The new release allows AI agents to view the customer’s screen, interpret on-page elements, spot friction points, guide users with on-screen annotations, and hand off to human agents with complete contextual history.

It combines these capabilities with enterprise-grade redaction, auditing, and privacy controls, positioning the solution as a major leap in safe, context-driven AI support.

Indeed, Corbrowse believes that only after outlining these capabilities does the core problem become clear.

As Zac Scalzi, Director of Sales at Cobrowse, told CX Today:

“AI agents transformed how customers communicate, but they still lack the context required to actually solve problems. Until AI can see what the user sees, every answer is an educated guess.”

The “Context Gap” Holding AI Back

Large language models allow virtual agents to understand intent and deliver increasingly natural conversations.

But as Scalzi notes, “they still don’t see the actual user interface, what the user is doing, what errors are shown, or the UI obstacles encountered.”

This lack of grounding is what Cobrowse calls the “context gap”: the fundamental reason AI often sounds helpful yet fails to deliver meaningful resolution.

Customers end up repeating themselves, agents resort to guesswork, and support escalations pile up.

Cobrowse’s official product page argues that “LLMs can interpret and relay information, but without visual context they cannot reason. They behave like a searchable knowledge base, not an intelligent support agent.”

The vendor is emphatic that solving this gap is essential for businesses to thrive in the next era of agentic AI.

What Cobrowse AI Actually Brings to Virtual Agents

The new Cobrowse AI platform introduces capabilities traditionally reserved for human-assisted cobrowsing, but now fully integrated with automated support flows. These include:

Real-Time Visibility into UI State

Virtual agents can observe the customer’s web or mobile session, enabling them to identify errors, locate confusing elements, and understand exactly where a user is stuck.

Situation-Aware Guidance

Cobrowse notes that AI can now “visually direct customers with drawing and annotation tools,” giving step-by-step guidance instead of generic instructions.

Intelligent Analysis of Friction

The product interprets UI behavior and friction points in real time, giving AI agents the context needed to provide precise and timely instructions.

Seamless Escalation

If a case requires human intervention, the AI hands over with full visual and conversational history – eliminating the need for the customer to restate the issue.

Enterprise-Grade Safeguards

The platform includes redaction controls, audit logging, and deployment options tailored for regulated industries.

Scalzi described the solution’s ambition clearly, stating:

“Cobrowse AI elevates existing AI strategies by giving agents the context they need to reason. It shifts AI from relaying information to resolving issues autonomously.”

Why This Release Matters for the Broader CX Landscape

Even as enterprises invest heavily in AI assistants and copilots, many remain disappointed by low containment and inconsistent accuracy.

According to Scalzi, that frustration stems from over-reliance on data inputs that lack situational understanding.

“Most companies try to feed AI more information, such as FAQs, documentation, and logs, but without visual grounding, the AI is still guessing,” he said.

Teams often attempt to patch the issue by building custom APIs that expose product state to the AI. Yet, according to Cobrowse, these approaches are “engineering-intensive, fragile, and often introduce privacy risks.”

Cobrowse AI aims to eliminate these workarounds, giving virtual agents the context they need without custom engineering or risky integrations.

Expected Outcomes for Support Organizations

Like any AI solution, it all boils down to whether or not the tool can really deliver measurable results.

Cobrowse highlighted the following areas where it believes adopters can expect meaningful gains:

• Higher containment: More issues resolved entirely by AI.
• Greater accuracy and understanding: Virtual agents can interpret intent with UI awareness rather than assumptions.
• Improved CSAT: Customers experience interactions that feel relevant and confident.
• Higher FCR: AI agents can complete end-to-end resolutions rather than pushing users through multiple steps.
• Better digital adoption: Users learn the product as they’re guided through real workflows.

The company’s website claims that Cobrowse AI “gives your virtual agents the context they need to guide, resolve, and drive digital confidence.”

A Step Toward AI That Truly Understands Customers

Cobrowse’s latest release delivers something that conversational systems have historically lacked: the shared visual context that makes human-to-human support efficient and intuitive.

The company argues that this advancement is essential for agentic AI, as it enables the technology to not only speak like a human, but also think and respond like one.

In discussing the broader context of AI evolution, Scalzi summed up this point nicely:

“Without context, AI is little more than a smart FAQ. With visual intelligence, it can finally operate with real understanding.”

For organizations seeking to scale automation without sacrificing quality, this release may signal a new path forward.

Rhys Fisher sits down with Thor Mitskog, CEO of Cyber Acoustics, for a no-nonsense deep-dive into one of the most overlooked security debates in modern customer experience: hardware vs. software noise cancellation.

As enterprises race toward the cloud, Thor breaks down the hidden compliance traps, IT headaches, and cybersecurity risks that come with that shift – and why hardware might just be the unsung hero of secure communication.

If you’ve ever wondered how something as simple as a headset could protect sensitive data in finance, healthcare, or contact centers, this conversation is a must-watch.

When it comes to noise cancellation, the question isn’t just “how good does it sound?” –it’s “how safe is it?”

Join Rhys Fisher and Thor Mitskog as they unpack the real-world security implications of cloud-based software tools and why leading enterprises are turning back to hardware-driven solutions for peace of mind and performance.

Key discussion points

Cloud security pitfalls: How moving audio processing to the cloud opens doors to compliance and data breach risks.
Hardware simplicity: Why plug-and-play devices slash IT setup time, cut costs, and sidestep configuration nightmares.
Industry sensitivity: How financial services, healthcare, and contact centers are leading the charge in hardware adoption for regulatory reasons.
Future trends: Why Thor predicts consolidation in cloud noise-cancellation software – and a new wave of intelligent hardware innovation.

Explore Cyber Acoustics’ latest hardware solutions for secure communication.

Subscribe to CX Today for more deep dives on tech, security, and CX innovation.

Share your thoughts below — is your organization still trusting the cloud for critical voice data?

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

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

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

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

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

The Rise of the Super Agent  

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

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

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

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

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

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

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

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

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

For Penn, CoPilot delivers the best of both worlds:  

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

Efficiency with Integrity  

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

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

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

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

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

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

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

Case Study: Insurance Company  

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

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

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

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

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

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

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

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

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

Empathy Through Enablement  

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

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

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

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

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

Enterprise Takeaway: What Leaders Should Do Now  

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

To realize that value, the Puzzel man recommends:  

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

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

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

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

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

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

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

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

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

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

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. 

Optimizely’s latest report, AI & The Click-less Customer, surfaces the scale of the shift.

According to its research, 52% of consumers frequently use AI to research products, and 14% now begin their shopping journey on platforms like ChatGPT and Gemini.

For younger shoppers in particular, this isn’t niche behavior; it’s now the default. The study notes that shoppers aged 18-44 are “around three to four times more likely to use AI daily to research products and services than those aged 55 and over.”

Another striking, and arguably worrying, statistic from the report, given the inconsistencies of AI summaries, is that 42% of consumers are willing to trust AI-generated product summaries without clicking through to a website.

If search engines once served as the high street for digital commerce, AI is fast becoming the front door. Brands, however, are largely standing inside, hoping customers still choose to knock.

A Discovery Channel Businesses Don’t Control

As AI tools work their way into shopping behavior, organizations must realize that the starting conditions of the customer journey are very different. Customers now get a summary, not a set of links. They see a shortlist, not a funnel.

In discussing the report, Optimizely’s SVP of Marketing, Tara Corey, captured the stakes clearly:

“AI isn’t just changing how people shop, it’s rewriting the rules of how brands are found.”

“The moment a shopper decides to learn more, they expect instant clarity, trust, and speed. If you’re not visible or ready in that moment, someone else is.”

That immediacy is crucial. AI-generated answers effectively compress discovery, comparison, brand familiarity, and early consideration into a single exchange.

If customers don’t feel compelled to click through, the traditional levers that retailers rely on (UX design, A/B-tested product pages, meticulously crafted landing pages) don’t matter until much later, if at all.

This is why Optimizely highlights the rise of GEO (Generative Engine Optimization) as a new battleground.

Rather than concentrating on ranking, brands need to prioritize representation by featuring accurately in AI summaries, with consistent product data and clear value propositions that AI can interpret and surface.

For Corey, Black Friday reinforces this point, claiming that the shopping holiday “has always been a test of performance. Now it’s a test of discoverability.

“The brands that understand how to show up in AI platforms, not just search engines, will be the ones consumers engage with first.”

ChatGPT Pushes Further into Product Research

The timing for this shift is amplified by OpenAI’s rollout of shopping research in ChatGPT, a feature designed specifically to guide buying decisions.

According to the company, the feature allows users to simply describe what they want with a prompt, such as “Find the quietest cordless stick vacuum for a small apartment”, and ChatGPT will “ask smart clarifying questions, research deeply across the internet, review quality sources, and build… a personalized buyer’s guide in minutes.”

OpenAI claims that the tool “turns product discovery into a conversation,” pulling up-to-date information on price, availability, reviews, specs, and images.

Indeed, the company didn’t mince words about how widespread this behavior already is, stating that “hundreds of millions of people use ChatGPT to find, understand, and compare products.”

The tool is built on a specialized version of GPT-5 mini trained for shopping tasks. It “reads trusted sites, cites reliable sources, and synthesizes information across many sources to produce high-quality product research,” while adjusting recommendations based on user feedback.

It’s also transparent:

“Your chats are never shared with retailers. Results are organic and based on publicly available retail sites.”

With nearly unlimited usage made available across ChatGPT plans for the holiday season, OpenAI has effectively turned its product research feature into a mass-market shopping assistant at the exact moment Black Friday demand peaks.

OpenAI is also narrowing the gap between product discovery and conversion with the introduction of Instant Checkout, a new feature that allows U.S. ChatGPT users to buy items directly within the chatbot.

The rollout begins with Etsy sellers, with Shopify merchants such as Glossier, SKIMS, and Spanx set to follow.

The system currently supports single-item purchases and will expand to multi-item carts and additional regions over time. It’s powered by the open-sourced Agentic Commerce Protocol, developed with Stripe, which enables secure transactions between AI agents, shoppers, and merchants.

Stripe users can activate it with minimal code adjustments, while others can connect through the Shared Payment Token API or the Delegated Payments specification.

For shoppers, the experience is seamless: product recommendations appear organically, payment and shipping details are confirmed inside the chat, and purchases are completed without switching platforms.

Merchants retain full control as the merchant of record, managing payments, fulfillment, returns, and support through their existing systems. OpenAI notes that while sellers pay a small fee on completed transactions, customer pricing remains unchanged.

The Journey No Longer Starts with the Brand

Optimizely’s data shows that 66% of consumers still start on search engines, but AI is eating into that lead quickly.

When AI tools handle product discovery before a brand’s website ever loads, the customer journey begins in a space the brand doesn’t own and can’t directly shape.

And while 45% of marketers say they have a GEO strategy, only 27% feel fully prepared for customers who first encounter them via AI.

That gap is becoming critical. Optimizely’s data from Black Friday 2024 underlines the intensity of peak-season traffic, which sees a 65% increase in website visits, 99.98% uptime, and over 7,400 A/B tests run across the weekend.

These numbers reflect how much brands still rely on their own channels to convert interest into purchase. But if AI increasingly determines whether customers ever reach those channels, discovery could become redundant.

Consumers are already signaling what they trust, with 31% saying they’re more likely to trust an AI-generated summary if it comes from a known brand, and another 31% preferring a mix of brand and product information.

It is clear that right now, the brand still matters – AI just mediates the introduction.

What Retailers Need to Do Next

Across Optimizely’s report, the following three priorities emerged:

1. Structure Product Data for AI, Not Just Search Engines

Optimizely stresses that AI platforms are becoming “the new front door to digital experiences” and that GEO ensures brands “show up accurately in AI-generated answers.”

Accurate and accessible product data is foundational for that.

2. Treat AI Summaries as an Extension of Your Brand

Optimizely notes consumers will often trust AI summaries without clicking, and trust increases when information comes from a brand they know.

This implies brands must manage how they appear inside AI answers – which is the core of GEO.

3. Prepare Your Site for Late-Stage Arrivals

The concept fits the report’s narrative: customers come to a brand’s site later, after AI does the early filtering.

Optimizely also highlights the need for site performance during peak loads (traffic surges, uptime, speed).

A New Era of Shopping Behavior

ChatGPT’s new feature doesn’t replace retail websites, but it reshapes their role. They’re no longer hubs for early research; they’re destinations customers reach after an AI-guided shortcut.

AI is removing friction from the top of the funnel, but it’s also removing brand influence. The brands that adapt quickly will treat conversational AI as the new storefront, not a secondary channel.

And the next generation of shoppers is already there.

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.   

Will Salesforce’s Informatica Acquisition Make Agentforce Unstoppable?

Salesforce has announced the acquisition of Informatica.

First reported back in May, the purchase has now officially been confirmed for approximately $8BN.

The deal will see Salesforce leverage Informatica’s AI-powered cloud data management capabilities to improve its agentic AI offerings – most notably, the Agentforce platform.

Alongside the data catalog, Salesforce will also gain access to Informatica’s integration, governance, quality and privacy, metadata management, and Master Data Management (MDM) services.

The end goal is to use these capabilities to build a unified data foundation for agentic AI, enabling safe, responsible, and scalable AI agents across the enterprise.

Indeed, in discussing the news, Salesforce Chair and CEO Marc Benioff described data and context as the “true fuel of Agentforce.”

“When companies get their data right, they get their AI right, and Agentforce becomes unstoppable.”

In terms of the specifics, Salesforce also detailed how Informatica’s capabilities will sharpen its Data 360 feature… (Read more).

Cloudflare Outage Disrupts Major Platforms, Payments, and Black Friday Plans

It’s becoming a familiar story: A technical glitch at Cloudflare, one of the biggest internet infrastructure providers, knocked a number of websites and services offline for a few hours on November 18, disrupting customer access and merchant payments.

X (formerly Twitter), ChatGPT, Claude, Perplexity, Spotify and payment giant Square were among those caught up in the fallout.

The trouble began just before 11:48 GMT, when Cloudflare posted that it was dealing with an “internal service degradation” causing intermittent outages across its service network. Users saw error pages, stalled logins, broken APIs, and sites claiming connections were blocked. There were a few conflicting signals about the restoration progress, as at one stage the company reported that services were beginning to recover, but then around 15 minutes later reverted to “continuing to investigate this issue.”

By 13:04 GMT, Cloudflare admitted that one of its fixes involved disabling WARP access in London entirely, temporarily cutting off users from its WARP performance-boosting and VPN service that helps secure and accelerate internet connections:

“During our attempts to remediate, we have disabled WARP access in London. Users in London trying to access the Internet via WARP will see a failure to connect.”

Cloudflare announced a fix five minutes later, but continued to receive “reports of intermittent errors” until close to 17:00 GMT… (Read more).

Five9 Targets CX Inefficiencies with New Genius AI Upgrades

Five9 has introduced a fresh wave of Genius AI updates designed to push the company’s “Agentic CX” vision further into the contact center core.

Announced at the company’s CX Summit in Nashville, the new capabilities span routing, quality management, analytics, and digital engagement, tying them more closely together to help organizations extract greater value from AI at scale.

As many enterprises attempt to take AI from pilot projects into day-to-day operations, fragmentation continues to slow progress.

Disconnected data, inconsistent reporting, and standalone AI experiments often make it difficult to achieve the continuous improvement leaders expect.

Five9’s latest releases aim to combat these challenges by treating AI not as an add-on but as the connective layer running across the environment.

Five9 Chief Product Officer Ajay Awatramani framed the shift as a more fundamental rethinking of how AI should function inside the contact center:

“Our Agentic CX vision is about creating systems that don’t just respond but also help teams better understand and anticipate customer needs.”

So, let’s take a closer look at Five9’s newest features… (Read more).

UJET Acquires Spiral to Address Customer Data Analysis Roadblocks

UJET has announced its acquisition of Spiral to bolster its AI capabilities.

The AI startup will allow UJET to continue its AI roadmap for enhanced customer service solutions.

This partnership will also address customer data analysis issues for UJET’s enterprise customers.

This acquisition is set to further UJET’s AI roadmap vision by bolstering the company’s AI capabilities and addressing customer experience concerns.

By highlighting these issues of visibility between customer and leader, organizations will be able to improve their customer issues before they reach escalation.

In fact, UJET has reported that organizations that are unaware of these individual customer problems are losing approximately $5MN-$30MN in customer churn revenue.

This can be linked to ignored or forgotten negative customer experience complaints, with organizations reportedly gathering only five percent of reported customer issues.

According to UJET CEO, Vasili Triant, customer churn remains a blind spot for many enterprises, arguing that customer interaction analysis is not done effectively… (Read more).

Yet, despite how prevalent the technology now is within the customer service and experience space, many organizations still view it as a separate tool that gets bolted on.

While this can be effective, to truly reap the rewards of AI, organizations need to start thinking bigger – treating AI as a foundation, not a toolkit.

That much is clear after speaking with Sirte Pihlaja, Head of Team at CXPA Finland, who is organizing the event.

This year’s event will see speakers and CX professionals from around the globe descending on Helsinki for tw0 days of talks and workshops on how AI-native organizations are the future of CX.

But what exactly does AI-native mean?

For Pihlaja, it is about moving far beyond surface-level AI adoption.

“We want to help organizations become AI-native,” she said, explaining how relying on scattered pilots or small internal experiments simply won’t cut it.

“If you think, ‘Ok, I’m going to use ChatGPT or Copilot’… you might only achieve like a 10% personal productivity gain from that. But you won’t be able to deliver the major improvements that you’re looking for.”

And this isn’t purely a CX issue. Pihlaja details how AI-native organizations can have a wider economic impact.

“Finland [Philaja’s home country] has been in an economic slump for almost 20 years,” she said, and AI offers one of the clearest pathways out of it, but only if organizations re-engineer themselves with enough urgency and ambition.

In her words:

“We have been handed the technology and the solution… it’s just a question of making people understand how vital this is for our society in the first place.”

That belief shapes this year’s CX Masterclass, which brings together 25 speakers from across business, government, and technology.

The goal is to give leaders the cross-functional perspective needed to go from dabbling with AI to embedding it in the operating model of the entire organization.

Building the Case for AI-Native Organizations

Pihlaja’s definition of ‘AI-native’ goes well beyond integrating agents into customer service or adding a handful of automation features; it means rethinking how processes run, how decisions are made, and how work is distributed between people and AI systems.

She points to Sitra – the Finnish organization responsible for public-sector funding – as an example of the shift that is already underway.

According to Pihlaja, Sitra has decided that if an organization does not have a plan in place to become AI-native, it will not be given funding.

While these measures may seem extreme, they emphasize how strongly Finland believes in the technology .

Pihlaja believes that CX sits at the center of this transformation. Not because CX teams own AI strategy, but because customer journeys are where the impact becomes tangible – and where broken processes reveal themselves most clearly.

Still, she is clear that this event is bigger than a CX audience alone.

“This is not just for CX people,” she said. “We have sent the invite out to any business people who are interested in developing their business and their organizations.”

Why the Broader CX Community Should Pay Attention

Many CX leaders are already feeling the pressure to adapt, but Pihlaja argues that the change coming next will be deeper and faster than anything the sector has faced in a decade.

For example, Nokia’s former Head of Culture and Leadership, Mark Hayton, will explore what it means to lead in a world where AI agents don’t just support teams – they may be the team.

Pihlaja summarized this by explaining that the industry is “on the brink of a big change… the future of work is going to look totally different when many of us will maybe have an AI agent as our team member… or a boss who is actually an AI.”

Although many a disgruntled employee might be reading this and joking with their colleagues about an AI boss being able to do a better job – but it is a serious point.

How will agents respond and adapt to a world where the contact center leaders might be AI?

Meanwhile, C-level leaders from Finnair, Fujitsu, Microsoft and others will discuss how enterprise strategy shifts when organizations must design with, not just for, AI.

CX leaders often talk about customer expectations rising. Pihlaja’s point is that expectations aren’t the only thing rising. The capabilities available to customers, agents, and AI-driven systems are rising too – and the organizations that fail to adapt their operations quickly will fall behind.

Machine Customers: Where the Shift Becomes Real

Linked with the overall theme of AI-native organizations is the role of machine customers.

This year’s Masterclass devotes significant attention to that subject, beginning with the opening keynote from Gartner VP Don Scheibenreif, who has spent more than a decade researching machine customers.

Historically, “machine customers” referred to connected devices, such as a fridge reordering groceries, or a car booking maintenance. But, as Pihlaja explained, “GenAI and agentic AI have reshaped what we mean by machine customers.”

These agents now have the capacity to negotiate, compare, choose, and transact at a level far beyond earlier definitions.

The closing keynote from Standard Chartered Bank’s Katja Forbes explores another angle: how machine customers will transform complex B2B environments.

“One of her tasks is to understand how machine customers are going to be affecting this kind of clientele,” Pihlaja said – referring to governments, institutions, and large corporates

Other sessions delve deeper into the operational implications. Nexi Group’s agentic commerce lead will lay out how identity, security and payments will function when AI agents initiate transactions.

And for those looking for practical next steps, day two focuses on hands-on capability building – from AI search optimization to agentic commerce tools that help organization’s “speak the same language with the AI… across different channels and different spots”

You can discover everything about CX Masterclass 2025, including the full list of speakers and events, by checking out the website today.

You can also tune in to the livestream here.

Announced at the company’s CX Summit in Nashville, the new capabilities span routing, quality management, analytics, and digital engagement, tying them more closely together to help organizations extract greater value from AI at scale.

As many enterprises attempt to take AI from pilot projects into day-to-day operations, fragmentation continues to slow progress.

Disconnected data, inconsistent reporting, and standalone AI experiments often make it difficult to achieve the continuous improvement leaders expect.

Five9’s latest releases aim to combat these challenges by treating AI not as an add-on but as the connective layer running across the environment.

Five9 Chief Product Officer Ajay Awatramani framed the shift as a more fundamental rethinking of how AI should function inside the contact center:

“Our Agentic CX vision is about creating systems that don’t just respond but also help teams better understand and anticipate customer needs.”

“With these innovations, AI moves from the periphery to the core of the contact center – linking data, people, and processes into a system more closely embedded with contact center operations in ways intended to support continuous learning, adaptation, and more efficient and meaningful customer experiences.”

So, let’s take a closer look at Five9’s newest features.

Four New Innovations in the Genius AI Suite

Agentic Quality Management (AQM)

Five9’s new quality management approach is designed to evaluate up to 100% of customer interactions.

The model focuses on generating insights that can inform routing decisions, coaching programs, and ongoing performance improvement.

Rather than operating as a separate analysis tool, AQM feeds data directly into other parts of the Genius AI ecosystem.

Genius Routing

This updated routing engine uses defined customer attributes, agent skills, and proficiency levels to determine the best match for each interaction

Because it can draw on real-time inputs from AI performance systems and self-service applications, it becomes easier for organizations to deliver faster resolutions and more personalized experiences.

OneVUE Analytics

OneVUE builds on Aceyus VUE technology but packages it in a more approachable, self-service analytics experience. Users can assemble custom dashboards with flexible metrics that cover both traditional CCaaS operations and multi-vendor environments.

It is built with AI-ready reporting in mind, giving teams a clearer view of the KPIs shaping customer interactions.

Adaptive Digital Engagement

The digital engagement update introduces a no-code Dynamic Web Messenger Configurator, allowing teams to launch or adjust webchat experiences instantly.

Five9 also revealed a new partnership with Meta, enabling native WhatsApp connectivity with template support, broadcasts, and AI Agents. The broader goal is to give organizations modular digital channels that plug easily into Five9’s AI layer.

Early Customer Feedback

Northwestern Mutual is among the customers already experimenting with the new capabilities.

Eric Schanno, the company’s Principal Solutions Engineer, explained how leveraging Five9 had allowed Northwestern Mutual to “move faster from insight to action.”

“We are very excited about the potential of Five9 AQM to help us elevate coaching across 100% of interactions. And OneVUE gives us a single source of truth for the metrics that matter.

“The combination is raising the bar on performance and helps deliver more consistent, high-quality experiences for our customers.”

A Step Toward Fully Agentic CX

Five9’s message seems to be that the next stage of CX transformation isn’t just about adopting AI tools, but weaving AI into the operational fabric of the contact center.

These Genius AI enhancements move the platform further in that direction, giving organizations a more unified foundation for delivering adaptive, insight-driven customer experiences.

More News from Five9

Afiniti has teamed up with Five9 to bring its AI Pairing technology into the Five9 Intelligent Cloud Contact Center, signalling a step forward for more sophisticated, behind-the-scenes AI in CX.

Rather than placing AI directly in front of customers, Afiniti analyses behavioural and contextual signals in the background to match each caller with the agent most likely to deliver a successful outcome. In effect, it acts as a matchmaking engine for the contact center.

IDC’s new MarketScape points to rapid momentum in Europe’s CCaaS sector, forecasting growth from $1.5BN in 2024 to $3.7BN by 2029 – a 20 percent CAGR.

Five9 is highlighted as a market leader, backed by its AI-driven platform, solid compliance credentials, and strategy tailored to European requirements.