To kick things off, there are six predictions that all examine what might be the most tangible shift taking shape in customer experience: agentic AI moving from underwhelming chatbots into systems that can actually handle real work.

After years of disappointing automation projects, the technology has reached a point where self-service might finally live up to its billing.

Meet the experts:

• Simon Thorpe, Director of Global Product Marketing for Customer Service & Sales Automation at Pegasystems
• Kishan Chetan, EVP and GM of Agentforce Service at Salesforce
• Matt Price, CEO of Crescendo
• Hakob Astabatsyan, Co-Founder & CEO of Synthflow AI
• Matthias Goehler, CTO in the Europe region for Zendesk
• Zeus Kerravala, Principal Analyst at ZK Research

The Chatbot Problem Nobody Wants to Talk About

Simon Thorpe, Director of Global Product Marketing for Customer Service & Sales Automation at Pegasystems, isn’t mincing words about where self-service has been.

“Look, everyone is talking about AI right now. And for good reason,” he says.

“But the thing that I’m really excited about is the fact that we can finally deliver self-service that actually works for our customers. You know, self-service that can get real work done. It’s able to resolve issues, complete tasks, deflect work from our centers and it’s self-service that our customers are actively going to want to use.”

Customers actively wanting to use self-service has been the unicorn of CX for the better part of two decades. Chatbots and IVRs promised a lot but mostly delivered frustration.

Simple queries? Sure. Anything remotely complicated? Straight back to the queue.

Thorpe sees agentic AI changing that dynamic because it can reason, adapt, and understand natural language in ways that rigid scripting never could.

He explains “What once took months is now going to take weeks, which is tremendously exciting.”

But there’s a catch. Speed without structure creates chaos, particularly in regulated industries where processes can’t just be improvised by an AI agent with good intentions.

“Without governance and workflow or workflow backbone, AI agents can go rogue. They can ignore processes. They can introduce risks.”

His solution is pairing agentic AI with enterprise-grade workflows that act as guardrails, ensuring “your rules, your regulations, your standards are consistently applied every single time.”

AI Agents Move from Pilot Projects to Production

Kishan Chetan, Salesforce’s EVP and GM of Agentforce Service, believes 2026 is when AI agents move away from experimentation toward becoming infrastructure.

“For me, the CX prediction for next year, the biggest one, is far more mainstream of AI agents,” he says.

“Companies across the board will use AI agents in their customer experience, and they’ll use that for different processes, and that’ll work seamlessly with their human service reps.”

The emphasis on working alongside humans rather than replacing them reflects how the conversation around AI has matured. Early hype suggested automation would eliminate jobs.

The reality is messier and more interesting: AI handles volume and repetition, humans manage complexity and judgment.

When AI Outperforms the Average Agent

Matt Price, CEO of Crescendo, makes a prediction that’s bound to spark debate: AI agents will become more empathetic and more efficient than humans in 2026.

“On average across all of the interactions between service agents and AI, AI will perform better because on average, AI assistants are able now to have great language, detect tone and respond appropriately to customers in the moment and have full access to all of the information that they need in order to give customers what they want, which is an answer.”

Price isn’t suggesting every AI interaction will beat every human one. Top-tier agents will still outperform AI. But AI doesn’t have bad days, doesn’t forget context, and doesn’t struggle with tone on the 200th repetitive call of the day. That consistency matters.

There’s also a perception angle here, as Price notes that “a lot of the time for clients, it’s not necessarily just how well you serve them, but how much effort you put in.

“And there’s nothing better than showing the amount of effort that’s been put in, than putting a human in the loop rather than an AI agent.”

So even as AI gains emotional intelligence, there will still be moments where customers want to know a person is involved.

The Innovation Slowdown (That’s Actually Good News)

Hakob Astabatsyan, Co-Founder & CEO at Synthflow AI, predicts 2026 will see a decline in forward-looking innovation and instead focus on making AI work at scale.

“My prediction for 2026 is that we will be seeing less groundbreaking innovation that we have experienced in the last two years and more ROE and value delivery to the customers, to enterprises.

“What I mean by that is more scalable, more reliable platforms that allow the enterprises to go into production and deploy agents, voice agents, but also chat, omnichannel chat and text agents into production and scale them to millions of calls.”

That might sound boring compared to the breathless pace of the last couple of years, but it’s what enterprises actually need. Over-the-top innovations don’t matter if the technology can’t handle production traffic without breaking.

From Reactive to Proactive

Matthias Goehler, CTO in the Europe region for Zendesk, sees AI shifting from solving problems to preventing them before customers even notice.

“My biggest prediction for 26 when it comes to CX is that AI will move from automation to anticipation,” he says. “Instant resolution still remains the biggest expectation of customers. But on top of that, customers also more and more expect personalized engagement.

“And then even on top of that, if companies could start to become more proactive and reach out to customers instead of customers having always to reach out to companies, I think then we’re really talking about the gold standard in service.”

That’s a higher bar than most organizations have reached, but the technology to get there has begun its early stages.

Customers Will Actually Prefer Virtual Agents (For Simple Tasks)

Zeus Kerravala, Principal Analyst at ZK Research, predicts that for straightforward requests, customers will start choosing virtual agents over humans.

“My CX prediction for 2026 is that virtual agents get so good that for simple requests, people start to prefer the virtual agent over humans,” he says.

“And you might think that this is contrary to everything we believe, but if you look back at the early days of online banking and restaurant reservations online, people said that back then that no one would prefer a computer over a person. And in both cases that certainly wasn’t true.”

Kerravala draws a parallel to other initiatives that originally faced skepticism but eventually became preferred options once they proved faster and more reliable.

“Virtual agents can do things faster and more accurately than people now for complicated tasks. We’re still going to do prefer to a human, but in 2026 the quality of virtual agents will get so good that for simple tasks we’re going to prefer machines over people.”

The prediction doesn’t suggest humans become obsolete. It suggests customer preferences will align with the strengths of each channel.

What This Means for 2026

The common thread across these predictions is straightforward: AI agents are maturing from disappointing novelties into reliable tools that can handle real customer service work.

Self-service that actually works, agents that operate alongside humans without replacing them, and systems that anticipate problems rather than just reacting to them.

These aren’t abstract possibilities anymore. They’re becoming baseline expectations.

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.

Yet, despite the hype, many rollouts stumble on a fundamental problem: machines simply don’t understand human voice as well as they should. 

Accents, background noise, and speech variability often trip up AI systems, leading to frustrated customers, escalated calls, and diminished trust 

As Sharath Narayana, Co-Founder of Sanas, puts it: 

“Ninety-five percent of AI projects have failed. People are very quick to jump to the conclusion that this will completely change everything – and then after six months, say, ok, this doesn’t work.” 

It’s a sobering reminder that automation alone doesn’t guarantee better CX. Customers don’t just want speed; they want to be heard and understood. 

Old Systems, New Pressures 

Sharath argues that much of the challenge comes from trying to retrofit AI onto enterprise systems built decades ago. 

“Think about the Fortune 500,” he says. 

“These companies have existed for a long time. Some of their CRM systems were built on mainframes. Nobody knows what code was written or who even wrote it. You can’t change those overnight.” 

While the boardroom pressure to “show impact with AI” is intense, enterprises are realising that the biggest bets carry too much risk. Instead, they’re seeking low-lift, high-impact solutions that deliver visible results quickly. 

That’s where Sanas is finding traction. 

From Empowering Humans to Supporting AI 

Sanas first made its name helping human agents communicate more clearly. 

By smoothing out accents and eliminating noise, the technology opened the door for thousands of new CX workers across India and the Philippines. 

“We’ve helped so many agents land jobs they would have never cleared without Sanas,” says Sharath. 

“When you lift one person out of poverty, you lift an entire family. That’s when you feel you’re building AI for good.” 

But recently, demand has grown for Sanas to support AI agents too. 

“All these automation stacks were built to serve humans,” Sharath explains. 

“We asked ourselves, ‘If we’re eliminating misunderstanding between two people, why not also between an AI agent and a human?’ That’s where we decided to build our SDK.” 

Real-World Impact 

The SDK is already finding powerful use cases: 

• Transcription Accuracy: “One of the largest transcription companies is using us to improve ASR accuracy by double digits,” Sharath says. 
• AI Call Handling: A global agentic company has seen abandonment rates drop by more than 30 points, thanks to better “turn-taking” (knowing when to pause or speak). 
• Telecom Synthetic Call Detection: A major telco is testing Sanas to spot synthetic calls, abuse, and fraud, which previously cost them millions to carry despite abandonment rates of 98 percent. 

These aren’t minor efficiency gains; they translate into smoother customer interactions and stronger enterprise outcomes. 

Why Voice Understanding Matters 

For Sharath, the key is empathy. Customers want authenticity, not robotic uniformity. 

“A lot of companies asked us early on, can you make everybody sound like Sheila from Texas? Our answer was no,” he says. 

“We always make a human sound like themselves. Because when there is a realness in the way you speak, that’s when empathy comes in. That’s where trust comes in.” 

This philosophy is also shaping Sanas’ new language translation tools, designed to ensure speakers always “sound like themselves” even when communicating in another tongue. 

A Balanced Future 

Looking ahead, Sharath sees a balanced role for human and AI agents. 

Short calls under two minutes may be automated, but for longer or sensitive conversations, the human touch remains indispensable. 

He is firm in his assertion that there will always be a human in the loop, highlighting the fact that “AI agents are not free,” and suggesting that the cost of compute, storage, and scaling is often equal to or higher than outsourcing. 

That makes technologies like Sanas even more critical, as they ensure that both humans and AI can interact in ways that are clear, authentic, and trusted. 

Building Trust in AI CX 

Enterprises may not be able to rewrite their legacy systems overnight, but they can still take steps to improve the experience for customers today. 

Voice understanding is fast emerging as the missing link, bridging the gap between automation and empathy. 

And with its SDK now in the hands of some of the world’s largest companies, Sanas is positioning itself as a leader in building that bridge. 

“With a bot that can relate to and empathize with the human better, maybe that world might change,” Sharath reflects. 

For enterprises under pressure to show AI impact without sacrificing customer trust, it’s a future that can’t come soon enough. 

You can find out more about Sanas’ accent translation technology by reading this article today.  

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

Of course, in the year 2025, when we’re discussing the latest tech phenomenon, we can only really be referring to AI.   

And, like the long list of tech advancements that have come before it, AI isn’t delivering as advertised.   

Indeed, for many contact centers, the reality has been a tangle of tools, dashboards, and alerts that leave agents feeling more burdened than empowered.  

However, despite these shortcomings, much of the customer service and experience sector appears to be doubling down on AI.  

This leaves an opening for companies that are brave and creative enough with their AI strategies to differentiate themselves in a crowded marketplace.   

One such company is Graia, which focuses not on replacing agents but on helping them do their best work.  

“When you’re redesigning the customer experience, it’s about how humans and technology coexist,” says Sahil Rekhi, CRO of Graia.  

“Our vision has always been to give agents ownership of the customer relationship, while providing the right technology to help them solve problems faster and more effectively.” 

It’s a philosophy that may strike a chord with many contact center leaders today.  

With agent turnover at record highs and tool fatigue spreading, organizations are beginning to realize that true efficiency comes not from automating people out of the process, but from augmenting human capability.  

The Problem with Current AI Tools  

The contact center industry has no shortage of AI solutions.  

From call transcription to performance analytics, the technology stack has grown dense. Yet many leaders are finding that more technology doesn’t always mean better outcomes.  

Rekhi notes that this “AI overload” often creates more friction than it removes.  

“Companies need to take a holistic view of what they’re trying to achieve. Too often, AI is rolled out as a quick fix – something the board wants to see in action – without a broader change management wrapper around it,” he explains.  

“That’s how you end up with tool fatigue and disengaged agents.”  

Instead, Rekhi argues for a phased, empathetic approach, stating that “AI isn’t a binary switch; it’s a journey over 12, 24, or 36 months. And that journey has to include people.”  

The Graia man believes that this people-first mindset must start with communication. He emphasizes the need for leaders to be transparent about why AI is being adopted and how it benefits employees.  

“There’s a lot of fear out there, agents wondering, ‘Is AI coming for my job?’ The reality is, it’s there to make their roles more valuable.” 

Agent Empowerment Through Empathic AI  

Graia’s platform is built on what Rekhi calls “empathic AI,” tools that simplify the agent’s job rather than complicate it.  

“The first thing we ensure is that the agent owns the interaction,” he says. “Technology should empower them to deliver faster resolutions, with the right context, tone, and empathy.”  

That context awareness is a cornerstone of Graia’s design. Through real-time voice and chat analysis, the platform tracks over 50 different emotional states, offering live guidance to agents on how best to engage.  

If compliance risks arise, it can flag them instantly. If sentiment dips, it nudges the agent toward a more constructive tone.  

“It’s like having a co-pilot that understands both the customer and the conversation,” Rekhi explains.  

“Agents come out of those interactions with a genuine sense of achievement, feeling like they delivered on what they were hired to do.”  

In multilingual environments, that support becomes even more powerful.  

Indeed, Rekhi details an example of Graia’s BPO customers, where agents serve ten or more markets.  

By deploying the vendor’s real-time voice and chat translation, which covers more than 100 languages, agents can deliver seamless service without actually speaking all those languages.  

“That’s where the idea came from: they’re not superhuman, just using Graia,” Rekhi says, referring to one of the company’s tag lines.   

Transforming Retention, Training, and Performance  

Another side effect of the influx of AI tools for agents has been a greater emphasis being placed upon the agent experience, which has emerged as a top driver of customer satisfaction.  

Rekhi believes that AI can actually play a pivotal role in helping to improve the agent experience, particularly when it comes to retention and training.  

“Happier agents mean happier customers, better revenue, and less churn. That’s proven in this industry,” he says.  

By using AI to automatically surface relevant knowledge, suggest next steps, and summarize calls, Graia helps reduce onboarding time and training overhead, as Rekhi explains:  

“No agent needs to be a subject matter expert anymore.” 

“They just need to know how to use the platform. AI takes care of the rest – tone, brand guidance, compliance prompts – all in real time.”  

For new hires, that means faster confidence and fewer early dropouts. For managers, it means consistent service quality without micromanagement.  

Rekhi adds: “It saves them precious time they can use to focus on high-value engagements, which ultimately drives better results for the business.”  

Beyond onboarding, Graia’s analytics feeds continuous improvement. By analyzing past interactions, both human and automated, the system identifies where agents typically need help, then refines training materials accordingly.  

Rekhi gives the example of a major automotive client using Graia to analyze dealership and roadside assistance calls, automatically generating training content and knowledge articles based on real customer interactions.  

Human Potential, AI-Assisted  

As AI becomes a fixture of the modern contact center, Rekhi believes the winning formula lies in alignment, transparency, and consolidation.  

“Try to bring everything behind a single agent desktop,” he advises. “That one-time pain of integration is worth it. The ongoing pain of swivel-chair setups and attrition isn’t.”  

For Rekhi, the goal isn’t to create superhuman agents; it’s to make ordinary agents feel capable, confident, and supported:  

“It’s about supercharging the employee, not replacing them. Companies that take that view will see AI adoption that truly sticks.”   

 

You can learn more about Graia’s approach to empathic AI by reading this article.   

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

Sales and marketing departments have entered a new era. One defined by rising customer expectations, fierce digital competition, and intense pressure on revenue performance.  

As growth slows and investor scrutiny increases, commercial leaders must deliver more profitable and customer-centric outcomes. Technology now sits at the heart of that mission. 

Long gone are the days of the salesperson with only charm in their utility belt. Modern sales teams are harnessing digital tools to personalize their message, deeply understand their target buyers, and streamline their own workflows. 

When it comes to marketing, the modern buyer wants more than catchy slogans and tech demos. More independent than ever, buyers respond to marketers who can provide real data and relevant content. 

This guide explores the capabilities, considerations, and opportunities within the evolving sales & marketing technology landscape. Its goal is to equip leaders with clarity, structure, and direction. To help them find the right tools to keep up – or dominate – in a competitive enterprise landscape. 

This comprehensive guide will help you understand: 

• What is Sales & Marketing Technology?
• The Key Trends Defining This Sector 
• The Different Categories Within Sales & Marketing Tech 
• What is in the Sales & Marketing Tech Stack?
• Why Enterprises are Struggling with New Tech Adoption
• Vendor Selection: How Do I Do It? 
• 2030 and Beyond: The Future Trends Defining Sales & Marketing 
• Where Do I Go to Find Out More? 

Sales & Marketing Technology, Explained: The Engine Behind Modern Growth

Sales and Marketing Technology refers to a connected ecosystem of tools and platforms. Their aim is to help organizations attract, engage, convert, and retain customers across the full buyer journey. 

Its purpose is to unite data, digital channels, and revenue teams so businesses can strengthen customer lifetime value.  

In today’s competitive climate, technology has become the backbone of modern commercial strategy.  

The New Rules of Revenue: Trends Reshaping Sales & Marketing

Analysts have identified three key areas where sales & marketing leaders are looking to improve – driving interest in new technology:

“CSOs must balance revenue generation with operational efficiency as investor demands rise.” – Gartner

 

“Leaders must prioritize improving revenue processes and customer-centric strategies.” – Forrester  

 

“The 2025 trend to watch for is the Personalization Renaissance.” – Mintel  

In other words, three capabilities have now become competitive differentiators. Better revenue generation, better personalization, and better processes among sales and marketing teams.  

Forward-thinking enterprises have responded to these challenges in a similar way – harnessing artificial intelligence. Here are three recent case studies where these areas were addressed with new AI products. 

• Better revenue generation:
  • PWC identified one key differentiator enabling some marketing teams to deliver nearly 80% more shareholder value than competitors. Their trick? Harnessing and investing in AI.
  • “Used narrowly, AI can make marketing less expensive – faster content, smaller budgets, leaner teams. Used strategically, it can make marketing indispensable – unlocking new growth, higher profitability, greater enterprise value”, it concluded.
  • Top performing marketing teams were found to regularly invest in new AI-powered capabilities. This in turn generates revenue, and funds marketing teams to invest in more tools – creating a cycle of enterprise success. 

• Better personalization:
  • A European telecom company found that customers receiving personalized messages took action 10 percent more than those who received generic advertising materials.
  • According to a 2025 McKinsey report, the company used GenAI to personalize messaging based on age, gender, and data usage.
  • McKinsey reported that it has “seen some marketers deploy gen AI to personalize content development 50 times faster than a more manual approach”. 

• Better processes:
  • Enterprise IT platform Workday saw 3,500% ROI after adopting an automation solution for client contracts. 
  • By connecting data across platforms, the Workday team saved hundreds of thousands of hours in its global projects. 

The Four Categories of the Sales & Marketing Technology Landscape

Whether you’re an inbound marketing expert, a sales team leader, or a CIO, you have an important role to play in harnessing and optimizing these technologies.  

The team at CX Today have distilled this wide-range of products into 4 core categories. 

1. Building Awareness (Acquisition)
   Tools that help organizations attract attention, generate demand, and capture leads through content, campaigns, and data-driven targeting.
2. Sales Enablement (Closing Deals)
   Platforms that empower sales teams with insights, automation, messaging, and training to shorten sales cycles and increase conversion.
3. Retaining Customers (Client Success)
   Technology that strengthens onboarding, engagement, renewal, and advocacy – protecting recurring revenue and reducing opportunities for churn. 
4. Omnichannel Connection (Linking Everything Together)
   Integration and automation technologies that unify data to deliver consistent experiences end-to-end. 

Your Tech Arsenal: Understanding the Tools That Fuel Revenue

Vendors are looking to support a broad spectrum of job titles within sales & marketing, with an aim to automate, optimize, and manage workloads. With the four categories as a dividing structure, here are some of the typical solutions that are found in the modern sales & marketing tech stack: 

Building Awareness 

This is the front door of the enterprise growth engine. Awareness tools help brands capture attention, generate qualified leads, and convert insights into pipeline opportunities.  

• Social media and content sharing: These platforms look to generate leads through inbound marketing where interested readers are prompted to engage further with the brand. This is particularly valuable for industries that rely on thought-leadership to grow brand awareness, such as professional & business services (consulting, legal, accounting). 

• Lead capture: This can include strategic forms where brands can gather intent data and contact details to set priority accounts for further marketing. It may also include web deanonymization, where brands can reach out to visitors to its site for sourcing potential leads. 

• Smart webpages & content journeys: Modern customers want more independence and feel more confident doing their own research. These platforms can help personalize and enhance how customers reach the decision stage, providing relevant content without being overbearing. 

• Related vendors: HubSpot, Hootsuite, 6sense, Uberflip 

Sales Enablement 

Once awareness is established, sales enablement technology equips revenue teams to engage prospects intelligently and close business deals efficiently. This leads to shorter sales cycles and higher conversation rates. 

• CRM systems: A strategic approach to converting sales relies on a centralized database which highlights opportunities, past interactions – in sync with the awareness-building marketing teams. 

• Smarter sales pitches: Salespeople are in a stronger position to increase conversions when they have personalised decks, playbooks, and pitches. These solutions help manage and track these sales content pieces. 

• Training and Coaching: These solutions put salespeople in a meeting with AI avatars, letting them practice their pitch in a risk-free environment. And after a genuine sales meeting, they can analyse transcripts to find areas for improvement. 

• Related vendors: Highspot, Seismic, Showell, Microsoft Dynamics 365 

Retaining Customers 

Retention and expansion are now as critical as acquisition. Customer success technology ensures that value delivery continues beyond the sale, and helps Chief Revenue Officers to better predict financial forecasts. 

• Customer Success Platforms: With data analytics and dashboards, these solutions can help client teams to understand the health of the accounts they manage. From there, they can upsell promising accounts or prevent churn. 

• Product Usage Analytics: One of the core ways to retaining customers is to ensure their onboarding and usage is going smoothly. These tools track this information and enhance the work of support teams who address problems. 

• Post-purchase communication tools: Once customers are onboarded, client teams can use a lifecycle communication tool to keep customers engaged and prime them for upsell pitches. 

• Related vendors: Qualtrics, Amplitude, Totango, Braze. 

Omnichannel Connection 

The final layer – and the most transformative – focuses on unifying data and experience across the organization. These platforms make marketing, sales, and service work as a single, insight-driven system. 

• Customer data & identity management: With these intuitive systems, client teams have a top-down 360° view of all customer touchpoints. This enables personalized content and predictive insights across the client journey. 

• Smarter digital engagement: When clients communicate with a company’s AI bots or human agents, omnichannel systems enable seamless conversations across sessions and channels. 

• Integration & automation layers: Acting as the connective tissue itself, these tools enable information to automatically flow to the right places and at the right times, feeding the 360° vision. 

• Related vendors: Salesforce, Zeta Global, Sprinklr, Zapier. 

Mind the Gaps: Adoption Pitfalls That Can Stall Your Tech ROI

While these solutions can open new doors for marketing & sales teams, adoption is not always a straightforward path. And the stakes are high.  

Putting aside financial consequences for the business, members of a buying committee may lose credibility if they support a purchasing decision that later fails to bear fruit. 

Going one step further, an unsuitable adoption journey can spark a negative reaction from customers, regulators, and investors.  

Vendors often pitch that their tools are simply ‘plug and play’. The reality is that enterprises must consider these adoption challenges: 

Privacy and first-party data:

Concerns around user privacy have been growing steadily, and this has resulted in a clamp down on legacy forms of data collection, such as third-party cookies. 

• Marketing teams today are expected to comply with all new privacy regulations. They should aim to responsibly collect consented data while fostering valuable personalization.
• Enterprise buyers must consider GDPR, CCPA, and other AI regulations – or risk the resulting backlash. 

Preventing underutilization:

A 2022 study from Gartner found that marketers were only utilizing 42% of their martech stack, a 16% drop from two years prior. The most common reason for this was an overlap of tech solutions, rendering part of the stack obsolete.

• With this in mind, sales and marketing teams can first consider how legacy solutions can be upgraded. Implementing a whole new solution may result in waste and underutilization. 

Employee satisfaction and skills:

One of the most common adoption challenges across every aspect of tech is ensuring the satisfaction of the humans that will be interacting with it day in and day out. 

• Adoption can be stalled when teams don’t have the skills or motivation to complete the onboarding. In relationship-driven industries such as sales, hesitancy about handing over the reins to AI is understandable.
• Secondly, if vendors can’t provide comprehensive support post-purchase, this will also extend the adoption process unnecessarily.  

Proving value:

“It’s not what you know, it’s what you can prove”. This isn’t just a stereotypical line from a legal drama, it’s also a key consideration when purchasing a new sales & marketing tool. 

• Due to the nature of outbound marketing, for example, it can be difficult for CMOs to prove that their department is truly delivering ROI. 
• More page views? More likes on a LinkedIn post? An increased email open rate? Consider which metrics are strong enough to justify a purchasing decision and whether a new solution will complement them. 
• For sales leaders, one common target is maximizing time with potential clients. This means they must prove that any new tech solution adopted is truly optimizing their time and workflow to open up their calendars for more calls. 

Rather than looking at the new features of an AI tool, tech buyers can start by looking at their own KPIs. Considering how a new solution will integrate and enhance existing processes of success measurement is key.

Cutting Through the Noise: A Buyer’s Guide to Selection Criteria

15,384.  

In kilometres, it’s roughly the distance between Guatemala City and Hyderabad. To count to from one, it would take you around four hours. But it’s also the number of Martech solutions that exist in 2025, according to a recent report. 

There isn’t even a recognised number of Salestech solutions out there, depending how wide one wants to cast their net, but it’s estimated to be over 1000. 

Rather than cause a serious bout of decision paralysis, these statistics are meant to illustrate how critical it is for enterprise buyers to have effective selection criteria. 

Here are some starting points to help you get from thousands to just one. The right one. 

Identifying your pain point:

Enterprise buyers will likely know what their primary growth constraint is, whether it’s more leads, more conversions, or a more comprehensive view of the entire customer experience. 

• Using the four categories system, buyers should ensure that there is a strategic fit with the relevant buying stage.  
• Keep in mind however, how processes upstream and downstream will impact your KPIs and growth. If you’re a sales leader struggling to convert meetings into successful deals, it could be that the marketing team is not generating targeted enough leads, for example. 

Data unification costs:

While it’s been established that AI can be a major differentiator in sales & marketing, but it is very much the ‘cherry on top’, as opposed to the solid foundation to build upon.  

• Without unifying new tools with existing data systems, creating a single, reliable source of truth becomes difficult.
• Without that, AI can expose more problems than it solves according to Tim Banting of Techtelligence. 
• Therefore, enterprise buyers should not expect a flashy new AI solution to be a ‘do-it-all’ tool. Instead, selection criteria should evaluate the integration costs that will create a fertile soil for AI to grow from. 

Success-centric vendors:

Vendors will already be thinking about how to prove their solution is right for you. Consider whether a vendor can provide a measurable realistic forecast of productivity gains that you can take to the buying committee.

• This mindset signals a shift away from promises, and more towards tying programs to results. 

The Future Trends Defining Sales & Marketing Technology

As we look toward 2030 and beyond, the sales & marketing technology landscape will undergo deeper transformation driven by greater automation, personalization, and data management.  

These teams can expect to see transformation in the content they produce, but also the workflows that define their workplace experience.  

Here are some future capabilities and trends to consider: 

The human and the machine:

While automation and AI will be embedded deeply, the differentiator will shift to how humans play a strategic role. Can they use technology to amplify creativity, judgement and empathy – not replace it?

• More than 50% of the internet is estimated to be AI-generated content. That trend has no signs of slowing. Companies that utilize AI, while amplifying their humanness, could capitalize on AI-fatigue among customers. 

Outcome-driven models:

As pressure mounts to prove ROI among sales & marketing teams, vendors may change how they frame their products.  

• The time of features, capabilities and upgrades, could be no more. 
• The future could vendors and buyers more oriented around business outcomes (e.g., increased net revenue retention, shorter sales cycles), rather than flashy products. 

Convergence of martech-adtech-servicetech:

The segmentation between “marketing tools”, “sales tools” and “customer service tools” will blur. This comes as omnichannel data connects everything and intelligence is found everywhere. 

• The future could see enterprises normalize an integrated revenue ecosystem with real-time feedback loops across the customer journey.

Final Takeaway: Building a Smarter, More Human Sales & Marketing Engine

Over the coming years, AI-driven orchestration, unified customer data, and outcome-based measurement will redefine what effective go-to-market execution looks like.  

Leaders who invest in scalable, ethical, and modular platforms today will find themselves better equipped to navigate tomorrow’s volatility. A future where buyer journeys are self-directed, expectations are personal, and attention is fleeting. 

Stay up to date on the latest sales & technology news by following CX Today on LinkedIn and keeping an eye on our new website category: Sales & Marketing Technology.

To hear from fellow buyers and tech decision makers, join the CX Today community group on LinkedIn where over 40,000 industry professionals are inspiring change. 

The cloud-based software company announced its third-quarter earnings report on Monday, revealing strong results across the board. 

This has led to ongoing investments into current and future AI products and initiatives. 

“We remain on track to exceed the $100MN in ARR from new products by the end of 2025,” said RingCentral CEO, Vladimir Shmunis, adding:

“Adoption of our new AI-led products is broad-based across various customer cohorts, from small businesses to large enterprises.

“Our GSP partners are also beginning to sell these new offerings, expanding our reach and accelerating adoption.” 

This expansion has led the company to roll out multiple AI product releases this week, increasing its customer base by branching out into emerging AI trends. The launches are aimed at enhancing communication experiences for both customers and agents.

This has resulted from an R&D spend of $125MN into its new AI portfolio. The company is already seeing consistent profitable growth, in the hopes of exceeding the year-end ARR target. 

RingWEM Adds AI Workforce Tools to Cloud Contact Center

On Monday, RingCentral released its latest AI product, RingWEM, an AI-powered workforce engagement management suite designed to enhance its native cloud contact center, RingCX. 

The suite offers four capabilities to strengthen customer experience across agent performance, customer satisfaction, and operational efficiencies by using AI-powered insights: 

AI Quality Management: Designed to give human agents the skills to improve their overall performance, the quality management tool will use personalized customer quality criteria to evaluate and provide extensive insight and feedback on customer calls. 

The tool is used to analyze and observe full customer interactions and agent workflows, delivering focused guidance for reskilling. 

Furthermore, the tool offers AI-based coaching recommendations to improve agent expertise, allowing enterprises to improve their workforce by viewing their agents’ performance analytics, common communication themes and advise them on next steps using the provided data-driven advice. 

AI Workforce Management: Used to improve quality in customer service, planning for potential challenges, and overall efficiency, this tool combines precise data forecasting and resourceful scheduling to align staff with probable tasks to tackle targeted demand. 

By using precise data forecasting, the AI workforce management tool can use algorithms to analyze past and current data trends and business drivers to predict the likelihood of contact quantities, allowing time for agents to tackle abnormal spikes before they occur. 

With intelligent scheduling, the workforce tool can generate actionable schedules for agents to follow that include agent preferences, adjustable service level actions, and business requirements to keep agents on track with tasks. 

These can be modified to fit irregular changes in working conditions to ensure that service levels stay the same to avoid customer friction. 

Additionally, customer agent supervisors can run probable scenarios to analyze the effectiveness of staff models and company changes before they are implemented. 

AI Interaction Analytics: This tool provides enterprises with high-level insights into customer satisfaction with interactions, compiling data taken from surveys and summaries from the interactions themselves to address negative customer experiences. 

AI Interaction Analytics can dissect customer conversations through voice tone, language preferences, and patterns in speech to assess satisfaction. 

The tool can use this and other conversations to further analyze key customer trends and issues as a whole, allowing businesses to proactively address these concerns before escalation. 

Screen Recording: Similar to the AI Quality Management capability, this tool allows supervisors to evaluate customer-agent interactions by collectively linking calls and screen recordings for a wider range of information into quality of conversation and workflow efficiency. 

These tools can be utilized to address underlying issues with agent performance and customer satisfaction and elevate operations in contact centers to deliver smarter service. 

RingCentral Debuts Agentic Voice AI Suite

RingCentral has also released its agentic voice AI communications suite, encompassing three tools that enhance communication experiences across the lifespan of each customer interaction. 

AI Receptionist (AIR): Before a conversation begins, this tool ensures that calls are not left unanswered. 

Using the voice AI ability to interact with customers, this AI agent can comprehend a customer’s reason for calling, answer questions, hand off real-time interactions to agents with summarized caller context, and identify and log potential opportunities that may require a human agent to follow up. These can help to avoid customer friction and repeated information. 

For scheduling interactions, AIR provides multi-calendar support across a company to integrate employee schedules and harmonize teams. 

Sales opportunities are collected and stored for future use in Salesforce, HubSpot, or with AIR’s own database. 

AIR can also be used on any SIP-based phone, allowing AI customer handling to be dealt with across the cloud, any premises, or hybrid setups. 

Brian Tucker, Chief Digital Officer at Televero Health, is a customer of RingCentral’s AIR tool. 

He said, “Using RingCentral’s AI Receptionist, the results are undeniable. We saw our monthly appointments increase 14 percent in the first four months, an increase in monthly revenue of over $200,000. 

“That kind of growth and return on investment is exactly what we need.”

AI Virtual Assistant (AVA): During a conversation, AVA can provide an agent with real-time assistance across customer interactions by implementing four key capabilities: 

• Real-Time Calls and Meeting Summaries: Identify the relevant information, questions, and actionable tasks during the span of a call or meeting, generating summaries and highlight reels to allow agents to keep track of the interaction’s objective during the call. 
• AI Writer to Create and Translate Communications: This capability can draft, edit, and translate conversations in multiple languages, allowing for seamless and customer-focused messaging. 
• Multi-Use Assistance Across Workflows: By adapting to a user’s communication method, via phone, text, or chat, this tool can provide intelligent prompts and relevant actions for each task. 
• Product Adoption and Feature Discovery: AVA can advise discovery and management methods to improve RingCentral’s overall customer enterprise experience. 

Kira Makagon, President & COO, RingCentral, explained the value of AVA in an enterprise workflow: “By putting trusted voice intelligence at employees’ fingertips, AVA makes work more productive and empowering,

“AVA is your personal virtual assistant that enables you to work smarter, faster, and more efficiently.”

AI Conversation Expert (ACE): After a conversation, ACE steps in to offer evaluated business insights from these interactions and adds it into one analytics and insights layer for a simplified outlook. 

It provides real-time insight into current customer satisfaction, trends in revenue, and overall agent team performance, giving context to performance data to allow leaders to act quickly. 

When requested, ACE can turn compound data into written summaries, recommend actions and examples of improvement, and be used an interactive interface to allow leaders to inquire related queries with instant results. 

Zach Jecklin, Chief Information Officer at Echo Global Logistics, and customer of ACE, uses the tool for improving company knowledge on customer calls and data trends. 

“AI Conversation Expert provides us with the detailed coaching for individual calls, and the dashboard connects the dots by rolling up all that data into a clear, concise view of the major trends impacting the entire business,” Jecklin said.  

“We used to have call data. Now, we have business intelligence. It’s that simple.”

RingCentral Pairs New AI Tools with Solid Growth

RingCentral has launched the new AI tools in conjunction with the announcement of its strong third earnings quarter. 

During its earnings call, the company reported a total revenue result of $639MN, seeing a growth of 5 percent from the previous year. 

Subscription revenue also increased, rising by 6 percent to $616MN, with a 23 percent rise of $130MN in free cash flow, which it intends to increase during the rest of the year.

In this interview, Deputy Editor Rhys Fisher sits down with Sharath Narayana, CEO and Co-Founder of Sanas, to unpack the challenges and opportunities surrounding AI voice technology.
With a background in engineering and over a decade of entrepreneurship, Sharath brings deep expertise in speech AI, offering fresh insights into how real-time speech synthesis and accent harmonization are transforming customer experiences.

This conversation is a must-watch for CX leaders navigating the hype and reality of AI in the contact center.

In this candid interview, Sharath Narayana explains how Sanas is redefining speech AI to make every agent a “super agent.” From real-time noise reduction to accent harmonization and breakthrough language translation, Sanas is building a future where understanding drives empathy, trust, and better CX.

Key takeaways from this video:

• Why Sanas is doubling down on human-first AI instead of voice bots that replace agents.
• How accent harmonization and speech enhancement reduce friction in global conversations.
• The real-world story that inspired Sanas to tackle bias in speech understanding.
• Why empathy, trust, and real-time speech synthesis are non-negotiable for next-gen CX.

In this interview, CX Today Deputy Editor Rhys Fisher sits down with Sahil Rekhi, CRO at Graia, to discuss how the company is reimagining customer experience by blending AI empathy with agent empowerment. With decades of industry expertise and a bold vision, Graia aims to close the gap between automation and authentic human interaction.

Customer experience is evolving fast – but too often, technology investments fail to deliver real value.

In this interview, Rekhi breaks down why empathy-driven AI is the missing link and how Graia’s platform is setting a new standard for both customers and agents.

What you’ll learn in this video:

• Why most AI deployments stall at pilot stage, and how to avoid the trap.
• How Graia’s Agentic Experience Platform blends omni-channel, opti-channel, and multimodal interactions.
• The role of AI-powered empathy in reducing attrition, boosting CSAT, and driving revenue growth.
• Why the future of CX isn’t about AI replacing humans, but empowering them.

By the end, you’ll see why Graia believes empathy isn’t just a buzzword; it’s a competitive advantage that could reclaim billions in lost revenue.

Watch the full interview to see Graia’s approach in action.

Explore Graia’s platform to discover how empathy-driven AI can transform your CX strategy.

Indeed, a recent study predicted that the CCaaS market is set to triple by 2030.   

A big part of this is the growing popularity of AI, automation, and digital-first customer expectations, which have completely changed the face of the contact center space.   

For many CX leaders, it creates a familiar headache: how do you pick a platform today that won’t look dated a year from now?  

It’s not an idle concern. Investments in customer experience tech are rarely small. Projects can take months to deploy, cost millions, and carry long-term implications for workflows and staffing.  

Nobody wants to discover six months in that their shiny new CCaaS system can’t adapt; or worse, that it locks them into processes they’ll soon outgrow.  

As Martin Kalinov, CMO at Voiso, puts it:  

“Onboarding a platform that feels modern today can sometimes feel outdated just a few quarters later.  

“Our approach to future-proofing is threefold: build on adaptable architecture, deliver features that solve today’s problems without locking customers in, and maintain a product roadmap grounded in direct customer feedback and emerging needs.”  

It’s a philosophy rooted in pragmatism. After all, the speed of AI development alone makes yesterday’s innovation today’s table stakes.  

Omnichannel: Still More Talk Than Action  

Take omnichannel. It’s been a buzzword for well over a decade, yet how many businesses can honestly say their channels are stitched together seamlessly?  

“The industry loves to talk about omnichannel, but few vendors actually deliver on it,” says Kalinov.  

The Voiso CMO believes the real challenge isn’t just enabling multiple channels; it’s connecting them in a way that feels cohesive to both agents and customers.  

Many providers bolt on chat or social as afterthoughts to a voice-first system. That doesn’t cut it.  

Voiso’s approach is to bake omnichannel into the core. That means every channel – voice, SMS, webchat, WhatsApp, Telegram, Viber, Messenger, Instagram DM – is unified inside a single interface.  

Agents don’t have to juggle tabs mid-conversation, and supervisors don’t have to reconcile fragmented reporting.  

That level of cohesion delivers consistency as well as efficiency.  

For a global businesses, delivering the same service standards across continents and languages is only possible if every channel sits under one roof. Anything else is firefighting.  

When AI Stops Being Just Hype  

No CX discussion in 2025 would be complete without mentioning AI.  

However, while the benefits of the tech are preached far and wide, companies remain wary.  

They’ve heard the promises before. What they want now is proof that AI can deliver, not just pilot projects and slick demos.  

For Voiso, one of the best examples of AI being used to solve real problems comes from the company’s Speech Analytics feature, which is designed to drive immediate, practical value, not just generate more data.  

Supervisors, for instance, use it for live keyword tracking and sentiment detection, identifying moments when agents need coaching while the call is still live.  

Moreover, the solution can also flag potential compliance breaches mid-conversation, allowing managers to step in before the issue escalates. That immediacy is why G2 recently named Voiso a leader in Speech Analytics for mid-market providers.  

Then there’s the predictive dialer, which Kalinov points to as an example of AI that’s been battle-tested:  

“It wasn’t just a smart-sounding feature; it delivered measurable gains from day one for companies like Realtree Properties, increasing call volumes by over 40% and cutting idle time in half.”  

These are the kinds of outcomes businesses want to see: faster coaching, fewer compliance risks, and tangible productivity gains. Not just a slide deck promising ‘transformation’.  

Innovation Without Empty Promises  

The tension between innovation and hype is familiar for contact center buyers.  

Vendors love to talk about their roadmaps, but the reality often lags. Kalinov is blunt about Voiso’s stance:  

“At Voiso, our rule is simple: no false promises. If we launch a feature, it’s because it’s ready to deliver value in practice, not just in theory.”  

This is an industry where credibility matters.  

CIOs have long memories of tools that over-promised and under-delivered, leaving them to pick up the operational pieces.  

The safer bet is often the vendor who delivers less marketing noise and more working features.  

What’s Next for the Future-Ready Contact Center  

So, where does all this leave CX leaders planning for the next three to five years?  

For Kalinov, the focus is on more intelligent systems, not bigger ones.  

“The future of business communication isn’t about piling on more tools; it’s about creating smarter, more connected systems,” he said.  

“Customers want platforms that make their teams faster, not just busier; more agile, not more complex.”  

Ease of integration is part of that equation. With open APIs and plug-and-play integrations, Voiso positions itself as a system that complements existing stacks rather than replacing them outright.  

For businesses, the move takes them beyond convenience to risk reduction.  

However, perhaps the most telling thing is how customers themselves describe the platform.  

“What we’re hearing from customers is that Voiso doesn’t just fit their needs today, it evolves with them,” Kalinov explains.  

“Whether they’re scaling internationally, pivoting to new channels, or automating parts of the customer journey, customers trust us to help them move faster without friction.”  

In other words, the future-proof contact center isn’t about predicting what’s next; it’s about being adaptable enough to handle it when it arrives.  

Discover more about Voiso’s AI approach by reading this article.  

You can also explore the company’s full suite of AI services and solutions by visiting the website today.