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.

Sales reps spend 70% of their time on nonselling tasks, according to a 2024 report from Salesforce. This includes time-intensive manual work like data entry, internal meetings, and admin.

For sales leaders weighing up how humans can focus on discovery, relationships, and negotiation – the case for sales automation is clear.

What is sales automation (and why now)?

CX Today defines this technology as solutions to assist and automate sales tasks, admin, and workflows.

Sometimes referred to Sales Force Automation (SFA), this software looks to maximise efficiency while keeping manual efforts to a minimum.

The impact of this can be huge. Gartner predicts AI-powered SFA could cut meeting preparation time by 50% within two years.

Where automation reduces repetitive workload

1. Automatic data capture and CRM hygiene

Manual logging is a morale killer – and can lead to human error lowering data quality.

Contemporary SFA systems auto-capture emails, meetings, call notes to ensure that all relevant data is present. It can then enrich these contacts automatically, so sales reps aren’t spending all day copying fields between systems.

The payoff isn’t just time saved; it’s more reliable pipeline data for managers and finance. This means sales reps can spend more time selling, while creating a clean paper trail for the rest of the team to build upon.

2. Smarter lead and account prioritisation

If everything is a priority, nothing is. Predictive models rank accounts by likelihood to convert based on engagement signals and historical patterns.

With this automation-created intelligence, sales reps know their next best action without having to think about.

When prioritisation is automated, teams spend less time guessing and more time dedicated to where it will make the most difference.

Beyond just the sales rep, this process will also help managers to distribute sales meetings in the fairest way. This will help boost morale and give each rep a consistent chance to earn their commission bonus.

3. Guided outreach and content automation

Sequencing tools can now generate drafts, personalise at scale, and schedule multi-step cadences – allowing sales reps to make a good first impression and beyond.

With AI refining tone, subject lines, and message length for each persona, the humans don’t have to deal with a repetitive copy-and-paste grind. This creates more engaging content and leaves more time for the humans to seal the deal in conversations.

This can be an especially powerful tool for new members of a sales team. It enables them to skip the tedious onboarding process and immediately start creating brand-safe messaging.

Helping people do the work only people can do

With SFA at their side, sales leaders and decision makers in the buying committee can expect to see 3 key rewards from their purchase:

• Time reallocation: With automation capturing activity and drafting first drafts, teams can claw back hours for customer work.
• Higher conversion rates: Account/lead scoring puts energy on high-yield targets while AI-assisted cadences lift reply rates without manual rewriting.
• Cleaner data: Automated hygiene improves forecast quality and reduces last-minute admin, giving managers more bandwidth for training.

By stripping out repetitive steps from the sales workflow, automation can surface what matters most.

The salesperson of the future will have the bandwidth to build trust, shape deals, and close business without having to worry about repetitive manual tasks.

To find out more about how your sales team can stay ahead in a competitive landscape, read CX Today’s Ultimate Guide to Sales & Marketing Technology.

New channels, new AI tools, new cloud platforms, each promising a faster route to smarter service.  

Yet when you look behind the success stories, you’ll find that most transformation journeys don’t start with a giant leap, but with a well-considered step.  

This more measured approach is beginning to gain traction across the enterprise landscape.   

Rather than ripping out legacy systems all at once, many CX leaders are taking a phased approach to modernization: layering AI, analytics, and cloud capabilities on top of proven infrastructure.  

The goal isn’t simply to “get to cloud,” it’s to evolve in a way that protects what already works while unlocking what’s next.  

“It’s not always realistic to move everything you’ve built over many years,” says Miguel Angel Marcos, Vice President of Operations at Enghouse Interactive.  

“Helping organizations move at their own pace – maybe migrating some agents or campaigns first – makes the whole process smoother and less disruptive.” 

That mindset is driving what Marcos calls flexible modernization, a model that allows enterprises to innovate without compromising operations, compliance, or financial stability.  

From Analytics to Agility  

Before the journey to modernization even begins, it’s essential to make sure that you have the necessary tools and equipment to complete said journey.   

You can’t just run out the door, Bilbo Baggins style, without walking shoes, a map, and plenty of water.   

The enterprise equivalent of this is data and analytics.   

Indeed, Marcos recalls one large contact center that started its transformation not by migrating infrastructure, but by introducing AI-driven analytics to its existing on-premises setup.  

“They had hundreds of agents,” he explains. “Supervisors were listening manually to hours of recordings to understand performance. AI gave them a consolidated view of every interaction, not just a few.”  

That change, he says, brought immediate value, as it allowed them to identify areas that needed improvement and act fast.  

“It’s an easy first step toward modernization – and it delivers quick wins without having to move the entire platform,” Marcos says.   

For some organizations, those early AI integrations – including quality management, speech analytics, and sentiment tracking – act as a bridge between the old world and the new.  

Once the data foundation is in place, they can decide which workloads make sense to run in the cloud and which to keep in-house.  

Cloud, But on Your Terms  

Like many organizations, Enghouse Interactive’s customers take a variety of routes 

Some organizations move directly to CCaaS solutions, such as the company’s CxEngage platform, which offers the pay-as-you-go flexibility of OPEX models.  

Others keep their core systems on-premises while spinning up cloud-based campaigns that demand speed and scalability.  

“We see customers who want to launch temporary or seasonal projects quickly,” Marcos explains.  

“Instead of waiting months to deploy on-prem, they run that campaign in the cloud where agents can switch between environments with no issues.”

This hybrid approach enables enterprises to balance CAPEX and OPEX models and mitigate the “big bang” risk that often derails large-scale migrations.  

It also helps teams adapt operationally, allowing them to retrain supervisors, fine-tune integrations, and adjust processes at a manageable pace, as Marcos details:  

“When companies move more deliberately, they don’t break their workflows. They can adapt integrations, protect previous investments, and keep business running while they modernize.”  

The Quiet Advantage of Going Slow  

In a market obsessed with velocity, taking time can actually be a competitive advantage.  

If only there were a famed fable to better illustrate this point.   

Marcos points out that slowing the pace of change – perhaps to the speed of say a tortoise – often protects both financial and human capital.  

“You might have systems still under support, or contracts that run another year,” he says.  

“Why waste that investment? Move when it makes sense, not just when a vendor says you should.” 

He also notes that gradual change helps organizations avoid the fatigue that comes with sweeping IT overhauls.  

“Big transitions affect not only IT, but operations. Giving teams the time they need to adapt means better adoption and fewer surprises.”  

A Future Defined by Flexibility  

Looking ahead, Marcos believes that flexibility – in deployment, in finance, and in technology – will remain the defining characteristic of successful CX organizations.  

He observes that political and regulatory factors are shaping how companies approach cloud, pointing to the fact that in some European markets, governments are more cautious about moving everything to public clouds due to data sovereignty concerns.  

“That’s why it’s important to offer options,” he argues.   

At the same time, the democratization of AI is reshaping who can compete on experience.  

“A few years ago, advanced analytics were only for big call centers with large budgets,” Marcos says.  

“Now, even operations with five or ten agents are asking for AI. It’s becoming a must-have.”  

The combination of scalable cloud technology and more modular architectures also gives decision-makers unprecedented freedom.  

Whereas previously, once a company invested in a solution, it was tied to it for years, today, it’s much easier to switch if something isn’t delivering.

This shift has placed the power firmly back in the hands of operations leaders.  

Modernization That Fits the Business  

Ultimately, modernizing a contact center isn’t about chasing the latest platform; it’s about creating a technology path that fits your business reality.  

Whether that means starting with AI analytics, moving certain functions to the cloud, or running hybrid environments indefinitely, the key is to build momentum without losing control.  

As Marcos puts it:  

“Every customer is different. Our job is to adjust to what they’re asking for and what they need – to help them evolve at their own pace.”

Because in CX, progress isn’t defined by how fast you move, but by how well every step brings you closer to the customer.  

You can discover more about Enghouse Interactive’s approach to modernization by checking out this article. 

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

For this writer, as much as it pains me to admit, it was the trademark side-swept fringe and uncomfortably tight jeans of an emo teenager.   

For younger readers, it might be the current, inexplicable obsession with Labubus, which one day you’ll look back on with confused nostalgia.   

Whatever your vice, the good news is that some mortifyingly embarrassing photos and a small amount of wasted cash are probably all you have to worry about.   

Unfortunately, for major enterprises delivering customer experiences that matter, deciding to hitch their wagon to the wrong trend can have far more damaging results.  

Right now, there is no bigger CX trend than AI. Be it chatbots, agent-assist tools, or QA, enterprises are experimenting with AI wherever and however they can.  

Of course, this isn’t to say that AI should be ignored; the technology’s potential to drastically alter and enhance CX is undeniable. But despite the hype, not every AI deployment delivers the results businesses expect.  

For Joseph Kelly, Solutions Architect at Miratech, part of the issue is the ubiquitous nature of the tech, as he explains:  

Everything is AI. But is it just AI for AI’s sake?  

“Customers really need to hone in on the right strategy to start with. In the CCaaS space, in customer experience and employee experience, first getting strategy right will help cut through a lot of the clutter and get to the heart of how AI can really help.”   

Kelly’s point hits at a real challenge: how to separate genuine AI value from marketing spin. 

Vendors are quick to slap ‘AI-powered’ on everything, from natural language understanding to speech recognition; the trick is knowing what will actually move the needle.  

The Hype vs. Reality  

When organizations are hype-driven, they run the risk of deploying technology without a defined goal, which often results in overspending.   

Kelly notes that even established tools like NLU IVRs have been ‘AI-powered’ in marketing terms for years, without fundamentally improving the experience.  

“It’s about cutting through the marketing and sales speak on what is really AI, and what’s not,” he says.  

“Then, you look at where you want to start to make real change. Are you looking to enhance your customer experience with AI? Or your agent experience with AI? That’ll help guide you where you’re trying to get to.”  

Enterprises that clarify their objectives – whether it’s reducing call volumes, boosting first-contact resolution, or improving agent workflow – are far more likely to see tangible benefits from their AI deployments.   

Start Small, Solve Real Problems  

For organizations just starting with AI, Kelly believes the best approach is to take things step-by-step. 

For example, he highlights practical pilots like agent-assist, smarter routing, and call deflection as good examples of “seeing how the technology can help agents provide more informed and efficient answers to customer inquiries.”  

Small-scale projects reduce risk and can produce immediate wins against clear goals to build on. Routing customers correctly the first time reduces wait times; agent-assist tools speed up complex resolutions. These early wins build momentum and justify wider adoption.  

However, in order for these pilots to be successful, he emphasizes the need for “good, accurate data that the AI can access.”  

Once pilot projects show value, scaling AI requires alignment with broader business goals. Efficiency, personalization, and agent experience must stay front and center. But again, data is at the heart of it all.  

“Where am I going to house all of this information?” Kelly asks.  

“Does it have to be in the CCaaS vendor’s platform? Do I need a way to connect these things so a change in one system propagates to another?”  

Kelly also cautions that adding AI won’t fix a weak foundation.  

 If you don’t have a really stellar customer experience today, adding AI is not going to provide the benefits you’re probably thinking it can. 

Consolidating data, optimizing knowledge management, and improving processes must come first.  

Avoiding AI for AI’s Sake  

For Kelly, the major contributors to AI project failures are vanity projects, poor integration, and a lack of adoption by agents and customers.   

To combat this, change management is critical.  

Agents need confidence in new tools, and customers must feel automation improves – not hinders – the experience. Without this, even advanced AI can underperform.  

This is where Miratech can help. By grounding AI projects in business needs and guiding enterprises through data strategy, integration, and adoption, the company turns AI investments into tangible, measurable business outcomes. 

This all means AI doesn’t have to be just a buzzword or trend. When used with clear goals, it can truly transform customer experience – improving efficiency, personalization, and agent empowerment.  

The key is to have a purpose: and then start small, scale strategically, and let AI serve the business, not the other way around.  

You can learn more from Joseph Kelly on how to maximize your CCaaS migration by checking out this article.

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

The communications platform disclosed its Q3 earnings on Monday, highlighting a strong growth from its customer experience portfolio. 

Zoom has also revealed its plans to grow product revenue further by enhancing its existing products with additional AI capabilities to drive AI-first customer experiences. 

During the earnings call, Zoom announced that the platform would be evolving from its traditional customer experience platform to an AI-focused one, aiming to drive productivity and relationships. 

Eric Yuan, CEO and Founder of Zoom, revealed that after its strong quarterly results, Zoom would be able to move forward with this vision. 

He said: “This performance reflects the durability of our business driven by the growing value we are delivering for customers as we evolve from a communications leader to an AI-first platform for work and customer experience. 

“Our vision is to be the AI-first work platform for human connection.” 

Zoom expects to accomplish this transformation by following its three strategic priorities: enhancing its existing products with AI, driving growth in AI products, and scaling AI-first customer experiences. 

Enhancing Existing Products 

During Zoomtopia 2025, the communications platform unveiled AI Companion 3.0, an updated version of AI Companion that utilizes agentic AI not only to respond, but also to act, advising on tasks such as meeting preparations, freeing up time, and call follow-ups. 

Zoom has embedded various AI capabilities and tools, including AI Companion, across its platform foundation, including: 

• Zoom Meetings: Zoom’s AI Companion, a proactive AI assistant tool, offers meeting summaries, follow-ups for next steps, and drives work forward. 
• Team Chat: Rising by 20% in active monthly users year over year, AI Companion supports the messaging product by providing customers with chat summaries, composition tools, and simplified search options for higher productivity. 
• Zoom Phone: This tool now offers Voice Intelligence for call transcription, summaries, noise cancellation, call routing, and analytics and insights for customer data collection, with over 10 million users now paying for Zoom Phone as of early Q3. 
• Zoom Contact Center: Working as Zoom’s cloud-based contact center solution, this platform has adopted AI tools such as Virtual Agent, an agentic AI chatbot offering complex tasks and responses for customers, and AI Expert Assist, allowing agents to utilize AI support in real-time with summaries and translations and offer possible agent responses during customer interactions. 

In fact, AI Companion usage has grown four times year-on-year, revealing that these AI features are seeing value from user activity, resulting in rapid adoption. 

By adding AI to these already-established products, customers are more likely to accept these capabilities once they’ve been integrated into the software. 

Driving Growth in AI Products 

By moving beyond its core communication tools and investing in greater agentic abilities, Zoom offers its customers further access to its AI tools to personalize them to their needs. 

This allows Zoom the chance to drive AI product revenue with product monetization, generating financial growth rather than just adding tools to products. 

In fact, 90% of Zoom’s top CX deals involve paid AI features to contribute to product revenue, offering both subscription and consumption models to suit the customer. 

This includes the development of AI tools such as Custom AI Companion, a paid version of the standard AI Companion model targeted towards enterprise-tier customers, allowing businesses to customize the tool to meet specific demands and policies. 

This also includes similar products such as Virtual Agent and AI Expert Assist, as well as Zoom’s recent acquisition of BrightHire. 

Scaling AI-First Customer Experiences 

Through utilizing tools such as Virtual Agent and AI Expert Assist, Zoom is using AI to transform interactions between customers and enterprises by expanding these products across the platform for automated workflows. 

These tools will involve automating routine requests and advise agents during workflow automation, voice, chat, and video calls for faster results. 

Zoom has also implemented a feature that allows enterprises to install either Zoom’s or a third-party’s AI tool, encouraging them to become familiar with AI usage while tailoring it to their needs. 

This strategy will also involve Zoom working with its largest customers to move AI agents into deployment; however, this may prove difficult. 

During the earnings call, Zoom noted that despite this upsurge in AI tool adoption, its net dollar expansion rate stayed at 98%, 2% lower than expected, likely suggesting that large customers had not been spending as much as hoped on Zoom’s products, with renewals on larger accounts proving difficult to resume. 

Zoom Key Earnings Results 

Zoom’s earnings results showed some strong areas of performance across enterprise and cashflow revenue results 

• Zoom’s total revenue reached $1.23BN, up 4.4% year-on-year 

• Its enterprise revenue grew 6.1%, totalling 60% of Zoom’s total revenue 

• Average monthly churn increased by 2.7%, similar to Q3 2024 

• Its operating cash flow increased to $629MN, up 30% year-on-year 

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

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

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

Agent Analytics

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

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

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

Agent Optimization

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

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

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

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

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

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

Agent Health Monitoring 

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

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

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

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

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

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

AI Implementation Report 

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

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

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

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

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

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

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

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

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

Scorecards, consistency checks, and compliance boxes have shaped the bulk of QA activity – and while these remain essential, their impact on customer experience often stops at the agent level.  

Many leaders still struggle to answer bigger questions: What’s driving call volumes? Where are customer expectations shifting? Which friction points keep resurfacing across journeys?  

The traditional model simply wasn’t built for that.  

As Derek Corcoran, CEO and Founder of Scorebuddy, puts it, those older processes are “baked into operational QA teams” and are “legacy in some ways.”  

Manual evaluation was always time-consuming, and teams were typically “only covering 2% of total conversations – and that’s on a good day.”  

With such a limited sample, it’s no surprise that QA often surfaced issues but rarely connected them to broader CX patterns.  

Instead of operating with a telescope, Corcoran says, teams were forced to work “with a microscope.” They were focused on agent performance rather than systemic insight. And until very recently, the technology didn’t allow for anything different.  

The Shift: From Passive Scoring to Meaningful Discovery  

AI and modern analytics are rapidly changing the QA equation.  

Today, QA leaders can step out of the purely operational view and into a far more strategic one. But the shift only happens when teams move beyond scoring into meaning-making.  

According to Corcoran, the first leap many customers take is simply getting out of spreadsheets and “streamlining” evaluation work. Immediately, this unlocks visibility:  

“Suddenly they’ve visibility they didn’t have before, and the agents are now directly engaged in the interaction with the evaluators and supervisors.” 

But the step-change occurs when that structured data meets analytics.  

Scorebuddy connects to multiple data sources – voice, chat, bots, and more – aggregating everything into a single picture.  

This lays the groundwork for a deeper analysis, including tagging, filters, dashboards, sentiment analysis, and custom forms that reflect what leaders actually want to understand, rather than just what they can tally.  

The key is scale. A manual team might evaluate a small percentage of conversations, but analytics sits “across a much wider set” and introduces what Corcoran calls a “multiplier effect.”  

Agents can no longer argue that the wrong calls were chosen, and leaders can finally see meaningful patterns in customer behavior.  

Most importantly, this resolves one of CX’s biggest pain points: explaining why customers are making contact.  

With analytics, “you almost have an inbuilt business analyst,” Corcoran explains. Leaders can see exactly when contact drivers spike, how they correlate to product changes, and what should be escalated beyond the contact center.  

Spotlight: When QA Data Reveals What No One Else Saw  

Take the example of a retail client navigating a Black Friday surge.  

As Corcoran recalls, the QA and analytics engine detected a sharp increase in calls linked to a specific delivery provider.  

“They could see that one particular service delivery company was missing its SLAs,” he explains.  

“This insight allowed the business to launch an action plan to which the provider responded, allowing them to drive down that cause of contact.” 

That ability to correlate contact drivers with operational issues outside the CX team creates direct, measurable impact on CSAT and customer effort – something that would have been nearly impossible using traditional QA alone.  

Another emerging hotspot is bot governance. As more companies deploy GenAI-driven virtual agents, they often assume these systems perform flawlessly unless complaints surface.  

But as Corcoran warns, bots “can go off script,” and a conversation that appears resolved “may not be the case.”  

Without structured QA on bot interactions, businesses risk unseen CX damage.  

One Scorebuddy customer now audits 50,000 bot conversations per week and compares bot performance side-by-side with human agents, providing a powerful lens for identifying where automation shines and where it creates risk.  

Building the QA–VoC Insights Loop  

In order to move from insights to analytics, QA teams must thread those insights back into operational decisions, product teams, and executive conversations.  

The strongest programs combine QA and VoC input into a single continuous loop, where quality signals highlight friction, VoC confirms customer impact, and analytics quantify scale.  

This integrated view is exactly where Corcoran sees QA heading:  

“We’re now getting visibility of other aspects in the business that we can then move into the C-suite if necessary.”  

From Problems to Priorities  

Scorebuddy’s evolution mirrors this broader industry shift.  

The goal is no longer to simply review calls but to understand why things go wrong, what’s driving customer effort, and what to fix first.  

QA becomes not just a safeguard for consistency but a discovery engine – one capable of shaping cross-functional decisions.  

In a world where contact centers must deliver more with less, this is where QA proves its worth: not as a reporting tool, but as a source of truth for customer reality.  

You can watch and listen to more of Derek Corcoran’s QA insights by checking out this exclusive interview with CX Today.   

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

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.

With enterprises under growing pressure to scale up their CX platforms, and legacy systems struggling to keep pace with the agility, insight, and innovation that today’s customers expect, CCaaS offers real hope.   

However, when it comes to moving to CCaaS solutions, the path isn’t always obvious.  

Should organizations go for a straight ‘lift-and-shift’, or adopt a more gradual, continuous approach?  

For Joseph Kelly, Solutions Architect at Miratech, the first thing to look at when considering a CCaaS migration is “how an enterprise can consume its transformation.”  

He emphasizes the importance of understanding the people who are going to be involved and how much work they can actually put into it: how much change can your agents handle at one time? How much can your customers consume?  

Kelly likens sudden, large-scale migrations to visiting a familiar website one day and finding it completely redesigned.  

“You spend most of the time trying to figure out where all the content you usually consume is,” he says.  

You really would look back and think this has taken way more time than I would have liked. You never want to get into that situation when moving from an on-premise system to a CCaaS platform.  

Lift & Shift or Lift & Shine 

In a nutshell, lift-and-shift migrations move existing systems and processes largely as they are.  

The appeal is clear: speed and simplicity. For enterprises seeking to consolidate operations quickly or retire on-premise infrastructure, this can seem practical.  

But there is an alternative that more fully exploits the capabilities of cloud and avoids just moving existing problems into a new environment: lift-and-shine. 

“Look at what you have today that works and what you can move over without being too disruptive,” Kelly advises. “And then modernize from there.” 

“That’s really the core of what we can call lift-and-shine.” 

The key is minimizing disruption for both agents and customers while maintaining continuity and improving over time.  

Continuous Iteration  

Continuous iteration – or “continuous enhancement,” as Kelly prefers – allows new capabilities to be rolled out gradually, and existing processes to be adjusted over time.  

“One of the main benefits is that you can time changes around contracts and new feature releases,” he explains.  

You want to make sure you’re getting the most out of that vendor contract, so you’re not leaving money on the table. 

This approach reduces the risk of overwhelming teams or customers, smoothing the change journey towards a fully-modernized CX environment.  

The main drawback of this approach is that it takes patience, something that is usually in fairly short demand in the CX tech space.   

Gradual adoption demands strong change management and the discipline to resist a ‘one-and-done’ mindset. 

Making the Choice  

Like any major customer service and experience implementation decision, multiple factors are in play when it comes to thinking about CCaaS migrations.  

Cost is certainly a big one: lift-and-shift may seem to be more cost-effective in the short-term, but it risks hidden expenses later.  

Continuous iteration, on the other hand, spreads costs and risk, but requires sustained effort.  

Business priorities matter too. Enterprises focused on speed to market or compliance deadlines may initially lean towards a lift-and-shift approach. But those prioritizing innovation, adaptability, and long-term CX improvements may prefer iteration.  

Aligning Strategy with Outcomes  

It is clear from Kelly’s insights that CCaaS migration is far more than a technical move; it’s a holistic choice affecting agents, customers, and business outcomes.  

Success requires understanding the organization’s capacity for change, evaluating risk, and aligning strategy with broader CX goals.  

Partnering with experienced CX experts can really help to get it right. “It’s about looking beyond platforms,” Kelly says.  

It’s about how you can deliver a better customer experience while moving toward continuous enhancement.  

At the end of the day, the right decision leads to an approach that will enable leaders to set up an organization for sustainable CX transformation, balancing efficiency with the evolving expectations of digital-first customers. 

You can hear more from Joseph Kelly on the truth about CCaaS migrations by checking out this exclusive interview with CX Today.  

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

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

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

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

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

Instead of scaling value, enterprises are scaling frustration.

From AI overload to orchestration clarity: Making CX systems sing

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

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

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

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

The buying shift: From AI expansion to workflow simplification

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

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

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

Less tech, more flow

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

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

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

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

Keep up to date with the latest tech buyer trends

Find Tim’s full analysis on Techtelligence.

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