussions. Here, teams regularly dissect challenging interactions together, examining decision points and alternative approaches.
• Metrics evolve beyond efficiency to include indicators such as time-to-confidence on new queues, reduction in repeat escalations, or peer contributions to shared knowledge spaces.
• Mistakes become assets. A failed save or difficult escalation is treated as a case study, anonymized, shared, and discussed, rather than a purely disciplinary event.

When teams feel safe surfacing missteps alongside wins, knowledge transfer accelerates. The contact center shifts from a place of individual execution to one of collective improvement.

Overcoming the Scaling Barrier

Despite its benefits, mentorship-driven learning has historically been difficult to scale. Programs often falter due to unclear objectives, burnout among top performers, or uneven access that unintentionally reinforces inequity.

The organizations that overcome these barriers focus on reducing friction rather than adding programs. They use technology not to replace the coaches, but to support the mechanics of learning:

• Automating mentor-mentee matching based on skill gaps.
• Enabling asynchronous, in-the-moment feedback.
• Capturing frontline “wins” before they disappear into individual experience.

When knowledge sharing becomes embedded in daily workflows rather than confined to formal sessions, mentorship stops being an extra burden and starts functioning as operational infrastructure.

The Future of the Function

As we move further into 2026, the contact center is no longer the back office; it is the front line of brand strategy. The organizations that thrive will be those that treat their frontline as a high-skill professional guild, supported by the tools and culture of lifelong learning.

By focusing on the transfer of tacit knowledge and embedding mentorship into the daily workflow, we don’t just improve customer experience (CX); we elevate the human experience of the people delivering it. The future of CX isn’t just about better technology; it’s about better-supported humans.

“Revolution” is a word that gets used often.

• The Industrial Revolution.
• The Internet Revolution.
• Now, the AI Revolution.

In many cases, the term feels overstated. In this case, it does not.

AI is already reshaping how contact centers operate. What makes this moment different is not just the pace of change, but the nature of it. Previous waves of technology have largely improved efficiency. This one is changing roles.

Generative AI agents can handle conversations, generate responses, and improve over time. That shifts the role of the human agent from executing work to overseeing, refining, and stepping in where judgment is required. Here’s how:

• Agents are no longer just responding. They are validating and deciding.
• Supervisors are no longer just managing people. They are managing systems and outcomes.
• Coaches are no longer just improving performance. They are helping redefine what “good” looks like.

That is a different kind of change. And it requires a different kind of leadership.

Recognizing the Human Impact

When change reaches this level, it is not just operational. It is personal.

For many contact center employees, confidence is built on experience. Knowing what to say. Knowing how to respond. Knowing how to solve the problem.

Introducing AI into that equation can create uncertainty, even if the long-term impact is positive:

• Am I still needed?
• What does my role become?
• Where do I add value?

These are not abstract questions. They are immediate and real.

If leaders do not address them directly, even the best strategy will struggle to gain traction.

What makes this moment different is not just the pace of change, but the nature of it… This one is changing roles.

The starting point is not the technology. It is the audience.

There is also a practical reality behind this. When people feel anxious, their ability to process information drops. They are not ignoring the message; they are not fully hearing it. That is why leaders who move too quickly to strategy often find themselves repeating the same message without progress.

Addressing the emotional side of change is not a “soft” step. It is a necessary one.

What’s Different About Generative AI

It is worth being explicit about what makes this shift different:

• Traditional automation removed steps.
• Generative AI changes decision-making.

That distinction matters.

In the past, technology might reduce handle time or route calls more effectively. But the human still owned the interaction.

Now, AI can participate in the interaction itself. It can draft responses, suggest next steps, and in some cases resolve issues independently.

That creates a new dynamic where humans are no longer the sole owners of customer conversations. Instead, they are also responsible for:

• Evaluating AI-generated outputs.
• Intervening when nuance or judgment is required.
• Managing exceptions and escalations.
• Ensuring the overall quality of the experience.

For supervisors, the shift is just as significant. Performance is no longer just about individual agents. It includes how effectively AI is being used, where it is falling short, and how the system improves over time.

For coaches, development shifts from correcting behavior to building new capabilities. Critical thinking. Decision-making. Knowing when not to rely on AI.

This is not a small adjustment. It is a redefinition of the work.

A Practical Leadership Approach

Communicating and leading through AI adoption requires intention. The following principles provide a practical framework for guiding teams through this transition.

1. Keep the audience point of view

Change is often experienced as loss, even when it leads to improvement. If people are anxious, they are not fully processing information. Addressing that emotional response is essential to effective communication.

2. Use reassuring language

Language shapes perception. Framing AI as a “next chapter” or an opportunity for growth creates a different reaction than positioning it as a disruption or replacement.

3. Reframe uncertainty as risk

Uncertainty creates hesitation. Risk creates clarity. Leaders should articulate both the opportunity and the cost of inaction, particularly as customer expectations continue to evolve alongside AI capabilities.

4. Provide a sense of control

While organizations cannot control the emergence of AI, they can control how it is implemented. Involving employees in testing, feedback, and rollout decisions helps reduce resistance and increases engagement.

5. Anchor in the familiar

People respond better to change when it connects to something they already understand. Aligning AI initiatives with existing language, workflows, and structures can ease adoption.

6. Model conviction

Uncertainty from leadership amplifies uncertainty across teams. Leaders do not need all the answers, but they do need to be clear and consistent about direction.

7. Act with transparency and honesty

Credibility is built through honesty. Acknowledging what is not yet known, while outlining how answers will be found, reinforces trust, and supports a culture of learning.

8. Use unifying language

Framing the transition as a shared effort reinforces alignment. “We” is more effective than “you” when navigating change of this scale.

9. Leverage communication channels

Consistent, multi-channel communication is critical. In the absence of information, people will fill the gap themselves, often with incorrect assumptions.

10. Stay close to the front line

Leadership visibility matters. Direct engagement with agents and supervisors provides insights that cannot be captured through reports alone and helps leaders adjust in real time.

Clarifying Where Humans Add Value

As AI becomes more capable, one of the most important responsibilities of leadership is clarity.

Not just about what is changing, but about what is not.

There are aspects of the work that remain distinctly human:

• Judgment in complex or ambiguous situations.
• Empathy in moments that require emotional understanding.
• Navigating conversations that do not follow a predictable path.
• Building trust with customers over time.

AI can assist in each of these areas. But it cannot replace them. In many ways, these skills become more important as AI handles more of the routine work. The role of the agent shifts up the value chain.

That is a positive shift. But only if it is clearly understood. If left unspoken, people will default to assuming their role is being reduced, not elevated.

Conclusion

AI will continue to change the contact center industry, just as previous waves of technology have. What distinguishes this moment is that it is not only changing how work gets done, but how people define their role in doing it.

That distinction is where leadership matters most.

As AI becomes more capable, one of the most important responsibilities of leadership is clarity.

Organizations that succeed will not be those that simply adopt AI tools. They will be the ones that communicate clearly, move with intention, and bring their people along through the transition.

Because ultimately, this is not just a technology transformation.

It is a leadership one.

There has never been more enthusiasm for AI in the contact center. There has also never been more waste. The gap between those two facts is where most organizations currently live.

AI deployment in contact center operations is failing, not because the technology is flawed, but because the thinking behind it is.

Boards are mandating it. Vendors are overselling it. And operations leaders, caught between pressure from above and skepticism from their teams, are implementing it in ways that solve nothing and sometimes actively make things worse.

This is not an argument against AI. It is an argument against deploying it badly.

The FOMO Trap is Real and Expensive

The pressure to “do AI” has become almost indistinguishable from the pressure to be seen doing it. Announcements on LinkedIn. Slide decks with capability roadmaps. Vendor partnerships marketed as transformation.

Behind a significant number of those announcements is a story that goes quietly untold:

• The chatbot that increased customer effort.
• The AI routing layer that confused agents.
• The automation project that required a manual workaround within six weeks.

Fear of missing out (FOMO) is not a strategy. When the stated business case for AI investment is “our competitors are doing it,” that is the moment to pause, not accelerate.

The contact centers seeing genuine ROI from AI are not the ones that moved fastest. They are the ones that were most deliberate. They started with a problem, not a product. They defined what success looked like before they signed anything. And they measured relentlessly.

The question leadership should be asking is not “Do we have AI?” It is “What specific friction are we eliminating, for whom, and how will we know we have succeeded?”

AI as a Replacement Strategy Costs More Than Money

Perhaps the most damaging framing in this space right now is AI as a headcount reduction tool. It is the wrong objectiveand it produces the wrong outcomes.

Customers are not asking for fewer human interactions. They are asking for better ones. They want their issues resolved quickly, with minimal effort, by someone or something that understands context.

The contact centers seeing genuine ROI from AI…are the ones that were most deliberate.

When AI helps deliver that, it succeeds. When it stands in the way of it, it fails regardless of how sophisticated the underlying model is.

Equally, when agents feel threatened by the technology around them, adoption stalls. Workarounds proliferate. The operational complexity that AI was meant to reduce actually increases.

The organizations consistently delivering results with AI are building it as a layer of support, not as a replacement for their staff. Like with:

• Real-time knowledge delivery.
• Automated after-call work.
• Intelligent quality scoring that coaches rather than polices.

These applications free agents to bring genuine skills to complex or emotionally sensitive conversations.

When AI is framed as a partner to the workforce rather than a threat to it, something predictable happens: people start using it. And when people use it, it improves. That is the cycle you want.

Understand Processes Before Automating Them

AI cannot fix a broken process. It can only make a broken process automated and appear faster, and therefore more visibly broken.

Organizations that attempt to implement AI on top of poorly understood or poorly documented processes end up automating the dysfunctions. Inconsistent handling becomes consistently inconsistent. Gaps in knowledge base content become systematically delivered wrong answers.

If you cannot map your most common customer journeys end to end, you are not ready to automate them. Process clarity is not a precondition that can be revisited later. It is a prerequisite.

Before any AI discussion begins, operations leaders should be able to answer the following:

• Where in the customer journey is the effort highest and why?
• Where are agents spending the most time on tasks that do not require human judgment?
• Where are quality inconsistencies concentrated and what drives them?
• What would a 10% improvement in first contact resolution (FCR) actually be worth?

These questions are not academic. They are the foundation of a credible AI business case and the baseline against which any deployment will ultimately be judged.

The Financial Case Must Be Honest

AI investment is frequently under-costed and over-benefited in internal proposals. Licensing tends to be the figure in the model. Implementation complexity, change management, training, integration with legacy systems, and ongoing optimization rarely feature with appropriate weight.

The result is that projects land with an ROI calculation that looked strong at approval and looks very different (and not in a good way) 18 months later.

A credible financial model for AI in the contact center should account for the full cost of deployment and the full timeline to value. For most organizations, meaningful ROI from AI is a 12-to 24-month journey, not a 90-day (yes, next quarter) one. Proposals that suggest otherwise deserve scrutiny.

The question is not whether AI has a financial case. It often does, and a strong one. The question is whether your specific deployment, in your specific environment, against your specific baseline has one. Those are different questions.

People Transformation Is Not Optional

Technology transformation without people transformation is not transformation. It is an installation. And there are serious resulting consequences for not incorporating your staff into the process:

• Agents who do not understand why AI is being introduced will not trust it.
• Team leaders who are not equipped to coach in an AI-augmented environment will revert to old behaviors.
• Operations leaders who do not have visibility into how AI decisions are being made cannot act on them.

The consequences do not stay internal for long. When agents disengage, customers feel it.

• Interactions become transactional.
• The warmth and ownership that defines a genuinely good contact center experience quietly erodes.
• Satisfaction scores drift, repeat contacts rise, and the quality consistency AI was supposed to deliver never materializes because the human layer it depends on is working around it rather than with it.

At a business level the damage compounds.

• Attrition increases, because people leave environments where they feel threatened or undervalued.
• Experienced agents take institutional knowledge with them.
• Recruitment and training costs accelerate from hiring and onboarding replacement staff.

Consequently, the AI investment meant to generate ROI ends up sitting on top of a disengaged workforce. The promised, hoped-for efficiency gains never arrive.

There is also a subtler risk that rarely surfaces in boardroom conversations. When staff are excluded from transformation, they become passive recipients of change rather than active participants.

As a result, they stop flagging when AI is producing wrong answers. The feedback loop that makes AI better over time ceases to exist: if it had a chance to be formed in the first place.

The organizations most focused on reducing their dependency on people through AI consistently end up discovering that getting the people part right matters more than ever.

When conditions are right, AI in the contact center…is a step change.

The change management component of AI deployment is consistently underfunded and underweighted. But in mature deployments, it is arguably the most important element.

The technology is increasingly commoditized. The ability to embed it in a way that actually changes how people work is not.

That fear starts earlier than most leaders realize. Long before go-live, agents are reading the headlines, listening in on vendor presentations, and drawing their own conclusions.

In the absence of honest communication, those conclusions are rarely positive. By the time the technology lands, resistance is already baked in.

Transparency matters here. Agents who understand what AI is scoring and why, and who can see how that connects to their own development, engage with it differently than those who experience it as an opaque monitoring layer.

Leaders also must address the replacement question directly rather than hoping it doesn’t come up, but it always does.

Organizations that acknowledge that fear, and which demonstrate through action that AI is there to support rather than threaten, see adoption and performance outcomes that those that stay silent simply do not.

When Should You Deploy AI?

When conditions are right, AI in the contact center is not an incremental improvement. It is a step change. Faster resolution. More consistent quality. Richer customer insights. Agents who are better supported and less worn down by repetitive work. The conditions that make deployment worthwhile are reasonably consistent:

• You have a clearly defined problem that AI is genuinely suited to address.
• Your processes are understood well enough that automating them will not amplify existing dysfunction.
• You have built a financial case that accounts for full costs and realistic timelines.
• Your people strategy is as developed as your technology strategy.
• You have defined success metrics before you start, not after.
• Leadership is aligned on the objective: AI as an enabler of better human performance, not a substitute for it.

When those conditions exist, deploy confidently. When they do not, the most valuable thing an operations leader can do is say so.

The Real Question Has Always Been the Same

How do you use AI in a way that makes the work more human and the experience more effortless?

That is not a technology question. It is an operational and cultural one, and it requires the same rigor, honesty, and clarity of purpose that any serious transformation demands.

The contact centers that will look back on this period as a turning point are not the ones that deployed AI first. They are the ones that deployed it properly.

There is still time to be one of them.

Today’s contact centers are faced with meeting, and ideally surpassing, customer expectations that are arguably being impacted by a tumultuous economy in an uncertain world: where their spending often fails to mask their underlying anxieties.

Adding to this are the promises, challenges, and, yes, fears of AI, for both customers and contact center employees.

In this environment, how do contact centers best coach and train their agents? To find out, I had a virtual conversation with consultant and Advisory Board member Laura Sikorski.

Q. What trends are you seeing in coaching and training? Have these changed/are there new ones that have emerged since last year?

I wish my crystal ball could counteract everything I am seeing. I was shocked to read that AI-enabled software appears to be in the forefront of managements’ time savings and software-generated agent assistance.

According to Delenta, a digital coaching platform, “The best coaching platforms are now AI-enabled, automatic note-taking, personalized content generation, intelligent scheduling. 56% of coaches now use AI tools in some capacity to track progress and provide personalized support.”

“…the best strategy is to hire the right people with the right expertise who understand empathy and who have great listening/communication skills.” —Laura Sikorski

Guess I am “old” school in my thinking that a coach becomes a mentor for each employee’s tenure.

Here’s another connected, related disturbing trend: the amount of hours coaches spent on what used to be actual physical “paperwork” (but done today mostly on computers and smartphones).

International Coaching Federation industry analysts project that the global coaching market will reach $5.8 billion by the end of 2026. Coaches who have adopted dedicated management platforms report reclaiming an average of 10-plus administrative hours per week.

Why are we looking at coaching administrative hours? Don’t we want our agents to feel that their coaches have their needs and wants at the forefront to help them be the best at their jobs, NOT what AI thinks they need?

For me, the best strategy is to hire the right people with the right expertise who understand empathy and who have great listening/communication skills.

It comes down to getting back to basics. A training program should include (and cover):

• A classroom/lab/reference library/work floor
• A schedule/detailed curriculum/role playing/tests
• Manuals/workbooks
• Soft skills
• Customer service
• Telephone etiquette
• Cross-training
• Follow-up training

A formal monitoring/coaching policy should be in place and detail:

• Performance guidelines
• Tools and measurements
• How reviews are scored and will be communicated.

Q. Are you seeing any changes in the skills that contact centers require agents to have and be trained on as compared to what they were hired for?

Fortunately, no. You need to hire staff who are skilled for the job they will do. Here are some interview questions that will help:

1. Teamwork

• Tell me about one of the toughest groups you have had to work with to achieve a task or objective.
• Tell me about a time when you were able to help a team member.
• Tell me about a stressful interaction you had with a team member. How did you handle it?
• Describe a situation when you felt a team member was not contributing enough. What steps did you take?

2. Customer Service

• Describe a situation when you had to deal with demands from an unreasonable customer.
• Tell me about when you went the extra mile for a customer.
• Describe a situation when you had to calm down a very angry customer and how you remained composed.
• Describe a complex problem you recently had to sort out for a customer.

3. Problem-Solving and Judgement

• What would you do if the customer is not happy with your answer or solution?
• How do you deal with a customer’s question that you do not know the answer to?
• Give me an example of a decision you had to make quickly while dealing with a customer recently.
• What steps did you take when you found out a problem was a result of inefficient service by your company or colleagues?
• What will you do when our computer systems shut down and you are speaking with a customer on the phone?

Q. Is AI changing coaching and training, and if so, how? For example, there has been much talk about integrating/having AI agents work with human agents.

Let’s be real about AI and human integration. By this I mean that the two will be interacting live on the same interaction. It is VERY expensive to implement; I do not foresee this happening in centers below 250 agent-seats.

CTI screen pops available in current CCaaS integration software has been around for years to help assist agents on a call and it works quite well.

Q. Have there been any changes in the skills sought from coaches, trainers, and supervisors? In what they are tasked with? how they are selected, trained, and managed?

I have not witnessed any skill changes required for these jobs and hope we never will.

Keep in mind HR must continue to look for management staff who can answer these behavioral competency questions without any hesitation:

• What does customer satisfaction mean to you?
• How would you improve customer satisfaction?
• What are the key attributes of a contact center agent?
• How do you handle work pressure?
• What is the key to a successful contact center?

Q. What are your best practices recommendations for contact centers?

Having 40 years of experience in the industry it is a bit daunting to reply to this question. Between articles, TV appearances, and audits on scores of clients there are so many recommendations that have become industry standards and best practices.

“Let’s be real about AI and human integration…It is VERY expensive to implement; I do not foresee this happening in centers below 250 agent-seats.”

I am most proud of advocating for and seeing the following positive changes in many contact centers:

1. Operational measurement that should be based on forecast accuracy, schedule fit, service level, quality, performance criteria, employee satisfaction, customer satisfaction, and strategic value (efficiency, profitability) to your organization.

Consider these contact center measurements:

• Service level percentage (x% in Y seconds, average speed of answer, abandon rates)
• First contact resolution (FCR), skill levels
• Average handle time (AHT), after-call work (ACW)
• Cost per call, conversion rates
• Employee satisfaction
• Customer satisfaction, e.g., interaction analysis, surveys, focus groups

2. Contact center software technology that should combine the best of human interaction and AI to be sure it is distributing accurate information to your staff on time, at the same time, and in the same format.

“…your employees want to know how they are doing and what they can do to be better at their jobs.”

3. Providing time-off from interaction work for:

• Special projects
• Career enrichment training
• Outside seminar attendance
• In-house seminars by outside professionals

4. Using the “task force” approach for:

• Selecting “complaining agents” to solve an issue.
• Creating a general task force in charge of creating ways to make their jobs less stressful or ways they could be empowered to solve customer complaints.
• Customers love to complain about “phone trees,” so how about a task force to simplify all those “press” options?

5. Enabling employee empowerment to bring out the best in your employees. To make that happen, you must:

• Have faith in their abilities.
• Give them the freedom needed.
• Rely on their integrity to make the right decisions.
• Provide encouragement, job tools, and authority.

To conclude, companies must develop training programs and coaching performance evaluation policies that improve customer service and experiences, staff productivity, and ultimately profitability.

In fact, your employees WANT to know how they are doing and what they can do to be better at their jobs. You are NOT spying on employees!

Finally, hiring practices need to be established so that you hire the appropriate people that have the same custome service values as your company does.

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.