Fairview, Texas – March 2026 – TrueCX, the AI platform for agent readiness and contact center training, announced today that it has obtained the internationally-recognized ISO/IEC 27001 certification for information security management systems.
This certification confirms that TrueCX has a complete and audited security framework in place that is capable of identifying, managing, and reducing security risks across the organization and for its customers. This security milestone builds on TrueCX’s existing SOC 2 Type II certification, reinforcing the company’s commitment to security, operational excellence, and continuous compliance.
“Security is fundamental to how we build for our customers.”
— Lonnie Johnston, TrueCX Founder and CEO
“Security is fundamental to how we build for our customers,” said Lonnie Johnston, Founder and CEO of TrueCX. “Our customers and partners trust us with customer conversations, agent performance data, and compliance workflows, and achieving ISO 27001 certification demonstrates that we have the systems and governance in place to maintain and protect that trust.”
“ISO 27001 is not a lightweight badge,” said Maria Citrowske, Vice President of Marketing. “It represents months of engineering rigor, documentation, risk analysis, evidence gathering, and internal training. We’re proud of the team for building this the right way.”
For contact centers and enterprise buyers, the certification provides meaningful advantages, including reduced operational and compliance risk and assurance that their sensitive data is managed under an internationally-recognized standard.
TrueCX’s security features now include ISO/IEC 27001 certification, SOC 2 Type II certification with zero exceptions, a fully-implemented information security management system, and continuous monitoring and risk management processes.
The company will continue to invest in its security infrastructure and compliance as it expands its footprint and customer base.
For more information about TrueCX, visit truecx.com.
About TrueCX
TrueCX is an AI-powered platform that enables contact center leaders to accelerate agent readiness, validate real-world performance behaviors, and improve operational outcomes before agents ever reach live customer calls.
By combining intelligent virtual customers (IVCs), applied learning validation, and performance analytics, TrueCX helps organizations reduce ramp time, lower early attrition, and improve customer experience outcomes.
Get in touch to learn more about TrueCX’s solutions.
AI is quietly reshaping many contact centers. With IVR handling balance checks, bots resetting passwords, and voice agents resolving simple billing questions, what’s left for your agents?
The answer: the most complex, emotionally charged edge cases that automation and AI simply can’t handle.
And while the call mix has changed, agent training hasn’t – or hasn’t changed enough.
Your agents know your policies, they’ve completed your onboarding modules, and they’ve shadowed a few calls. But they haven’t practiced in realistic, high-pressure environments.
The result is not just a slower learning curve or more escalations – its true operational losses.
Let’s break down where that cost shows up.
Cost Per Lead
In many industries like utilities, home services, and insurance, calls are revenue opportunities. Marketing and sales teams have spent real time and resources to generate inbound and outbound leads.
Here’s what could happen if a new agent mishandles these calls:
The potential customer hangs up
The potential customer doesn’t call back
The potential customer delays a purchase by several more touches
The potential customer chooses one of your competitors
The lost revenue opportunity and increase in cost per lead digs away at your bottom line; each additional minute on the phone or additional touchpoint to re-activate a potential customer adds up fast.
Customer Satisfaction Score (CSAT) and Loyalty
Consider a customer calling into your contact center with a highly emotional issue. Maybe their power was shut off, or their insurance claim was rejected, or they are stranded after a flight cancellation.
When a new agent hesitates, provides unclear information, puts that customer on hold for too long, or transfers them multiple times, the customer experience degrades fast, and their sentiment dips from bad to worse.
This affects more than just customer satisfaction and CSAT surveys. It affects renewal, churn, revenue, and trust. A single bad interaction during a critical moment can undo years of positive service and brand loyalty.
Average Handle Time (AHT)
Without proper preparation in true-to-life circumstances, new agents will simply take longer to do their jobs. They’ll put customers on hold more frequently and for longer periods of time, re-read scripts before speaking, search for answers across multiple systems, and escalate when they’re not 100% sure of a solution.
Even a one-minute increase in AHT per call compounds quickly. Multiply this by your calls per month and see the costs start to add up in:
Longer queues
Higher call abandonment
Higher staffing requirements
Overtime
Each extra minute of AHT chips away at your bottom line metrics and overall efficiency. But there is a cascade effect, too:
More compliance risk, as agents rush to recover time later on other calls
More fatigue for agents, as longer calls signal complexity and strain
Less time for coaching, because supervisors are covering escalations
Lower customer satisfaction, as customers spend longer on the phone for issues that should have been resolved quickly
Operational Dispatches
In companies with an element of field work, like property management, home services, and utilities, agents may default to dispatching a team member on-site as a safe way to de-escalate and end a conversation.
But if an issue could have been resolved remotely, this creates a serious operational burden. Consider the hours a member of your team spends traveling, the money spent on gas, and the potential, worthy on-site visits they could have been doing in the meantime.
And if the onsite visit wasn’t necessary to begin with? You risk eroding customer trust, too.
Now multiply that by tens or hundreds of avoidable dispatches per month.
Escalations
When new agents struggle, the issues don’t stay with them. Experienced agents or supervisors step in to provide additional training, QA, coaching, and escalation support. This all adds up to minutes or hours where your MVPs are off the phones.
Your best performers should be on the front lines, not cleaning up training gaps or doing reactive firefighting.
Ask yourself:
For top agents: Who is now taking calls instead of your top performers? If your highest-converting, highest-performing agents are pulled into support or escalations, your calls will shift to mid-tier or new agents. This redistribution quietly lowers conversion and CSAT and raises AHT and risk.
For supervisors: Where could that leadership capacity be going instead of doing reactive coaching? What broader improvement initiatives are being put on the backburner? Every minute spent resolving preventable issues is time not spent analyzing trends, reigning workflows, improving systems, or coaching. Over time, this resource scarcity puts your supervisors in reactive mode instead of proactive mode.
Attrition
It’s no secret that early performance is directly correlated to churn in an agent’s first 90 days. In a COPC study, only 71% of agents felt that their onboarding adequately prepared them for success, down 3% from previous years.
When agents are thrown into emotionally intense situations without realistic practice, confidence plummets fast. And low confidence leads to stress, burnout, and voluntary exits.
Imagine that a new agent logs in for their first live shift on day one. The low-hanging fruit of password resets and balance checks are automated, and the first call routed to them is a customer whose power has been shut off and is worried about losing refrigeration for their grandmother’s medication.
The agent knows your policies in theory – they covered them in training – but now the customer is audibly upset. There are compliance implications to consider, system notes to catch up on, and customer satisfaction to consider all at the same time.
So the agent hesitates. They put the customer on hold. They escalate. This happens over and over again, and by the end of their first week, the agent is dreading each and every call. By the end of their first month, they’re questioning whether this is the right job for them.
Replacing that agent, who could have been a top performer if properly set up for success, costs thousands in recruiting, training, and lost productivity.
And if the reasons behind churn haven’t changed, this becomes a self-fulfilling prophecy.
A cultural expectation that new agents won’t be here long leads to lower overall expectations, failure as a status quo, and the perception of your contact center as a cost center – also a self-fulfilling prophecy.
But there are real ways to stop the cycle.
Don’t Turn Your Customers Into Coaches
In many contact centers, live calls still function as one of the primary classrooms for new agents. But your customers are the most expensive coaches imaginable.
The alternative? Improved training and coaching that leads to real agent readiness.
Tools like Intelligent Virtual Customers (IVCs) allow your agents to build confidence and readiness with realistic AI customers who talk, respond, and react like your actual customers.
Compare the cost of improving your training to the math of what unprepared agents really cost you, and ending the cycle of churn and burn becomes a no-brainer.
Contact center agent onboarding has followed the same arc for decades: start new hires on simple calls and build confidence through repetition and gradual complexity. But with the introduction of AI, that arc is starting to feel unreliable.
This shift isn’t happening everywhere, and it’s not happening all at once, but it’s happening often enough that onboarding feels harder than it used to for agents and contact center leaders alike.
The opportunity to warm up on low risk, simple calls is lower, and new agents are facing complex, emotionally-charged conversations and edge cases early and often. This is the time to question long-held assumptions about what onboarding should look like.
This post breaks down five ways AI is reshaping contact center onboarding, and what teams can do to adapt without sacrificing confidence, performance, or retention.
Challenge #1: “Easy” Calls Are Disappearing First
AI and self-service usually absorb the simplest customer interactions first.
Balance checks, password resets, shipping status, basic account updates. These were once the lowest rung of the onboarding ladder. They gave new hires repetition, rhythm, and a low-risk way to build confidence before handling more complex situations.
Although AI adoption isn’t equal across all industries, these entry-level questions are slowly disappearing as AI quietly redirects simple issues away from human agents.
This means that agents have fewer low-stakes interactions to practice with, and they reach nuanced or complicated conversations sooner – before they feel fully settled into their roles.
Industry example
The first call Ryan receives during his first day on the phones is from a customer whose power was shut off and is worried about losing refrigeration for his insulin.
The routine questions Ryan practiced during onboarding are now automatically answered by IVR. The calls that reach him are edge cases, escalations, and emotional situations. He technically knows the utility company’s policies, but he hasn’t been able to practice in a low-risk environment and build confidence before things get personal.
Challenge #2: Early Mistakes Carry More Risk
When “easy” calls disappear, so does the margin for error. Trust, compliance, and revenue are impacted – among other key metrics – when avoidable mistakes happen during high-stakes customer conversations.
Onboarding completion, at face value, doesn’t say much about how an agent will actually perform under real stakes. Now that early performance matters more, teams need better ways to observe, assess, and support agents during onboarding itself.
Intelligent Virtual Customers (IVCs) allow this by allowing teams to evaluate real performance, behavior, and training gaps before agents ever get on the phone with a live customer.
Industry example
Sam finishes his onboarding and passes all of his required knowledge checks. During his first week talking to real customers, he gets overwhelmed and misses an important compliance step. This leads to escalation, manager intervention, and a big confidence hit for Sam.
In industries like finance and healthcare that are highly regulated, early mistakes often carry outsized consequences. The goal shouldn’t be to speed up agent time-to-floor, but to ensure that true readiness will actually translate into compliance.
Challenge #3: Confidence Falters Early
When new agents struggle, it is easy to assume they lack knowledge, skill, or motivation. More often, the issue is overwhelm and cognitive load.
As first-call complexity increases, agents have to listen, interpret, decide, and respond under emotional pressure, all while navigating brand new tools, policies, and time constraints.
This pressure shows up quickly: agents hesitate mid-call, second-guess themselves, or over-rely on escalation. Stress rises, confidence drops, and what might have been a temporary wobble becomes a pattern. Over time, this can be one of the strongest predictors of early churn.
Industry example
Leia is on back-to-back calls from stranded passengers during a severe storm. She knows her company’s policies, but the emotional pressure, time constraints, and sheer amount of calls slows her down.
After several highly-emotional conversations, she begins hesitating, putting customers on hold, and escalating issues she knows she could normally resolve on her own – though she isn’t so sure anymore.
Without regular reinforcement and training, even the most capable agents can start doubting themselves and making avoidable missteps.
Challenge #4: The Training Ladder Doesn’t Match Reality
Contact center onboarding programs have traditionally involved learning the basics before progressing towards more complex scenarios. That approach is less relevant now that basic calls are gradually being replaced with AI at many contact centers, and complexity is the new status quo.
This is not a training failure, it’s an opportunity to introduce new approaches, tools, and processes and train a new generation of flexible, prepared, and confident agents.
Industry example
Ray, a new agent, did great on his training scenarios during onboarding. Once on the floor, however, he was met with a mix of edge cases and emotional calls from day one. His reality didn’t match what the training ladder taught him to expect, and his confidence – and the customer experience – suffered as a result.
Challenge #5: Readiness Signals Haven’t Kept Up
Even as customer conversations grow more complex, many onboarding metrics remain designed for a simpler era: completion rates and time-to-floor remain the main indicators of success.
While these metrics are easy to track, they don’t actually reflect how prepared an agent is for the calls they’ll face.
This gap affects culture, morale, and decision-making:
Leaders and tenured agents hesitate to trust new agents
Supervisors and managers are asked to make training longer without evidence it will help – or worse, they’re asked to accept a “churn and burn” norm
Agents can feel judged by outcomes that don’t reflect their learning curve.
Industry example
Priya finishes her onboarding on schedule, but during her first week, she struggles to manage troubleshooting, compliance checks, and distressed customers.
Her performance begins to slip, and escalations increase. Priya is taken off the phones and put back in training, slashing her motivation and morale because readiness was declared too early, using signals that measure completion rather than performance under real conditions.
AI Can Make Contact Center Onboarding Easier, Too
The same technologies that have changed the status quo and made onboarding feel harder also have the potential to make it more effective, predictable, and cost effective.
Used intentionally, AI can reduce risk on the floor, and ensure agents are set up for success on day one.
The key is redefining readiness. When we have the right tools to adequately assess performance before agents get on calls, AI can become a way to move learning out of live queues and into lower-cost, lower-risk environments.
Intelligent Virtual Customers (IVCs), for example, allow agents to simulate real calls with an AI customer to see how they handle pressure, volume, objections, and edge cases before real metrics like CSAT and retention are at stake.
The payoff is real: fewer escalations, less agent churn, and a better customer and agent experience. AI gives operations leads a way to teach, measure, and improve readiness without paying for it in real time, with real customers.
An agent’s first day on the phones sets the tone for everything that follows. Confidence. Performance. And even retention.
Many companies struggle with the same issue: they confuse contact center agent training completion for true readiness. After completing training, agents may have memorized the material, but they still have no experience handling real conversations in real conditions.
That gap between contact center agent training and readiness is where Day One one often breaks down.
From Trained to Ready
Teams that incorporate realistic, repeatable call practice with Intelligent Virtual Customers (IVCs) tend to see stronger Day One outcomes.
When agents can practice realistic conversations in a true-to-life environment without pressure from live customers, they build confidence faster and make fewer avoidable mistakes once they hit the floor.
Day One Readiness Checklist
To help learning and development teams assess readiness before agents go live, we put together a short and simple Day One Readiness Checklist.
It focuses on four areas that help predict early success:
Agent Fundamentals: Systems, audio, documentation, and coaching plans are ready before Day One begins. (check out this best ANC headphones guide.)
Call Readiness: Agents have practiced and aced real conversations, not just reviewed scripts or completed mock calls.
Floor Readiness: Agents know how to put calls on hold, handle escalations, and solve inevitable technical issues.
Support in the First 24 Hours: Call center agent training, coaching, feedback, and check-ins are clearly defined.
The checklist is designed to be saved, shared, and used as a final readiness check. Before agents take their first live call, count how many boxes you can confidently check off:
Few boxes checked means high risk.Agents are likely to feel overwhelmed or stressed.
A moderate score means agents may survive Day One, but confidence will lag.
A strong score means agents are set up to perform and recover, even when things go wrong.
Most contact centers wait until agents are live on the phones in order to measure performance, but by that point, the stakes are already sky-high. Mistakes affect real customers, escalations pile up, supervisors are pulled in, and new agents feel under immense pressure to perform immediately.
When performance issues show up after an agent hits the floor, training teams are forced to be reactive instead of proactive. Tracking the right metrics allows for intervention at the contact center agent training stage, shortening ramp time and protecting both agents and customers when it matters.
This guide covers the metrics to track during agent onboarding and training so you can prevent problems and set agents up for success before they take a real call.
Here are the key metrics to track before an agent takes their first call:
Readiness and Confidence Metrics
If an agent doesn’t feel prepared to take live calls, they are far more likely to struggle the moment a conversation goes off-plan. In this way, readiness and confidence metrics are early predictors of churn.
Low confidence leads to hesitation, hesitation leads to mistakes, and mistakes create stress and early exits.
By tracking readiness and confidence alongside call center agent training completion, L&D teams can keep their finger on the pulse of which agents are ready, which need a little more practice, and which need targeted support.
Readiness and confidence metrics include:
Success Rate
Number of “Reps” to Reach Competence
Improvement Over Time
Self-Reported Confidence
Call Handling Quality Metrics
Keeping an eye on call handling quality metrics during training helps avoid QA issues down the line. But with traditional contact center agent training, it’s hard to simulate the real-world scenarios that could lead to sup-bar QA scores in the real world.
With Intelligent Virtual Customers (IVCs), agents can have true-to-life conversations with AI customers who sound, respond, and react like real customers. IVCs make call handling quality metrics trackable on day zero, far before real customers are on the line.
Call handling quality metrics include:
Script Adherence
Information Accuracy
Objection Handling
Compliance Adherence
Escalation and Recovery Metrics
Agents who escalate frequency or struggle to recover from escalations will experience higher stress and burnout once they’re live on calls. Frequent escalations also put an additional burden on supervisors and top agents who will likely be called in for support.
When agents aren’t exposed to realistic, challenging scenarios in their training, those first few difficult calls can feel entirely overwhelming. Evaluating escalation and recovery skills before agents go live, and training them with IVCs, makes it possible to improve agent performance without risking the real customer experience.
Example metrics include:
Escalation Frequency
Time to De-escalation
Successful De-escalation Rate
Why Contact Center Agent Onboarding & Training Metrics Matter
Tracking these key metrics before agents ever talk to a real customer means your training organization can move from reactive correction to proactive readiness, putting in place best practices before bad habits have the opportunity to take hold.
These early indicators help teams:
Reduce churn
Improve QA scores
Strengthen compliance scores
Lower escalation rate
Reduce average handle time
Protect CSAT and NPS
Taken together, these metrics lead to a more consistent customer experience, higher-achieving agents, and a stronger bottom line.
But measuring these core metrics requires realistic practice, and classroom training and traditional roleplay cannot replicate the actual experience of being on a call with a customer. By creating lifelike practice environments for your agents, IVCs can help you measure readiness metrics and ensure your agents hit the floor running on day one.
Turn Early Signals Into Better Results
Doing fundamental training when agents are already on calls is a quick way to negatively impact your contact center’s bottom line. The risk to customers and agents alike is too high to ignore; the earlier your learning and development team can measure, monitor, and train these foundational metrics, the better.
Readiness, quality, and escalation issues appear during onboarding, and they can be stopped during onboarding, too. When these signals are tracked in advance, trainers can intervene sooner and reduce the downstream operational impact that shows up once live customers are in the mix
For operations leaders, this means fewer surprises and more predictable performance. For learning and development leaders, it means clearer proof that call center agent training directly influences business outcomes.
Get in touch if you want to learn more about TrueCX and how Intelligent Virtual Customers (IVCs) can help you measure business-critical metrics as early as their first day of onboarding.
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For the past few years, conversations about AI in contact centers have brought with them a lot of anxiety. Will AI replace jobs? De-skill teams? Will it turn L&D into something cold or automated?
The short answer? No.
At TrueCX, our opinion is that AI will enable contact center teams to do more. And for L&D, that change can mean clearer impact, more compelling data, and a better seat at the table.
Here are the top five ways that AI is turning L&D into a business-critical function in 2026:
1. AI Has Transitioned From Experiment to Infrastructure
For a lot of contact centers, AI is no longer something to pilot or try out: it’s part of how work gets done each and every day.
Teams are using AI to move faster, do more with less, and extract insights, patterns, and actions from mountains of call data.
The conversation, in turn, is shifting from “AI hype” to grounded practicalities. Leaders aren’t chasing the next big thing; they’re looking for tools that help their teams do better work without burning out.
Among the L&D leaders I speak to, AI is being viewed more and more as a potential support system rather than a threat.
2. As AI Automates Routine Tasks, Soft Skills Become a Major Differentiator
One of the clearest themes I’ve picked up on in conversation with L&D leaders is that AI has definitively not made human skills any less important.
In fact, it’s made them more important. And more visible.
When routine and straightforward tasks are automated, what remains are the high-stakes moments that are harder to script: handling a frustrated customer, navigating an emotional call, or de-escalating a bad experience.
Empathy, active listening, creativity. These are the skills that separate average performers from top agents, and they can’t be automated.
L&D is the key here. The table stakes conversations will be automated by AI, and L&D will have the critical task of making sure the conversations that remain are handled by excellent agents with a strong grasp of strong skills. Training is more important than ever.
3. Traditional Training Doesn’t Work
The other side of the token in #2 is that traditional training will no longer cut it.
Onboarding that teaches agents the answers to frequently asked questions and then sends them to the call center floor doesn’t match the reality of what they’ll actually face on the phones.
In a contact center environment increasingly shaped by AI, training has to invest in agent confidence and soft skills just as much, or even more, than the product and compliance information they’ll need to know.
TrueCX can help with that by providing Intelligent Virtual Customers (IVCs) so your agents can refine their soft skills in a failure-free, true-to-life environment.
4. Readiness is the Metric That Matters
As a result of this shifting landscape, many L&D leaders are rethinking what they measure.
Instead of checking for completion (who finished a program or course), leaders are looking for readiness (can this agent actually handle the moments that matter?).
This shift changes everything about how learning programs are designed and evaluated.
Measuring readiness requires visibility: knowing which skills are strong, which need work, and how agents are progressing over time. AI makes this possible at a scale that wasn’t realistic before, turning onboarding data into a business-critical metric.
5. AI Turns Training Into a Dynamic, Scalable System
One of the most powerful changes I’ve discussed with L&D leaders is the ability for AI to turn training into something continuous, personalized, and measurable.
Instead of one-size-fits-all programs, AI makes customized training scalable and lets agents practice real scenarios that mirror their day-to-day and suit their particular skill gap. Agents receive timely and tailored feedback, and L&D leaders can see patterns and address gaps with relevant data about performance.
With AI, L&D teams no longer have to choose between resource-intensive, bespoke training or ineffective blanket programs. Personalized training can scale with your team and meet every agent where they are to help them build readiness and confidence.
And with trustworthy measurement, L&D teams can easily spot high performers, agents in need, and major skill gaps early in the training cycle. This allows for better segmentation and a more informed approach, as well as the ability to better track and show improvement over time.
L&D as a Strategic Partner
All of these AI trends are reshaping the role of L&D. When learning teams can draw a clearer line between training, readiness, and performance, their work becomes visible in new ways, and they can actively influence business outcomes.
AI doesn’t replace L&D teams; it gives them a seat at the table.
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TrueCX introduces Experience Intelligence, which uses lifelike virtual customers to measure and improve customer interactions across every channel.
Fairview, Texas – December 2025 – WizeCamel, the company that first introduced lifelike AI phone simulations for agent training, has officially rebranded as TrueCX.
TrueCX was founded in 2024 with the mission of improving the way contact centers prepare their agents for real, high stakes customer interactions. Traditional methods such as mock calls, classroom training, and roleplays can’t accurately recreate the stress, emotion, and unpredictability that agents face on real customer calls. This leads to slower onboarding, costly errors, and higher churn.
The company’s foundational solution helped agents master calls faster and reduced supervisor burden by providing realistic, dynamic AI conversations through Intelligent Virtual Customers, or IVCs. IVCs behave like real customers and allow agents to gain confidence in a safe environment so they can reach proficiency faster.
This insight—that IVCs are the new gold standard in true-to-life call simulations—set the stage for a wider effort to extend their usefulness across the full customer experience.
With its transition to TrueCX, the company is now expanding IVC technology across the entire customer experience lifecycle.
Introducing Experience Intelligence
TrueCX’s new solution, Experience Intelligence, extends IVC capabilities beyond agent training. It lets companies audit and evaluate real customer experience across every channel.
“Our customers saw clear coaching gaps, yet their CSAT scores told them nothing new. That gap pushed us to evolve customer experience measurement beyond traditional surveys. TrueCX gives leaders the truth of the interaction so they actually know where to act.”
— Lonnie Johnston, TrueCX Founder and CEO
With TrueCX, every customer interaction is now digital and measurable, so companies can evaluate the real customer experience directly, rather than rely on survey memory and self-selection.
The company’s Experience Intelligence offering can call your support line, test your chat workflows, submit email inquiries, and evaluate the experience as a real customer would. In turn, companies receive accurate, direct data on core metrics like time to response, resolution rate, empathy, and more.
Experience Intelligence can also be directed outwards: TrueCX now has the capability to assess competitor performance on these key metrics, so you can establish a baseline for improvement and ensure your product is truly the best in the market.
Why Experience Intelligence Matters
Most companies rely on mechanisms like CSAT and NPS to assess customer experience. These metrics, while helpful, are incomplete: they give a score without a story, and rarely provide a clear path for improvement.
They capture what a customer remembers, not what they actually experience on a call.
Even if a company’s tech stack already includes metrics like average handle time and first call resolution, they are often shown in isolation rather than as part of a larger journey. A customer might reach out over email, then move to chat, and finally call into a voice channel. Most tools can’t evaluate this holistic experience from a customer perspective.
Experience Intelligence solves this problem. Acting as a real customer, IVCs evaluate your processes from end to end. They map the real steps customers take and expose delays, handoff issues, and broken paths that surveys alone can’t surface.
This shifts CX from opinion to evidence, which is the core purpose of TrueCX.
A Unified IVC Platform
TrueCX now offers three complementary solutions:
TrueCX Train prepares agents for real conversations in a true-to-life, risk-free environment
TrueCX Measure assesses your real customer experience so you know where to focus your improvements
TrueCX Compare shows you how your customer experience compares across the market.
All three solutions run on the same IVC engine, so training, measurement, and benchmarking draw from one continuous experience dataset. Together, the solutions give companies a clear view of their customer experience, and the tools they need to improve it—without surveys or stitched-together tools.
“Becoming TrueCX is our way of doubling down on how much value AI customers can bring across the entire customer experience. Launching the Experience Intelligence category feels like a natural next step, because it grew directly from what our customers have been asking for: a unified, evidence-based view of customer experience.”
— Maria Edington, TrueCX VP of Marketing
Learn More
Whether you want to improve agent training, understand the reality of your customer experience, or get a better sense of what your competitors are doing, TrueCX can provide you with a tailored, no-pressure demo.
Get in touch to learn more about TrueCX’s solutions.
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AI Training for Contact Center Agents, The Future of Onboarding & Readiness
Reinventing Agent Readiness in the Age of AI
Contact centers are facing a training and onboarding crisis. Traditional methods, classroom sessions, generic scripts, and shadowing, were built for a world where new agents could start with simple, low-stakes calls and gradually build their skills. But that world is gone.
Automation and self-service have stripped away those entry-level interactions, leaving new hires to confront complex, emotionally charged issues on day one. This has turned into longer ramp times, higher attrition, and costly inefficiencies that directly impact customer experience.
AI presents a new path forward. By leveraging AI training for contact center agents, leaders can reimagine onboarding as a scalable, personalized, and data-driven process. Through contact center training simulations powered by Intelligent Virtual Customers (IVCs), new hires can safely practice realistic customer conversations, receive instant feedback, and progress only when they demonstrate true readiness. This approach not only accelerates proficiency but also boosts agent confidence, reduces trainer burden, and builds resilience in an era where every customer interaction matters.
For contact center executives: AI isn’t just transforming how agents serve customers, it’s transforming how we prepare them to succeed.
Rising Complexity of Customer Interactions
The contact center has always been a challenging environment, but the landscape has fundamentally shifted. Low-effort, transactional calls – think about password resets, simple account lookups, straightforward policy questions – they are now handled by self-service portals, automation, or chatbots.
What remains are the higher-stakes, emotionally charged, and technically complex conversations. New hires often face these situations from their very first live call, without the benefit of building skills gradually.
The “Broken Ladder” Effect
Historically, onboarding followed a natural learning curve. Agents began with simple issues, gained confidence, and then worked their way up to more complex customer interactions.
With AI taking over those easier calls, that ladder has collapsed. Agents are no longer “cutting their teeth” on manageable tasks — they’re being thrown straight into the deep end. The result is high stress, low confidence, and an increased likelihood of burnout or attrition in the first 90 days.
Inefficiencies in Legacy Training Models
Despite these changes, many training programs still rely on outdated methods:
Static classroom modules that don’t adapt to individual strengths or weaknesses.
Generic scripts that fail to mirror the real-world variety of customer conversations.
Trainer-heavy shadowing sessions that are costly, inconsistent, and difficult to scale.
These methods are no longer sufficient for preparing today’s workforce. They consume significant resources while failing to deliver measurable readiness.
The Business Impact for Leaders
For contact center leadership, the consequences of ineffective onboarding are profound:
Longer time-to-proficiency: New hires take longer to reach productivity benchmarks.
High early attrition: Frustrated or overwhelmed agents leave within the first few months.
Customer experience risks: Rookie mistakes in live interactions directly affect CSAT and brand reputation.
Escalating costs: Increased trainer hours, re-hiring, and re-training compound the financial burden.
The message is clear: the traditional onboarding playbook no longer aligns with the realities of a modern, AI-driven contact center environment. Leaders need a strategy that acknowledges this shift and equips agents for success from day one.
Why AI is a Natural Fit for Agent Training
Safe Experimentation
Unlike customer-facing AI deployments, training and onboarding take place in a back-office environment. This makes it a safe proving ground for innovation. Leaders can integrate AI into agent development without risking customer satisfaction or brand perception. Mistakes made in training stay in training, allowing agents to build confidence before going live.
Scalability Without Burnout
Traditional training models are constrained by human capacity — trainers can only run so many roleplays, shadowing sessions, or feedback cycles in a day. AI removes these limitations. It can facilitate unlimited practice sessions for as many new hires as needed, all at once, ensuring no agent is left waiting for time or attention.
Personalized Learning at Scale
Every agent enters onboarding with different strengths, weaknesses, and learning styles. Static curricula fail to account for this variation. AI dynamically adapts to each agent’s performance: slowing down when mastery hasn’t been achieved, accelerating when skills are demonstrated, and targeting development where gaps are most pronounced. The result is a more efficient, individualized path to proficiency.
Data-Driven Readiness
Traditional onboarding relies heavily on observation and intuition. With AI, every practice interaction generates objective data points: how well the agent followed compliance rules, whether they maintained empathy under pressure, how quickly they resolved a simulated issue. These metrics give leaders a clear, measurable picture of readiness — and a more confident transition from training to live calls.
AI Training in Action: Contact Center Training Simulations
For decades, new agents were eased in through job shadowing, classroom roleplay, and a few low-stakes calls. That world doesn’t exist anymore. Today, AI-powered simulations restore that learning curve by letting agents practice with Intelligent Virtual Customers (IVCs) before they ever take a live call.
These simulations aren’t static scripts. They feel real — voices with tone, emotion, and unpredictability — and they adapt to how the agent responds. More importantly, they rebuild the ladder of learning that automation has taken away:
Realistic roleplay: IVCs replicate actual customer conversations, preparing agents for the messy reality of live interactions, not just “happy path” calls.
Progressive challenge: Training begins with simpler questions and gradually scales to complex, emotionally charged scenarios.
Real-time feedback: Instead of waiting for a trainer’s notes, AI flags hesitation, tone issues, or missed compliance steps instantly.
Confidence building: With repetition in a safe environment, agents walk into production already battle-tested, not wide-eyed rookies.
The combination of immersion, structure, and feedback turns training into something more than knowledge transfer — it becomes confidence transfer. By the time agents meet their first real customer, they don’t just know what to do; they know they can do it. And that makes all the difference.
From Vision to Execution: A Blueprint for AI-Driven Training
Adopting AI in training doesn’t require tearing down your existing onboarding program overnight. Instead, it works best as a staged transformation — layering AI simulations into the areas where they add the most value first, then scaling over time. Here’s a proven framework for leaders:
Step 1: Audit Existing Onboarding
Every contact center has unique challenges, but the pain points often look similar:
Extended nesting periods where new hires sit idle or over-rely on floor support.
Trainer bottlenecks, with valuable supervisors tied up in repetitive shadowing.
Rookie errors in live calls that frustrate customers and drive early attrition.
By mapping these inefficiencies, leaders can pinpoint where AI-powered simulations will deliver the biggest ROI first.
Step 2: Pilot AI Simulations
Start small. Select a new-hire cohort and supplement their onboarding with AI-powered practice calls. Focus on a narrow set of use cases — for example, your top three call drivers or common compliance scenarios.
Piloting does two things:
Proves the concept with measurable results (faster ramp, higher confidence).
Builds buy-in with trainers and frontline managers, who see the impact firsthand.
Step 3: Integrate into Existing Systems
AI training shouldn’t exist in isolation. To maximize value, it should connect seamlessly with the tools leaders already rely on:
LMS platforms for centralized learning journeys.
QA scorecards to ensure consistency between simulation and live performance.
Performance dashboards so leaders can track progress across the entire workforce.
Integration ensures that AI simulations aren’t a side experiment — they’re a core part of the development ecosystem.
Step 4: Expand and Specialize
Once the pilot proves successful, broaden the scope:
Create a scenario library that mirrors the real contact center environment: technical troubleshooting, high-emotion complaints, billing disputes, and compliance-heavy conversations.
Introduce specialized training paths for cross-skilling (e.g., moving a seasoned chat agent into voice support).
Regularly update scenarios to reflect new products, policies, or customer expectations.
This library becomes a living, breathing asset — one that scales as fast as the business evolves.
Step 5: Measure and Iterate
The final step — and the most important for leadership — is measurement. AI-driven training produces hard data, making it easier to track progress:
Time-to-proficiency compared to traditional cohorts.
Attrition rates in the critical first 90 days.
Customer experience metrics, such as CSAT or First Call Resolution (FCR), for new agents.
Trainer hours saved, representing direct cost reduction.
Leaders can then refine the approach, double down on what works, and demonstrate ROI to the executive team.
Beyond Onboarding: Unlocking the Full Potential of AI Training
AI training isn’t limited to getting new hires up to speed. Once the foundation is in place, the same simulations and feedback loops can be applied across the employee lifecycle — strengthening skills, supporting career growth, and preparing leaders for the future.
Ongoing Upskilling Customer expectations and business priorities change constantly. AI simulations allow agents to rehearse new product launches, updated policies, or seasonal campaigns before they ever reach customers. Instead of learning on the fly, agents walk into change fully prepared.
Cross-Skilling Across Channels Moving an agent from chat to voice, or from service to sales, has traditionally required significant retraining. With AI, simulations can replicate each channel’s dynamics — tone of voice, pace, and interaction complexity — making transitions smoother and more cost-effective.
Leadership Readiness The benefits don’t stop with frontline staff. Supervisors and managers can use AI-driven roleplay to practice coaching conversations, performance discussions, and even conflict resolution. This prepares leaders to handle high-stakes interpersonal moments with the same confidence that agents bring to customer calls.
In short, AI training creates a continuous development ecosystem. It doesn’t just shorten the path to proficiency for new hires — it provides an adaptable platform that grows with agents, leaders, and the organization as a whole.
Measuring What Matters: Key Metrics for AI-Driven Training
For contact center leaders, adopting AI in training isn’t just about innovation — it’s about impact. The success of any program must be tied to clear, measurable outcomes that align with business priorities. By tracking the right metrics, leaders can prove ROI, refine their strategy, and ensure agents are truly ready for live customer interactions.
Here are the most critical measures to monitor:
Speed-to-Proficiency: How quickly do new hires reach productivity benchmarks compared to traditional onboarding? Faster ramp times mean lower costs and earlier contributions to customer experience.
Early Attrition: Monitor dropout rates within the first 90 days. A strong AI training program should reduce early exits by giving agents the confidence and preparedness they need to stay.
First-Call Resolution (FCR) & QA Scores: Track whether new agents are resolving customer issues on the first attempt and adhering to quality standards. These are leading indicators of training effectiveness.
Trainer Hours Saved: Calculate the reduction in time supervisors and trainers spend on shadowing and repetitive roleplay. Freeing up leadership capacity creates both cost savings and strategic flexibility.
Agent Confidence Scores: Use post-training surveys or self-assessments to measure how ready agents feel before going live. Confidence correlates directly with performance under pressure.
When these metrics move in the right direction, leaders gain not only proof of value but also a framework for continuous improvement. The data transforms training from a cost center into a measurable driver of workforce effectiveness and customer satisfaction.
The Future of Agent Training: Intelligent Virtual Customers Take Center Stage
As contact centers evolve, so must the way we prepare the people at the heart of them. Intelligent Virtual Customers (IVCs) represent the next generation of training — moving the industry beyond static scripts and human roleplay into an era of AI-driven, hyper-realistic simulations.
What makes IVCs transformative is not just the technology, but the strategic outcomes they unlock:
The Evolution of Simulations IVCs behave like real customers — unpredictable, emotional, and varied — creating training that feels authentic, not rehearsed. This bridges the gap between theory and live customer interactions.
A Strategic Advantage for Leaders By adopting IVCs, contact centers can shorten onboarding cycles, build agent confidence before day one, and reduce the costly churn associated with rookie stress and burnout. Early adopters will see measurable performance gains and a stronger competitive edge.
Creating a New Category IVCs are not just another training tool; they are the foundation of a new category in workforce development. Just as AI assistants revolutionized self-service, IVCs are poised to redefine how organizations prepare agents for the realities of modern customer service.
For executives, the implication is clear: IVCs are no longer optional. They are the cornerstone of a future-ready workforce strategy.
Why Now Is the Time to Redefine Agent Training
The contact center is no longer defined by simple transactions. Agents step into complex, emotionally charged interactions from the moment they go live, and traditional onboarding models are no longer sufficient to prepare them. This new reality demands a new approach.
AI training for contact center agents, powered by Intelligent Virtual Customer (IVC) simulations, offers that approach. By restoring the broken learning ladder, providing safe but realistic practice, and delivering data-driven insights into readiness, AI-driven training transforms onboarding from a cost center into a competitive advantage.
For VPs and Directors, the decision is clear. This isn’t about testing a new tool on the margins — it’s about redefining how your workforce is built, developed, and sustained in an AI-first era. Leaders who embrace IVC-powered training will not only shorten time-to-proficiency and reduce attrition, but also build a confident, resilient agent base ready to deliver exceptional customer experiences from day one.
The future of customer service belongs to organizations that prepare their people as thoughtfully as they design their technology. With AI-driven training, that future starts now.
TL;DR: AI Training for Contact Center Agents
The challenge: Traditional onboarding is broken. Easy “starter calls” are gone, leaving new hires overwhelmed by complex issues on day one.
The solution: AI training powered by Intelligent Virtual Customers (IVCs) restores the learning ladder with realistic simulations, adaptive feedback, and measurable readiness.
The impact:
Faster speed-to-proficiency
Lower early attrition
Higher FCR and QA scores
Reduced trainer hours
More confident, resilient agents
The opportunity for leaders: This is not a side experiment. It’s a strategic imperative for VPs and Directors to future-proof their workforce and sustain performance in the AI-first era.
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From Six Months to 30 Days: How Borland Groover’s New Hires Beat Tenured Agents
Borland Groover cut training ramp time, slashed errors by 20%, and boosted agent quality — outperforming tenured staff within 30 days with TrueCX.
At Borland Groover, one of the nation’s largest privately held gastroenterology practices, the patient support team faced a familiar challenge: how to onboard new agents quickly and consistently in a highly complex scheduling environment. Traditional shadow-based training took weeks, left agents unprepared for live calls, and slowed hiring at a time when the center was already understaffed. That changed with TrueCX. By introducing AI-powered training simulations on day two, Borland Groover cut ramp time dramatically, reduced scheduling errors by nearly 20%, and saw new hires outperform tenured staff within their first 30 days on the floor.
“The first class trained with TrueCX outperformed my tenured agents in just 30 days. That’s something I’ve never seen before.”
— Susan Tyrrell, Director of Patient Support Services, Borland Groover
Borland Groover’s patient support center was under pressure. The team needed to hire and train dozens of agents to handle complex GI scheduling calls, yet the existing training model was slow, inconsistent, and ineffective.
Inefficient onboarding: New hires spent two weeks shadowing a supervisor, picking up inconsistent habits depending on who trained them. Training stretched to four weeks, and even then, agents struggled on live calls.
Staffing shortfall: Despite needing a much larger team, Susan had far fewer agents in place, and the long onboarding process made rapid growth impossible.
High error rates: GI scheduling is uniquely complex, requiring knowledge of multiple procedures, providers, and variables. Agents routinely made nearly 200 errors per month, creating rework, patient frustration, and revenue risk.
Painful first calls: Without structured practice, agents’ first live calls were overwhelming—longer than they should be, error-prone, and stressful.
“It was painful at best. Every supervisor trained their way, nothing was repeatable, and new hires took far too long to become productive.”
— Susan Tyrrell, Director of Patient Support Services, Borland Groover
How Borland Groover Reimagined Training with AI
AI-driven training & simulation: Introduced TrueCX early in onboarding (day 2).
Practice with AI personas: Agents trained on realistic scenarios before live calls.
Actionable reporting: TrueCX score provided customer service and business-aligned metrics, not just form-based checks.
Flexibility & scaling: Ability to increase difficulty, test empathy, catch language barriers, and identify poor-fit hires quickly.
Borland Groover began its TrueCX journey in beta with small training groups, experimenting with how AI-driven simulations could replace outdated shadowing practices. Early results showed promise, but the real breakthrough came when Susan shifted from mock calls and classroom-style “nesting” to day-two simulations with TrueCX.
This change allowed new hires to practice realistic call scenarios almost immediately — building confidence and surfacing performance insights far earlier than before. For the first time, Susan’s team could scale training to larger groups of 12–15 agents at once, instead of the 4–5 limit imposed by traditional methods.
The rollout soon expanded beyond Borland Groover’s U.S. operations. When applied to the organization’s nearshore teams in Colombia, TrueCX proved invaluable in catching language comprehension issues early, ensuring only the right candidates advanced to live calls. By standardizing training across geographies, Susan was able to deliver consistent performance regardless of where agents were located.
“I didn’t have to wait until someone hit the phones to know if they’d succeed. By the first week, I could spot which agents weren’t going to make it — and act early.” — Susan Tyrrell, Director of Patient Support Services, Borland Groover
What Happened When Agents Hit the Floor
The impact of TrueCX at Borland Groover was immediate and measurable. Within weeks of rollout, Susan’s team saw improvements across efficiency, quality, and business outcomes that fundamentally changed how the contact center operated.
Operational Efficiency
Ramp time was cut dramatically. Instead of taking six months for new hires to reach full productivity, agents trained with TrueCX were performing at a high level in just 30 days. The new model also allowed Susan to scale training classes from 4–5 agents to 15 at once — without any drop in quality.
Quality & Accuracy
The results on call quality were striking. The first class trained with TrueCX not only matched but outperformed tenured agents within 30 days. Errors fell by nearly 20%, dropping from roughly 200 per month to around 110. In fact, Susan noted that these new hires would have qualified for quality bonuses in their very first month — something that had never happened before.
Productivity & Adherence
TrueCX also drove consistency on the floor. Handle times improved, call flows became more standardized, and schedule adherence rose from ~90% to over 92%. Agents reported less frustration and greater confidence in their roles, keeping them engaged and on task.
Catching Issues Early, Scaling Growth
Beyond the numbers, TrueCX helped Borland Groover make better workforce decisions. Susan could identify low performers within the first week, preventing costly mis-hires and reducing churn. Stronger screening and faster ramp times also meant the clinic could increase appointment capacity, driving direct revenue growth — results Susan is actively quantifying.
“Our error rate dropped nearly 20%, our new hires outperformed tenured agents in 30 days, and for the first time, they would have bonused in month one. That’s game-changing.” — Susan Tyrrell, Director of Patient Support Services, Borland Groover
Why TrueCX?
When Susan evaluated other solutions, she found that most competitors promised to replace mock calls — but fell short where it mattered. Their scoring models simply compared performance against a checklist, without offering deeper insights into customer service quality.
TrueCX stood out because it provided customer service scoring that went beyond compliance. Its ability to measure empathy, communication skills, and industry-specific behaviors gave Susan confidence that her agents were being trained for real-world conversations — not just scripted accuracy.
Unlike others, TrueCX was built with the realities of contact centers in mind. The platform delivered true soft skills assessment and business alignment, ensuring new hires weren’t just technically competent, but also able to deliver patient-centered, empathetic care in a complex GI environment.
“Competitors could load a quality form, but they couldn’t tell me if an agent actually demonstrated empathy or built trust with a patient. TrueCX could.” — Susan Tyrrell, Director of Patient Support Services, Borland Groover
Why GI Clinics (and Beyond) Need Human Agents
Gastroenterology brings a unique challenge: scheduling is so complex that automation alone isn’t enough. Unlike a dental or primary care appointment, GI scheduling involves countless variables that influence when and where a patient should be seen. A human agent has to make the final decision, which makes consistent, confident training essential.
TrueCX gives those agents what they need. By blending efficiency with empathy, the platform ensures staff are ready for real-world conversations. The result is fewer errors, faster scaling, and better patient experiences in environments where bots simply can’t keep up.
The lesson extends beyond GI. Any specialty or industry where interactions are complex and high-stakes — from oncology and dermatology to airlines — can benefit from TrueCX’s approach to accelerating training and preparing agents for success.
“We can’t automate GI scheduling — it’s too complex. That’s exactly why TrueCX is so valuable. It makes our people better, faster.” — Susan Tyrrell, Director of Patient Support Services, Borland Groover
Proving That Efficiency and Patient Care Can Coexist
Borland Groover’s experience shows that even in highly complex specialties like gastroenterology, it’s possible to achieve efficiency, accuracy, and scale without sacrificing patient experience. By transforming training with TrueCX, the organization accelerated ramp time, reduced costly errors, and empowered new hires to outperform seasoned staff — all while improving adherence and morale.
For GI clinics and other specialty contact centers facing similar challenges, TrueCX offers a proven path to faster ROI and stronger patient outcomes.
Contact TrueCX today to learn how you can reduce training time, improve quality, and capture revenue growth in your contact center.
95% of AI Projects Fail. Don’t Let Your Call Center Be One of Them.
By now, you’ve probably heard the stat: 95% of AI projects fail. It’s been splashed across headlines and whispered in boardrooms ever since MIT’s 2024 study on enterprise AI adoption found that the vast majority of pilots fizzle before delivering measurable business value (MIT Sloan, Windows Central, The AI Navigator).
That failure rate isn’t just academic. It’s a warning sign for executives under pressure to “do something with AI.” Boards are demanding results, employees are skeptical, and customers are unforgiving when half-baked solutions make their experience worse. Nowhere is this pressure more acute than in call centers, where AI has been sold as the silver bullet to reduce costs and transform customer experience.
The problem? Most call center AI projects don’t even make it out of the pilot phase. The technology may be powerful, but when the rollout is rushed, misaligned, or poorly integrated, the results are predictable: frustrated employees, wasted budgets, and a public failure that makes the next project even harder to sell.
But here’s the thing—failure isn’t inevitable. A small percentage of organizations are already proving AI can make call centers faster, smarter, and more resilient. The difference isn’t the tools they buy. It’s how they implement them.
Only 5% of AI projects make it to success — a reminder of the challenges and discipline required to deliver real value.
This article will break down why so many call center AI projects fail, and more importantly, what you can do to ensure yours doesn’t.
The Real Reasons Behind the 95% Failure Rate
If we peel back the headlines, the real story behind AI’s 95% failure rate is that most projects collapse under the same set of avoidable mistakes. In call centers, the pressure to “do something with AI” often leads to rushed pilots, unclear success metrics, and cultural resistance long before the technology itself has a chance to prove value. To understand how not to become another cautionary tale, it’s worth starting with the most common—and most fatal—mistake: launching without a clear path to ROI.
1. No Clear ROI
Executives are under pressure to “do something with AI,” so projects often start for the wrong reasons: to appease a board, to follow competitors, or to run with a vendor’s shiny demo. But without a clear business case—shorter handle times, fewer escalations, lower attrition—pilots rarely connect to the P&L.
This is why so many projects stall out after the pilot phase. They look impressive in a slide deck, but when budget reviews come around, leaders ask the one question no one wants to answer: what value did this actually create? If the answer isn’t measurable, the project dies.
2. People and Culture Problems
AI adoption isn’t just about technology—it’s about trust. Bridging the gap between leadership’s ambitions and employees’ readiness is the real transformation.
AI transformation doesn’t happen in a vacuum. It happens through people—and too often, people are an afterthought.
Agents see AI as a threat to their jobs. Managers see it as a top-down initiative they weren’t consulted on. And executives underestimate how much training, communication, and cultural readiness is required for adoption. The result? Resistance, slow uptake, and even outright sabotage.
A recent survey by Boston Consulting Group found that less than 20% of frontline employees feel confident using AI in their day-to-day work. If your people don’t understand it, trust it, or see “what’s in it for them,” no amount of investment will make it stick.
3. Broken Plumbing (Integration + Data)
AI isn’t magic—it runs on infrastructure. And in call centers, that infrastructure is notoriously complex. CRMs, telephony systems, workforce management tools, QA software… if the AI solution doesn’t plug into them seamlessly, it creates more friction than it solves.
Then there’s the data problem. Call centers produce mountains of data, but much of it is siloed, messy, or incomplete. “Garbage in, garbage out” isn’t just a cliché—it’s the reality. Poor data hygiene leads to bots giving wrong answers, analytics missing the mark, and employees spending more time cleaning up after AI than doing their actual jobs.
4. Misplaced Bets
Finally, there’s the temptation to swing for the fences. Leaders want big, customer-facing wins—chatbots that deflect thousands of calls, or voice AI that handles entire conversations. The problem? These are the riskiest bets. Failures are public, employees lose trust, and customers are quick to share horror stories on social media.
Meanwhile, the boring stuff—back-office automation like compliance checks, call routing optimization, or transcript QA—quietly delivers reliable ROI. But because it’s less flashy, it often gets overlooked until budgets are burned and credibility is gone.
The Pattern
Call center AI projects don’t fail because the technology isn’t ready. They fail because organizations underestimate the cultural lift, overcomplicate the rollout, and bet on the wrong projects.
Until those fundamentals are addressed, AI will remain a boardroom talking point instead of a bottom-line driver.
Solutions: How to Avoid Being in the 95%
1. Reduce Variables: Start Small, Not System-Wide
Simplify integration—launch where dependencies are low. The biggest AI failures are not due to the technology; they’re due to how organizations deploy it. Pulling off an enterprise-wide automation without ironing out integration and infrastructure first is a high-risk move guaranteed to detonate mid-flight.
A recent TechRadar Pro analysis labels this the “last-mile problem,” where grand digital transformation plans derail when hitting legacy systems, tangled data governance, and real-world constraints.
Big transformations carry big risks. Start small: a low-dependency pilot offers safety, control, and confidence before scaling.
The lesson: “implementation is strategy”—not just choosing the tech, but ensuring it works in practice.
Similarly, Gartner reports that a whopping 77% of engineering leaders say integrating AI into existing applications remains a major challenge, and advises selecting platforms with cohesive ecosystems rather than patching together disparate tools.
Where to start: low-dependency, high-ROI projects
Call Routing Automation Use AI to intelligently pre-route calls based on simple metadata (region, priority, agent skill set), which often requires minimal CRM integration but delivers clear impact on handling times and customer experience.
Workforce Scheduling Support Implement AI assistants that leverage historical patterns for smarter shift assignments or adherence monitoring—again, typically interacting only with workforce management modules, not full CRM pipelines.
Quality Assurance Automation Instead of automating agent-facing scripts or customer interactions, choose an internal process—like analyzing call transcripts for compliance or sentiment—that runs independently and delivers immediate insight and ROI.
Select initial projects with low system coupling—components that can run nearly standalone or work within well-defined scopes. These “minimum viable integrations” reduce complexity while proving value in real business terms.
2. Build Employee Buy-In Early
From skepticism to empowerment: Make AI feel like a help, not a threat.
Set the Stage with Data
Employee sentiment around AI adoption is fraught with concern. A recent GoTo survey found that 62% of employees believe AI is significantly overhyped, and 86% admit they aren’t using it to its full potential—mainly because they lack confidence in how or where it fits into their day-to-day work.
Meanwhile, a Pew Research Center study shows that only 16% of workers use AI at all, and a staggering 80% do not—highlighting a gap between access and adoption.
These trends reveal a hidden truth: resistance isn’t about stubbornness—it’s about uncertainty.
Focus: Education Before Automation
Instead of positioning AI as a replacement, frame it as a tool that makes agents’ lives easier. Provide contextual training tailored to real workflow scenarios, and walk through how AI can reduce mundane tasks—like auto-sorting inbound calls or flagging compliance breaches—not replace human judgment.
Pilot with Employee Champions
AI adoption spreads best through peer advocacy, not top-down mandates. Identify a group of motivated agents—trusted individuals who are curious and coachable—and involve them early. They act as localized influencers: shaping adoption norms, providing feedback, and demonstrating AI’s value in their own workflows. This grassroots approach builds momentum from the frontline upward.
Build Trust Through Communication
Trust in leadership strongly influences trust in AI. A Harvard Business Review insight underscores that employees are skeptical about AI when they don’t trust the leadership behind it—especially if they feel AI is being used without transparency or benevolent intent.
Open dialogue about AI’s role, limitations, and safety—tracks not just outcomes, but message clarity—makes adoption feel intentional, not imposed.
3. Automate the Back Office First
Minimize risk—let quiet wins build credibility.
While chatbots struggle in the spotlight, behind-the-scenes automation drives efficiency and reliability.
“Automate the back office first” may sound like an overused mantra, but it’s popular for a reason: starting where AI has fewer customer-facing risks gives organizations the breathing room to prove ROI without the PR nightmare of a failed chatbot rollout.
Back-office functions—compliance, transcription QA, performance analytics, and Intelligent Virtual Customers (IVCs)—are ideal launchpads. They’re process-heavy, measurable, and less exposed to the customer’s direct line of sight.
What to Automate First
Compliance Checks: Automate auditing call transcripts to flag regulatory or policy issues.
Transcription QA: Use AI to analyze recordings for accuracy, sentiment, or script adherence.
Performance Analytics: Spot patterns in agent productivity, escalation trends, or customer sentiment shifts.
Intelligent Virtual Customers (IVCs): Synthetic customers designed to simulate real conversations. Instead of risking failure with live customers, IVCs let you test, train, and refine AI models against realistic scenarios—quietly, safely, and cost-effectively.
Case in Point: Commonwealth Bank’s Cautionary Tale
When Australia’s Commonwealth Bank (CBA) pushed AI voice bots directly into customer service, the outcome was public and painful. Bots failed to resolve issues, call volumes rose, and 45 jobs were cut prematurely before the bank had to backpedal amid backlash.
It’s a textbook example of chasing a headline instead of proving AI’s value in safer, internal domains first.
Why It Works
Low visibility = low risk: Errors happen behind the scenes, not in front of customers.
Proof of value: Automating “boring but critical” processes shows real, measurable ROI.
Foundation for scale: Early wins build executive and employee confidence for more ambitious rollouts.
4. Vendor Strategy: Safe Bet vs. Fast Bet
Choosing the right partner can make or break your AI project.
Option 1: Incumbent Vendors — The Safe Bet
Large, established vendors (think your existing CRM, workforce management, or cloud providers) come with undeniable advantages: scale, security, and the credibility that reassures your board. They’ve delivered before, and they’ll integrate into your existing tech stack with less friction.
The trade-off? Speed. Big vendors often move slowly, layering AI into their products incrementally. You’ll sacrifice agility for stability—but for some executives, especially those under scrutiny from boards or regulators, that’s the right call.
Option 2: Startups — The Fast Bet
Smaller, specialized vendors often innovate faster. They can spin up pilots in weeks, customize deeply for niche workflows, and push the boundaries of what’s possible with AI.
But there are risks: limited resources, unproven scalability, and the potential for hiccups that frustrate employees or erode credibility with customers. A failed startup partnership can set your AI agenda back years—not because the tech was bad, but because your organization loses confidence.
Vendor Strategy: Safe Bet vs. Fast Bet
Factor
Incumbent Vendor (Safe Bet)
Startup Vendor (Fast Bet)
Speed to Deploy
Slower, incremental rollout
Fast, agile pilots
Integration
Strong alignment with existing stack
Flexible, but may require workarounds
Credibility with Board
High — proven track record
Mixed — depends on reputation
Risk of Failure
Low technical risk, slower ROI
Higher risk of hiccups, potential setbacks
Innovation
Steady, but rarely disruptive
Cutting-edge, niche solutions
Scalability
Enterprise-grade, reliable
May struggle at large volumes
Best Fit When…
Board/regulators demand stability; credibility matters most
Speed and differentiation are critical; appetite for risk is higher
Hybrid Strategy
Use for customer-facing or mission-critical AI
Use for back-office pilots and innovation sprints
The Executive Framework: Choosing Your Path
When deciding between safe and fast, align the choice to your risk appetite and board expectations:
If credibility matters most: Stick with incumbents. They provide a defensible, low-risk path to AI adoption.
If speed and differentiation are critical: Partner with startups. Be ready to embrace hiccups as the price of innovation.
If you want both: Consider a hybrid strategy—pilot with a startup in the back office (low risk, high learning), while aligning your customer-facing roadmap with a trusted incumbent.
Bottom line: There’s no “right” choice, only the choice that fits your strategic posture. The wrong vendor isn’t just a missed opportunity—it can turn your call center into another 95% statistic.
Executive Playbook: Making Call Center AI Work
AI success in call centers isn’t about chasing the flashiest tools. It’s about discipline, focus, and choosing battles you can win. Here’s the checklist every executive should keep in mind before greenlighting the next AI project:
✅ Tie Every Pilot to Measurable ROI
If you can’t connect the project to the P&L, don’t start it. Define success upfront in hard metrics: reduced handle time, lower attrition, higher CSAT, or compliance cost savings. Every pilot should answer the board’s question: “What business value did this create?”
✅ Pick “Low Surface Area” Projects First
Start where integration is simplest and dependencies are minimal. Call routing, workforce scheduling, and QA automation deliver quick wins without touching every system in the stack. Prove value before attempting system-wide transformations.
✅ Train Employees and Align Incentives
AI doesn’t work if people won’t use it. Invest in education that shows employees how AI helps their workflows, not replaces them. Reward early adopters, celebrate quick wins, and use employee champions to spread momentum.
✅ Prioritize Back-Office Before Customer-Facing
Public-facing AI failures destroy credibility fast. Back-office automation—compliance checks, transcription QA, performance analytics, Intelligent Virtual Customers (IVCs)—delivers ROI quietly while giving you space to refine the technology.
✅ Match Vendor Choice to Risk Appetite
Don’t let vendor selection be an afterthought. If stability and credibility matter most, lean on incumbents. If speed and differentiation are critical, partner with startups. Better yet, build a hybrid strategy: use startups for low-risk pilots, then scale with trusted incumbents.
The Bottom Line
AI projects succeed when leaders treat them as business initiatives, not tech experiments. Anchor every step in ROI, simplify your first moves, bring employees along for the ride, and choose vendors with your strategic posture in mind. Do this, and your call center won’t just avoid being part of the 95%—it will help define the playbook for the 5%.
TLDR; The 5% Opportunity
The numbers may be grim—95% of AI projects fail—but they’re not destiny. For call centers, success isn’t about betting on the flashiest AI or rushing to impress the board with a chatbot demo. It’s about focus, realism, and cultural readiness.
The difference between the 95% that fail and the 5% that succeed isn’t the technology. It’s leadership. Leaders who demand measurable ROI, start small, bring employees along, and place smart vendor bets are already proving AI can make call centers more efficient, resilient, and customer-centric.
As an executive, you don’t have the luxury of treating AI as an experiment. Your job, your team, and your customer experience depend on getting it right. The good news: you can get it right—if you build deliberately, not reactively.
So here’s the call to action: Don’t chase the hype. Build the foundation that makes your call center part of the 5%.
