The Great CX Disconnect: Reality Is Falling Short of Expectations

There is an unprecedented disconnect between customer expectations and the experiences contact centers are delivering. According to Oracle, while 83% of customers expect immediate help when they reach out to an organization, with 90% defining “immediate” as within 10 minutes, the majority of contact centers consistently fail to deliver timely support. Alarmingly, 89% of customers are likely to switch to a competitor after just one negative experience.

Research from Gartner and Forrester shows that fewer than one in five customers successfully resolve issues through traditional IVR systems. Most either abandon the process in frustration or turn to live agents, highlighting persistent dissatisfaction with current voice self-service options.

Meanwhile, voice interactions cost $7+ per contact, making voice the most inefficient channel in a contact center portfolio. The US Bureau of Labor Statistics has found contact centers struggle with turnover rates of 30-45% at a replacement cost of $6,440 per departing employee, as cited by the Society for Human Resource Management.

Why Legacy Voice Systems Fail and Why It Matters

Traditional CX voice solutions consistently disappoint for six key reasons, all of which create frustration and inefficiency:

“Repeat Yourself” Fatigue: Customers are forced to re-explain issues due to a lack of platform memory, which spikes dissatisfaction and call times.

Menu Maze Confusion: Static, deep menus that are difficult to navigate leave customers confused and boost abandonment rates.

Misunderstood Inputs: Poor speech recognition and limited comprehension cause incorrect routing and more live-agent escalations.

Inflexibility During Change: Slow, manual updates make efficiently adapting to change nearly impossible, meaning missed opportunities during crises or market shifts.

Low Containment Rates: Complex requests still require live agents, reducing ROI from automation and raising operational costs.

Lack of Personalization: Generic, one-size-fits-all responses with no contextual memory threaten loyalty and upsell opportunities.

As customer expectations evolve, these outdated IVRs and chatbots simply can’t keep up, proving that a smarter, more adaptable CX approach is no longer optional but essential.

Customer Intent Is Contextual, Not Demographic

To address the CX disconnect, organizations must understand that satisfying customers requires more than faster response times. It demands genuine, human-like support. Customer behavior has been proven to be driven by circumstances, not demographics. A Gen Z customer might call for the exact same reason as a baby boomer when they’re in a stressful or urgent situation. McKinsey has found that when complex issues arise, 82% of customers escalate to voice regardless of their age or channel preference.

High-stakes moments such as medical emergencies, financial disputes, and travel disruptions all lead customers to choose voice. Instead of building for demographic segments, organizations must build based on emotional and circumstantial states.

The Empathetic AI Breakthrough: Engineering Human Connection at Scale

This is where empathetic voice AI comes in—a technology that not only processes words but also understands the full spectrum of human communication in real-time. Empathetic voice AI operates through three advanced detection mechanisms that create genuine emotional resonance:

Prosody Analysis: The technology analyzes pitch, pace, and volume patterns to understand emotional state. A customer speaking rapidly with a rising pitch triggers immediate empathy protocols. For instance, a user sounding frustrated gets a slower, more empathetic reply.

Lexical Cues: In combination with transcribing speech, the AI detects repetition patterns, word choice intensity, and linguistic stress markers. When someone says, “I’m fine,” the system analyzes context and vocal patterns to understand the true meaning. It recognizes that “‘I’m fine’ could mean “I’m upset”.

Behavioral Signals: The platform monitors interruptions, silence patterns, and conversation flow disruptions that indicate confusion, frustration, or disengagement.

The groundbreaking differentiator of empathetic voice AI is that it focuses on emotional resonance, adaptability, and trust building through tone awareness, intent recognition, and emotional intelligence that detects and responds to emotion in real time.

The Architecture Revolution from Monolithic to Modular

To understand how empathetic AI achieves this emotional resonance, it’s essential to look at how its underlying architecture differs from traditional systems. Traditional monolithic IVRS lock teams into rigid architectures where everything is intertwined, meaning if one thing needs to be changed, everything needs to be changed. It’s slow, rigid, and difficult to improve.

The AI-native platforms powering empathetic voice AI are designed on modular architectures where you can update or improve one part without disturbing the rest. It’s faster to deliver, easier to fix, and works across any channel.

Key Components:

Streaming Speech-to-Text: Real-time language processing with higher accuracy and reduced latency

Large Language Models: Context-aware response generation with conversation memory

Emotion Detection Engine: Multi-modal analysis combining prosody, lexical cues, and behavioral signals

Intelligent Text-to-Speech: Natural, expressive speech output matching emotional context

Real-Time Decisioning: Millisecond response adaptation based on customer state

Empathy as a Measurable Advantage

Organizations that use empathetic voice AI in their CX strategy are transforming every interaction from transactional to relationship-building. The solution combines operational efficiency with human understanding, providing several compounding benefits:

Higher Satisfaction, Retention, and Loyalty: Emotionally intelligent responses help customers feel genuinely heard and valued, driving satisfaction scores up to 50% higher, as found in Bain & Company research. This provides major reductions in churn and long-term loyalty through connection-driven conversations.​

Operational Efficiency at Scale: Modular AI architecture enables faster deployment, lower escalation rates, and reduced handling times. McKinsey reports that AI-driven automation can cut customer service operating costs by up to 30%, delivering scalable efficiency while maintaining quality support.

Smarter Personalization: Real-time emotional analysis lets every conversation adapt to a customer’s tone and intent, ensuring more relevant and empathetic interactions. McKinsey finds containment rates for AI-driven support systems often exceed 50% and claims up to 80% of tickets are handled autonomously in mature AI environments, confirming how tailored support drives better resolution outcomes.

Employee Empowerment and Engagement: With AI managing routine interactions and emotional context, agents are free to focus on complex, higher-value cases, resulting in improved morale, engagement, and satisfaction among staff.​

Sustainable Differentiation: Emotional intelligence becomes a brand signature, distinguishing forward-thinking leaders in competitive markets, driving higher customer lifetime value, and nurturing a culture of continuous innovation.​

When empathy and efficiency are prioritized in every interaction through emotionally aware AI, customer experience becomes faster while also more resonant, personal, and enduring. Empathetic voice platforms like Engage, Mosaicx’s AI-native solution, are driving these advances across industries. Engage turns empathy-driven conversations into measurable business outcomes by combining intelligent automation, multichannel capabilities, and robust compliance in one seamless experience.

The Framework: Automate, Inform, Connect

The most successful empathetic AI deployments follow a strategic framework:

Automate: Deploy AI-powered self-service for routine transactions to create fast, personalized, conversational interactions.

Customer: “I want to pay my credit card bill.”

AI Response: “No problem. Your balance is $123.27. Would you like to use your enrolled account?”

Inform: Provide AI-assisted information through a generative AI knowledge base to explain eligibility, next steps, and alternatives.

Customer: “Can I increase my credit card limit?”

AI Response: “You might be eligible for a credit limit increase! I can help you check.”

Connect: Reserve human interaction for only complex inquiries. The AI will have already gathered context, detected emotional state, and prepared the optimal handoff.

The automate, inform, and connect approach ensures inquiry complexity is matched to the appropriate resource level while maintaining emotional intelligence at every stage.

Always On, Always Responsible AI

Advanced empathetic AI platforms operate under an “Always On, Always Responsible” principle that prioritizes operational excellence and ethics, ensuring AI systems are available 24/7 while being trustworthy, fair, and accountable at every moment.

Guardrails in empathetic voice AI are essential, as they protect customers and businesses alike. Content filters, source checks, and transparency features prevent errors and confusion. Feedback and bias-reduction practices ensure the AI treats everyone fairly, while security ensures emotional data is safe and not linked to anyone’s identity.

Turning Conversations into Actionable Insights

Understanding emotion and intent in real time is just the beginning. Equally important is turning these interactions into insights that guide smarter decisions and elevate the customer experience. By analyzing patterns across voice, digital, and AI-assisted channels, organizations can uncover trends, identify friction points, and guide strategic improvements.

By leveraging advanced analytics, leaders can:

• Spot trends and pain points across channels: Identify where customers struggle, what issues reoccur, and which interactions drive satisfaction or churn.
• Optimize resource allocation: Determine which inquiries can be handled by AI versus human agents, ensuring staff focus on high-value, complex engagements and improving overall efficiency.
• Measure effectiveness and ROI: Track outcomes such as resolution rates, sentiment shifts, and operational efficiency to validate the impact of CX strategies. These insights make “empathy as a measurable advantage” concrete, showing the business value of emotionally intelligent interactions.
• Enable continuous improvement: Use real-time insights to refine conversational flows, personalize experiences, and anticipate customer needs before they arise—turning reactive CX into proactive experience management.

When analytics is applied in this way, customer interactions are no longer just reactive—they become a strategic source of intelligence, informing both operational decisions and long-term CX innovation. Organizations that integrate emotional understanding with actionable insights gain a 360-degree view of their customers, ensuring every interaction informs smarter, data-driven strategies for loyalty, efficiency, and competitive differentiation.

Lead or Be Disrupted

Legacy systems and impersonal automation are no longer sustainable for organizations that prioritize loyalty and growth. As customer expectations evolve, success now depends on technology that feels human. Empathetic voice AI bridges that gap by adapting instantly to emotional cues, turning every interaction into a faster, more meaningful, and emotionally aware exchange. Gartner has concluded by 2029, agentic AI is projected to autonomously resolve up to 80% of common customer service issues, widening the gap between CX leaders who adopt and those committed to outdated approaches.

To see how empathetic AI works in practice, learn more about  Mosaicx Engage, a platform that empowers organizations to deliver connected, compliant, and emotionally intelligent engagement across every channel.

Mosaicx Engage is designed to align with proven best practice frameworks and ethics for customer engagement, including human-centric empathy, transparent compliance, continuous user feedback, and actionable analytics. By choosing Engage, organizations implement a solution built for measurable, responsible, and meaningful interactions at scale.

Organizations prioritizing emotional intelligence alongside operational efficiency will set new CX standards, defining the next era of customer engagement. The data points to those who act decisively now going on to lead the market, while others risk being disrupted and left behind.

The post Empathetic Voice AI Brings a New Competitive Edge to CX appeared first on Execs In The Know.

Lance Child, ASAPP Account Director, shared the session with Strategic Consultant Crystal Collier and Principal AI and CX Consultant Chance Whitley, both of PTP. Here are the key takeaways from their session.

Why most generative AI pilots fail

Despite the raft of generative AI solutions being built and deployed, the majority of generative AI initiatives don’t make it past the pilot phase. A recent MIT report found that a staggering 95% of pilots fail to make a measurable impact on the bottom line. But the reasons for these failures aren’t just technical—operational choices and cultural conditions often deserve the lion’s share of the blame.

Three core obstacles stand out:

• Learning gap in tools and teams: Spinning up an AI agent demo is easy. But successfully deploying one that learns, adapts, and consistently improves customer experience is much harder. Mismatches between chosen tools and the actual problem, paired with unprepared teams, lead to disappointing results.
  
• Resource misalignment: Far too many organizations chase high-profile, revenue-generating use cases. But more substantive gains are often realized in functions with less visibility, like the contact center.
  
• Avoiding friction: Many pilots are designed as “happy path” demos, rather than real-world trials meant to tease out failures and tough issues early, before scaling up. But identifying sources of friction early in the process is key to creating agents that eventually work at scale.

What sets successful AI pilots apart

ASAPP’s Lance Child noted that the ASAPP team has helped a number of iconic brands realize value with a generative AI agent pilot, setting the stage for even greater returns with expansion. Based on that experience, he highlighted some consistent best practices:

• Tool and partner alignment: According to the MIT report, two-thirds of the successful pilots partnered with specialized external experts, rather than attempting to build a solution in-house. These partners bring essential perspective, mature frameworks, and robust capabilities for live simulation, testing, and ongoing tuning.
  
• Empowering line managers: Success with an AI deployment is far more likely when those closest to the workflows take the lead. When a central AI team drives the rollout without crucial input from line managers, that leads to blind spots and missed insights that put success out of reach.
  
• Deep integration and adaptation: Successful pilots go beyond basic adoption to absorption. They aren’t focused on simply adding a new tool to your kit. They embed new capabilities into everyday workflows, training, and continuous improvement processes. Adoption is just a checkbox of whether you have AI. Absorption means the AI agent is actually changing how work gets done.

The innovation tolerance model

The PTP team introduced a helpful framework for guiding your strategy and decision-making with an AI agent pilot. As they explained, before launching your pilot, it’s important to assess your organization’s innovation tolerance. In other words, what’s your capacity for change and risk? The answer is rooted primarily in your company culture.

You’ll need to consider a few key elements:

• Culture: How comfortable is your organization with change, experimentation, and occasional failure? Are your executives open to incremental, iterative gains, or are they more likely to expect big wins from the outset? Tuning the pilot’s scale and ambition to match culture minimizes resistance and increases your ability to demonstrate true value.
  
• Expectation management: It’s better to land small wins that build momentum than to aim too high and fail publicly. Right-sizing the pilot requires more than technical planning. You’ll also need to secure internal buy-in and clear alignment on what success looks like.
  
• Organizational readiness: How much appetite is there in your organization to tackle the challenges that will inevitably arise? Are the teams that will be involved prepared technically, operationally, and culturally to learn and adjust as failures occur?

If you’ve honestly assessed your organization’s innovation tolerance, and you’re disappointed with the results, don’t be overly concerned. You can still deploy an AI agent and realize significant value by aligning your pilot with your organization’s innovation tolerance profile. That alignment is a crucial lever for maximizing learning and ROI.

Use case selection: Start small, but think big

The session emphasized the critical importance of selecting the right use case for your AI pilot program. While it’s tempting to automate simple, low-risk tasks, those don’t always deliver the learning or measurable ROI needed for scale.

Key criteria for smart use case selection:

• Low risk, high impact: Choose customer intents that the AI agent can solve and that will make a noticeable impact on scenarios where pilot errors won’t be catastrophic, but where successfully solving them will be noticeable—such as basic FAQs that still drive operational costs and customer satisfaction.
  
• Alignment on outcomes: Ensure agreement across executive and operational levels on how you’ll measure the success of the pilot. Success metrics should reflect contact center and business KPIs, like first contact resolution (FCR), customer satisfaction (CSAT), and cost to serve, rather than AI performance benchmarks.
  
• Commitment to data quality: Many pilots falter because underlying data and knowledge bases aren’t up to par. Disparate, outdated, or competing data can undermine an AI agent’s ability to provide accurate answers or perform tasks necessary to help the customer. Broad commitment to ongoing data cleanup and maintenance is essential.
  
• Cross-functional buy-in: Avoid pilots led solely by engineering, even if that includes a team of AI experts. You’ll need broader input and oversight to keep the project aligned with business goals. Engage relevant line managers, customer experience teams, and change management experts to ensure the pilot addresses the full range of needs and concerns.
  

Testing, measurement, and iteration: Musts, shoulds, and nevers

Rigorous, context-sensitive testing can make the difference between a pilot bound for production and one that’s doomed to fail. Testing a generative AI solution is very different from testing traditional software. So, it’s useful to approach it with a different mindset. Getting a generative AI agent ready to serve your customers is a bit like getting a new human agent ready. You’ll need to feed the AI agent relevant training material, run live simulation tests based on real customer conversations, and layer in continuous tuning.

The practical framework described by PTP’s Chance Whitley divides the process of ongoing testing and optimization into three categories:

• Musts: Critical compliance, accuracy, and safety requirements. For instance, an AI agent handling billing inquiries must never give a customer the wrong total or account number. These are “do not harm” criteria, and failures require immediate intervention.
  
• Nevers: Actions that absolutely cannot happen, such as sharing protected personal information or providing advice beyond compliance guidelines.
  
• Shoulds: Improvement opportunities, things that are desirable but don’t reach the critical level of a must. These include clarity in responses, conversational nuance, and improved empathy, which would improve the customer experience. But they’re not showstoppers if they don’t happen yet.

Prior to launch, you should focus on the musts and nevers. Those are about ensuring your AI agent operates safely, accurately, and remains compliant with regulations and company policy. Once you’ve addressed the musts and nevers, you can turn your attention to the shoulds. The shoulds are about enhancing the customer experience, improving resolution rates, and increasing the AI agent’s ability to handle exceptions.

The process of identifying and addressing the shoulds will continue post-launch. It’s an ongoing effort. As you continue to monitor and measure your AI agent’s performance, you’ll want to pay close attention to these key metrics:

• First contact resolution (FCR)
• Conversation quality scores
• Containment rates
• Transfer and abandonment rates
• Repeat customer interactions

Keep in mind that monitoring customer feedback through surveys might not be enough, especially as customer behavior changes with automated service. You’ll also need to mine conversation data and continuously refine agent performance through real-time review and cross-functional input.

Change management and customer enablement

Beyond technical deployment, pilots only succeed when everyone impacted is engaged. That includes your front-line human agents and even your customers. That means change management can’t be an afterthought. The viability and value of your AI agent depend on it.

Here are a few best practices to keep in mind:

• Ongoing communication: Regular updates and training for internal teams will keep processes aligned and expectations realistic.
• Customer enablement: When launching an agent externally, it can be helpful to treat it as a new product launch. Announce changes, guide users, and smooth transitions as you would with significant branding or website updates.
• Iterative improvement: The most impactful pilots treat shoulds as fuel for iteration, folding feedback into ongoing tuning rather than relying exclusively on lagging indicators like customer surveys.

Friction is your friend: Designing pilots for improvement

When it comes to the customer experience, you might be aiming for frictionless CX. But for an AI agent pilot, friction is good. You should be actively seeking it out. Challenges uncovered during deployment, testing, and even close monitoring immediately after launch all open the door to rapid improvement. Problems that surface only after you scale up are riskier. They could damage customer relationships and create costly problems for your customer service operation.

Our best advice is to steer your AI agent pilot toward use cases aligned to your organization’s innovation tolerance, surface issues early before they become costly public problems, and treat the cycle iteration as an investment instead of a setback. With that approach, your pilot has a good chance of delivering significant value that you can build on through scaling and expansion. 

Final takeaways

Generative AI agent pilots aren’t run-of-the-mill technology evaluations. At their best, they’re also cultural and operational inflection points. Success relies on understanding your organization’s innovation tolerance, taking a pragmatic approach to choosing use cases, focusing on data readiness, and rigorously measuring musts, shoulds, and nevers. Engage your whole team, invest in change management and customer enablement, and actively pursue sources of friction. That’s how you achieve continuous improvement and real returns. 

By Stefani Barbero, Writer, ASAPP

ASAPP creates AI solutions that solve the toughest problems in customer service. With native AI at our core, our solutions get beyond basic automation to dramatically increase contact center capacity. At the center of our approach is GenerativeAgent®—an AI agent platform that autonomously and safely resolves complex customer interactions over voice and chat.

PTP delivers innovative self-service and contact center solutions that cut costs, enhance investments, and improve customer satisfaction. We offer comprehensive support from strategy and design to integration and implementation.

deep implementation expertise to tackle the most complex challenges in enterprise contact centers.

The post Piloting with precision: Increasing your chances of success with a generative AI agent pilot appeared first on Execs In The Know.

After the call ends, it’s logged in a CRM, tagged in a support system, maybe mentioned in a weekly recap — and then it disappears.

But what if that feedback didn’t stop at the agent level?

What if every customer conversation automatically became a signal that improved marketing campaigns, optimized operations, and fixed technical issues before they went viral?

That’s what a modern post-interaction strategy and Voice of the Customer (VoC) program are designed to do — and with AI, the process can now run automatically.

Why Post-Interaction Strategy Matters More Than Ever

Customer support is no longer just a department that solves problems; it’s a data engine that fuels company-wide learning. Every conversation holds three types of insight:

1. Reactive insight – What customers are asking for or struggling with right now.
2. Diagnostic insight – What’s consistently going wrong beneath the surface — recurring issues, PII incidents, or broken experiences that need to be resolved upstream.
3. Transformative insight – What the company could do differently to improve loyalty, reduce costs, or grow faster.

Without a system to capture and route these insights upstream, teams are forced to react to symptoms rather than solve the root cause.

For example:

• A surge in “I can’t find my order” calls may actually trace back to a broken tracking link on the website.
• Complaints about a loyalty reward not applying may point to a marketing system sync issue, not an agent error.
• Frustration about long wait times may indicate an operations misalignment rather than a staffing shortage.

Support already hears these problems first — the challenge is making sure the right teams see them, fast.

The Voice of the Customer: Beyond Surveys and CSAT

Many organizations claim to have a “Voice of the Customer” program, but too often, it relies on after-the-fact surveys or manual reporting. While CSAT and NPS scores can be helpful, they rarely tell the whole story.

The real voice of the customer is hidden in the words, tone, and sentiment of everyday interactions — the conversations your agents have thousands of times per week.

When analyzed collectively, these interactions can reveal:

• Trending frustrations (e.g., “My discount code won’t work”)
• Moments of delight (e.g., “Your agent fixed it so fast, thank you!”)
• Confusion around new features (e.g., “I don’t understand this update”)
• Emerging risks (e.g., “I might switch to another brand”)

That’s intelligence every department can act on — if it’s captured and shared fast enough.

How AI Changes the Game

Historically, this kind of insight required manual tagging, surveys, or dedicated analysts. But now, AI can listen at scale, identify patterns, and automatically route findings to the right departments in real time.

Here’s how a modern AI-powered feedback loop works:

1. Listen across all interactions — Calls, chats, social mentions, and ticket notes are analyzed automatically.
2. Detect patterns and sentiment — AI groups similar issues, tracks emerging themes, and assesses whether the customer left happier or angrier.
3. Route insights upstream — Findings are shared instantly with the right teams: IT for technical errors, Marketing for loyalty issues, Operations for process delays, and so on.
4. Enable real-time fixes —  When diagnostic tools flag a customer profile error or data mismatch, agents can correct it instantly during the interaction instead of escalating or reopening the case later. This prevents small data issues from snowballing into bigger customer problems — and builds trust in every touchpoint.

In other words, AI turns every customer conversation into real-time business intelligence — reducing friction, preventing churn, and improving efficiency across the board.

Enter harpin AI: Making Insights Actionable Across Teams

This is where harpin AI comes in.

harpin AI was built to connect the dots across customer conversations, operational data, and system logs — turning the noise of daily interactions into a clear, validated signal.

Here’s what that looks like in practice:

• Pattern Detection: harpin AI identifies recurring themes across customer interactions — such as loyalty discrepancies, delayed confirmations, or account errors that frustrate customers across channels.
• Automated Routing: When an issue surfaces repeatedly (like “checkout button not working”), harpin AI automatically notifies IT, marketing, or RevOps, depending on the source.
• Sentiment Analysis: It tracks whether customers leave the interaction happier or more frustrated, surfacing coaching opportunities for agents and broader process fixes for leadership.
• Proactive Resolution: Because harpin AI connects validated data across systems, it can flag inconsistencies or profile-level errors as they happen — allowing agents to correct them in real time and preventing future incidents by alerting the right teams upstream.

The result: a company that learns faster from every customer it serves.

Why This Feedback Loop Benefits Everyone

When the voice of the customer travels freely, every team improves:

• Support Teams resolve fewer repeat issues and can focus on higher-value interactions.
• Marketing Teams can identify friction in campaigns or loyalty programs before it impacts brand perception.
• Sales and RevOps can turn support-derived signals into real revenue intelligence — spotting churn risks, account-level friction, or pipeline leakage that traditional lead-scoring might miss.
• IT and Engineering gain early visibility into bugs or usability problems that frustrate customers.

And for executives, this creates something far more valuable: a real-time pulse on customer health, powered by the people who interact with customers every day.

Turning Data Into Action

The key to a successful post-interaction strategy isn’t just collecting feedback, it’s activating it.

That means:

• Setting up workflows that turn customer conversations into alerts, summaries, or tasks for other teams.
• Equipping agents with visibility into trending issues so they can empathize and resolve faster.
• Using sentiment analytics to measure how interventions are working — not just for customers, but for agent morale and retention too.

When AI connects these dots, the organization moves from reactive firefighting to proactive problem-solving.

The Future of Customer Support: From Cost Center to Strategic Command Center

Customer support has always been the front line of brand reputation. But now, with the right tools and feedback loops in place, it’s also the command center of customer intelligence.

AI doesn’t replace human empathy, it amplifies it. It ensures the stories agents hear every day are not lost, but leveraged to build a better business.

Support teams already hold the key.

AI — and solutions like harpin AI — simply unlock the door.

The post How AI Turns Customer Support Insights Into Company-Wide Action appeared first on Execs In The Know.

The real-time nature of this session was certainly a new one for me, and it was inspiring. The interactive format sparked some of the most meaningful conversations I’ve had at an event workshop. It allowed us to focus on the real problems that attendees needed help with – not just what sponsors wanted them to hear – and encouraged all of us to imagine the possibilities.

I found this session and the topics discussed reflected much of what we, here at Quiq, hear from our customers, prospects, and partners. So I wanted to share the five key topic areas that emerged, and enable a larger audience to learn from these incredible CX leaders. Here are the challenges discussed and the main takeaways from our collaborative session:

1. Gaining C-Suite Buy-In for AI Projects

A major discussion point was the difficulty contact center (CC) leaders and project executors face when trying to get executive buy-in for AI initiatives.

Key Takeaways:

• Educating the C-Suite is Required: Contact center operations and leaders find that AI project knowledge can range from oversimplified to extremely risk adverse at the executive level, and their complexity and risk isn’t well understood. A primary task for CC leaders is to better educate the C-suite on the nuances of these projects.
• Clarifying Risk Tolerance: Perspectives on acceptable risk for AI projects vary widely within an organization. Transparency is critical—not just about risk, but also how your chosen solution addresses that risk with guardrails, safety checks, etc. Business users need support in articulating potential risks and managing different viewpoints to align the organization.
• Defining AI Value Realization: Executive expectations for the return on AI can range from replacing all agents to questioning whether the project will pay at least for itself. Creating standardized AI Value Realization plans is essential for setting clear, realistic goals—whether it’s reducing agents, reallocating resources, or driving revenue.
• Navigating Self-Education: With countless vendors making big promises and news articles highlighting project failures, it’s difficult for leaders to educate themselves and their teams. A lack of industry standards and concrete examples compounds this challenge. One way to get around this? Look for success stories and how that success was achieved, especially ones in your industry that resonate with the challenges you’re facing and goals you have.

2. Managing the Complex Contact Center Ecosystem

The ever-expanding ecosystem of vendors, systems, and BPOs in the contact center creates significant complexity. The group focused on where to begin when trying to reconcile it all.

Key Takeaways:

• Assess Your Current Tech Stack: Start by taking inventory of your technology. Document the use cases and customer journeys supported by your current stack. Determine if it’s still meeting your needs, and identify opportunities for consolidation or elimination based on workflows.
• Prioritize Legacy Tech: Address challenges with legacy technology first, before modernizing, consolidating, or making new investments. Seek best-of-breed new technology where it’s required.
• Leverage BPO Partnerships and Trusted Vendors: Leverage your BPO partners and trusted software vendors with resources and experience in the contact center space to better understand the broader ecosystem. They often provide valuable insights and flexible options.

3. Creating and Proving Measurable Value

This discussion focused on defining executive-level metrics and driving tangible value within the contact center.

Key Takeaways:

• Connect Metrics to the Bottom Line: While leading indicators are useful for daily operations, the metrics you articulate and track for executives must clearly impact the bottom line. For example, Quiq customer Brinks Home™ demonstrated a reduction in cost per contact by 67%, showing that less dependency on live agents drove costs down.
• Focus on Long-Term Outcomes: Outcome-based measures are what matter the most. Prioritize long-term results and use calculations to project their impact over time. For example, framing a savings of $100,000 in one month as “$1.2 million over the year” or “$6 million over five years” is far more powerful.

4. Balancing AI with a Human Touch

This group explored how to effectively balance the human element within an AI strategy to increase both agent and customer adoption.

Key Takeaways:

• Implement AI that Empowers Both Agents and Customers: Introduce AI solutions that assist agents, in addition to customers. Automating repetitive tasks allows agents to focus on more complex customer interactions. Providing them with better information and tools can help soften the transition.
• Treat Agents with Respect: Many agents fear their jobs will be replaced. While some roles may change, this transformation will also create new opportunities. Be transparent and treat agents like adults, communicating how resources will be reallocated to other valuable areas of the business.
• Communicate Results Widely: Share the intended and actual results of your AI initiatives with both executives and frontline representatives to maintain transparency and build trust.

5. Modernizing Quality Assurance with AI

The conversation here focused on how AI is transforming conversation and ticket QA, and how to effectively QA the AI itself, as the business, technology, and customer needs evolve.

Key Takeaways:

• Onboard Your AI Like an Agent: Treat your AI system like a new hire, not a new piece of tech. First, educate it. Then, test it on designed conversations. Next, have it handle live conversations with human oversight. You can limit the volume, review the interactions, and tune the AI before moving to fully live conversations with ongoing sampling.
• Utilize AI Agent Analytics: Your AI solution should have its own analytics that perform self-review and QA using an AI model. This data will show where the AI agent needs to be tuned or updated.
• Apply AI for QA Across All Conversations: Use AI tools for all conversation QA and analysis. For agent performance, AI can automate the review of large volumes of interactions to identify valuable training opportunities and areas for focused improvement.

Final Thoughts

From securing buy-in for AI projects and navigating complex ecosystems to proving value and maintaining the human touch, my conversations with CX leaders highlighted exactly where we’re at with challenges today.

But there’s plenty of good news, too: By embracing new technology thoughtfully and keeping the lines of communication open, CX leaders are better positioned to deliver meaningful outcomes for their organizations. And of course, the right technology and partner matter. Visit quiq.com to learn more about how we empower CX teams to overcome each of these challenges—and download our free guide to change management, AI-Ready CX: A Leader’s Guide for Change, Adoption, and Impact.

By Mike Zinne, Chief Experience Officer at Quiq

The post 5 Key Challenges CX Leaders Face Today: A Think Tank Recap appeared first on Execs In The Know.

To provide direction and guidance, TP conducted a comprehensive study across different service delivery leaders to understand where they are finding value. What we discovered is that despite widespread ambition and countless pilot projects, a significant gap remains between potential and execution. Clients tell us that most are in the early stages of making AI work at scale and that there is a clear disconnect between strategic goals and operational reality, held back by gaps in readiness, technological limits, and the challenge of getting customers on board.

This blog post summarizes the insights from over 100 top CX leaders to understand the current state of AI adoption, the persistent importance of human agents, and the path forward for businesses looking to truly scale smart.

The State of Play: Ambition Meets a Human-Centric Reality

CX leaders are clear on their motivations for adopting AI and automation. The top two drivers are improving the customer experience and reducing costs. However, achieving these goals is proving more complex than anticipated. A major finding is that digital containment remains a significant challenge. Only 34% of enterprise leaders report that more than half of their customer interactions are fully handled by self-service or
automation.

This points to a critical area of focus: many organizations are still struggling to optimize self-service resolution and drive customer adoption across digital channels like mobile apps and websites. The data suggests a large portion of interactions, between 0% and 30%, are powered by digital or self-help tools, indicating a heavy reliance on traditional support methods.

Despite strong ambitions for automation, most organizations continue to depend heavily on human support. A striking 70% of surveyed leaders state that over half of their customer interactions are still managed by human agents. While initiatives to reduce this dependence are underway, progress is slow. This highlights a gap between ambitious strategies for automation and the actual operational readiness to implement them. The reality is that the human touch remains central to today’s customer experience.

The Evolving Role of the Human Agent

The persistence of human-led support doesn’t mean AI has no role. Instead, it signals an evolution. The future of CX isn’t about replacing humans with AI but augmenting them. The role of the human agent is rapidly shifting from handling routine inquiries to managing high-value, emotionally intelligent problem-solving.

According to TP’s study, agents are becoming resolution experts, equipped to handle interactions that automation cannot. The top scenarios best served by human agents are:

1. Complex and sensitive cases (56%)
2. Sales, retention, and commercial negotiations (28%)
3. Emotionally sensitive and high-empathy interactions (9%)

This shift is reshaping the workforce. Looking ahead one to three years, TP clients expect agent roles to evolve toward handling complex and escalated issues, focusing more on CX and empathy, and collaborating directly with AI tools. Agent augmentation, where AI is embedded into workflows to boost productivity and service quality, is a key trend. This hybrid model, blending digital efficiency with human touchpoints, is emerging as the formula for a more agile and effective customer experience.

Widespread Pilots, Limited Maturity

The journey toward scaled AI is paved with pilot programs. Nearly 70% of organizations have conducted AI pilots and projects over the last 12 to 18 months. The most common use cases being tested are:

• Agent Assist tools (69%)
• Customer-facing bots (67%)
• Interaction and sentiment analytics (64%)
• Case summarization (58%)

While experimentation is widespread, the results are still unclear for most. A massive 68% of leaders reported it was “too early to tell” if their recent AI initiatives delivered on their objectives. Only 6% confirmed that their projects “fully delivered,” while 24% said they “mostly delivered.”

This data reveals a critical bottleneck. Organizations are successfully launching pilots but are struggling to move beyond them to achieve mature, scalable implementations. The technology is being tested, but its full impact on the customer experience is still emerging.

Readiness Gaps and Implementation Hurdles

Why is it so difficult to scale AI in CX? The research points to significant readiness gaps across industries. Overall, only 30% of enterprise clients rated themselves as “High” in terms of readiness to deploy and embed AI across their operations.

Industries that are heavily regulated or complex, like Government and Insurance, show the lowest AI readiness. These sectors have a minimal presence in the high-maturity category and are concentrated in the low-maturity tier. In contrast, Technology and Healthcare show a more mixed profile, with a notable share in the “moderate” range, suggesting active experimentation but remaining implementation gaps.

When CX leaders were asked about their biggest concerns and risks, the challenges of scaling came into sharp focus. The top three risks identified were:

1. Technology and Infrastructure (25%): Barriers related to scaling AI automation.
2. CX and Customer Adoption (21%): The difficulty of getting customers to use new AI-powered tools.
3. Data Privacy and Security (16%): A persistent and critical concern in any AI implementation.

Furthermore, organizations rated their own capabilities in key areas as moderate to low. Process re-engineering and the existing tech ecosystem were identified as weak points, confirming the challenges in scaling AI solutions and driving customer adoption. This internal view aligns with the external reality: without the right foundations, scaling AI remains an uphill battle.

The Outlook for 2026: Muted Impact and Continued Experimentation

Looking ahead, what can we expect? CX leaders are planning to continue focusing on the same core AI use cases. For the next 12 to 24 months, the top planned initiatives are again agent assist tools, bots, sentiment analytics, and summarization tools. This suggests that organizations are still working to perfect and scale the foundational elements of AI in CX rather than moving on to more advanced applications.

While newer concepts like agentic AI—autonomous systems that can manage end-to-end processes—are on the radar, adoption is in its infancy. Only 47% of clients are exploring these solutions, and half of them are in the very early stages of discovery.

This cautious approach will likely mute the short-term impact of AI on interaction volumes. While client aspirations are high, our estimates show a modest 4-5% volume reduction impact by 2026. The projected impact from automation on outsourced work is expected to grow from 12% in 2025 to just under 15% in 2026. The experience from 2025 shows that given the challenges with readiness and pilot success, it will still take another 12-18 months for scalable AI solutions to mature within enterprises.

Actionable Strategies to Bridge the AI-CX Gap

The path from AI strategy to CX impact is challenging, but not impassable. Organizations can take clear, actionable steps to bridge the gap and unlock the true potential of AI.

As a valued Execs In The Know partner, TP offers the following recommendations for AI-enabled CX:

1. Prioritize Scalable and Proven Technologies
Instead of chasing every new AI trend, focus on what works. Prioritize scalable technologies like agent assist tools, customer-facing bots, and sentiment analytics. These tools boost efficiency and enhance the customer experience by enabling human agents to focus on what they do best: handling complex and emotional interactions.

2. Invest in Upskilling Your Workforce
Your human agents are more critical than ever. Invest in transforming them into specialized experts. Provide training that focuses on empathy, complex problem-solving, and negotiation. An upskilled workforce, augmented by AI, is a powerful combination that can manage sensitive cases and drive customer loyalty in ways automation alone cannot.

3. Move Beyond Pilots with a Clear Roadmap
Experimentation is valuable, but it can become a trap. To break the cycle of endless pilots, develop a clear roadmap for scaled AI adoption. This means integrating AI into core workflows, establishing sustainable deployment practices, and setting measurable goals for success. Define what a “win” looks like and build a plan to get there.

4. Address Foundational Challenges Head-On
Don’t ignore the roadblocks. Actively address key challenges, including technology infrastructure, data privacy, compliance, and financial risks. Building a robust foundation is essential for any scaling strategy. This may involve modernizing your tech stack, strengthening data governance, and creating a cross-functional team to manage change.

5. Shift Toward Consultative Partnerships
For many businesses, navigating the AI landscape alone is daunting. Shifting toward consultative, value-driven partnerships with CX experts can provide tailored solutions, continuous improvement, and industry-specific expertise. This is especially crucial for regulated sectors where AI readiness is lower and the compliance stakes are higher.
By embracing these strategies, organizations can move from ambition to impact, transforming their customer experience and building a smarter, more scalable future.

Guest post written by Jojo Pacis, Chief Transformation Officer at TP

TP is a global leader in digital business services, which consistently seeks to blend the best of advanced technology with human empathy to deliver enhanced customer care that is simpler, faster, and safer. To learn more about AI-enabled CX, visit tp.com

The post Bridging the Gap: AI Strategy vs. CX Reality appeared first on Execs In The Know.

And in today’s attention economy, that’s not a soft skill. It’s survival.

The Cost of Distraction

Brown opened with a staggering statistic: the average employee context-switches 1,200 times a day, bleeding nearly 40 minutes of productivity—over three hours a week—into the cracks of distraction.

“The world is vying for your attention,” he said, “but your world needs it.”

The message landed because every leader in the room knew the truth of it. More dashboards, more alerts, more meetings. The question isn’t whether information is available; it’s whether the right information is cutting through the noise at the right altitude.

The Discipline of Focus

Today, as Senior Director of Campus Teams at Christ’s Church of the Valley (CCV) in Phoenix, he oversees 18 campuses, 400 employees, and a weekly footprint of 60,000 people. His conviction is clear: focus isn’t innate, it’s trainable.

Brown challenged leaders to ask themselves a deceptively simple question: Am I giving my attention to the right thing, at the right time?

That reframing sparked a ripple in the audience. It’s not about doing everything, everywhere, all at once. It’s about intentionality. In his words: “In light of where I want to go and who I want to be, is the information I’m consuming beneficial? Are the voices I’m listening to beneficial?”

For CX leaders wrestling with competing dashboards and conflicting advice, this was a gut check.

Curating the Voices That Matter

Brown shared that clarity comes not from having all the answers, but from tuning into the right voices. Leaders often confuse noise for knowledge. But great leaders, he suggested, curate their inputs: listening to peers, mentors, and, yes, their customers, who can offer perspectives that translate into action.

In an age of artificial intelligence (AI), shifting employee expectations, and nonstop data streams, clarity is a competitive advantage.

Altitude Matters

One of the most powerful moments came when Brown urged leaders to consider their “altitude.” At what level are you currently operating? Are you buried in weeds, scanning the horizon, or lost in the clouds? Leaders must develop the agility to shift altitude, zooming in when details matter and out when teams need vision.

The Leadership Challenge

As he closed, Brown left attendees with a challenge: In a world of constant distraction, clarity is no accident; it’s a choice. The discipline of focus—on the right thing, at the right time, at the right altitude—is what frees leaders to guide their teams with confidence.

A special thank you to Travis for sharing his insights and leadership with us at the Customer Response Summit. Your dedication to innovation, service, and human connection continues to set the standard for the CX industry. We appreciate your time, expertise, and the inspiration you brought to the stage!

Keep an eye on your inbox. We’ll be sharing more of Travis’s story and the lessons CX leaders can take from it in the October 2025 issue of CX Insight.

The post The Discipline of Focus: Lessons from a Former NFL Quarterback appeared first on Execs In The Know.

Peloton, once synonymous with “the Bike,” has evolved into something far bigger: an ecosystem of connected fitness products, premium content, and a global community of millions. But as Spring reminded the audience, growth doesn’t just test your business model; it tests your ability to support, empower, and elevate every single member along the way.

When Success Outpaces Structure

Rewind just a couple of years. Peloton was riding a wave of demand that few companies ever experience. However, with that success came turbulence: overstocked accessories, scattered middle-mile logistics, service and repair gaps, and a global support structure that was stretched too thin.

In Spring’s words, Peloton had “disproportionate capacity and inefficiently staffed global support.” In other words, the model that got them here wouldn’t get them there.

Building the Foundation for Member Excellence

What followed was a deliberate reset. In 2022, Peloton rebalanced its operations. In 2023, the team streamlined its BPO footprint, introduced Salesforce, and initiated a company-wide upskilling initiative. By 2024, they were operating with just two BPOs—98 percent of support now U.S.-based—while training “universal agents” capable of handling membership, order experience, and technical service with equal fluency.

That shift wasn’t about cost-cutting. It was about creating a foundation where every member’s touchpoint felt seamless and trusted.

Technology That Works for Humans

Spring emphasized that technology should never be a barrier between a member and their goal. It should clear the path. That’s why Peloton transitioned to AWS for its phone system, integrated Service Cloud Voice, and deployed Agent Assist to help frontline staff classify cases, recommend replies, and generate summaries.

The result: agents aren’t bogged down in process, they’re freed up to be human. And members get what they came for: support that is fast, personal, and aligned with their fitness journey.

From Contact Center to Concierge

One of the most compelling moments of Spring’s keynote was her description of two new programs redefining the member experience.

• Social Care: A team that proactively engages with members on platforms like Facebook and Instagram, meeting them where they already are.
• Human Concierge: A pilot program offering new members a dedicated guide during their first 90 days, ensuring they feel supported, celebrated, and connected

These aren’t just initiatives. They’re a philosophy that member support isn’t about handling problems; it’s about creating a sense of belonging.

Data as Culture, Not Just Metrics

Under Spring’s leadership, Peloton also began building a stronger data culture. Weekly performance visibility and clear metrics gave her teams not just numbers to chase, but stories to tell, including how their work translated into efficiency, loyalty, and long-term engagement.

Data-driven decision making wasn’t a line item in a presentation. It became a way of seeing, much like Peloton teaches its riders to measure cadence, resistance, and output.

The Leadership Question

Spring’s keynote left the room buzzing with a question: Are we as CX leaders designing support that solves issues, or are we designing support that elevates the entire customer journey?

Because Peloton’s story is more than a case study in operational turnaround, it’s proof that CX leaders can transform turbulence into trust, scale into service, and, most importantly, transactions into relationships.

A special thank you to Dawn and the entire Peloton team for sharing their insights and leadership with us at the Customer Response Summit. Your dedication to innovation, service, and human connection continues to set the standard for the CX industry. We appreciate your time, expertise, and the inspiration you brought to the stage!

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As tech visionary Ray Kurzweil once said, “Our intuition about the future is linear. But the reality of information technology is exponential, and that makes a profound difference. If I take 30 steps linearly, I get to 30. If I take 30 steps exponentially, I get to a billion.”

We’re in the era of exponential AI acceleration, which means the biggest risk isn’t doing the wrong thing. It’s doing nothing at all. So how do you balance bold vision with practical, low-risk execution? 

We’ve developed a proven framework we call the Red Circle Method to help you.

The Cost of Over-Planning

First, though, some real talk: getting stuck in the endless planning cycle is costing you—and maybe more than you think.

• Lost competitive edge: While you’re perfecting a strategy, others are testing, learning, and iterating.
• Operational drag: Agents continue a battle against inefficiencies chipping away at customer satisfaction.
• Financial impact: Each month of delay means missed opportunities to improve AHT, CSAT, and FCR.

Bottom line? Delay is the most expensive choice you can make.

Think Bigger: Reimagining the Possibilities

When it comes to successful AI implementation, vision matters. Incremental optimization won’t dramatically transform your contact center—but wholesale reinvention isn’t realistic, either. The key is to imagine without constraints, then translate that vision into quarterly stepping stones.

Before we get to the Red Circle Method, let’s start with a thought exercise.

Ask yourself: What would your contact center look like if you built it today—with no legacy systems, no headcount constraints, and AI-native from day one? Don’t worry about feasibility yet, just dream big. Then, for each reimagined process, consider how you might get there from where you are now. Identify three or four stepping stones, each one reasonably achievable within a quarter, that move your contact center closer to that transformation.

Consider how one real-world telecommunications company struggling with an ineffective IVR phone tree put this exercise into action. Instead of trying to improve the phone tree, they imagined a whole new first-contact experience—one where conversational AI could understand a customer’s needs, route them to the correct agent, and even handle many issues itself. And so, over the course of four quarters, they targeted smaller use cases that led them to their ideal  state.

• Quarter 1: Basic balance inquires and bill explanations
• Quarter 2: Service outage reporting and updates
• Quarter 3: Plan changes and upgrades
• Quarter 4: Complex troubleshooting and technical support

Each step was small and manageable, but together, they reshaped the customer service experience.

The Red Circle Method Framework

Now that you have an idea of where you want to go, it’s time to act. And the Red Circle Method offers a proven, disciplined, operator-first approach to quarterly progress.

Step 1: Audit your current pain points. Review QA problem areas, top call drivers, and handle time distribution. Ask your agents what wastes the most time in a typical call. You’ll quickly realize you have all the data you need to find ROI opportunities.

Step 2: Red-circle two or three of the most targeted, measurable, solvable issues. Avoid vague statements like “improve customer experience.” Something like “reduce password reset handle time by 30 seconds” works because it’s testable and impactful. Remember, focused goals prevent wasted effort.

Step 3: Build a bottom-up business case. Numbers create cross-functional alignment, so attach dollar values to AHT, CSAT, and QA scores and run A/B tests or time studies to validate impact. Make sure stakeholders in Finance, IT, and CX are on the same page regarding ROI expectations.

In practice, this framework sets you up for something we like to call the 33% Rule. Say you pilot three use cases—the ones you identified in Step 2—per quarter. Even if only one out of three is successful each quarter, you’re building a repeatable win rate, along with a culture of momentum. That’s where long-term transformation begins.

Momentum Is the New Strategy

Visionary thinking combined with disciplined execution creates a powerful rhythm: Dream without limits. Then test and prove value one quarter at a time. Repeat. 

This dual approach avoids the extremes of analysis paralysis on one end and reckless moonshots on the other. Your contact center will feel a positive impact in a matter of weeks.

And if a use case doesn’t work out as expected? It’s much easier to pivot after three months than after a year or more of planning and evaluating.

Beyond that, quarterly micro-pilots also build credibility for scaling. As teams adopt a test-and-learn mindset, you create a culture where AI is embraced rather than tolerated or ignored. The best technology is the technology that gets used, after all—and these quarterly stepping stones provide demonstrable progress for boards, employees, and customers.

In a nutshell: Perfect plans don’t win. Progress does.

We’ve already seen the Red Circle Method help CX leaders balance bold imagination with disciplined execution. Now it’s your turn to transform strategy into reality, one quarter at a time. Try red-circling just one challenge this quarter, and let it prove what’s possible.

Guest blog post written by Laivly.

Laivly helps leading brands operationalize AI in their contact centers through Sidd, its AI platform. Sidd integrates directly into existing customer service operations, transforming AI from abstract potential into measurable results that drive data consistency and customer satisfaction.

The post The Red Circle Method: From Vision to Reality, One Quarter at a Time appeared first on Execs In The Know.

The current economic climate, coupled with the emergence of deeply transformative technology, has prompted leaders to seek out added efficiency. Indeed, Zendesk, one of TELUS Digital’s technology partners, found that 70% of CX leaders believe generative AI (GenAI) makes every digital customer interaction more efficient. If these leaders are right, and the brands they represent are able to connect their customers with what they’re looking for in the ways that they’d expect, the added efficiency made possible by new technology will be a considerable boon for brands and customers alike.

But it’s important to remember that efficiency alone doesn’t necessarily improve loyalty or profitability. Loyal customers are the lifeblood of sustainable business growth, often spending more, costing less to serve, and acting as brand advocates. This makes nurturing loyalty through meaningful interactions a critical driver of long-term profitability. Underscoring this point, Calabrio research reveals that nearly all consumers (97%) say service interactions influence their loyalty. Again, there is an important question to consider: Are brands optimizing away the very moments that build lasting — and profitable — customer relationships?

The evidence suggests that many brands might be. While 65% of customers want brands to adjust to their expectations, 61% feel most brands treat them like a number, according to Salesforce. This disconnect highlights that empathy is often overlooked in the pursuit of efficiency. In fact, 86% of consumers say showing empathy is powerful in building strong brand relationships, per Sitecore.

“As artificial intelligence and automation reshape customer experiences, organizations can feel they have to choose between efficiency and empathy. This is a false dichotomy,” says Jim Mitchell, vice president of customer experience and digital innovation at TELUS Digital. While AI and automation effectively handle routine tasks, human agents remain essential in building trust and loyalty through empathetic interactions and complex problem-solving. The challenge — and opportunity — lies in finding the sweet spot where technology enhances, rather than replaces, human connection.

Ultimately, the best experiences are designed for humans, by humans. 

The hidden value of “inefficient” customer interactions

In the drive for efficiency, it’s easy to lose sight of what customers value: being heard, understood, and genuinely cared for. Mitchell adds, “While speed and convenience are undoubtedly important, they’re not the whole story. Consider the frustration of having a support ticket closed before your problem is actually resolved, or the exasperation of being unable to reach a human when you really need to talk things through.” These are the moments where efficiency, taken to an extreme, can actually erode customer loyalty.

The most memorable, loyalty-building customer experiences often emerge from interactions that might seem “inefficient” on paper. These experiences may not align with traditional efficiency metrics, but they can create the kind of emotional connections that turn customers into loyal advocates.

Examples of experience investments that build lasting loyalty

Some forward-thinking companies have embraced customer experience strategies that prioritize long-term value over short-term efficiency. These approaches, although potentially resource-intensive, yield significant returns in terms of customer loyalty and brand advocacy. 

Examples include:

• Ritz-Carlton Hotels encourages every employee to spend up to $2,000 per guest to solve any problem or improve their stay, without needing manager approval. Empowering every member of their team to act decisively prevents bureaucracy from getting in the way of the resolutions. It creates a perception that the brand is willing to go above and beyond for guest satisfaction.
• Costco Wholesale maintains a generous return policy for most items, even accepting returns of items purchased years prior and with signs of use. While this policy could be seen as financially inefficient, it builds immense trust among its member base. 
• Chewy, a retailer of pet-related products, shows that they understand pet owners and the bonds that they form with their animals. When Chewy learns that a customer’s pet has passed away, they go out of their way to find the pet’s picture on social media, create an oil painting of the pet, and send it to the customer with a handwritten note — all without being asked and at no charge.
• Patagonia transforms Black Friday into an opportunity to strengthen connections with its community of environmentally conscious customers. Instead of running traditional sales, they speak directly to shared values with a message on their website: “We know shopping a good sale can feel great. But the frenzy of Black Friday never sits well with us. So, we’ve got a better deal. Stick to buying the quality gear you need any day of the year, and we’ll give away the profits to save our home planet.”

If efficiency were the sole focus for evaluating CX programs, these practices would likely never make it past the drawing board. Yet, these industry-leading brands recognize an important truth: Strategic investments in empathy can yield returns that outweigh their apparent costs.

The business case for empathy in CX

The business case for these so-called inefficient practices becomes clear when we consider that a mere 5% increase in customer retention can increase profits by up to 85%, per Bain & Company. This staggering ROI on customer loyalty underscores why CX leaders are rethinking how they measure success.

“Traditional metrics like net promoter score (NPS) and average handle time (AHT) have their place, but they don’t tell the whole story,” explains Mitchell. NPS gauges loyalty but doesn’t reveal specific pain points, while AHT can incentivize speed over quality, potentially leading to unresolved issues and lower customer satisfaction. Recognizing these limitations, next-generation KPIs such as customer effort score (CES), customer journey satisfaction (CJS), and revenue per interaction are emerging to provide a clearer link between CX improvements and business outcomes.

What’s promising is that the majority of CX leaders recognize that a single metric does not define success, with 40% reporting using three different KPIs and 38% tracking four or more — according to TELUS Digital research, in partnership with Statista. This multi-faceted approach to measurement reflects a growing understanding that in customer experience, it can’t only be about efficiency. The most successful organizations are those that find ways to measure — and optimize for — both operational excellence and human connection.

Leveraging AI to enhance human connection

The conversation around AI in customer experience often focuses on automation and efficiency — and for good reason. AI’s ability to take on repeatable tasks, power self-service options and process vast amounts of customer data promises to transform how organizations deliver support. However, leading organizations are discovering a more nuanced approach: using AI to amplify human empathy rather than replace it. According to Zendesk, 75% of CX leaders see AI as a force for amplifying human intelligence. In practice, this means the same technology that drives efficiency can also enhance our capacity for understanding and responding to customers empathetically.

Both options make it clear that the interpretation about enhancing empathetic capacity is our analysis, separate from Zendesk’s statistics.

This perspective represents a significant shift in how we think about AI’s role in CX. Rather than viewing AI solely as a tool for automation, keen leaders are exploring how it can create opportunities for more meaningful human connections, not fewer. The key to success lies in asking the right questions about AI implementation. Mitchell asks, “How can we use AI to identify emotional cues in customer interactions? Where can AI provide agents with real-time emotional intelligence support? How can we ensure AI-driven personalization feels genuinely empathetic?”

Forward-thinking organizations are finding answers through several strategic approaches. They’re implementing AI-powered sentiment analysis to gauge customer emotions in real-time and route complex or emotionally charged interactions to the most suitable human agents. AI is being used to provide agents with empathy cues and suggested responses based on customer context, amplifying their ability to connect on a human level.

These companies are also using AI to detect early warning signals that might prompt support requests, improving self-help options based on analyzed patterns and enabling proactive outreach before issues escalate. They’re creating AI-driven personalization that considers past interactions and preferences, while maintaining clear paths to human support and enabling customers to easily schedule human interactions when needed.

The goal isn’t to make every interaction more efficient, but rather to use AI to create space for human connection where it matters most. By handling routine tasks and providing agents with better insights, AI frees up human agents to focus on complex, emotionally nuanced interactions that build lasting customer relationships.

Upskilling human agents for the AI era

As AI is used to handle more routine interactions, the role of human agents is evolving to focus on more complex, nuanced customer needs. This shift demands a new approach to developing future-ready CX talent — one that emphasizes emotional intelligence, problem-solving skills and the ability to identify opportunities for deeper customer engagement.

“The future of customer experience delivery isn’t just about resolving issues,” explains Mitchell. “It’s about equipping agents with the skills and tools to handle increasingly complex interactions while recognizing opportunities to strengthen customer relationships.” This means training agents not just in traditional customer service skills, but in using AI-powered tools effectively and reading emotional cues that technology might miss. However, there’s currently a significant gap in agent preparedness. Only 45% of agents claim to have received AI training, and less than half of those (21%) are satisfied with that instruction, according to research from Zendesk. This disconnect between technology adoption and agent readiness risks undermining both efficiency and empathy goals.

The opportunity extends beyond problem-solving to building deeper customer relationships. “If I have good support, if I have 30,000 contacts on a monthly basis where 95% or 96% of these interactions are positive, I have a great opportunity,” explained Cami Ferreira, global business strategist and CX speaker, on a recent episode of Questions for now: She envisions support teams as powerful drivers of business growth, noting that every positive interaction creates potential for referrals, upsells and cross-sells. “What if we invest in artificial intelligence to predict what customers would want from that particular organization?” she asked. “If you have artificial intelligence predicting, in a personalized way, what you can offer once you have that customer on the line, that’s the time, that’s the momentum to leverage to sell more.”

The key is ensuring agents have both the technical skills to leverage AI effectively and the emotional intelligence to know when and how to make these deeper connections. Through innovative approaches like AI-powered training simulations that accelerate time-to-proficiency, organizations can help agents hone both the technical and emotional aspects of modern customer experience. This balanced approach turns customer service interactions into opportunities for relationship building, making every conversation count toward long-term customer value.

Get help balancing efficiency and empathy in CX

As organizations navigate the AI-driven future of customer experience, the most successful will be those that find ways to balance efficiency with empathy, using technology to create more opportunities for meaningful human connections, not fewer. 

TELUS Digital partners with innovative brands to achieve this balance, helping them leverage AI to enhance both operational excellence and human connection. Our approach integrates AI-powered efficiency with empathy-driven strategies, helping brands create memorable customer experiences that build loyalty and long-term value.

Ready to explore how your organization can realize opportunities for both efficiency and empathy? Contact the TELUS Digital team today.

Guest blog post written by TELUS Digital.

The post The Strategic Value of Embedding Empathy in the World of AI-Driven CX appeared first on Execs In The Know.

For many brands, this vast middle is a blind spot. Decisions are often made based on the opinions of the loudest voices, leaving untapped insights buried in the silence. The challenge—and the opportunity—is learning to listen to what’s not being said.

Here’s how brands can start uncovering these hidden insights:

1. Shift Your Perspective on Feedback
   Feedback isn’t just the complaints or praise, it’s also the quiet signals. Look for patterns in the middle of the spectrum: repeat behavior, subtle comments, or even the absence of engagement. These signals often point to areas that truly impact the customer experience.
2. Use Technology Wisely
   AI and analytics tools can be powerful mirrors for your data, but they aren’t mind-readers. If the data you feed them is biased toward extremes, the insights will be too. Always pair technology with human judgment to capture the nuance of neutral or “silent” feedback.
3. Build a Culture of Trust and Curiosity
   Listening to customers starts internally. If your employees don’t feel safe speaking up, you’ll never hear the full story from your frontline teams. Psychological safety isn’t just an HR initiative, it’s the foundation of world-class customer experience.
4. Correct for Bias, Act with Transparency
   Every feedback system has blind spots. Being transparent about how you collect, analyze, and act on feedback builds trust with both customers and employees. This encourages honesty and leads to insights you can actually rely on.

The silent majority may not leave a review, but they’re leaving clues everywhere. When we learn to tune into them, both through data and through the voices of our employees, we unlock a level of insight that can transform the way we connect with customers.

I’m excited to be presenting a panel on this very topic at Execs In The Know’s Customer Response Summit in San Diego on September 18.

Our session, hosted by Lisa Oswald, is titled “The Signals We’re Missing” and will explore how to truly listen to the silent majority of customers, the ones whose feedback isn’t captured in traditional surveys, filtered through bias, or lost in outdated systems.

The Execs In The Know Advisory Board will tackle questions like:

1. How do we build trust so more customers are willing to speak up? Where are surveys falling short, and what’s replacing them?
2. What cultural blind spots are baked into how we collect and interpret feedback?
3. How can AI-powered sentiment detection uncover unspoken truths and emerging emotions, when applied intentionally and ethically?

If we want to meet customers where they are, we must learn to listen even to what they don’t say. I’m really looking forward to this panel and the insights we’ll share.

Guest blog post written by Ruth Rose, VP, Strategic Growth at Transcom

The post Listening Beyond the Stars: How to Hear What Your Customers Aren’t Saying appeared first on Execs In The Know.