In many ways, agents have never faced more pressure. They’re expected to manage an ever-expanding mix of digital and voice channels, resolve complex issues faster, and maintain empathy throughout every interaction – all while navigating shifting expectations and compliance demands.  

In this environment, AI is often framed as the solution: a way to ‘do more with less.’ Yet the most progressive contact centers are discovering that AI’s real potential isn’t in replacing people; it’s in augmenting them.  

A new generation of intelligent assistive tools, often referred to as ‘AI co-pilots,’ is helping agents make faster, smarter decisions, reducing cognitive load, and strengthening the very human qualities that customers still value most.  

“Our approach is pretty simple,” says Will Penn, Senior Sales Engineer at Puzzel.  

“We’ve got tools available to make sure the agent can do the best job possible; not to replace them, but to enhance the human part of the experience.”

The Rise of the Super Agent  

Unfortunately, the ‘Super Agent’ being discussed is not some sort of Captain America/James Bond hybrid – as cool as that would be.   

No, this is routed less in the fantastical and more in the mold of delivering real, everyday results.   

Rather than some questionable serum or a boatload of fancy gadgets, the tool that transforms contact center agents into ‘Super Agents’ is the AI-powered copilot.   

By sitting alongside agents, AI co-pilots can analyze conversations and surface contextual guidance in real-time.   

Instead of switching between multiple systems or digging through outdated documentation, agents receive live prompts from a connected knowledge base that continuously suggests next best steps.  

“Puzzel’s CoPilot is connected to a well-formatted and organized knowledge base,” Penn explains.  

“It can constantly suggest those next best steps throughout the call, guiding the agent without them needing to put the customer on hold or go searching across multiple databases.”  

The impact is immediate: shorter handling times, fewer errors, and smoother compliance processes.  

In highly regulated industries such as insurance or finance, that combination of speed and accuracy is crucial.  

For Penn, CoPilot delivers the best of both worlds:  

“It’s better for the agent because everything they need is right in front of them. And it’s better for the customer because they get their answers faster. Everyone benefits from it.”

Efficiency with Integrity  

Penn’s comments aren’t just the biased championing of his own company’s solution; they’re backed up by facts.   

Indeed, early adopters of AI co-pilot tools are reporting measurable improvements.  

According to Puzzel, organizations have seen up to eight times faster wrap-ups, four-fold ROI on agent time, and a 23% reduction in average handling time (AHT).  

Those numbers matter, but they tell only part of the story. Behind each gain is a reduction in the hidden strain agents face: fewer repetitive tasks, less information overload, and a greater sense of control.  

In particular, Penn believes that the removal of “annoying admin” is “huge.  

“Consistency improves, accuracy improves, and the agents are freed up to do those more complex tasks that need their full attention.”  

By aligning efficiency with integrity, AI co-pilots are helping contact centers achieve something many thought impossible: operational speed and human quality, simultaneously.  

Case Study: Insurance Company  

While co-pilots might be one of the most renowned AI solutions in the contact center, they are far from the only game in town.   

Puzzel recently launched its Live Summary feature, which leverages AI to create editable, CRM-ready call notes in seconds, ensuring accuracy, consistency, and compliance.  

A clear example of how this tool encapsulates the potential of the human-AI partnership in the customer service and experience space can be seen from one of their customers, a Nordic legal insurance provider that manages complex, documentation-heavy cases.  

Before deploying Puzzel Live Summary, its agents spent significant time writing and reviewing notes after each call, a process that often delayed lawyer preparation and case progression.  

With Live Summary automatically capturing and structuring conversation details in seconds, those delays have been drastically reduced.  

While the efficiency gains are impressive, Penn stresses that Puzzel’s priority “isn’t just speed; it’s quality.   

“With Live Summary, we’re not only creating notes faster, but the quality and consistency of those notes have improved. “

“For this customer, it means better handovers to their lawyers and a smoother experience for their customers.”  

By automating these administrative tasks, agents can focus on higher-value interactions, such as listening, clarifying, and showing empathy, rather than typing.  

Empathy Through Enablement  

Despite all the talk about automation, most customers still prefer human interaction for complex or emotionally charged issues.  

Puzzel’s research shows that 75% of customers want to speak to a real person when problems get serious. That preference underscores a simple truth: empathy remains crucial to top-level customer experience.

AI co-pilots support, rather than dilute, that empathy. By taking away the distractions of note-taking, knowledge retrieval, and compliance checking, agents have more bandwidth to actively listen and respond with understanding.  

“The co-pilot is there to enhance the human experience,” says Penn  

“It gives that guidance throughout the interaction, so the agent can focus on what really matters: the customer in front of them.”  

Enterprise Takeaway: What Leaders Should Do Now  

For enterprise CX leaders, it is clear from Penn’s insights that AI’s most strategic value lies in empowering people, not sidelining them.  

To realize that value, the Puzzel man recommends:  

• Auditing the agent experience: Identify where repetitive or manual tasks are limiting empathy and productivity.  

• Introducing AI assistants that complement, not complicate: Seamless integration and transparent guidance are key.  

• Defining success beyond speed: Quality, accuracy, compliance, and customer sentiment are the real metrics that matter.  

As Penn notes, the evolution of AI in the contact center is as much about mindset as it is about technology.  

“There does need to be a mental shift,” he says.  

“Moving away from thinking AI is there to automate service, and towards thinking of it as a collaborative tool that enhances the human experience.”  

While it’s true that AI can bring confusion to the contact center space, the pros far outweigh the cons.   

When implemented correctly, the reward isn’t just faster resolutions; it’s happier agents, more trusting customers, and a more resilient CX operation.  

You can learn more about how your customer service department can deliver efficiency and empathy by checking out this article. 

You can also discover Puzzel’s full suite of solutions and services by visiting the website today.   

They’re also a must-have in an era where first-party data has become crucial. In retail and eCommerce, CDPs now decide whether personalization feels seamless or invasive. For healthcare teams, they determine if data moves fast enough between portals, contact centres, and clinics to help patients instead of slowing them down. In finance, they’re becoming a line of defence -unifying records, flagging anomalies, and proving compliance to regulators.

What Is a CDP and Why It’s Not Just a CRM Upgrade

For years, the CRM has been treated as the central hub for customer data. But its design is narrow. It logs contacts, tracks deals, and manages service cases.

What it doesn’t do is unify every fragment of data a company holds – website visits, app behaviour, consent records, transactions, service interactions, marketing touches – into one, live profile that can be activated anywhere. That is the role of the Customer Data Platform.

A Customer Data Platform pulls first-party data from every corner – websites, apps, call logs, sales systems – and brings it into one place. The mess gets cleaned, duplicates are resolved, and what’s left is a living record of the customer that keeps updating. From there, it can do a lot: fuel AI models, trigger automated steps in the background, or simply give a frontline agent the context they usually waste time piecing together.

As Salesforce describes it, the platform moves data “from disconnected silos to real-time engagement.” Adobe takes it further, positioning CDPs as the only way to deliver “personalization at scale, powered by first-party data.”

For many CX teams, CDPs are now the foundation of any strategy for future growth. That’s part of why the market is growing so fast, at a rate of around 39.9% CAGR.

Measurable Customer Data Platform Benefits and ROI

Technology investments rise and fall on one question: do they pay back? In the case of Customer Data Platforms, the answer is increasingly clear. CDP benefits are measurable, and enterprises are using the data to justify spending at the board level.

The financial case is strong. A Forrester Total Economic Impact study of Oracle Unity found that companies achieved an ROI of 158% with payback in just seven months. The study pointed to efficiency gains in segmentation, campaign orchestration, and service response times.

The pattern repeats across industries.

• Telecoms: Spark NZ, working with Tealium and Snowflake, cut campaign delivery time by 80%. That speed translated into millions saved in media spend and higher response rates.
• Membership services: At AAA Washington, Salesforce Data Cloud unified data across channels to power faster, more relevant roadside support. The result was a jump in satisfaction scores and measurable cost savings as manual lookups disappeared.
• Retail: Klaviyo’s customers often cite faster payback than expected. At DKNY, Klaviyo helped to increase click rates and improve conversions by enabling end-to-end personalization.

But the benefits of a CDP often go further than expected, particularly in the age of AI and automation.

Customer Data Platforms as the Foundation for AI, Automation and Orchestration

Artificial intelligence and automation are boardroom priorities, but both depend on the quality of the data underneath. Without a trusted foundation, even the smartest models deliver the wrong outcomes. That is why CDP benefits now reach beyond marketing: they are powering AI decisioning and workflow orchestration across the enterprise.

Salesforce describes its Data Cloud as a “zero-copy CDP,” able to unify data across systems and activate it in real time. In practice, that means generative AI assistants like Einstein or Agentforce can draft responses, recommend actions, and even guide self-service troubleshooting – because the context is accurate. Adobe has taken a similar line, stressing that a CDP is the only way to make personalisation “real-time and scalable.”

Case studies bring this to life. Vodafone used Tealium to launch what it called an “AI-powered customer revolution”. By unifying subscriber data, the telecom giant improved cross-channel engagement by 30% and created new opportunities for automated retention campaigns.

Castore, a fast-growing sportswear brand, turned to Klaviyo’s CDP to scale its customer journeys and marketing campaigns, branching into four new SMS territories, and increasing click-through rates for AI-powered, automated campaigns.

Trying to automate on top of scattered data just makes the noise louder. With a CDP in place, orchestration feels sharper: actions run faster, responses land with more relevance, and journeys stay consistent. The same profile sits underneath everything – whether it’s an AI copilot, a bot handling routine work, or a self-service flow guiding a customer through a fix.

Customer Data Platform Benefits for Compliance, Security and Trust

Data is now under the same scrutiny as financial records once were. A misstep doesn’t just create headlines; it creates fines that hurt. Since GDPR came into force, regulators have handed out more than €5.6 billion in penalties, and the pace has only picked up in the last two years. For banks, insurers, and healthcare providers, fragmented data is a liability.

This is where a Customer Data Platform earns its keep, allowing enterprises to build compliance into the data layer itself. Every profile carries its own consent state. Every interaction can be logged against that consent. Sensitive fields can be masked automatically before they ever reach a campaign or a dashboard. The result: marketers can move quickly, while legal and risk teams know the audit trail is intact.

There are practical examples. Legal & General uses its CDP to improve real-time engagement, but compliance was part of the business case from the start. Data policies are enforced centrally, so every department works from the same set of rules.

A CDP doesn’t guarantee compliance on its own, but it gives organisations the framework they need to protect customers, and themselves.

Improved CX: Personalization and Omnichannel

Most people don’t notice a database. What they do notice is the experience: the email that lands with the wrong name, the app that asks them to log in again, the service agent who doesn’t know what happened last week. A Customer Data Platform exists to stop those seams from showing. That’s where some of the clearest CDP benefits come through.

The promise is simple enough: take signals from across the business, stitch them into a live profile, and feed them back into every channel. In practice, it means fewer repeated questions, less wasted spend, and more relevant offers. Analysts at Aberdeen have noted that organisations with a CDP in place see campaign response rates more than double.

An example? L’Oréal turned to Tealium to power personalization and improve media efficiency worldwide. The CDP became the bridge between consented first-party data and campaigns running in dozens of markets, ensuring local teams could tailor messages without compromising privacy.

Fisher and Paykel used the CDP from Salesforce to improve customer experience in another way – by giving clients a more efficient self-service experience. Agentforce now handles 65% of the company’s interactions, and 45% of customers book directly through AI. The company also benefits from a 50% reduction in call handling times, that means agents have more time to focus on other tasks.

CDP Benefits for Employees and Service Teams

Customer Data Platforms are usually sold on their marketing value, but the upside for employees is just as important. When data is scattered, staff spend time chasing records, re-entering details, or trying to match information between systems. It drags down productivity and morale. Unifying that data is one of the simpler benefits of a CDP, yet it can be the most visible inside the organisation.

For agents on the service desk, a unified profile means they no longer need to piece together a caller’s history from multiple screens. Instead, they see the journey in context – recent transactions, open tickets, even preferences collected elsewhere. Handle times drop, but so does frustration on both sides of the line.

There are clear examples. AAA Washington turned to Salesforce Data Cloud to give service agents a single view of member profiles. The organization described how AI agents could act on those profiles to dispatch roadside help faster and more accurately, without the risk of burnout.

Customer Data Platform Benefits for Business Leaders: Unified Insights and ROI

For executives, the challenge is rarely a lack of reports. It’s the opposite. Every department has its own dashboards, each with different numbers, often telling different stories. Decisions slow down because no one is sure which version of the truth to trust. This is where one of the more overlooked benefits of a CDP comes in: a single, reliable view that leadership can use with confidence.

There’s a strategy angle too. Once unified profiles start feeding into AI, executives can pick up churn risks before they hit the numbers, forecast with more certainty, and shift spending based on what customers are actually doing right now. It cuts down the boardroom surprises – and avoids the frantic catch-up when forecasts and reality don’t match.

The Adecco Group is a case in point. By implementing Salesforce Data Cloud across its global workforce solutions business, the company gave executives access to a 360-degree view of both clients and candidates. Its Chief Digital & Information Officer explained that AI now powers recommendations across the business, backed by trusted data. The CDP benefits in this case weren’t limited to marketing metrics; they reshaped how Adecco planned and delivered strategy.

The Customer Data Platform Benefits Enterprises Can’t Overlook

Much of the discussion around CDP benefits centers on personalization or marketing efficiency. Important, but not the whole story. Some of the biggest advantages are the ones that don’t always make it into vendor slides.

One is data quality for AI. Models are only as strong as the information they’re trained on. A Customer Data Platform acts as a safeguard, filtering personal data where it shouldn’t be used and ensuring inputs are consistent. That makes AI outputs more reliable – and less risky when it comes to privacy or bias.

Another is collaboration. Increasingly, organizations are using data clean rooms – neutral environments where they can share insights with partners without exposing raw records. CDPs can plug directly into these environments, allowing a retailer and a brand, or a hospital and a research partner, to coordinate using anonymised datasets.

Governance often gets left until last, but a CDP brings it right into the data layer. Consent tags travel with the customer wherever their information is used. If someone withdraws permission, that state follows them, blocking future use automatically.

These Customer Data Platform benefits aren’t flashy like a dashboard or a campaign, but they carry more weight. They shape whether AI scales responsibly, whether data partnerships work, and whether trust with customers holds firm.

For enterprises, that’s both a challenge and an opportunity. Handled properly, feedback reveals exactly where things are and aren’t working. It tells support teams which moments frustrate. It tells product teams what’s missing, and it tells the C-suite what customers value enough to fight for.

That’s the real job of customer feedback management, turning scattered input into structured insight, then routing it to the teams that can actually do something with it.

The best CFM systems don’t just capture data. They:

• Map feedback across the full journey, not just surveys
• Spot trends early, before they show up in churn
• Connect insight directly to actions: faster support, better products, clearer messaging

In short, modern customer feedback management platforms give enterprises a new kind of muscle: the ability to listen deeply, move early, and improve continuously

What is Customer Feedback Management?

Customer feedback management is the discipline of collecting, interpreting, and acting on customer sentiment – not just from surveys, but from every channel where customers leave a mark.

That might mean tracking a drop in CSAT after a product update, combing through live chat logs, or decoding a two-star review on Trustpilot. In most enterprise settings, it means building a feedback loop that crosses teams: product, marketing, service, and operations all relying on the same source of truth.

The best customer feedback management software doesn’t just store responses. It translates them into structured insight, surfacing trends, routing complaints, and pushing alerts to the right place, fast. It’s the glue between listening and resolution.

To work at scale, feedback systems typically include:

• Multichannel ingestion: Web forms, support tickets, NPS, app reviews, even social DMs. Every signal matters, even if it’s unstructured.
• Theme detection and prioritization: Tools flag repeat issues or keyword clusters before they become reputational risks.
• Workflow integration: A refund complaint can notify finance. A delivery bug can trigger a ticket in product ops.
• Dashboards and reporting: With the help of AI systems, leaders get a filtered view of real insights by product line, geography, or channel.

Leading companies aren’t collecting feedback in a vacuum. They’re wiring it directly into CRM systems, contact center tools frontline workflows, so the right people can act without delay. The tighter the integration, the faster teams can respond, fix what’s broken, and strengthen customer relationships that last.

Where Feedback Fits: Feedback Management, VoC, and EFM

Feedback is only useful if it leads somewhere. That’s where terminology starts to matter. Voice of the Customer (VoC), customer feedback management, and enterprise feedback management (EFM) are often used interchangeably. They shouldn’t be.

Customer feedback management is the engine room. It handles collection, sorting, tagging, and routing. Think of it as the operational layer that turns raw input from surveys, ratings, and comments into tasks and decisions. This is where data moves from inboxes and dashboards into action plans.

Voice of the Customer (VoC) goes broader. It doesn’t just listen to what customers say, it listens to how they feel, how they behave, and where they’re frustrated or delighted without necessarily saying it outright. A good VoC program blends direct feedback with behavioral signals and sentiment analysis. It’s about seeing the full picture.

Enterprise feedback management (EFM) stretches even further. It includes employee and partner insight, compliance triggers, internal process reviews, and often sits closer to risk management than CX. In highly regulated or distributed organizations, EFM is essential infrastructure.

At enterprise scale, feedback management isn’t just a support tool. It’s part of the system of record: connected to customer data platforms, CRMs, business intelligence tools, and employee engagement systems (WEM tools).

Each of these frameworks adds something. The most mature organizations use all three as parts of one loop: listen, understand, and act.

What is Customer Feedback Management? Feedback Types

Customer feedback isn’t always a form or a star rating. It’s often informal, unstructured, or buried in systems where no one’s looking. Recognizing the different types is the first step toward building something that works across departments and channels.

• Direct Feedback: The most visible kind. Surveys after support calls. CSAT and NPS prompts. Product reviews submitted through apps or portals. It’s usually structured, timestamped, and easy to analyze. But it’s also the most filtered. The people who answer tend to be at the emotional extremes, either thrilled or annoyed. Everyone else stays quiet.
• Indirect Feedback: This is what customers say when they’re not talking to you directly. Tweets. Public forum threads. Online reviews. Complaints posted to third-party sites. In many organizations, this insight slips through the cracks. But today’s customer feedback management platforms use NLP and sentiment tools to bring these comments into view before they become brand problems.
• Inferred Feedback: This is the feedback customers don’t say out loud, but show in what they do. Dropping out halfway through checkout. Asking the same question in three different places. Bouncing between help pages without finding what they need.

On their own, these signals can be easy to miss. But together, they reveal patterns of frustration that direct surveys might never surface.

Why Customer Feedback Management Matters

There’s no shortage of dashboards in a modern enterprise. But few of them speak with the voice of the customer. That’s what feedback management changes. It shifts insight from lagging reports to live reality, focusing on the real-time pulse of what customers need, want, and expect.

For enterprise leaders focused on customer experience, this isn’t a soft metric. It’s operational. According to Bain & Company, companies that excel at customer experience grow revenues 4%–8% above their market. But growth doesn’t come from tracking satisfaction scores alone. It comes from turning those scores into action.

Here’s where feedback becomes a business driver:

• Alignment Across Teams: Sales hears one thing. Support hears another. Product has a third backlog entirely. When feedback lives in separate systems, teams solve different problems. When it’s centralized, patterns emerge, and teams move in the same direction.
• Early Signal Detection: A broken link on a signup form. A billing process that’s confusing in one region. A surge in cancellation requests. Customer feedback management platforms surface these issues before they hit churn reports. The earlier the fix, the lower the cost.
• Smarter Roadmapping: Feedback isn’t just a support signal, it’s a product roadmap tool. Tracking customer insights, linking them to outcomes, and activating responses leads to strategic action. Teams can prioritize features that drive loyalty.
• Competitive Advantage: Every brand says it listens. Few can prove it. Companies that consistently close the loop visibly earn trust. In a market where switching costs are low, trust is often the only real moat.

The case for customer feedback management software isn’t just about efficiency. It’s about agility, spotting the next risk or opportunity while competitors are still guessing.

How to Build a Customer Feedback Management System That Works

Enterprises don’t lack feedback. They’re swimming in it. The challenge isn’t collection, but coordination. Scattered responses, siloed ownership, and no clear plan for what happens next. That’s where customer feedback management becomes a system, not just a task.

1. Start with What You Already Have

Before adding new tools or channels, map what’s in play. Most enterprise teams already gather feedback across:

• Post-interaction surveys
• Help desk conversations
• Social and review platforms
• Product feedback forms
• Sales and account notes

But it’s often fragmented, or locked in spreadsheets, CRM fields, and third-party platforms. Start by listing every touchpoint where customers leave a trace. Then identify who owns that data, how it’s reviewed, and whether it drives action.

2. Build a Shared System, Not Just a Repository

A true customer feedback management system isn’t just a bucket. It’s a hub. One place where cross-functional teams can view, analyze, and act on insights. That requires more than storage. It needs structure. Look for tools that:

• Integrate with your CRM system and CDP
• Tag feedback by source, product line, sentiment, urgency
• Offer role-specific dashboards for ops, product, CX, compliance
• Allow for routing, escalation, and response tracking

Consider other integrations that might be helpful too, such as connections to your ERP and business intelligence platforms, or workforce management tools.

3. Design a Feedback-to-Action Pathway

Without clear ownership, feedback dies in the backlog. Teams need to agree on what gets prioritized, who responds, and how it loops back into service design, training, or product fixes.

The strongest systems:

• Flag urgent or high-impact issues automatically
• Route insights to the right teams (with deadlines)
• Track outcomes, not just volume
• Communicate resolution back to the customer

When that loop works, feedback becomes part of how the business runs.

How to Use Feedback to Improve Business Results

Most companies collect feedback. Fewer actually do something meaningful with it. In mature organizations, feedback isn’t just a sentiment report, it’s a driver of change. Done right, it informs strategy, sharpens execution, and reduces churn.

• Prioritize patterns over outliers: It’s easy to get caught up in the latest complaint or viral review. But high-performing teams step back. They look for volume, frequency, and trends, not just anecdotes. That could mean mapping repeat issues to product features, or tracking common service pain points over time.
• Feed insight to the right systems: Don’t keep customer feedback on a CX dashboard. Use it to inform product roadmaps, workforce planning, pricing models, training strategies, and anything else that impacts the customer experience.
• Expand your metrics: Go beyond NPS and CSAT. Think about customer effort scores, overall retention rates and churn. Determine the KPIs you want to keep track of in advance, and make sure everyone is watching them, including the C-Suite.

Choosing Customer Feedback Management Software

Customer feedback is everywhere. What separates good companies from great ones is what they do with it. That’s where the right customer feedback management software comes in, to make insights actionable, accountable, and accessible across the enterprise.

Start With the Business, Not the Tool

Software selection should begin with the problems it’s meant to solve. Are customers dropping off after onboarding? Or are service complaints slipping through the cracks? Are product teams getting insight too late to act?

Clear goals tend to point to the right tool:

• Real-time alerts for contact center agents?
• Text analytics for unstructured NPS comments?
• Trend reporting to inform product roadmaps?

Once those use cases are clear, it becomes easier to separate the platforms built for scale from those that just tick boxes.

Integration Over Isolation

In a modern tech stack, no system should sit alone, especially not feedback.

Customer insights gain power when connected to:

• CRM platforms, where individual records tell a full customer story
• Contact center solutions, where timing and channel matter
• CDPs, which consolidate behavioral and transactional data
• BI tools, for deeper cross-functional reporting
• Broader ERP, WEM, and business management tools

Make sure your platforms feed the systems powering decisions.

Think Long-Term: Governance, Scalability, and Fit

Even the most powerful platform will struggle without strong foundations. For enterprise buyers, that means focusing on operational readiness:

• Can the system support multiple teams and regions with clear permissions?
• Are escalation workflows and approvals built in?
• Does the vendor offer strong uptime guarantees and compliance controls?
• Is the reporting flexible enough to satisfy both executive leadership and front-line teams?

Ease of use matters too. If agents, analysts, and leaders can’t find value in it quickly, feedback won’t flow where it’s needed most.

Discover the best customer feedback management solutions:

Customer Feedback Management Best Practices

Technology may capture customer sentiment, but it’s what companies do next that separates good intentions from real improvement. At the enterprise level, feedback shapes products, and defines brand reputation, retention, and revenue.

Here’s what the most effective teams get right.

• Track consistently: Feedback isn’t a file to review later. It’s a feed that’s active and ongoing. Companies need to review regularly, discuss in depth, and build around it.
• Make feedback cross functional: Operations needs visibility into service complaints, marketing needs to know where messaging misses, and HR should see how poor feedback is affecting teams. Get everyone involved.
• Close the loop: Replying to feedback, or acting on it, is crucial. Customers want to know their input mattered, and teams want confirmation their fix was felt. Ensure that your action is clear, powerful, and visible.
• Read between the lines: Surveys are useful, but raw behavior can say more. Combine behavioral insights, structured survey data, and conversational analytics for a comprehensive view of what customers really feel, not just what they say.
• Make it easy to act: Help teams fix issues quickly. Check if workflows are in place for feedback routing, and whether CX agents can escalate recurring problems. Give people the tools they need to act.

Customer Feedback Management Trends

Customer expectations haven’t just shifted, they’ve splintered. Channels have multiplied. Responses move faster. The tools used to manage it all are catching up. Here’s what’s defining feedback management right now:

The Rise of AI-Powered Analysis

Enterprise teams spent years circling AI as a concept. Now it’s operational. The strongest feedback systems today don’t just categorize responses, they break them down by tone, urgency, and underlying cause.

Platforms like Medallia, NICE, and Sprinklr are using natural language processing and conversational analytics to surface issues before they mutate. Instead of waiting for quarterly survey analysis, teams can spot sentiment drops and recurring themes as they happen.

Feedback Is Becoming Embedded

Feedback used to live in standalone forms: a survey here, a rating box there. That’s changing. Leading platforms now capture signals from everyday interactions: chat logs, call transcripts, even app usage.

Feedback is moving closer to the moment. A delivery delay triggers a quick prompt. A cancelled subscription opens the door to ask why. Systems are listening all the time, and they’re getting smarter about what to listen for.

Structured Feedback Loses Traction

It’s not just about ticking boxes. The most valuable insights often show up in open comments, social threads, or long-form email replies. That unstructured data used to be hard to sort. Now, it’s where the action is.

Enterprises are investing in platforms that can handle nuance: that can understand sarcasm, spot emotion, and cluster feedback without a human reading every line. Forrester calls this shift “human insight at scale”, and it’s showing up as a core capability in nearly every customer feedback management platform leader.

Everything Connects Or It Doesn’t Work

Feedback is most valuable when it flows. Into support platforms, product roadmaps, agent scripts, and CX dashboards. But that only happens when systems talk to each other.

Leading tools now integrate out-of-the-box with CRMs, contact center systems, VoC platforms, and enterprise resource planning (ERP) solutions. That allows customer concerns to influence decision-making across the business, not just in service.

Privacy Remains Crucial

The line between “listening” and “surveilling” is thin, and enterprise buyers know it. In a post-GDPR, opt-out-default world, customer feedback strategies need to include transparency.

That means clear consent prompts. Data handling disclosures. Anonymization features. Especially in regulated sectors, ethics now sit beside analytics in the buyer’s checklist.

What is Customer Feedback Management? The Voice of CX

Customer feedback management It affects product decisions, shapes brand reputation, and drives loyalty at scale.

Done well, it connects dots across departments, from support and sales to marketing and operations. It puts real-time customer truth in front of the people who can do something about it.

But it only works when the systems are connected, the insights are trusted, and the loop is truly closed. That’s why enterprise teams are investing in modern customer feedback management platforms to operationalize input.

For companies focused on loyalty, innovation, and experience, the question isn’t whether to invest in customer feedback tools. The only real question is: which one will help you act faster, and smarter? CX Today is here to help:

• Join the Community: Be part of a dynamic CX-focused network. Swap ideas with thought leaders and elevate your feedback strategy.
• Test the Tech: Discover the top-rated platforms, meet vendors, and explore trends at live and virtual events.
• Plan Your Next Investment: Use our CX Marketplace to explore top vendors in feedback, VoC, CDP, and contact center tech.

Or visit the ultimate CX guide for enterprise experience leaders, for insights into how to build a better CX strategy, one step at a time.

This comes after its annual report revealed that AI implementation had increased by 282% since 2024. 

These tools enable enterprises to deploy AI agents without worrying about the reliability and safety of their performance within a system. 

Salesforce’s observability tools provide AI agents with the capabilities to analyze performance, optimize interactions, and monetize stability. 

Agent Analytics

This capability allows enterprises to view how well an AI agent is operating through monitoring its movements, how it’s improving/declining, and where these pain points are coming from. 

This can be turned into performance data, trends, and insights to understand how efficiently these agents are performing and take actionable steps to improve their usage. 

This can also be done across all implemented agents, allowing enterprises to view their agents’ overall effectiveness on customer interaction and support their continuous improvement. 

Agent Optimization

As a key observable capability, Optimization offers customer enterprises full transparency with each agent interaction. 

Customers can uncover how agents make decisions and what led them to make those choices, highlighting performance gaps and session flows to diagnose any issues and deduce the steps needed to improve its performance. 

This can include prompt, rule, or data source adjustments to solve misinterpreted information, inconsistent results or agent hesitation. 

Salesforce provides access to end-to-end visibility for customers to view each agent’s response and action, even with larger, complicated action chains. 

For less varied issues, similar requests can be accumulated to uncover larger problems in patterns or trends. 

Customers can also identify an agent’s configuration issues to pinpoint how an agent’s behaviour is affecting its operation and uncover which areas need to be retrained or personalized further for improved performance. 

Agent Health Monitoring 

This capability can monitor an AI agent’s reliability and safety level to ensure that it is running as expected. 

It provides almost real-time visibility and alerts when the agent is performing unpredictably, notifying the company before any significant damage takes hold. 

It measures an agent’s ability to handle requests, time taken to respond, and tracks incidents such as failures, breaks in activity, or invalid responses. 

By leveraging the capability, teams can speedily detect and resolve issues to minimize agent downtime and continue productivity. 

This tool is formed by two of Agentforce’s components, acting as the foundation for the observability tool by supplying the data and governance structure needed to monitor agents: 

• Session Tracing Data Model: By logging every agent interaction, the data model can store all its data in Data 360 and provide the observability tool the means to generate reliable analytics, error identifiers, and support optimization for unified visibility.
• MuleSoft Agent Fabric: This enables enterprises to control, register, and review agents to justify how they function and interact. 

AI Implementation Report 

In a report published in November, Salesforce announced that AI implementations had increased to 282% since last year. 

This data reveals that companies are now at a far better position to deploy pilot projects at scale rather than risk the threat of experimentation. 

Despite this, data governance, security, and trust remain high priorities, requiring risk management across workflows. 

This means that more companies are going to require higher visibility and control across large-scale AI deployments, which is where Salesforce’s observability tools come in. 

By supporting enterprises with agent interactions, Salesforce’s observability tools can decrease operational risk by allowing teams to keep up to date with agent visibility and analytics to keep agent deployments stable. 

Reddit, a customer of Salesforce, highlighted how Salesforce has allowed the customer enterprise to scale agents securely through consistent visibility. 

John Thompson, VP of Sales Strategy and Operations at Reddit, stated: “By observing every Agentforce interaction, we can understand exactly how our AI navigates advertisers through even the most complex tools.  

“This insight helps us understand not just whether issues are resolved, but how decisions are made along the way. 

“Observability gives us the confidence to scale these agents, continuously monitor performance, and make improvements as we learn from their interactions.”

The problem usually isn’t the model. It’s the data behind it. When knowledge bases are out of date, fragmented, or inconsistent, even the smartest AI will confidently generate the wrong answer. This is why knowledge base integrity and RAG governance matter more than model size or speed.

The urgency is clear. McKinsey reports that almost all companies are using AI, but only 1% feel they’re at maturity. Many also admit that accuracy and trust are still major barriers. In customer experience, where loyalty is fragile, a single hallucination can trigger churn, compliance headaches, and reputational fallout.

Leading enterprises are starting to treat hallucinations as a governance problem, not a technical one. Without governed data, AI becomes a liability in CX. With it, organizations can build automation that actually strengthens trust.

What Are AI Hallucinations and What Causes Them?

When customer-facing AI goes off-script, it usually isn’t because the model suddenly turned unreliable. AI hallucinations in CX happen when the system fills gaps left by bad or missing data. Picture a bot telling a customer they qualify for same-day refunds when the actual policy is 30 days. That’s not creativity, it’s a broken knowledge base.

Hallucinations tend to creep in when:

• Knowledge bases are outdated or inconsistent, with different “truths” stored across systems.
• Context is missing, for example, an AI forgetting a customer’s purchase history mid-conversation.
• Validation checks are skipped, so the bot never confirms whether the answer is still correct.

The risks aren’t small. 80% of enterprises cite bias, explainability, or trust as barriers to using AI at scale. In CX, inaccuracy quickly turns into churn, complaints, or compliance headaches.

There are proven fixes. Enterprises just need to know what to implement before they go all-in on agentifying the contact center.

The Real-World Impact of AI Hallucinations in CX

The stakes around AI hallucinations in CX translate directly into lost revenue, churn, and regulatory risk. A bot that invents refund rules or misstates eligibility for a benefit doesn’t just frustrate a customer – it creates liability.

Some of the impacts seen across industries:

• Retail: Misleading warranty responses trigger unnecessary refunds and drive shoppers to competitors.
• Public sector: Incorrect entitlement checks leave citizens without services they qualify for.
• Travel: Fabricated policy details can mean denied boarding or stranded passengers.

The financial burden is real. Industry analysts estimate that bad data costs businesses trillions globally each year, and the average cost of a single data-driven error can run into millions once churn and remediation are factored in.

Case studies show the impact, too. Just look at all the stories about ChatGPT, creating fictitious documents for lawyers, or making up statements about teacher actions in education. Every hallucination is a reminder: without knowledge base integrity and RAG governance, automation introduces more risk than reward. With them, AI becomes a growth driver instead of a liability.

Why Hallucinations Are Really a Data Integrity Problem

It’s tempting to think of AI hallucinations in CX as model failures. In reality, they’re usually symptoms of poor data integrity. When the information feeding an AI is out of date, inconsistent, or fragmented, the system will confidently generate the wrong answer.

Knowledge base integrity means more than just storing information. It’s about ensuring accuracy, consistency, and governance across every touchpoint. Without that, CX automation is built on sand.

Common breakdowns include:

• Outdated articles: A policy change goes live, but the bot still cites the old rules.
• Conflicting records: Multiple “truths” for the same customer, leading to contradictory answers.
• Ungoverned logs: Data pulled in without privacy controls, creating compliance exposure.

Some organizations are already proving the value of treating hallucinations as governance problems. Adobe Population Health saved $800,000 annually by enforcing stronger data controls, ensuring agents and AI systems pulled only from validated knowledge sources.

Building the Foundation: Clean, Cohesive Knowledge

Solving AI hallucinations in CX starts with building a solid data foundation. No model, no matter how advanced, can perform reliably without knowledge base integrity. That means every system, from the CRM and contact center platform to the CDP – has to point to the same version of the truth.

A few steps make the difference:

• Unified profiles: Use CDP to connect IDs, preferences, and history across systems. Vodafone recently reported a 30% boost in engagement after investing in unified profiles and data quality.
• Agent-ready records: Golden IDs, schema alignment, and deduplication stop bots from improvising. Service accuracy depends on knowing which record is the right one.
• Data freshness: Expired knowledge is one of the fastest routes to hallucination. Setting SLAs for update frequency ensures AI doesn’t serve answers that are weeks, or years, out of date.
• Governance layers: Microsoft’s Purview DLP and DSPM frameworks, for example, help enforce privacy boundaries and ensure sensitive data is never exposed to customer-facing AI.

Clean, governed data is what allows automation to scale safely. In fact, Gartner notes that automation without unified data pipelines is one of the leading causes of failure in AI deployments.

The lesson is clear: AI only works if the underlying knowledge is accurate and consistent. RAG governance begins not at the model layer, but in how enterprises treat their data.

Choosing Your LLM Carefully: Size Isn’t Everything

When automating CX workflows, the assumption that “bigger means better” often backfires. In fact, purpose-built, smaller language models can outperform broad, heavyweight counterparts, especially when they’re trained for specific customer service tasks.

Here’s what’s working:

• Smaller, tailored models excel at soft-skill evaluations. In contact center hiring, they outperform general-purpose LLMs simply because they understand the nuances of human interaction better.
• Efficiency is a major advantage. Smaller models require fewer computational resources, process faster, and cost less to run, making them ideal for real-time CX workflows.
• They also tend to hallucinate less. Because they’re fine-tuned on targeted data, they stay focused on relevant knowledge and avoid the “overconfident bluffing” larger models can fall into.
• Distillation, teaching a smaller model to mimic a larger “teacher”, is now a common technique. It delivers much of the performance without the infrastructure cost.

Choosing the right model is a strategic decision: smaller, domain-specific models support RAG governance and knowledge base integrity more effectively, without blowing your budget or opening new risks.

RAG Governance: Why Retrieval Can Fail Without It

Retrieval-augmented generation (RAG) has become a go-to strategy for tackling AI hallucinations in CX. Companies like PolyAI are already using RAG to make voice agents check against validated knowledge before replying, cutting down hallucinations dramatically.

Instead of relying only on the model’s training data, RAG pulls answers from a knowledge base in real time. In theory, it keeps responses grounded. In practice, without proper RAG governance, it can still go wrong.

The risks are straightforward:

• If the knowledge base is outdated, RAG just retrieves the wrong answer faster.
• If content is unstructured, like PDFs, duplicate docs, or inconsistent schemas, the model struggles to pull reliable context.
• If version control is missing, customers may get different answers depending on which copy the system accessed.

That’s why knowledge base integrity is critical. Enterprises are starting to use semantic chunking, version-controlled KBs, and graph-RAG approaches to make sure AI agents retrieve the right data, in the right context, every time.

Vendors are also moving quickly. Google Vertex Agent Builder, Microsoft Copilot Studio’s RAG connectors, and open-source projects like Rasa’s extensions are designed to enforce cleaner retrieval pipelines. Companies like Ada are proving that governed RAG can cut down false answers in sensitive workflows like background checks.

RAG is powerful, but without governance, it risks becoming a faster way to spread bad information. Grounding AI in trusted, validated sources, through structured retrieval and strong RAG governance, is the difference between automation that builds trust and automation that erodes it.

The Model Context Protocol for reducing AI hallucination

Even with RAG governance, there’s still a missing piece: how the model itself connects to external tools and data. That’s where the Model Context Protocol (MCP) comes in. MCP is emerging as a standard that formalizes how AI systems request and consume knowledge, adding a layer of compliance and control that CX leaders have been waiting for.

Without MCP, connectors can still pull in unreliable or non-compliant data. With MCP, rules can be enforced before the model ever sees the input. That means:

• Version control: AI agents only access the latest, approved policies.
• Schema validation: Data must meet format and quality checks before it’s used.
• Integrity enforcement: Broken or incomplete records are automatically rejected.

This is particularly relevant in regulated industries. Financial services, healthcare, and the public sector can’t risk AI fabricating eligibility or compliance-related answers. MCP provides a structured way to prove governance at the system level.

Vendors are already moving in this direction. Salesforce’s Agentforce 3 announcement positioned governance and compliance as central to its next-generation agent framework. For CX leaders, MCP could become the difference between AI that “sounds right” and AI that is provably compliant.

Smarter Prompting: Designing Agents to Think in Steps

Even with clean data and strong RAG governance, AI hallucinations in CX can still happen if the model is prompted poorly. The someone asks a question shapes the quality of the answer. That’s where smarter prompting techniques come in.

One of the most effective is chain-of-thought reasoning. Instead of pushing the model to jump straight to an answer, prompts guide it to reason through the steps. For example, in a travel entitlement check, the AI might be told to:

• Confirm eligibility rules.
• Check dates against the customer record.
• Validate exceptions before giving a final response.

This structured approach reduces the chance of the AI skipping logic or inventing details to “sound confident.”

Other strategies include:

• Context restating: Have the model summarize customer inputs before answering, to avoid missing key details.
• Instruction layering: Embedding guard phrases like “If unsure, escalate” directly into prompts.

Better prompting changes how the AI reasons. Combined with knowledge base integrity and retrieval grounding, thoughtful prompt design is one of the simplest, most cost-effective ways to cut hallucinations before they ever reach a customer.

Keeping Humans in the Loop: Where Autonomy Should Stop

AI is getting better at handling customer requests, but it shouldn’t be left to run everything on its own. In CX, the cost of a wrong answer can be far bigger than a frustrated caller. A single AI hallucination in CX around something like a loan decision, a medical entitlement, or a refund policy can create compliance risks and damage trust.

That’s why most successful deployments still keep people in the loop. Routine questions like order status, password resets, and warranty lookups are safe to automate. But when the stakes rise, the system needs a clear off-ramp to a human; no company should try to aim for limitless automation.

There are simple ways to design for this:

• Flagging low-confidence answers so they’re routed to an agent.
• Escalating automatically when rules aren’t clear or when exceptions apply.
• Training models with reinforcement from human feedback so they learn when to stop guessing.

Real-world examples prove the value. Ada’s work with Life360 showed that giving AI responsibility for repetitive queries freed agents to focus on tougher cases. Customers got faster answers when it mattered most, without losing the reassurance of human judgment for sensitive issues.

The lesson is straightforward: automation should extend, not replace, human service.

Guardrail Systems: Preventing AI hallucination

AI can be fast, but it still needs limits. In customer service, those limits are guardrails. They stop automation from giving answers it shouldn’t, even when the data looks clean. Without them, AI hallucinations in CX can slip through and cause real damage.

Guardrails take different forms. Some block responses if the system isn’t confident enough. Others make sure refund rules, discounts, or eligibility checks stay within company policy. Many firms now add filters that catch bias or toxic language before it reaches a customer.

The goal isn’t perfection. It’s layers of protection. If one check misses an error, another is there to catch it. Tucan.ai showed how this works in practice. By adding guardrails to its contract analysis tools, it cut the risk of misinterpreted clauses while still saving clients time.

For CX teams, guardrails aren’t about slowing automation down. They’re about trust. Customers need to know that the answers they get are safe even when they come from a machine.

Testing, Monitoring, and Iterating

AI systems drift. Policies change, data updates, and customer expectations move quickly. Without regular checks, those shifts turn into AI hallucinations in CX.

Strong CX teams treat testing and monitoring as part of daily operations. That means:

• Running “red team” prompts to see how an agent handles edge cases.
• Tracking hallucination rates over time instead of waiting for customer complaints.
• Comparing different prompts or retrieval methods to see which reduces errors.

Enterprises are starting to put this discipline into place. Retell AI cut false positives by 70% through systematic testing and feedback loops. Microsoft and others now offer dashboards that log how models use data, making it easier to spot problems early.

The principle is straightforward. AI is not a one-off project. It’s a system that needs continuous oversight, just like a contact center workforce. Test it, measure it, refine it.

The Future of AI Hallucinations in CX

Customer experience is moving into a new phase. Contact centers are no longer testing basic chatbots. They are rolling out autonomous agents that can manage full interactions, from checking an order to triggering a refund. Microsoft’s Intent Agent, NICE’s CXone Mpower, and Genesys’ AI Studio are early examples of that shift.

The upside is clear: faster service, lower costs, and better coordination across systems. The risk is also higher. A single AI hallucination in CX could mean a compliance breach or a reputational hit that takes years to repair. Regulators are watching closely. The EU AI Act and ISO/IEC 42001 both push for stricter rules on governance, transparency, and accountability.

The market is responding. Salesforce’s move to acquire Convergence.ai and NiCE’s purchase of Cognigy show how major vendors are racing to build platforms where governance is built in, not added later. Enterprises want systems that are safe to scale, not pilots that collapse under risk.

The reality is that hallucinations won’t disappear. Companies will need to learn how to contain them. A strong knowledge base, integrity, tight RAG governance, and frameworks like MCP will differentiate brands that customers trust from those they don’t.

Eliminating AI Hallucinations in CX

The risk of AI hallucinations in CX is not going away. As enterprises scale automation, the cost of a wrong answer grows, whether that’s a compliance breach, a lost customer, or a dent in brand trust.

The good news is that hallucinations are not an unsolvable problem. They’re usually data problems. With a strong knowledge base, integrity, clear RAG governance, and frameworks like MCP to enforce compliance, organizations can keep automation reliable and safe. Guardrails, smarter prompting, and human oversight add further protection.

Together, these measures turn AI from a liability into an asset. Companies that treat governance as central will be able to roll out advanced agents with confidence. Those that don’t risk being left behind.

Tim Banting doesn’t mince words. “Given that we’ve just had Halloween, I’m introducing the term: Frankenstack,” says the Head of Research at Techtelligence. The definition?

“A horrible cobbled together layering of bots and automation and analytics.”

It’s a vivid metaphor for a very real enterprise challenge. In their race to modernise customer experience, many organisations have piled on AI tools, each solving isolated problems but collectively creating confusion. He explains:

“They’ve hit this wall where adding tech adds cost and complexity and it doesn’t provide any degree of clarity.”

Instead of scaling value, enterprises are scaling frustration.

From AI overload to orchestration clarity: Making CX systems sing

The pendulum, Banting argues, is now swinging back. “What we’re looking at now is this resurgence of journey orchestration,” he says. “It offers a way to make existing systems talk to each other and automate handoffs between these Frankenstack systems.”

He explains that AI excels at optimizing moments within the customer journey, for example, agent assist tools or chatbots handling simple transactions. However orchestration optimizes the full journey.

Banting compares it to a conductor leading an orchestra: “You don’t have the brass section doing their own thing and percussion doing their own thing. It really does require something at the top to help guide it, coordinate it, schedule it and orchestrate that journey.”

Ultimately, the goal should be not more machinery but a smoother flow.

The buying shift: From AI expansion to workflow simplification

Techtelligence’s latest data backs up this trend. “Buyers aren’t hunting for new AI platforms,” Banting confirms. “They’re researching workflow orchestration, data unification, and process simplification.”

This is the latest chapter in 2025’s quietly growing trend – a lean towards ‘cost to serve’ as the key metric for success. Especially as enterprises are under pressure to do more but with a fewer headcount.

When “every customer interaction involves three or four different systems and multiple handoffs, your cost to serve really skyrockets”, Banting says. Automating this process is where orchestration shines, enabling enterprises to increase productivity.

Less tech, more flow

As enterprises consolidate, one message rings clear: the AI arms race is over; orchestration wins the war on complexity.

“There’s no one platform to rule them all,” Banting concludes.

“You really need to do your due diligence and talk about workflow integration with vendors. That will become more important to get the best productivity, both from individuals and also from teams.”

Orchestration is the quiet revolution bringing order to the AI chaos – and the smartest CIOs and CX leaders are already tuning in.

Keep up to date with the latest tech buyer trends

Find Tim’s full analysis on Techtelligence.

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Unsurprisingly, despite the industry hype, modernization has proved to be far trickier than many anticipated.  

The contact center, in particular, has become a battleground of cloud-first messaging, aggressive vendor roadmaps, and ambitious AI promises.  

However, while the pandemic accelerated adoption and innovation, it also underscored a harsh reality: modernization doesn’t have to mean moving at someone else’s pace.  

As Miguel Angel Marcos, VP of Operations at Enghouse Interactive, puts it:  

“During the pandemic, organizations learned very quickly that agility was critical. But perhaps more slowly, they are learning that control and compliance are non-negotiable.”  

That sentiment reflects a growing reality in the enterprise CX space. While many organizations rushed to the cloud over the last few years, the dust is now settling, and so too is a more measured, pragmatic approach.  

According to Marcos, recent research shows that after the initial surge of migration, nearly 50% of organizations remain cautious about moving fully to the cloud:  

“The truth is that many businesses want to modernize and improve customer experience, but not all of them want to do it at the same pace or in the same way.”

The Pace of Change and the Power of Choice  

It’s nice to have choices.   

You wouldn’t want to turn up at a restaurant and find there’s only one thing on the menu. Equally, it wouldn’t be much fun if you rocked up to your local cinema only to discover that they’re showing the same film all day, every day.   

The same thing is true for enterprises. Legacy investments, regulatory requirements, and internal workflows often mean a one-size-fits-all approach simply isn’t viable.  

Instead, they need a wider array of deployment choices from CX vendors so they can choose their own path and control their own journey to modernization rather than being stampeded. 

Marcos explains that for some customers, “going fully CCaaS is completely fine, we offer that.” Others, however, “can’t or don’t want to move everything to the cloud.  

“For them, modernization can still happen on-premises, with full AI and CX functionality.”  

He highlights that hybrid models are increasingly becoming the bridge between traditional infrastructure and the cloud, with more and more customers layering in AI capabilities in the cloud, while keeping their core systems on-premises.  

“It’s the best of both worlds; enabling innovation without disrupting business continuity,” he explains.   

For example, a government contact center may retain on-premises systems for data sovereignty reasons but integrate AI-driven quality management and analytics from the cloud.  

Others are running specific campaigns or BPO operations on cloud platforms while maintaining in-house control over core technologies.  

Marcos stresses:  

“The important thing is that customers can modernize at their own pace, in their own way, without compromise.”

Flexibility First  

The principle of flexibility and “modernizing on your terms” is at the heart of Enghouse Interactive’s approach.  

Marcos is clear that the company’s technology strategy is built around interoperability, not rigid migration paths.  

“Choosing shouldn’t mean losing,” he says. “Framing everything as ‘cloud or nothing’ oversimplifies the message and forces compromises.  

Whether you’re on-premises, private cloud, hybrid, or full CCaaS, you can get the same robust CX capabilities: AI, analytics, omnichannel, and so on.”  

Enghouse Interactive’s mindset aligns with a broader industry shift.  

Enterprises are increasingly wary of vendor lock-in and are prioritizing open architectures that let them evolve as their business needs change.  

The future, Marcos suggests, lies in giving organizations the freedom to adapt, not forcing them into a predefined roadmap.  

Modernization That Starts Where You Are  

So, what practical steps can enterprises take to begin this kind of modernization journey?  

For Marcos, it starts with incremental innovation rather than wholesale change.  

With AI available across cloud, hybrid, and/or on-premises environments, Marcos suggests that  many companies could begin by leveraging their existing on-premises setup to start experimenting with AI integrations.  

In doing so, they will gain a sense of what the tech can and can’t deliver for them, without having to make a significant decision that could be difficult to reverse.  

He points to Enghouse Interactive’s AI-driven quality management tools as an example of “hugely practical” modernization.  

These solutions offer voice-of-the-customer analytics and agent interaction insights, which are designed to help contact center leaders better understand service quality and customer sentiment.  

“That’s the kind of innovation that drives value right away,” Marcos says. “It helps you improve customer experience today, without committing to a full-scale migration.”  

Beyond Cloud Hype  

While the market narrative often positions cloud as the only path forward, Marcos cautions that this can create unnecessary friction for many enterprises.  

“True modernization should be about balancing innovation with business continuity,” he says.  

“It’s not all cloud or nothing. It’s about interoperability, flexibility, and avoiding vendor lock-in, enabling organizations to evolve on their own terms.” 

That idea resonates strongly across the enterprise CX landscape. As digital ecosystems grow more complex, the winners will likely be those who blend agility with stability — modernizing intelligently, not reactively.  

In other words, the future of CX isn’t just about how fast you move. It’s about moving smart — and making sure that when you do, you’re the one setting the pace.  

You can hear more insights from Miguel by checking out this exclusive interview with CX Today.

You can also pick up more tips on how to spot AI that actually delivers by checking out this buyer’s guide. 

Like Harry Potter and Voldemort, Superman and Lex Luthor, and Kendrick Lamar and Drake – it can often feel like the two cannot coexist.   

The drive for faster resolutions, lower handling times, and cost efficiency has historically pulled in one direction, while the desire to provide genuine, human-centered service tugs in another.  

However, as AI continues to mature, a growing number of CX leaders are re-evaluating that divide.  

“When the AI boom kicked off, it was reasonable to assume you could only have one and not both,” said Will Penn, Senior Sales Engineer at Puzzel.  

“You say things like ‘chatbot’, and people picture a big metal robot: heartless, delivering your customer service.  

“But that’s a false choice. Efficiency and empathy can be complementary, but only when the right parts of the journey are automated, and the right moments are reserved for the human part of the experience.”

It’s a sentiment backed by fresh data.  

In Puzzel’s recent survey of 1,500 CX leaders, 43 percent said a hybrid model – blending AI efficiency with human empathy – represents the future of the contact center.  

That finding reflects a wider trend across the industry: companies are beginning to automate more strategically, not more blindly.  

 The False Dichotomy: Efficiency vs Empathy  

The assumption that faster means colder, or that human warmth comes at the cost of productivity, stems from the way many organizations still measure success.  

Traditional QA metrics such as average handling time (AHT) and first-contact resolution (FCR) – while invaluable for assessing service efficiency – can sometimes overlook the emotional context of a customer interaction. 

Penn argues that these metrics should be complemented with new forms of insight that capture empathy and emotional connection. 

“When we lead with empathy, we see agents who connect with customers on a personal level and address their needs in ways AI just can’t do on its own,” he said. 

“If you combine that with metrics that reward empathy, not just handling time, you start creating an environment where agents can truly do their best work.”  

While finding metrics that can measure empathy may sound like a tall task, advances in speech and text analytics are making it a reality.   

Organizations are now able to analyze 100% of customer interactions. The insights they extract – such as intent, sentiment, and emotional peaks – reveal not just what customers are asking, but how they’re feeling.  

This shift from anecdotal QA sampling to comprehensive emotional insight is quietly redefining the concept of efficiency.  

From Automation to Orchestration  

The next evolution of CX isn’t about replacing people with bots; it’s about orchestrating them together.  

Penn argues that “automation should take care of the routine, repeatable, boring tasks, so that the human agent can focus on the listening, problem-solving, and empathy.”  

That principle applies differently across sectors. In insurance, for instance, bots might accelerate claims intake while ensuring complex or sensitive cases are escalated to humans.  

In housing associations, analytics can flag vulnerability cues – mentions of mold, asthma, or damp – prompting proactive, empathetic outreach.  

Utilities can combine automated billing support with emotional-context detection, avoiding tone-deaf responses during times of hardship.  

This orchestration mindset is what Penn calls “AI that enables, not replaces.”  

Tools like Puzzel CoPilot and Live Summary demonstrate the idea in practice. 

CoPilot supports agents in real-time with next-best-step suggestions, while Live Summary handles after-call note-taking and context capture – saving time, but more importantly, freeing mental bandwidth, as Penn outlines:  

“When agents are free from admin, their emotional bandwidth increases. Empathy becomes something that’s more measurable, and more affordable to deploy.”

Empowering Agents Through Insight  

The other side of the orchestration coin is coaching.  

By analyzing full-interaction data, brands can identify top-performing agents and model their best practices.  

“We’re not going in saying, ‘look at all our cool AI functionality,’” Penn explains.  

“We start with understanding what customers are doing today. We use conversational intelligence to learn what customers are saying, what top agents are doing, and use that as a roadmap for development, whether that’s automating a process or using it as a coaching tool.”  

The results can be tangible. For example, one of Puzzel’s insurance customers recently shared how Live Summary streamlined the capture of legal information during its calls.  

Penn explained that a lot of the company’s calls were legal in nature, but not every agent is a qualified lawyer.  

“By prompting Live Summary to pull out the legal elements automatically, they’ve been able to send concise, complete notes to the lawyers – improving accuracy and efficiency.”  

That story illustrates how automation and empathy can converge, with technology quietly handling the structure, while humans handle the substance.  

The Enterprise Roadmap  

So how can enterprise CX leaders apply these lessons in their own companies?  

According to Penn, it starts with an “insights-first” mindset:  

1. Start with understanding, not assumptions: use data to uncover the moments where empathy truly matters.  

1. Automate selectively: Target routine tasks, not entire journeys.  

1. Iterate and refine: measure outcomes – emotional and operational – and feed them back into the loop.  

This philosophy turns AI from a blunt instrument into a precision tool. It’s not about doing more with less; it’s about doing the right things faster and the human things better.  

“Customers still want quick solutions,” Penn concluded.  

“But the contact center agent can’t be overlooked; they’re as important as ever. The technology should amplify their empathy, not erase it.”

You can hear more insights from Penn by checking out this exclusive interview with CX Today.  

You can also learn more about the company’s approach to conversational AI by reading this article.   

Why Staying Still Hurts Customer Retention 

When companies stick to reactive customer experience strategies, the cost of customer churn begins to mount. Research finds that many customers will walk away after just a single bad experience, so maintaining a high retention rate demands more than just firefighting when problems surface. Reactive support doesn’t just lose customers; it inflates costs with longer calls, repeat issues, and compensation efforts. 

By contrast, a proactive model intercepts issues before escalation, reducing the amount of support required and increasing the efficiency with which issues are resolved. In fact, organizations that adopt a proactive support strategy see ticket volumes drop by 20–30% over 12 months, and 25% lower support operating costs. 

The Cost of Reactive Customer Service 

When your business’ customer service strategy remains reactive, the hidden costs include: 

Rising churn: Customers who feel unsupported or undervalued will quietly drift away. 

Lost lifetime value: Retention is cheaper than acquisition; every percentage drop-in retention rate is revenue left on the table. 

Higher support costs: Fixing problems after they’ve occurred is often more expensive than prevention. 

Reputation damage: Negative experiences spread; poor service becomes part of your brand story. 

Innovation stagnation: A reactive model focuses on “putting out fires” rather than designing better journeys. 

Why Predictive CX Pays Off 

A proactive approach to CX means anticipating needs, spotting friction points ahead of time, and intervening early.  

Anwesha Ray, CX Today:

“Stay one step ahead of your customers’ needs … rather than waiting for them to contact you.” 

That kind of mindset shift matters for three inter-linked metrics: customer satisfaction, customer retention, and customer retention rate. 

1. By anticipating and preventing friction, you keep customers happier (higher satisfaction). 
2. Happier customers are more likely to stay (higher retention). 
3. Maintaining a higher retention rate reduces the churn cost and boosts lifetime value. 

When companies move from reactive to proactive service, they see fewer support escalations, lower costs, and stronger brand reputation. 

How AI Predicts Customer Needs 

Artificial intelligence has become the backbone of modern predictive customer experience. By analysing patterns in customer behaviour, sentiment, and interaction history, AI enables brands to anticipate issues before they arise. Predictive CX analytics can identify when a user is likely to churn, when a product might fail, or when satisfaction levels begin to drop – allowing businesses to intervene early with tailored solutions.  

These capabilities not only boost customer satisfaction but also improve customer retention by transforming reactive support into pre-emptive engagement. AI-driven insights give organisations the foresight to act with precision rather than urgency, helping them deliver value faster while reducing the cost of customer support.  

Implementing Predictive CX

Implementing predictive customer experience doesn’t mean starting over – it’s about improving your strategy and mindset. Here’s how to make the change, step by step. 

1. Map the customer journey: Identify the key touchpoints where customers are most at risk of frustration. Create a “living” map, not just a static diagram. 
2. Leverage customer data: Use feedback, behaviour analytics and support ticket trends to spot warning signals. That gives you the early warning system you need.  
3. Empower your teams: Equip frontline employees with the tools, metrics and authority to act before escalation. Proactive culture matters.  
4. Embed proactive outreach: Automated reminders, maintenance alerts, and tailor-made suggestions are effective ways to strengthen retention.  
5. Track the right metrics: Beyond support volume or resolution time, monitor rising or falling retention rate, churn cost, customer satisfaction trends. These provide the business case for change. 

Act Now or Pay Later 

If you’re evaluating whether to adopt predictive customer experience, it’s important to keep one hard truth in mind – the cost of inaction isn’t often visible until damage is done. Ignoring the shift from reactive to proactive may preserve the status quo today, but it risks higher costs, lower customer satisfaction, and weaker retention tomorrow. Thankfully, AI can help you predict customer issues before a ticket is ever raised.  

Ready to stop firefighting and start future-proofing your CX?

Check out our Ultimate Guide to AI & Automation in Customer Experience

These agents are being used in different, new, and exciting ways by different companies to advance their customer experience.

These providers put forward the following participants for this month’s roundtable:

• Jonathan Barouch, VP & GM of Zendesk for Contact Center
• Kevin McNulty, Senior Director of Product Marketing at Talkdesk
• Adam Spearing, VP of AI GTM EMEA at ServiceNow
• Vinod Muthukrishnan, VP and GM at Webex Customer Experience 
• Mike Szilagyi, GM of Product Management at Genesys

Below, these industry experts discuss the latest industry trends, predictions, and more.

AI Agents for Customer Support: The Trends

AI Agents in Action

Barouch: We are moving from the “smart IVR” era to AI Agents that understand intent, execute procedures within a controlled framework, and can escalate you to a human when needed.

This is a world of human and bot collaboration that heralds the end of the touchtone IVR. We see this as a fundamentally different phase for the contact center where AI Agents can consume internal knowledge or even AI generated knowledge based on the wealth of data within your customer service tickets.

This is the proactive, intelligent and integrated resolution based approach to AI.

Multi-Agent Orchestration

McNulty: AI agents are advancing from handling single tasks to working together through multi-agent orchestration.

They can now share context, divide responsibilities, and coordinate across systems to solve more complex customer issues end to end.

This level of interoperability is a major step forward—it allows AI agents to operate more like teams than tools.

Within the broader movement toward Customer Experience Automation, these agents are learning from outcomes and from each other, becoming faster, more accurate, and more adaptive with every interaction.

AI Is The New User Interface

Spearing: A defining trend is that AI is becoming the new UI for customer support.

Instead of navigating complex menus or portals, users can now engage naturally with AI agents through text, voice, or images to get instant, personalized help.   

ServiceNow’s “AI Experience” embodies this shift, turning conversational, intuitive AI interfaces into the front door of enterprise service.   

By understanding intent and context, AI becomes the seamless layer between people and workflows, making support faster, smarter, and more human.  

Single To Multi-Agent System

Muthukrishnan: The most significant trend is the rapid evolution from simple, single-purpose AI agents to sophisticated multi-agent systems capable of orchestrating complex customer journeys.

Previously, AI agents focused on handling straightforward tasks, but now we’re seeing the commercial adoption of AI agents that not only fulfill actions but also make real-time decisions—such as choosing the best next step for a customer based on context, business rules, or predictive analytics.

These agents can dynamically coordinate across multiple backend systems, access context, and seamlessly transfer between specialized sub-agents, all within a single customer conversation.

This enables a unified, “one conversation” experience where boundaries between sales, support, and service are blurred.

As these multi-agent orchestration and decision-making capabilities mature, we’re moving from experimentation to real-world deployments, unlocking more intuitive and human-like interactions, and setting the stage for the next generation of customer experience. 

Fully AI-Driven Customer Experiences 

Szilagyi: AI in the customer experience will soon be fully agentic as AI agents gain the awareness to understand context and act on it.

They’re learning to reason through complex interactions, coordinate across channels and carry forward the customer’s intent from one moment to the next.

This evolution marks the rise of skill-based, semi-autonomous systems that can deliver personalized, empathetic experiences with speed and consistency.

It will transform how service teams operate and how customers feel seen.

AI Agents for Customer Support: The Predictions

A New Contact Center Era

Barouch: AI Agents will fundamentally change how Contact Centers are operated.

Everything from reporting and analytics (what does AHT or Agent Occupancy even mean in a world of AI-based service?) all the way through to staffing rules will be thrown out the window.

The role of the human contact center agent will become more complex, picking up higher order work and where individual contributors morph into AI supervisors and coaches.

This change won’t happen over night but we expect contact leaders will adapt their hiring and team structuring strategies in 2026.

Proactive Customer Support

McNulty: AI agents are about to completely change what customer support looks like.

We’ll move from reacting to issues to a world where agents anticipate needs, team up in real time, and solve problems before customers even reach out.

These agents will reason, collaborate, and adapt—pulling in the right data and actions from across systems to deliver real outcomes, not just answers.

As Customer Experience Automation takes hold, support will feel less like a process and more like a living network that gets smarter, faster, and more intuitive with every interaction 

Multi-Functional AI Assistance

Spearing: By the end of this decade, AI agents will evolve from digital helpers into autonomous colleagues, operating across every function, anticipating needs, and resolving issues before customers even ask.   

ServiceNow sees a future where AI is the control tower for enterprise service, orchestrating people, data, and workflows seamlessly.

These agents won’t just respond; they’ll run support operations, guided by natural language and voice, integrating every system of record.  

As enterprise AI matures, customer service will shift from reactive resolution to proactive value creation, where AI delivers outcomes instantly, and humans focus on empathy, innovation, and growth.  

Personable Customer Experiences

Muthukrishnan: In 2026, AI agents will fundamentally transform customer support by enabling brands to deliver truly human-like seamless, personalized experiences.

I predict the distinction between support, sales, and service interactions will increasingly disappear from the customer’s perspective.

With AI acting as the orchestrator, customers will engage in a single, fluid conversation with the brand, where their needs are anticipated, context is preserved, and resolution is immediate.

Human agents will focus exclusively on complex or emotionally nuanced cases, empowered by AI-driven insights and automation.

This shift will turn contact centers into “experience centers”, making customer support a strategic driver of loyalty and growth.  

Predictive, Agentic Support

Szilagyi: We’re entering a phase where customer support will move from response to anticipation.

Semi-autonomous and autonomous AI agents will be able to recognize friction before it escalates and take action to resolve it.

They will schedule follow-ups, adjust processes and offer proactive solutions.

This agentic evolution will enable organizations to be always on, always aware and always improving.

Humans can then focus on the interactions where empathy and creativity drive the most value.

AI Agents for Customer Support: The Providers

Zendesk

Barouch: Zendesk acts as a trusted partner; not just a vendor.

With security and safety at the core of our proposition we focus on customer resolution as the metric and even charge customers based on resolutions.

All of our AI Agent use cases are practical and in the market today; they don’t require complex bolt-ons or heavy integrations.

Customers get fast time to value from a company with strong market adoption (with nearly 20,000 customers already using Zendesk AI).

Talkdesk

McNulty: Talkdesk is built for this moment.

Our Customer Experience Automation platform—Talkdesk CXA—is designed to power the next generation of AI agents for customer support.

At its core is the Talkdesk Data Cloud, which unifies structured and unstructured data—including the incredibly rich conversational data most companies struggle to use.

As organizations look to build data lakes that AI agents can access and learn from, we make that easy, governed, and real-time.

CXA then brings that intelligence to life through multi-agent orchestration, allowing AI agents to collaborate across channels and systems to deliver fast, contextual resolutions.

Combined with our history in CCaaS and deep industry expertise, Talkdesk helps companies move beyond simple chatbots to fully operational ecosystems where data, AI, and human insight work together to create smarter, more proactive customer experiences.

ServiceNow

Spearing: ServiceNow is uniquely positioned to deliver AI agents for customer support because our platform unifies data, workflows and automation into one trusted environment.   

Our latest “Zurich” release emphasises agentic AI built into the platform, giving customers scalable, governed, enterprise-grade agents from day one.  

We combine pre-built AI agents for common use cases with a low-code studio to customize, and built-in governance (AI Control Tower) so you can deploy quickly with confidence  

Cisco

Muthukrishnan: Cisco stands out as an ideal partner for AI-driven customer support due to our commitment to open, interoperable platforms and responsible AI innovation.

We design our solutions to adapt to any customer’s needs, whether on-premises, hybrid, or cloud.

This ensures security, data sovereignty, and compliance are never compromised.

Webex Contact Center leverages advanced AI to deliver measurable results, such as significant reductions in call abandonment rates and faster resolutions.

What differentiates Cisco is our holistic approach: we blend cutting-edge AI, deep integration capabilities, and a foundation of security and trust, enabling organizations to modernize customer experience at scale while maintaining flexibility and control.

We also build on Cisco’s advanced AI capabilities across our entire collaboration portfolio, allowing us to innovate rapidly and tailor solutions specifically for evolving CX needs.  

Genesys

Szilagyi: Delivering agentic AI responsibly requires a platform that unites intelligence, orchestration and governance.

The Genesys Cloud platform will empower AI agents to operate with autonomy and accountability guided by built-in guardrails and full visibility into how decisions are made.

It’s how we help businesses move confidently into the agentic era while keeping human connection and trust at the center of every experience