Yet many organisations still rely on traditional automation systems that struggle to meet these demands.  

Rigid IVR flows, generic chatbot scripts and siloed customer data often create more frustration than value, leaving customers repeating themselves and brands losing control of the customer journey. 

According to Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, many companies suffer from what he calls the “customer journey black hole” – a gap where context and customer history fall through the cracks, resulting in broken experiences and unnecessary friction. 

“Think about a typical banking interaction,” Anand says.  

“A customer fills in a loan application online, then calls the contact centre for support, only to be asked to provide the same information again. It’s no surprise that customers become frustrated.  

The consequence isn’t just dissatisfaction – 92 percent of customers say they’ll leave a brand after two or more poor experiences.

The Limits of Traditional Automation 

Even as businesses invest in automation to manage scale, traditional systems are increasingly showing their age.  

Script-based chatbots struggle to interpret nuanced intent.  

IVR systems force customers into predefined paths that rarely reflect what they actually want.  

And behind the scenes, data remains fragmented across CRM systems, ticketing platforms, and communication channels. 

“Legacy automation solves tasks, not outcomes,” Anand explains. “It might complete a form or look up an account, but it doesn’t understand the end goal of the interaction. It doesn’t collaborate with other systems.  

“It doesn’t adapt when the customer deviates from the script. Ultimately, it can’t orchestrate a full journey.” 

As customer journeys become more complex and decentralised, these limitations are becoming untenable.  

Organisations are now looking for a more intelligent and adaptive approach that can engage customers in real time, maintain continuity, and drive tangible results. 

Agentic AI in Action 

This is where agentic AI comes into play.  

Unlike traditional automation, agentic AI is built around autonomous, outcome-driven agents that can reason, collaborate and take contextual decisions.  

These agents can be trained for specific use cases such as cart abandonment recovery, KYC completion, proactive service notifications or multi-step issue resolution. This helps brands transition from basic automation to autonomous actions and AI decisioning.  

“Agentic AI is purpose-built,” Anand says. “Each agent understands the goal it needs to achieve, but it also knows how to work with other agents throughout the journey.  

“So you may have one agent focused on customer onboarding, another handling verification, and another coordinating follow-ups – all sharing context in the background.” 

This type of orchestration is increasingly essential for large enterprises. In e-commerce, for example, an agentic AI flow can detect a customer abandoning a cart, trigger hyper-personalised reminders across SMS, WhatsApp or email, and follow up based on engagement. If the customer expresses confusion or dissatisfaction, the agent can switch channels or escalate to a human agent with full context. 

“You’re no longer relying on one-size-fits-all automation,” Anand adds.  

You’re creating a dynamic loop that adapts to each customer’s needs and behaviours.

Voice AI: Transforming Real-Time Interactions 

The rise of voice AI is taking things a step further.  

Advanced speech-to-speech models now enable natural, human-like interactions that go far beyond traditional voice bots.  

These systems can understand real intent, detect emotion, and respond conversationally – making voice channels significantly more efficient and engaging. 

“For many customers, voice is still the channel of choice,” Anand notes.  

“But the experience has often been painful because legacy IVR is so restrictive. With voice AI, customers can speak normally and get real-time problem solving without navigating menus or waiting for an agent.” 

Tata Communications is seeing growing demand for voice AI in sectors such as banking, utilities, retail and travel, where customers frequently need rapid support with complex queries.  

When combined with agentic AI, voice agents can collaborate with other AI systems, retrieve information, complete tasks and escalate with full context when human support is required. 

“The beauty of voice AI is that it doesn’t break the flow,” Anand says. “If an escalation is needed, the human agent gets the full transcript, sentiment analysis and journey history. The customer never has to start again.” 

A Unified Approach 

Tata Communications has integrated these capabilities into a unified platform that connects multiple AI agents, voice systems and human support teams through powerful APIs and data connectors.  

The goal is to create a single interaction fabric that ensures continuity across every channel. 

“When an AI agent hands over to a human, or vice versa, all context is preserved,” Anand explains.  

“This is critical. If customers have to repeat themselves, the customer feels unheard and the journey becomes painful. Our platform eliminates that friction by ensuring that every agent – human or AI – understands the full picture.” 

The company has already seen strong results.  

One electric vehicle brand achieved a 25 percent increase in customer follow-through after deploying agentic AI-driven outreach.  

A large e-commerce marketplace reduced return-to-origin orders by 45 percent following the introduction of AI-powered WhatsApp workflows. 

“These are not incremental improvements,” Anand highlights. “They are major operational gains driven by intelligent automation that understands the customer’s intent.” 

Human-First, Outcome-Driven CX 

Despite the advances in AI, Anand emphasises that human expertise remains essential.  

Tata Communications’ approach is intentionally hybrid – using AI to handle repetitive tasks, streamline journeys and provide real-time insights, but ensuring humans remain central to complex, high-empathy interactions. 

“The best CX strategy is human-first,” he says. “AI should enhance human capability, not replace it. When AI and humans collaborate, you deliver outcomes that are personalised, proactive and genuinely valuable. That’s the future of customer experience.” 

As enterprises look to modernise their digital engagement, agentic AI and voice AI are emerging as critical technologies that can close the customer journey black hole and deliver the seamless, context-aware experiences customers expect. 

To explore how your organization can overcome the customer journey black hole and create seamless, unified experiences, contact Tata Communications to learn more about their integrated CX platform capabilities.   

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.

A loyal customer isn’t just someone who comes back. They spend more. Stay longer. Recommend faster. They open emails, tolerate hiccups, and ignore your competitors’ ads. They’re also far cheaper to retain than any lead your sales team is chasing right now.

Loyalty isn’t a lucky break. It’s the outcome of moments that go right consistently, and often quietly. A first experience that flows without friction. A support interaction that resolves more than just the issue. A product that keeps its promise. Each of these moments builds equity in the relationship.

When those touchpoints connect  across teams, systems, and time something stronger than repeat business takes shape. Customers begin to trust. They stick around, not because it’s the easiest option, but because the experience earns it.

What is Customer Loyalty?

Customer loyalty reflects a decision: the conscious choice to stay with a brand when alternatives are just a click away. It’s not just about satisfaction, plenty of satisfied customers churn. Loyalty runs deeper. It’s emotional, earned through consistency, value, and trust built over time.

In practical terms, loyalty shows when customers return after a poor experience, because they believe it’s the exception, not the norm. It shines when existing buyers refer peers, opt into updates, or upgrade without needing a discount.

But for enterprises, this isn’t a soft metric. It’s measurable, in retention rates, customer lifetime value, and referral growth. In fact, increasing customer retention by just 5% can boost profits by 25% to 95% depending on the industry. Loyalty doesn’t just pay off; it compounds.

Now, it matters more than ever. With CX as a key battleground, loyalty becomes a lead indicator of business resilience, and a hedge against rising acquisition costs.

The ROI of Customer Loyalty

Customer loyalty used to be a feel-good metric. Now it’s a board-level priority.

Retaining a customer isn’t just cheaper than winning a new one, it’s smarter. The cost of acquisition has spiked over 60% in the last five years, especially across digital channels. Meanwhile, repeat customers spend more, refer faster, and support brands longer, even when things go wrong.

The return is measurable:

• CAC Down, Margins Up: Brands with strong loyalty programs don’t need to outspend rivals on ads. Their customers come back organically. Acquisition costs are up to 7x higher than retention costs, and rising. Loyalty brings those numbers down.
• Predictable Revenue: Returning customers are more consistent. They know the product, trust the brand, and often skip the comparison stage altogether. That makes forecasting easier, pipelines more stable, and marketing spend more efficient.
• Loyalty = Resilience: In downturns, loyal customers stick. They’re more forgiving of glitches and slower to churn. A loyalty strategy isn’t just about growth, it’s about survival when market headwinds hit.
• Better Intelligence: Good loyalty tools are also listening tools. They track not just transactions, but behavior: redemptions, preferences, referrals, and feedback. That kind of data can feed customer journey strategies and help pinpoint why loyalty is rising or falling.
• Cross-Functional Buy-In: Loyalty isn’t a marketing-only game anymore. When programs sync with CRMs and support channels, they empower every team that touches the customer and help break down the silos that usually hurt CX.

What is Customer Loyalty Management?

Loyalty isn’t a byproduct of good service; it’s the result of managing relationships with intent. For enterprises, customer loyalty management is the discipline of designing and maintaining systems that keep the right customers coming back, staying longer, and contributing more value over time.

Loyalty doesn’t come from running rewards programs on cruise control. It starts with clarity; knowing who your most valuable customers are, what keeps them engaged, and how to stand out even when competitors promise more for less.

The best loyalty strategies don’t operate in a silo. They’re part of the broader customer experience engine, connected to feedback, support, product usage, and behavioural cues. Managed well, these strategies turn loyalty into a dynamic input, not just a passive output. It’s not a metric at the end of a funnel, it’s something built and reinforced at every stage of the journey.

Loyalty Management Tools and Platforms

The strongest tools today aren’t just managing point balances or sending birthday emails. They’re helping organizations understand loyalty as a behavior, not a program.

At a basic level, these platforms centralize loyalty data: engagement patterns, redemption activity, repeat purchase signals, and more. But the more advanced systems go further. They apply machine learning to spot early signs of churn, flag disengaged segments, and recommend next-best actions in real time.

What sets the leading loyalty management platforms apart is their ability to fit inside a broader CX tech stack. That means:

• Integrating with CRM to unify customer context
• Connecting to feedback loops for real-time insight
• Embedding in messaging infrastructure like CPaaS to deliver hyper-personalized moments that actually land

Many also support predictive analytics, using behavioral data to calculate loyalty risk scores, tailor rewards dynamically, or prompt human intervention when relationships are at risk.

How to Measure Customer Loyalty

Loyalty isn’t a single number. It’s a pattern, and like most patterns in enterprise CX, it takes a mix of metrics to see the full picture.

Behavioral signals still lead the pack. Metrics like repeat purchase rate, frequency of interaction, average order value, and churn give a direct read on what customers are doing, and where that behavior changes over time.

Behavioural signals often say more than surveys. A customer who slows their spending, skips repeat purchases, or stops logging in is sending a message. Something has shifted, in the experience, the product fit, or the perceived value.

Behaviour tells you what happened. But it won’t tell you why. That’s where customer sentiment comes into play.

Tools like Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) dig beneath the surface, giving teams a clearer sense of how customers actually feel about their experience. When behavioural dips show up, they offer the context needed to act fast, and fix the root cause before it costs more.

For many organizations, this layer is captured across touchpoints with VoC tools, then analyzed over time to correlate sentiment with spend or attrition.

What’s changing now is the rise of emotional loyalty metrics. These tools look beyond direct feedback, using conversational analysis, sentiment trends, and inferred emotional cues to understand attachment, not just satisfaction. It’s especially useful for brands competing on experience, not price.

Taken together, these data points create a more reliable model. Not just who’s loyal today, but who’s likely to stay, spend, and advocate tomorrow.

How to Choose Loyalty Management Software

The wrong loyalty platform won’t break a business, but it will stall progress. What looks slick in a demo can crumble under pressure if it can’t sync with existing systems, surface usable insights, or grow with you.

Enterprise teams evaluating loyalty management software need more than a feature checklist. They need to know how the tool will hold up six months in, with multiple departments relying on it.

Here’s what separates the useful from the disruptive:

True Integration

No platform works in isolation. If loyalty data sits in a separate bucket from customer service, CRM, or analytics tools, there’s a problem.

That means:

• Real-time syncing with CRM systems
• Alignment with VoC platforms
• Integrations with Business intelligence solutions

Most loyalty management platforms also seamlessly connect with CCaaS platforms, conversational analytics tools, and ERP software.

Dashboards That Get Used

Too many platforms surface metrics. Fewer tell you what they mean.

The strongest systems flag what matters: declining engagement from a once-loyal segment, a regional drop in redemption rates, churn triggers hiding in feedback. Ideally, these insights feed into broader customer intelligence tools.

Ask the vendor: When loyalty starts to dip, how will your platform show it, and who will know?

Scalability

Will it handle loyalty across multiple brands? Markets? Languages? Can it adapt to tiered models, emotional loyalty, partner programs?

Look for:

• Configurable logic, not hard-coded structures
• Clean admin interfaces for rule management
• Role-based controls that keep compliance teams comfortable

If it takes a developer to adjust a points rule, it’s not enterprise-ready.

Discover who’s driving results in the loyalty management software market here:

Best Practices for Improving Customer Loyalty

Loyalty doesn’t just emerge from a points program or a fun campaign. For enterprises, it’s a byproduct of consistent, intentional experience design, built into service flows, product strategy, data models, and frontline decision-making.

Build Feedback Loops That Actually Close

The fastest way to erode loyalty? Ignoring input – or worse, asking for it and doing nothing.

Instead of measuring feedback volume, measure action: How many product updates were driven by complaints? How often are support teams looped in to resolve themes emerging from surveys? Connect your loyalty program to customer feedback management tools that can drive real changes, not just reporting.

Use Tiering: But Don’t Let It Turn Transactional

Tiered loyalty still has its place, but only when it’s designed with purpose. Value shouldn’t just reflect spend. It should acknowledge engagement in all its forms. Early adopters, advocates, testers, even those who provide consistent feedback – they’re all part of the loyalty equation.

In B2B especially, tiers work best when they reflect mutual success. Think retention milestones, shared KPIs, or collaborative innovation, not just contract size.

Let AI Do More Than Segment

Yes, AI can slice customer cohorts faster. But real value comes when it flags what’s slipping before it shows up in churn.

Modern loyalty management tools increasingly come with predictive features: surfacing customers at risk of disengagement, nudging reps to check in, or adjusting loyalty offers based on sentiment and behavior patterns. Don’t just use AI to automate, use it to alert.

Tie Service Quality to Loyalty Outcomes

When loyalty starts to dip, it’s often not marketing’s fault, it’s a missed service expectation, or a support gap that never got escalated.

Bring loyalty and service metrics closer together. Track whether NPS dips after a long resolution time. Monitor whether loyalty program members get faster assistance, and whether that’s noticed.

Reward the Behavior You Want More Of

Discounts create habits, and not always good ones. If you reward spend alone, you build deal-seekers, not advocates.

Instead, reward the moments that drive growth:

• Referrals
• Feedback submitted
• Community contributions
• Self-service engagement
• Event participation

Loyalty isn’t a transaction, it’s a signal. Recognize the signals that drive real business value.

Localize Where It Matters

For multinational brands, loyalty can’t be global by default. Preferences shift by market, so should campaigns.

Consider:

• Local holiday-based promotions
• Regional tier naming conventions
• Local influencers or ambassadors

Global strategy. Local flavor. That balance keeps loyalty human.

Customer Loyalty Management + Service: The Critical Link

Loyalty doesn’t just live in a dashboard or a rewards app. It’s won or lost in moments that often feel small: a delivery delay, a billing dispute, a misunderstood policy. The way a brand responds in these moments is often more influential than any discount or points tier.

And that makes customer service a cornerstone of customer loyalty management.

When Service Is Seamless, Loyalty Feels Earned

Customers don’t demand flawlessness. But they do expect clarity, speed, and respect when things go wrong. Loyalty isn’t tested during moments of delight, it’s tested when something breaks. Support teams who can see a customer’s history, loyalty status, and previous interactions don’t just fix problems faster. They solve them with more context, more care, and often, more impact.

This is where integration matters:

• CRM systems should surface loyalty data
• CPaaS platforms can enable proactive outreach
• Ticketing systems can reflect VIP status or churn risk

Proactive Service = Preventative Loyalty Loss

The best loyalty moves aren’t reactive. They’re invisible, because the problem was handled before the customer noticed.

For example:

• Flagging shipping delays and sending apologies before the complaint
• Alerting high-value customers when products they love are low in stock
• Following up after negative sentiment is detected in chatbot interactions

This requires orchestration. But the payoff is reduced escalation volume, increased trust, and loyalty built on more than transactions.

Empower Agents Like They’re Brand Ambassadors

Loyalty lives or dies with the agent experience. If the frontline team feels unsupported, overworked, or stuck with legacy tools, they can’t deliver the kind of service that loyalty depends on.

Modern workforce engagement platforms are helping here, giving agents better training, clearer knowledge bases, and visibility into customer journeys. This isn’t just an ops upgrade, it’s a loyalty investment.

Customer Loyalty Management Trends to Watch

Enterprise loyalty strategies evolve with the customer, and the customer continues to change.

Over the past two years, loyalty has shifted from tactical marketing add-on to boardroom-level priority. Why? Because retention has become the fastest route to stable revenue.

Here’s what’s changing right now.

• Loyalty Is Getting Smarter: Rather than shouting about rewards, top brands are building invisible loyalty, systems that work behind the scenes, adjusting experiences based on behavior, purchase history, and product use. The loyalty isn’t in the point balance. It’s in the recognition. AI and predictive analytics are playing a bigger role here, helping teams act on churn signals before the customer ever says a word.
• Emotional Loyalty Takes the Lead: Price cuts don’t build loyalty. They build expectations. Enterprise buyers are shifting from transactional incentives to emotional loyalty strategies, things like exclusive experiences, consistent service, and values-based alignment. In B2B markets, that might look like strategic co-development, VIP access to product roadmaps, or account-based reward systems.
• Loyalty Hardwired Into CX: The strongest loyalty programs don’t operate in isolation. They’re woven into the wider customer experience stack, touching CRM, CPaaS, contact center platforms, and data systems. This allows brands to reward customers in real time, based on meaningful actions, not just spend.
• Consent-First Design: The days of collecting data “because we can” are over. Modern loyalty programs are being rebuilt around trust and transparency. That means clear value exchanges, upfront permissions, and control for the customer. Loyalty is no longer about how much data you can gather, it’s about how responsibly you use what you have.

Customer Loyalty Management Beyond the Transaction

Customer loyalty isn’t a finish line. It’s an ongoing, intentional outcome earned across every interaction, reinforced with every decision, and protected by every system put in place.

For enterprise teams, managing that loyalty means more than launching a rewards program. Managing loyalty well means making it easier for customers to stay than to leave. That’s not about discounts or perks, it’s about designing experiences that feel effortless, relevant, and personal.

Whether the goal is improving retention, boosting lifetime value, or gaining a clearer view of customer behaviour, the right strategy starts with the right tools, and the right insights.

CX Today offers a range of resources to help enterprise teams build loyalty systems that actually move the needle:

• Explore the Marketplace: Compare top loyalty management vendors with features tailored for growth, data integration, and security at scale.
• Join the Community: Learn how CX and marketing leaders across industries are evolving loyalty strategies in the CX Community.
• Track What’s Changing: Follow new developments in AI-powered loyalty, cross-channel experience design, and customer journey intelligence with research reports.

See how loyalty fits into the broader CX ecosystem. Visit our Ultimate CX Guide for a practical deep dive into the people, platforms, and processes driving customer-led growth.

A recent study by Twilio found that businesses in the Europe, Middle East and Africa (EMEA) region are accelerating their investment in conversational AI to enhance customer service and strengthen loyalty as they look to modernize their digital operations.

The report, Inside the Conversational AI Revolution: How to Win the Race to Deliver Exceptional Experiences, found that around 60 percent of organizations are in the final stages of developing conversational AI. That is slightly behind the global average of 66 percent, Twilio noted.

But businesses in EMEA aren’t just pushing ahead with conversational AI, they’re staffing up for it. They tend to have larger teams supporting their initiatives than businesses in other regions. Twilio surveyed more than 4,800 consumers and 457 business leaders in 15 countries, including the UK, France and Germany. On average, EMEA companies dedicate 49 team members to developing and maintaining their AI systems, well above the global average of 36 specialists, Twilio said.

More people on the ground often translates to deeper integration work and broader experimentation.

Around 54 percent of businesses expect to keep their current system in place for more than a year, suggesting that they’re treating AI as a long-term operational capability rather than a short-term experiment.

That mindset also shows up in how these companies are structuring their systems. The vast majority, around 88 percent, are running multiple models at once, mixing different large language models and providers to balance strengths and minimize weaknesses. Instead of betting everything on one system, they’re spreading risk and optimizing for flexibility.

This reflects a broader industry trend to avoid relying on a single AI vendor, as they aim to build more resilient AI stacks in anticipation of rapid model development and evolving regulatory expectations. Peter Bell, VP of Marketing, EMEA at Twilio, said:

“The commitment to conversational AI across EMEA is striking—we’re seeing serious investments in budget, people, and resources. As AI matures, the challenge for leaders is to remain ruthlessly agile, ensuring their systems keep pace with customer needs while prioritizing transparency and building lasting trust.”

Legacy Systems and Data Privacy Risks Hold Back Conversational AI

Despite this momentum, legacy systems are emerging as one of the biggest brakes on AI progress in EMEA. Businesses in the region are nearly twice as likely as their counterparts in the US or Asia-Pacific to cite challenges with existing infrastructure compatibility as their biggest barrier to deploying conversational AI, Twilio found.

Many organizations say their infrastructure isn’t built to support the kinds of real-time, data-hungry applications that conversational AI demands. Fragmented databases and rigid workflows make it harder to plug in new capabilities.

That is part of a broader theme seen across large enterprises. Businesses are eager to roll out smarter customer interactions, but AI adoption is accelerating much faster than the modernization of the systems underneath it. That gap limits the scope of what AI teams can deliver.

The study makes it clear that enthusiasm for conversational AI in EMEA comes with a dose of caution. Only 42 percent of businesses fully trust the models they’re using and almost half, 46 percent, lean on location and device data when personalising interactions. It’s a controlled approach, shaped as much by regulatory expectations and customer confidence as by technical constraints.

That caution is hardly surprising in a region governed by GDPR, active debates over AI governance, and a public that generally expects firms to justify how they collect and use personal data.

Research shows that while consumers are willing to share some data, they are reluctant to give up detailed personal information.

Enterprises are aware that any misstep around transparency or consent carries reputational and legal risks. As a result, AI systems tend to be deployed with tighter guardrails and closer oversight than in other regions.

In the US and parts of Asia-Pacific, businesses face fewer compliance hurdles and, as a result, show more risk appetite when it comes to adopting new capabilities quickly. While other regions prioritise speed, EMEA prioritises accountability, which shapes the approach to AI deployments and the types of data that businesses are comfortable relying on.

In EMEA, more than half of businesses (59 percent) are mainly using conversational AI for technical documentation and FAQs, with 54 percent concentrating on website chat, rather than social media.

Rather than focusing on cost cutting or increasing efficiency through automation, 58 percent of businesses cite brand innovation as the main driver, with 52 percent also citing agent efficiency and 48 percent focused on customer loyalty, showing an emphasis on relationship building.

The study hints at a reset in how companies evaluate the performance of their AI solutions. Businesses are paying closer attention to whether customers are actually happier and sticking around. Around half of organizations say they are prioritizing complaint resolution and 42 percent customer retention to measure ROI, reflecting a pragmatic approach to AI’s value in improving service quality.

Using conversational AI offers enterprises a chance to build trust and strengthen ties with customers over time to deliver long-term value.

On Wednesday, the enterprise giant released its quarterly earnings report, reaching a total revenue of $3.407BN, a 22% increase. 

Away from the finances, the company also used the call to outline its commitment to expanding its AI-driven and CRM solutions. 

Transforming Solutions

As part of its plan to bolster AI across enterprises, ServiceNow has emphasized its goals to transform AI agents, customer relationship management, and return on investment results for customers.

AI Agents

During the call, ServiceNow execs discussed the company’s aim to transform AI for its customer enterprises through its real-time AI control tower, to monitor, track and govern its use. 

By using its configuration management leadership to help with AI governance under one system, enterprises can simplify their AI management and integration process to deliver faster ROI. 

Regarding its agents, ServiceNow has created its workflow ‘road map’ for them to function effectively across an enterprise and a tech stack, going from a simple chatbot to a complex AI tool. 

This also includes voice, text, image and data options to allow customers to unify their AI experience. 

The company expressed its vision for a fully hybrid workflow future, with human and AI agents working together to solve and complete customer issues, tasks and actions. 

In fact, the company reported a sharp increase in activity for its AI control tower in Q3, with more than 4 times the traffic volume quarter-over-quarter.  

This also included a 55x increase in AI Agent Assist consumption in the past five months. 

In conversation with CX Today, Simon Harrison, Analyst and Executive Partner at Actionary, explained how ServiceNow positions its agentic AI as a reliable automation. 

He said, “ServiceNow’s approach to Agentic AI is pragmatic and brings guardrails, governance, and visibility to what can otherwise be chaotic experimentation with AI agents. 

“The company’s strength lies in how its highly usable approach is anchored in a mature workflow and data model, making its version of Agentic AI more controllable, measurable, and enterprise-ready than most.”

With AI turning into the new user interface, ServiceNow introduced AI experience earlier in Q3, creating AI assistants with a unified interface for a more seamless experience. 

This move from traditional UI avoids the repeated manual research through workflow engines and isolated agents by replacing them with one interface for all actions. 

CRM Investment Expansion

To revamp the customer service market, ServiceNow aims to drive higher growth and loyalty by turning its CRM into an AI-first system of action. 

By reconstructing CRM to center on automation, it can unify enterprise departments to drive actionable outcomes as well as data tracking. 

This means that by delivering more specific, automated capabilities, the integrated AI can understand customer context to resolve issues. 

This is used in customer service, where AI agents can resolve issues of consumer dissatisfaction, flag risk cases, and remind teams to respond before SLAs are missed. 

It also allows agent-to-agent orchestration to deliver faster, qualitative results. 

ROI

As pointed out in the earnings call, ROI is often a failure for tech enterprises due to its poor integration. 

However, ServiceNow’s AI platform changes this outcome by working throughout departments and tech stacks. 

This speeds up enterprise deployment, automation, and payback times, allowing investment outcomes to arrive within quarters, rather than years. 

The AI agents and control towers also provide cost benefits for ROI with faster resolutions. 

Customer Results

ServiceNow’s third quarter has also benefited from its enterprise customer base, with 553 customers each achieving at least $5MN in ACV. 

This was followed by the group of customers who had generated over $50MN increasing by 20% year-over-year. 

The company has also teased a partnership expansion plan with CcaaS giant, Genesys to focus on unification for agent-to-agentic orchestration between their two respective AI-powered platforms. 

This will allow both companies’ customers to use these systems under one environment to deliver faster, autonomous customer service. 

Enterprise customers are also seeing their personal results from ServiceNow’s efforts this quarter. 

In one instance, the US Federal boosted strong results in Q3 after signing a OneGov agreement with ServiceNow, beating ACV expectations. 

This was contributed to by ServiceNow’s Now Assist AI’s agency conversion and their AI control tower’s success in governance outcomes, resulting in strong ROI payoffs. 

With this deal, ServiceNow can provide government-wide agencies with its products including its AI and workflow solutions, allowing more agencies to use ServiceNow due to faster implementation. 

In return, ServiceNow has estimated to save the federal government billions over the coming five years, by boosting its overall efficiency levels by 30%. 

Key Earnings Results

Below is a breakdown of some of ServiceNow’s key financial results for Q3 2025:

• In its third quarter, ServiceNow outmatches its earnings expectations, with an increase in total revenue of over $3.4BN. 
• Its top earning result, subscription revenues, saw a quarterly earning result of $3.299BN, a 21.5% year-on-year growth
• The company also expects to reach its new AI revenue target of $1BN in 2026, after already exceeding its $500MN target for the year

As enterprises attempt to navigate how to integrate agents into their workflows, a more coordinated approach to multi-agent orchestration is emerging, in which multiple agents work together to deliver smoother customer interactions at scale.

There needs to be a demystification around AI agents so that enterprises can move forward with confidence, Garry Ovenell, Regional Vice President Solution Engineering at Talkdesk, told CX Today in an interview on the sidelines of the recent Engage Customer Experience Summit:

“People are talking about AI agents, but they talk about them in the singular. That’s not what it’s about.

“What it’s really about is, how do I take AI agents that are really powerful and really clever at what they do, and orchestrate them just like a conductor within an orchestra to be able to deliver an end-to-end outcome?”

That represents a shift in how enterprises think about the role of AI. More than a tool to provide faster responses, it can deliver a network of agents that work together toward measurable outcomes.

“From an AI perspective, it’s not just about the typical bots or your typical support that an agent may get. It’s about actually, how do I really affect the outcome in a consistent, scalable fashion?” Ovenall said.

“This is where the market should be going, and I think people are beginning to wake up to that.”

Multi-agent orchestration can start with a customer interaction or a trigger from an application. The orchestration platform can then engage the agents, much like human workers, to solve problems. “We’re seeing a lot of trends to start and drive that sort of thing,” Ovenall explained.

The technology is becoming the enabling factor underneath systems that deliver a holistic approach to customer interactions.

“We’re beginning to grow up a little bit. What I mean by that is we’re beginning to realize that the outcomes associated with end-to-end experience and the automation opportunities that sit in that need to be understood before you try and do anything.”

Too often, large enterprises buy tools before defining outcomes. While a majority of enterprises have started implementing AI initiatives, studies show that they are doing so without a clear view of the objectives they want to achieve.

A recent ArvatoConnect survey, for example, found that just 53 percent of businesses gathered end-user insight before making changes, and the same number hadn’t sought feedback on how new systems performed once in place.

“People don’t look far enough ahead to where they want to go. If you say, ‘what are your business goals for the next year, two years, three years?’ sometimes they don’t know that,” Ovenall said.

“Rather than launching AI projects and expecting them to automatically work,” enterprise leaders should “think about where you want to get to… You can always change the destination, but if you don’t have a destination, how can you drive to success?”

The place to start is taking a look at processes, James Mackay, Regional Sales Manager at conversational AI firm Rasa, told CX at the event. “We’ve been through cycles and cycles of large banks who haven’t done that, and they end up choosing a technology that fits their compliance structure rather than the customer outcome they’re trying to drive. That’s really challenging.”

Eating the Elephant One Bite at a Time

From that starting point, enterprises need to prove that their AI implementation projects address specific opportunities to succeed.

“That comes down to not just trialing it, but actually understanding what you’re trying to achieve, what you’re trying to measure, and then going after that. If you go after that in a proof-of-concept fashion, show the success, then that breeds further success,” Ovenall said.

“It’s like the old adage. You don’t try and eat the elephant at once. You try and eat it one bite at a time. There’s a lot of people that have just jumped in and tried to eat the elephant, and it’s failed.”

A much-debated MIT study suggesting that 95 percent of initial AI projects have failed, reflects that enterprises are “dabbling” and not measuring outcomes, Ovenall said, adding that AI is not a “magic bullet.”

“Yes, it’s one piece, and we’ve got some great intelligence out there to be able to have conversations. But just like a human being, we need knowledge to be able to solve problems, and just like human beings, we need a process to be able to follow, to be able to optimize the business outcome we require.”

“The AI engines still need that information. Where do they get that from? If that’s all locked up in your agents’ heads today, how do you get it out in such a form you can exploit it?”

Enterprises need to consider these factors before they get started to ensure that the coding and training they do on the platform will be successful.

Preparing Data for Smarter Multi-Agent Workflows

One of the key challenges to implementing multi-agent orchestration is fear around AI infiltrating customer data, Mackay said. “For these solutions to work, they have to talk to each other. And it’s talking to each other that everyone’s really nervous about.”

In highly regulated industries such as financial services, enterprises may not even want to share customer data between teams.

“It’s a real challenge. You have to make sure [organizations] have got that mapped out. Otherwise, you can have half a project and then they say, ‘actually we’re not allowed to do that’, and that’s it.”

And when enterprises can use data, it’s a challenge to wrangle unstructured information in usable ways. But there are ways to get started, one proverbial bite of the elephant at a time.

“Be honest about it,” Ovenall advises. “Start small. You can’t board the ocean. If you’ve got messy, unstructured data, go find a use case which requires a subset of data that you can understand and capture… and learn from that. There are plenty of tools on the market that will help you structure that data.”

Understanding the limitations of AI in handling data is key. While humans can usually tell when something doesn’t look right, AI lacks those instincts and will simply use the data it’s given. That’s why taking a more deliberate approach to checking and interpreting its outputs is crucial.

Managing AI Agents Like Human Teams

The trick to successful multi-agent orchestration is monitoring.

“Keep an eye on it, treat it just like you would teach a brand-new agent you just employed. You’re going to help them; you’re going to coach them,” Ovenall said.

“Very quickly, they’ll actually get very good, and… you’ll have this AI agent that could scale 10 times, 20 times, 1,000 times, 10,000 times, and it will be your best agent. But then you still have to… look after these tools to make sure they’re doing people right.”

Among the key questions for enterprises incorporating AI into their customer interactions is whether automated agents can truly grasp language and culture to deliver authentic experiences. Understanding words is one thing, understanding idioms, humor, cultural nuance and regional difference is another.

But Ovenall noted that humans can have similar misunderstandings — the answer is to communicate and learn from mistakes.

“You have the same problem with a human agent. They may be able to speak the language, but can they really speak the language, and do they truly understand the culture of people at the other end? That’s a learning process, so treat it as a learning process,” Ovenall said.

“Don’t panic about it, because that would happen in a real world. Just capture it and then iterate for it so that it doesn’t happen a second time.”

Many enterprise buyers remain cautious, continuing to evaluate individual AI agents before going all-in, while the technology is already moving ahead toward orchestration. The challenge is how to adapt and take advantage of these new ways to interact with customers.

“You can’t run away from it… the technology is not going to go anywhere, but how do we use it? We’ve seen this happen in the market a few times. With Netflix coming on and kicking out Blockbuster, you still watch movies.”

Amidst all the change, the need to serve customers is not going anywhere, Ovenall said. “In this space, we’re still going to want help. We’re still going to need to buy things; we’re still to have to service… the way we do it is changing rapidly and the opportunity for both customer and business alike is huge if they’re willing to start jumping and start driving things.”

The agency laid off about 1,800 call center employees in May and June. But it has since brought back roughly 160 and extended others’ contracts, as the issue was escalated to Canada’s Auditor General.

The CRA said it was answering 77 percent of calls by late September, having set a target of 70 percent by mid-October. The agency handles massive call volumes — more than 32 million per year, peaking at 300,000 daily during tax season — making it one of the most heavily used government contact centers in the country.

Since implementing the plan in early September, the CRA has rolled out several self-service improvements aimed at making it easier for Canadians and businesses to access help without having to call its contact centers.

These include extended online chat hours from 08:00 to 20:00 ET, simplifying its website and launching new self-serve options in its digital accounts. The CRA has also reported growing use of its chat tool, with more than 5,400 users between October 6 and 10. An expanded AI-powered chatbot is slated for release in early November to answer a wider range of questions and further reduce call volumes.

The agency also highlighted a national Quality Monitoring Program launched in 2024 that reviews more than 100,000 call recordings annually to improve accuracy and training.

The CRA received a government investment of $400MN in 2022, to support its call center operations in anticipation of call volumes remaining above pre-pandemic levels. This was intended to enable CRA to maintain a service standard of answering 65 percent of calls within 15 minutes of a caller opting to speak with an agent. That standard has been lowered from 80 percent in 2017.

But many Canadians report seeing little improvement in the service, spending hours on hold when trying to call the agency.

As part of its efforts, the CRA added a feature to its website telling taxpayers how long they can expect to wait before they get through to a service agent when they call. But test calls by CBC News at different times and days found its estimates to be wildly inaccurate, a gap that’s eroding public trust. Unlike many private-sector call centers, the CRA still doesn’t consistently offer a callback feature, leaving callers to wait on hold for as long as it takes.

The Minister of Finance and National Revenue and the Secretary of State directed the CRA to implement the plan in early September to strengthen services, improve access and reduce delays. The focus is on four key areas: “increasing call centre capacity, expanding online self-service options, tackling the root cause of service issues, and accelerating service modernization.”

Taxpayers’ Ombudsperson François Boileau said that while the CRA has made progress, he’s concerned about what happens next.

“With some processing delays far exceeding the CRA’s usual service standards, it is unlikely that the CRA will reduce the backlog to a sustainable level by the end of the 100-day period.”

“A longer-term commitment and adequate resources will be necessary. By reducing its processing delays, the CRA could reduce the number of calls it receives and reduce wait times for taxpayers.”

Boileau noted that a report released by the Auditor General this week, found a direct link between staffing levels and service performance, a familiar story for any large contact center. He also questioned what’s driving such high call volumes in the first place, suggesting that better digital design and faster case resolution could reduce the need for people to call at all.

Unsurprisingly, the Union of Taxation Employees (UTE) echoed those comments, calling for the CRA to immediately increase its contact center staffing, as well as the training and support it provides its agents. In August, the union launched a national “Canada on Hold” campaign denouncing the job cuts.

The Auditor General’s report shows that staff shortages, a lack of training and pressure on contact center employees are compromising the quality of the service the agency provides to the public, the union stated. Marc Brière, UTE National President, said:

“Our members, who work tirelessly, are often exhausted, overworked, and under pressure to respond to as many calls as possible as quickly as possible, to the detriment of quality service.”

Why Automation Alone Can’t Fix Contact Center Challenges

The CRA’s experience is a textbook case in what happens when contact center staffing, digital experience, and operational design fall out of sync. Even with new automation tools and AI chatbots, poor workforce planning and outdated processes can quickly undermine customer satisfaction.

The agency’s troubles may sound familiar to the UK’s HM Revenue & Customs (HMRC), which has wrestled with a similar dilemma — how to shift customers toward digital self-service without alienating them in the process. HMRC has been pushing taxpayers to use digital channels such as chatbots and online forms to reduce call volumes. But when the tax authority announced plans to close its self-assessment tax helpline for half the year, and drastically cut back others, it sparked an immediate public outcry. Within 24 hours, HMRC reversed its decision, conceding that it had misjudged how far it could push customers toward digital-only interactions.

For enterprises in any sector, self-service and digital tools can only succeed when supported by the right human infrastructure. Contact centers need skilled, adequately staffed teams who can handle complex interactions that automation cannot. And, as the CRA’s situation shows, service improvement needs to be treated as an ongoing commitment, not a one-time campaign.

As the 100-day plan wraps up, the CRA faces growing pressure to tackle the systemic issues that continue to generate high call volumes.

Boileau noted, “the CRA is measuring its performance before the peak volumes it normally experiences during tax season.”

“What will happen to the progress it has made after the 100-day plan ends and tax season begins? What will happen when the calls increase? Will the contact centers still have the resources to answer the calls?”

Many of its service users will be watching to see if its attempt to improve service and reduce wait times leads to lasting change.

From chatbots to voice assistants, AI is increasingly handling interactions that were once the domain of human agents.  

The promise is clear: faster response times, 24/7 availability, and reduced operational costs. Yet many enterprises are discovering that efficiency alone doesn’t equate to effective service.  

Customers still crave understanding, context, and emotional intelligence – qualities many automated systems struggle to deliver. When AI fails to grasp nuance or respond empathetically, frustration quickly builds, and loyalty suffers.  

“More often than not, people come in, implement this technology, and forget about it. When they do this, they’re losing the human touch and the human element of how agents have traditionally interacted with customers,” says Sahil Rekhi, CRO of Graia.  

This challenge – often referred to as the empathy gap – spans industries and geographies.  

Legacy AI deployments, rigid workflows, and rule-based bots can leave both customers and agents dissatisfied.  

For enterprises with complex operations and high-value clients, the stakes are especially high: missed opportunities, frustrated customers, and eroded trust can all have a direct business impact.  

As CX leaders look ahead, a new question is emerging: how can AI scale efficiency without sacrificing the human touch?  

Empathic AI is emerging as a potential answer, bridging the gap between automation and meaningful human connection.  

Against this backdrop, Graia has entered the market with a clear mission: to build AI that doesn’t just act fast, but acts thoughtfully, supporting agents and enhancing customer interactions without losing sight of the human element.  

From Cost Optimization to Emotional Intelligence  

When generative AI burst into the mainstream in late 2022, many organizations raced to automate as quickly as possible.  

The initial pitch was simple: reduce support costs, speed up resolution times, and lighten the load on human agents.  

“Ever since ChatGPT came onto the market, AI-driven automation and cost optimization have become buzzwords,” Rekhi reflects.  

“Everybody wants to use GenAI technology to create content, drive summarization, automate customer queries… and that’s probably the area for the biggest spend we see.”   

But what seemed like a silver bullet soon exposed its limits. Countless deployments relied on static workflows and brittle logic, producing interactions that were efficient but emotionally flat.  

Rekhi explains how too many businesses underestimated the importance of the human touch.  

“You’re missing the meaningful CSAT outputs, the NPS uplift, the loyalty. That’s where empathy needs to come into everything we do with this technology,” he said.   

While this may have been overlooked initially, there have been plenty of cautionary tales since the 2022 GenAI boom.   

Klarna, one of the highest-profile adopters of automation, dramatically reduced its contact center headcount – only to reverse course when customer experience began to suffer, as Rekhi explained:  

“They decided to get rid of 3,000 agents and then thought, actually, no, people still appreciate the value of talking to people.  

“At the end of the day, people buy from people.”  

While Klarna may be one of the more renowned examples of AI backtracking, it is far from the only company.   

Many CX leaders are moving beyond binary cost-saving models toward empathic AI strategies that can scale without losing the human layer.  

Enter Graia: Building Empathy into the Framework  

While the name may be new, Graia draws on the combined heritage of Bulb Technologies, Geomant, and Buzzeasy – three established players in CX technology. 

Graia is the world’s first Agentic Xperience platform, combining artificial intelligence with emotional intelligence to create experiences that are both autonomous and deeply human. 

Built on 25 years of customer engagement expertise, Graia unifies AI and empathy into a single engine that powers every customer interaction, rather than treating them as separate concerns like traditional contact center solutions. 

What sets Graia apart is its decision to anchor its platform in empathy, ethics, inclusivity, and compliance. 

It’s not simply layering ‘emotional’ language onto existing bots but rethinking how AI behaves in real conversations. 

For Rekhi, Graia’s goal is not to create technology that is “out there just to replace the human capital.  

“We want to create customer experiences that are transformative in the way both technology and humans can work together to drive the right outcomes.”   

Empathy in Action: How the Platform Works  

A key part of Graia’s approach is its empathy index score, which was developed by psychologists.  

Rekhi describes this metric as the “overarching goal you want to achieve for every customer.”  

When the platform is deployed, each interaction receives a baseline empathy score. Graia then uses adaptive AI to guide interactions toward a more empathetic outcome, whether through tone, timing, escalation, or language adjustments.  

But empathy isn’t dictated solely by the machine.  

“We give our clients control so they can set up six or seven different parameters or traits to dictate how empathy needs to come out,” Rekhi explains.  

“Listening skills, responsive skills, language, detail, even where the pauses need to be in the interaction.”  

This combination of structure and flexibility is particularly valuable in regulated industries.  

By baking in ethical and compliance guardrails, Graia enables enterprises to scale empathetic experiences confidently and consistently.  

Rethinking ROI: Beyond Cost Reduction  

For Rekhi, empathy isn’t just an aspirational value; it’s a business strategy.   

While many companies often view AI as purely a means to optimize costs, Rekhi argues that “if AI is deployed correctly – and AI through empathy is deployed correctly – you can actually drive revenue uplift and feed your growth ambitions.”  

His advice to CX leaders is pointed:  

• Look at ROI Holistically: Cost optimization is only part of the story. Empathy drives loyalty, retention, and revenue.”  

• Redesign the Process: Don’t just automate your current workflows; focus on the outcomes you want for customers.  

• Get the Data Right: Empathy works best when the underlying data enables informed, contextual responses.  

Empathy as a Strategic Differentiator  

In a nutshell, empathic AI is about ensuring automation works with people, not against them.  

For large enterprises navigating both rising customer expectations and operational pressures, that could prove to be a decisive edge, as Rekhi explained:  

“We believe empathy is a great differentiator for us in the market. And the way we implement it is going to extend that even further.” 

As the CX industry moves into its next chapter, the most successful organizations won’t just deploy smarter bots; they’ll build experiences that feel genuinely human.  

When deployed correctly, empathic AI can be a powerful strategy that might just define the future of customer service.  

For more information about Graia and its emphathetic approach to AI in the customer service space, visit the website today. 

Dreamforce 2025: The Top Announcements, ft. Agentforce 360, The New Slack, & Apromore Acquisition

This week, 50,000 people descended on San Francisco for one of the biggest events in the enterprise technology calendar: Dreamforce.

The 23rd annual conference was Salesforce’s chance to showcase its latest innovations, share success stories, and enable customers to network.

Below is a snapshot of some of the biggest revelations from the event:

• Agentic Enterprise Vision: Salesforce unveiled the “Agentic Enterprise,” where every employee works with an AI partner that autonomously handles tasks, creating a 24/7 intelligent and augmented workforce.
• Agentforce 360 Platform: The new Agentforce 360 Platform extends Salesforce beyond CRM, embedding AI agents across apps like Sales, Service, and Marketing to automate workflows across all departments.
• Slack as the Agentic OS: Slack becomes the central interface for agentic AI, enabling employees and AI to collaborate and act directly within Slack without switching apps.
• Acquisition of Apromore: Salesforce acquired process intelligence firm Apromore to give customers end-to-end workflow visibility and enable smarter, automated agent deployments.
• OpenAI Partnership: Salesforce and OpenAI expanded their collaboration, integrating GPT-5 and ChatGPT into Slack and Agentforce 360 to enhance AI-powered workflows and commerce.

You can find out more about all the major Dreamforce announcements here.

Zoom Announces 15,000-Seat CCaaS Megadeal with Oracle, Advances Broader Partnership

Zoom has confirmed that its contact center solution will be used to support Oracle’s customer service operations.

The 15,000-seat CCaaS megadeal will bring Zoom CX to Oracle’s global service agents.

Zoom first teased the deal back in February, reporting that the company had landed its largest-ever contact center deal, but has now confirmed that the unnamed Fortune 100 company mentioned at the time is Oracle.

The announcement is part of an expanded partnership between the two tech firms, which will see Zoom CX now available on Oracle Cloud Infrastructure (OCI).

The vendors believe that the collaboration between their solutions will allow enterprises to enhance customer engagement, boost workforce productivity, and advance business outcomes.

In discussing the availability of Zoom Contact Center on OCI, Chris Morrissey, General Manager of Zoom CX, claimed that the companies were “empowering organizations to unify customer interactions, employee workflows, and data into a single intelligent system.

The outcome is faster resolutions, stronger relationships, and measurable value at scale.

Christine Sarros, Senior Vice President of Enterprise Engineering at Oracle, also commented on the expanded partnership, stating that the combination of OCI and Zoom’s communications platform will “give enterprises a foundation for AI-driven engagement.” (Read more…).

Snapchat AI Spill Shows Why Chatbots Aren’t Ready to Run Customer Support Alone

A Cyber News experiment has once again exposed the cracks in an AI chatbot, this time Snapchat’s My AI, offering a stark reminder for companies rushing to put artificial intelligence in the customer experience driver’s seat.

Cyber News researchers recently tested Snapchat’s AI chatbot, which is used by over 900 million people worldwide, with some creative prompting that framed requests as storytelling exercises to trick the bot into sharing instructions for making improvised explosive devices, like Molotov cocktails.

While Snapchat’s safeguards block direct queries about weapons, the chatbot recited historical “how-tos” under the guise of a narrative when the team prompted it to tell a story about the Winter War between Finland and the Soviet Union and include details about how incendiary devices were reportedly made at the time.

This instance raises concerns about what other dangerous content could slip through, especially to younger users.

The Cyber News team explained:

“While the bot may never directly provide instructions on how to build improvised weapons, it will tell you a realistic and detailed story of how improvised weapons used to be built without any hesitation. This raises concerns about dangerous AI information availability for minors.”

The researchers notified Snapchat, but the vulnerability wasn’t patched immediately (Read more…).

Microsoft Expands AI Customer Service Strategy with L&G Contact Center Deal

UK financial services provider Legal & General (L&G) is working with Microsoft to build an AI-powered customer service platform.

The new system will use Dynamics 365 Contact Center to help employees provide faster and smoother assistance to L&G’s 12.4 million customers.

The platform, which will be integrated with Microsoft’s Copilot AI assistant, will give customer service teams a complete, real-time view of each customer’s relationship with the business. The first phase will focus on customers who have workplace savings, retail protection, and annuities, with additional product lines to follow.

The move aims to simplify the service experience. Dynamics 365 Contact Center will analyze conversations with customers, highlight useful tools to support the conversation, suggest relevant next steps, and prompt outreach across customers’ preferred channels. The system is designed to reduce complexity for employees by consolidating multiple tools and minimizing the need to transfer calls.

The agreement with Microsoft is part of a broader process at L&G to use technology to transform its customer service interactions, Laura Mason, Chief Executive Officer, Retail at L&G, said.

We recently launched the first fully digitized claims process, cutting average claim times by nearly two weeks, while our pensions app is the highest rated among peers, using digital tools to help people take control of their savings.

This new collaboration takes that ambition further, using AI to raise the bar while ensuring our teams can tailor support for customers who need us most (Read more…).

Cyber News researchers recently tested Snapchat’s AI chatbot, which is used by over 900 million people worldwide, with some creative prompting that framed requests as storytelling exercises to trick the bot into sharing instructions for making improvised explosive devices, like Molotov cocktails.

While Snapchat’s safeguards block direct queries about weapons, the chatbot recited historical “how-tos” under the guise of a narrative when the team prompted it to tell a story about the Winter War between Finland and the Soviet Union and include details about how incendiary devices were reportedly made at the time.

This instance raises concerns about what other dangerous content could slip through, especially to younger users.

The Cyber News team explained:

“While the bot may never directly provide instructions on how to build improvised weapons, it will tell you a realistic and detailed story of how improvised weapons used to be built without any hesitation. This raises concerns about dangerous AI information availability for minors.”

The researchers notified Snapchat, but the vulnerability wasn’t patched immediately.

Snapchat says My AI is trained on a broad range of texts and built with safety features it claims are “unique to Snapchat.” The training process “was designed to avoid amplifying harmful or inaccurate information, and the model was also fine-tuned to reduce biases in language and to prioritize factual information — though it may not always be successful,” Snapchat’s website states.

The experiment indicates that Snapchat’s claimed guardrails might not be as safe as they seem.

“Of course, no one’s rushing to Snapchat for lessons in destruction. But the experiment shows just how easily an AI can be pushed past the limits of what it was meant to do,” according to Cybernews.

It also underscores how easily AI systems can be pushed beyond their ethical or operational limits.

Snapchat isn’t alone. Meta, Lenovo, and other platforms have all faced similar AI “jailbreaks,” where chatbots are manipulated into providing unsafe or sensitive information.

Back in August 2025, Cyber News researchers tricked Lenovo’s AI chatbot Lena into exposing sensitive company data using a 400-character prompt.

This was enough to manipulate the bot into running unauthorized scripts on corporate machines, spill active session cookies, and sift through past conversations. Attackers can abuse such XSS vulnerabilities as a direct pathway into a company’s customer support platform.

Lenovo patched the flaw quickly, but the vulnerability shows how companies deploying AI chatbots can be exposed to massive data breaches that compromise customer trust.

These aren’t just abstract security risks; they have implications for customer experience teams relying on AI assistants to provide accurate information.

Another recent example is AI startup Anysphere and its AI-powered coding assistant Cursor’s chatbot, Sam, which responded to a customer query with a company policy that did not exist.

Several Reddit users shared their frustrations publicly and stated that they were cancelling their subscriptions to Cursor. By the time Anysphere responded three hours later, the story of a chatbot inventing a policy had already gone viral.

The Cursor incident illustrates the same fundamental problem as the Snapchat exploit. Chatbots can confidently present false, or even harmful, information as fact. This raises a crucial question for CX teams—how much can you trust the AI handling your customer interactions?

AI can speed up support and handle repetitive tasks, but companies need to combine automation with oversight, routinely testing AI behavior and keeping humans in the loop for high-stakes or policy-sensitive interactions.

The question isn’t whether AI will make mistakes, it’s how prepared you are to catch them before your customers do.