By integrating Microsoft 365 products into the software company’s platform, Zendesk customers can access Agent 365 capabilities for intelligent productivity. 

In turn, Microsoft has implemented Zendesk Agent within 365, allowing its customers to access tools to enhance service productivity and workflow efficiency. 

Craig Flower, Chief Information Officer at Zendesk, highlighted how the partnership expansion would improve Zendesk’s ability to deliver a superior customer experience. 

“Our collaboration with Microsoft on Agent 365 and Zendesk Agent for Microsoft 365 Copilot is a pivotal moment for Zendesk,” he explained. 

“This collaboration not only solidifies our position as a leader in enterprise AI automation but also ensures that Zendesk remains at the forefront of the evolving digital worker landscape.  

“By integrating with Agent 365 and Microsoft 365 Copilot, we are empowering our customers with both autonomous and streamlined support capabilities, optimizing operations, and ultimately delivering a more efficient and reliable employee experience within Microsoft 365.” 

Improving Service Experience 

This partnership aims to upgrade small business experiences by implementing both tools to generate tailored needs. 

By establishing Microsoft Agent 365 within Zendesk’s platform, the AI offers autonomous ticket management support for Zendesk’s customers for reduced human intervention. 

These capabilities include ticket creation, handling, status monitoring, and communication management within Microsoft’s environment to ensure data governance requirements are met. 

This allows human service agents to shift away from constantly reviewing routine queries and return to high-demand, complex tasks. 

In return, Zendesk Agent has been integrated into Microsoft 365 Copilot to support its core apps with ticketing capabilities, such as ticket submissions, status monitoring, and following up tasks without the need to switch tools. 

Similar to the first integration, this capability is managed within Microsoft’s environment, resulting in limited friction for tool management and deployment.  

As a result of the integration, agents can experience direct AI-assisted support in several routine task areas, resulting in higher responsiveness, resolution, and reduced waiting times. 

This AI integration allows smaller businesses to elevate their service demands to the level of any well-established company, including delivering higher productivity and service levels. 

By implementing these tools directly within a business, teams can manage their workflows effectively without agent intervention. 

Furthermore, both tools offer customers secure and compliance management for handling adoption risk within a governed ecosystem. 

Targeting The Small Business Market 

The integration follows a similar trend in recent months of larger vendors trying to dominate the small enterprise customer corner by offering tailored products and services to fit their needs. 

Earlier in November, Zoom had secured its commitment to providing service capabilities to companies of various sizes with simple, straightforward tools to enhance their businesses. 

The communications giant notes how businesses with smaller teams require different demands than larger ones, forcing some to juggle various workloads across the board to keep up with demand. 

This means vendors will need to personalize their tools and approaches to cover more ground and advance these smaller businesses to the industry standard. 

This has been a well-documented issue in the CX industry, as various companies have recently eliminated support for enterprise customers that don’t meet their size standards. 

Unfortunately, some customer enterprises that are unable to provide businesses with desirable profit results may be asked to cancel their subscription if the company can no longer provide the services needed or intend to solely focus on its largest customers. 

However, companies such as Microsoft and Zendesk have offered support for this neglected market, supplying these customers with both tools to elevate their teams while prioritizing their unique requirements. 

Srini Raghavan, Corporate Vice President for Microsoft Copilot and Agent Ecosystem, explained how the tool collaboration will offer these enterprise customers support across a range of business needs, and allow them to elevate their issue resolutions even at their current capacity. 

He said, “AI is transforming how organizations deliver employee service, and Microsoft’s collaboration with Zendesk is leading that change by enabling a new era of intelligent support. 

“We’re combining the power of Microsoft 365 Copilot’s intelligence with Zendesk’s modern service platform, enabling employees to resolve IT, HR, and Finance issues seamlessly within the tools they use every day.” 

Digital sovereignty, the ability for an organization to maintain clear control over how its data is stored, accessed, and governed, has moved from a technical concern to a board-level priority. As organizations expand their digital footprints and accelerate cloud adoption, rising regulatory scrutiny and growing customer expectations are forcing businesses to rethink how they manage data.

Sovereignty means different things to different people, the panelists noted, but the common thread is the need to take control over customer data, which has become essential to maintaining trust. The pressure to demonstrate that control is now shaping transformation plans, vendor choices and long-term customer experience strategies.

Control of Critical Data Is Becoming a Strategic Must

The energy crisis following the invasion of Ukraine exposed the geopolitical dimension of critical infrastructure, reinforcing the need for systems that can operate independently in extreme circumstances.

“Digital sovereignty is about stability and resilience,” said Julia Weberberger, Head of Corporate Strategy at Energie AG Oberösterreich, describing it as a source of power. “[W]e have to make sure that we operate our critical data on our own. We operate our own data center, with emergency power supply, and rely on a multi-provider strategy to create redundancies… It’s also very important that we build expertise in digital sovereignty in Europe, but also within our company.”

Europe is developing a new mindset built on innovation and security, Weberberger said, shaping companies, knowledge, opinions and even social narratives. In this environment, European data sovereignty is becoming a key strategic concern that requires balance.

As Martina Saller, Public Sector Sales Lead at Microsoft Austria said:

“It’s not a black and white discussion. It’s not about choosing the path of sovereignty or choosing the path of innovation. It’s about balancing and orchestrating… a risk-based approach.”

That layered approach should separate highly sensitive workloads from those suited for cloud-based innovation.

Public administrators highlighted that sovereignty is multidimensional: technical, legal, economic and emotional. What customers want above all is visibility and choice. As one leader emphasized, beyond control over data processing and storage, true sovereignty also means being able to choose the parts of a technology package they need rather than being required to buy licenses for bundles, which drives up costs.

Procurement rules, however, are still playing catch-up. With different requirements scattered across the EU, organisations often end up doing the same work multiple times. A more unified approach that allows for shared certifications and tech that plays nicely across borders would make it easier for businesses and public bodies to build modern, sovereign digital systems. And to make sure those sovereignty rules help innovation instead of getting in the way, organizations say they need clear guidance and strong partnerships with their tech providers.

What Customers Need from Cloud Partners

A recurring message throughout the discussion was that sovereignty cannot be achieved in isolation. Customers expect their cloud partners to help them meet changing regulatory, security and operational demands.

As Norbert Parzer, Certified Public Accountant, Tax Advisor and Partner at EOS put it, “first find the companion before you start the journey.”

To address concerns around extraterritorial data access, Jeff Bullwinkel, VP and Deputy General Counsel, Corporate External and Legal Affairs at Microsoft EMEA, detailed the steps the vendor has taken to provide assurance and legal protection.

The tech giant has built the EU Data Boundary for the Microsoft Cloud to “mitigate the risk, or reduce the surface area of risk by just reducing situations in which data is transferring from one continent to another.”

Just as crucial is Microsoft’s assurance that it will resist demands from governments to divulge customer data, Bullwinkel said:

“When Microsoft gets a request or a demand in order for data from any government around the world, we have a contractual obligation to litigate against that order whenever there’s a lawful basis for doing so. And we have quite a history of doing that…with a view toward guarding against that kind of risk and so we will continue in the future as well.”

Microsoft has also expanded its sovereign controls and confidential computing to ensure that customers hold the keys to their data.

The vendor recently announced expanded capabilities for its Sovereign Public Cloud and Sovereign Private Cloud. By the end of this year, customers in four countries—Australia, the United Kingdom, India and Japan—will have the option to have their Microsoft 365 Copilot interactions processed in-country. This will be expanded to 11 more countries in 2026: Canada, Germany, Italy, Malaysia, Poland, South Africa, Spain, Sweden, Switzerland, the United Arab Emirates, and the U.S.

These capabilities directly address customer expectations for operational autonomy and regulatory compliance.

Partnerships help empower organizations to keep control over their processes and architecture, so that digital transformations are secure and interoperable. Organizations across sectors are embracing AI, but they need to be sure that the models they use preserve transparency and control.

“There are many areas we see it’s important to have a good collaboration. And for that, trust is… obligatory. It’s the absolutely necessary thing. And it cannot just be a marketing promise,” Weberberger said.

The use of large language models (LLMs) raises critical questions when it comes to maintaining control over customer data, Weberberger noted, highlighting the need for transparency around who trains the data, who defines which information AI models are allowed to use, how ethical principles are implemented and who has the control and influence over the models.

“We need answers in the future when it comes to… how these LLM models are trained. Many providers tell us ‘we don’t use the customer data to train our LLM.’ But for us, still, the question remains, but how do the providers develop their LLMs when they don’t use the customer data to train them? Here we need clear agreements that we all know how it works, and openness to trust.”

For critical sectors like energy, innovation must align with stringent risk-management requirements without compromising safety or resilience.

Data Sovereignty as a Shared European Project

Panelists underscored the need for different regulators in Europe to get on the same page when it comes to digital rules, to create a clearer, more unified set of standards that works in practice and gives organizations the confidence to keep innovating.

“Policy makers and industry representatives should work together on defining clear, understandable and practical frameworks, which has not always happened in the past,” Parzer said.

“It’s about establishing certainty for market participants at the end… They should understand that innovation is not a luxury. It is just an enabler for our economic growth and insurance for our future. So it is all about defining rules that are going to balance innovation with compliance.”

And when those standards line up, it doesn’t just cut down on compliance headaches — it makes it easier for governments and regulated industries to embrace AI and cloud tools, giving them the guardrails they need to move ahead with confidence.

The conversation made one point clear: sovereignty is no longer a static concept. It is a shared responsibility shaped by policy, technology, and partnership. Customers expect cloud providers not only to deliver secure platforms, but also to collaborate, openly and continuously, on the frameworks, tools, and governance models that will define Europe’s digital future.

As the panel demonstrated when customers, policymakers, and technology providers align around transparency, control and trust, Europe can innovate at the pace required to remain resilient and competitive.

“I think we cannot expect this topic is going to go away,” Bullwinkel said. “These things are front of mind, absolutely, for our customers, for our partners, for government leaders… Things we’ve been talking about… around data privacy, around data security, around resilience, around data residency, these are all things that will continue to inform the conversation.”

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.

It’s a fascinating concept. The idea isn’t to replace the human on the phone but to give them a teammate that actually does things.

Before we get into the details, if you are trying to keep track of everything happening this week, you can find our full AWS re:Invent 2025 Event Guide here and our full re:Invent hub of news here.

The headline news centers on ‘Agentic AI’—a term you are likely familiar with by now. Amazon Connect is rolling out 29 new capabilities designed to show it’s more than just a buzzword. Unlike the rather rigid chatbots that would get confused if you phrased a question the wrong way, these agents can reason, look up accounts, and process requests. The goal is to handle the drudgery, the notes, the summaries, the form-filling, so the human agent can focus on being, well, human.

“We’re now entering an era of agentic AI in Connect.” — Pasquale DeMaio

Here is what that actually looks like for the people doing the work.

Agents Get Instant Access to Enterprise Knowledge

There is nothing worse than being on the phone and not knowing the answer. It’s awkward for everyone.

To fix this, Amazon Connect is connecting its AI agents directly to enterprise knowledge bases via Amazon Bedrock. It means the AI can pull accurate answers instantly during a conversation.

They have also added support for the Model Context Protocol (MCP). It sounds technical, but it essentially means the AI can talk to other systems—like inventory databases or order management platforms—without a fuss. And for those already invested in the ecosystem, these AI features now extend seamlessly into the Salesforce Contact Center.

Amazon Connect Just Made Proactive Outreach Easier for Service Teams

Ideally, you fix the problem before the customer has to call you.

The new “Journeys” feature allows businesses to design multi-step experiences that adapt based on what the customer does. Combined with new predictive insights, the system can spot churn risks or purchasing interests and suggest reaching out proactively.

They have also added WhatsApp support for outbound campaigns. Given how much of the world lives on that app, it feels like a necessary addition.

Amazon Connect Delivers Complete Visibility for Teams Trusting Autonomous AI

Handing control over to an AI can feel a bit risky. To calm those nerves, Amazon Connect has introduced “enhanced observability.”

You can now see exactly why the AI made a decision, what tools it used, and how it got there. It provides a level of transparency that has been missing. They have also added tools to simulate thousands of interactions, so you can test how the AI behaves before you let it loose on real customers.

Global Brands Get Genuinely Human Voice Interactions

Robotic voices are usually a bit odd. They kill the mood.

Amazon Connect is launching “Nova Sonic” voices. These are designed to sound genuinely human, with the ability to handle interruptions gracefully and understand different accents.

If you prefer other flavors, they have also opened the platform to third-party speech tools like ElevenLabs and Deepgram. It gives businesses a choice, which is always nice.

• Read Amazon Nova Sonic: The End of the “Robot Pause” in CX?

Amazon Connect Removes Analytics Headaches for Managers with Natural Language Queries

If you have ever stared at a complex dashboard wondering why call volumes are spiking, this might appeal to you.

Amazon Connect is introducing an AI assistant for managers that lets you ask questions in plain English. You can simply ask, “Which agents need coaching on product knowledge?” or “What is causing the spike in call volume today?”

The AI digs through the data and gives you an answer. It removes the friction of needing to be a data scientist just to run a contact center, which seems like a rather sensible move.

What’s Next?

I began to wonder if we are moving toward a world where the “superhuman” agent is the standard, not the exception. It is a lot to digest.

We will be digging into this all week. Stay tuned for exclusive video interviews and a few more scoops from the event floor here on CX Today.

Around 65% of consumers now use messaging apps like WhatsApp to engage with companies, research from Salesforce shows. WhatsApp, owned by Meta Platforms, has evolved from a simple personal messaging tool to a platform for customer engagement and loyalty.

Using messaging apps like WhatsApp is about meeting customers where they are. As the third most popular app worldwide, WhatsApp has a whopping 2 billion users, of which 1.54 billion interact with WhatsApp for Business accounts, making it impossible for brands to ignore.

WhatsApp for Business includes a range of communication tools—a free app, an advanced API platform, and an ad format that directs users to the app, along with extra features that help businesses of any size handle customer communication at scale.

Around 68% of WhatsApp users believe it’s the most convenient way to engage with brands, according to Freshworks. That suggests the app should be a key element of a brand’s omnichannel customer service strategy, offering a way to connect with customers in a channel they prefer to use.

WhatsApp offers the convenience of asynchronous communication, allowing customers to reach out to brands in their own time. By providing 24/7 availability through AI agents and live support, as well as Quick Replies and Automated Messages, companies can eliminate the frustration of only being available during business hours, when customers themselves may be unavailable.

Businesses can use the app to create engaging, personalized customer interactions with its rich media features incorporating images and videos.

In addition, with security a growing concern among businesses, it’s worth noting that WhatsApp uses end-to-end Signal encryption to keep messages private and ensure customer information is protected. Meta ensures WhatsApp Business Platform is compliant with the General Data Protection Legislation (GDPR) and other key regulatory requirements.

WhatsApp for Business Becomes Default Loyalty Channel in Asia-Pacific

Brand outcomes in Asia-Pacific highlight the importance of having a WhatsApp strategy. Of the 1.54 billion WhatsApp Business users, around 748 million are in Asia-Pacific, compared with 130 million in North America. With so many people in Asia using the app every day, the region shows how naturally companies can work it into their customer conversations.

Regional insights from polling firm YouGov show that 32-43 percent of consumers in the Asia-Pacific region use business messaging to complete purchases, track orders, and connect with brands, especially during major sales and seasonal campaigns. Similarly, Infobip’s Messaging Trends Report points to Asia-Pacific as the world’s fastest-growing messaging region, driven by dynamic markets like Indonesia, the Philippines and China. The region saw a 51 percent increase in conversational messaging in 2024, led by a doubling in WhatsApp growth to 100 percent.

As business messaging has become integrated into the customer lifecycle, WhatsApp has, in effect, become the default loyalty channel for high-intent customer engagement in the region, according to Meta.

“We are seeing the shopping journey become more conversational because consumers now want dialogue, not one-way marketing,” said Vicky Yiu, APAC Strategic Partnership Manager, Business Messaging at Meta.

“WhatsApp is increasingly becoming the commerce layer for brand engagement in Asia. Customers don’t just browse — they consult, request support, complete transactions, and revisit brands all within messaging threads.”

L’Occitane’s WhatsApp-First Strategy

At the recent Commerce Leadership Forum held at Meta Singapore’s office, French beauty brand L’Occitane stated that WhatsApp now accounts for more than 80 percent of its inbound and outbound customer conversations in Asia-Pacific, becoming its primary customer touchpoint across different markets.

“By using WhatsApp as a unified touchpoint, we can move customers seamlessly from online discovery to in-store engagement without losing context,” said Terrence Siu, Chief Information Officer, APAC at L’Occitane. “Customers enjoy a consistent and personalized experience wherever they interact with us, be it on Facebook, Instagram, or WhatsApp.”

The brand extends this into a loyalty journey by staying connected with customers through the app so that they continue to feel guided and valued after the first transaction.

“By distributing samples and exclusive VIP offers directly through WhatsApp, we can convert interest into purchase immediately in the same channel the customer already uses,” Siu added. “This approach has lifted our coupon redemption rate to 87%. Once the customer is connected, we continue the journey through personalized refill and replenishment reminders, helping us maintain an ongoing relationship rather than a one-time transaction.

“WhatsApp has become a loyalty bridge — from sampling, to purchase, to repeat purchase — all within one seamless customer journey.”

Across the region, it has become apparent that loyalty sticks only when customers are actively engaged, rather than passively included. As Alan Chan, CEO and Founder of Omnichat put it:

“Points sitting in an app don’t translate into customer relationships, but loyalty delivered through WhatsApp stays close to the customer’s daily behavior.”

Rather than waiting for customers to remember a loyalty program, brands can link QR codes to product samples, in-store touchpoints and messaging-based rewards to proactively engage members in real time.

“This is the difference between a loyalty database and a loyalty journey. When customers are reminded, recognized and rewarded in the channel they already use every day, loyalty becomes a habit,” Chan said, adding:

“[A]nd unlike apps, no one uninstalls WhatsApp.”

Using WhatsApp to keep customer relationships active means retention doesn’t have to be expensive. Brands in Asia-Pacific are showing the way by moving away from one-time campaigns and toward relationship-led loyalty that leans into conversational commerce where customers are already having real conversations.

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.

At AWS re:Invent 2025, the team introduced Amazon Nova Sonic, and it feels like they might have finally bridged that gap. It’s a new speech-to-speech foundation model designed specifically to make conversational AI feel, well, conversational.

Rather than just transcribing what you say and reading back a script, it listens, understands, and responds in real-time—much like a person would. It’s rather impressive, if a bit eerie at first.

The “Under the Hood” Bit

To understand why this is different, you have to look at how we used to build voice bots. The old way was a bit of a relay race: your voice was turned into text, sent to an LLM, processed, turned back into text, and then synthesized into speech. That relay race created lag.

Amazon Nova Sonic uses a unified speech-to-speech architecture. It processes audio input and generates audio output directly. Because it doesn’t have to constantly translate speech into text and back again, it cuts out the latency. It uses a bidirectional streaming API, which is a fancy way of saying it can listen and talk at the same time—just like a telephone call.

The “Vibe Check”: Why Audio-First Matters

There is a subtle but critical technical shift here. By moving to a native speech-to-speech model, we aren’t just stripping out latency; we are keeping the “data” that usually gets lost in translation.

In the old “Speech-to-Text” method, if a customer sighed heavily or sounded sarcastic, that emotional data was often stripped away when it was converted to plain text for the LLM. The bot read the words, but missed the mood.

Nova Sonic processes the audio directly. It hears the sigh. It detects the hesitation. It allows the AI to respond to the mood of the conversation, not just the transcript. In the contact center, that is the difference between solving a problem and losing a customer.

Where this actually changes the game (Use Cases)

It’s easy to get lost in the specs, but the real question is: where does this actually fix a broken experience? I’ve been looking at a few scenarios where that ultra-low latency is non-negotiable.

1. The “Panic” Call (Banking & Insurance)

When a customer calls because they’ve lost their credit card or had a car accident, they are already stressed. The old three-second “robot pause” between sentences spikes that anxiety. It feels like the machine is failing.

Nova Sonic’s ability to match the customer’s pace and tone—calm, efficient, and immediate—can de-escalate a situation before a human agent even needs to intervene. It’s not just about efficiency; it’s about digital bedside manner.

2. The “Messy” Booking (Travel & Hospitality)

Have you ever tried to change a flight with a voice bot? It’s usually a disaster because humans don’t speak in linear commands. We say things like, “I need to fly to London on Tuesday… actually, make that Wednesday morning, oh, and I need an aisle seat.”

Because Nova Sonic handles “barge-ins” (interruptions), the customer can correct themselves mid-sentence without breaking the bot’s logic. It mimics the fluid, messy nature of real human planning.

3. The Patient Tutor (Education & Training)

AWS highlighted Education First as an early adopter, and it makes perfect sense. In language learning, “latency” kills the flow. If you’re practicing French pronunciation, you need instant feedback, not a delayed grade.

The model’s ability to detect non-verbal cues—like a hesitant pause before a word—allows it to offer encouragement (“Take your time”) rather than just staring blankly into the digital void.

For the Builders: Getting Started is Surprisingly Simple

For the developers and architects reading this, you might expect a nightmare of integration. Usually, stitching together speech recognition, an LLM, and text-to-speech engines is a fragile “Frankenstein’s monster” of plumbing.

AWS has simplified this rather elegantly. Because it’s all one model, you don’t need to manage the hand-offs. You simply toggle access in the Amazon Bedrock console and use their new bidirectional streaming API. It handles the input and output streams for you, much like a standard phone connection.

The most refreshing part? Defining the bot’s personality doesn’t require complex code. You just set a system prompt—something as simple as “You are a friend, keep responses short”—and the model handles the nuance. It lowers the barrier to entry from “PhD in Linguistics” to “Standard Developer,” which is exactly what the industry needs to scale this tech.

Why this matters for CX Leaders

We often talk about “empathy” in CX, but it’s hard to be empathetic when there’s a delay after every sentence. Amazon Nova Sonic removes the friction that makes automated service feel like a chore.

It allows brands to build agents that can handle complex, multi-turn conversations without making the customer want to hang up. And in an industry obsessed with efficiency, making the robot sound a little less like a robot might be the most efficient move of all.

Sources: Amazon Nova Sonic, AWS News Blog

This approach enables enterprises to access information instantly across various environments, providing their agents with automated, complex responses. 

Deepdesk was previously recognized for its approach at the Microsoft Hackathon Awards in September, winning the highest score among CX vendors in the category ‘Ready to Scale’, strengthening its relationship with Microsoft. 

The Platform-Agnostic AI Approach 

Platform-agnostic AI is an approach that enables AI to operate independently from a platform without the need to rebuild or redesign itself. 

And whilst traditional AI models are typically linked to a single system, Platform-agnostic AI can run seamlessly across different CX platforms whilst maintaining consistency and independence from a single vendor. 

This means that enterprises can function flexibly across multiple environments, such as Microsoft or Salesforce systems. 

In 2024, Deepdesk was acquired by CX provider AnywhereNow to advance its product suite with Deepdesk’s AI assistant tool. 

In conversation with CX Today, Jonathan Quayle, Product Evangelist at AnywhereNow, explained how this approach can operate smoothly across different environments. 

He said, “Platform-agnostic AI means organizations don’t need to rebuild or replace their existing CX infrastructure to benefit from intelligent automation.  

“Instead, it integrates with the existing CX stack – whether that’s Genesys, Salesforce, or a proprietary system – with the AI capabilities, such as Microsoft Copilot, layered on top of it.”  

The Benefits  

This AI layer approach aligns with hybrid AI models, enabling humans and agents to coexist within the same system without replacing one another. 

It enables the AI to support repetitive tasks such as research, response suggestions, summarizing, and automating workflows, while humans remain in the driver’s seat for customer interactions. 

Qualye continues: “Achieving this involves decoupling the AI logic from the underlying architecture, which allows us to orchestrate agent assistance, knowledge retrieval, and automation across environments. 

“This approach allows for consistent performance and context retention, regardless of the platform. It also means organizations retain control over their tech stack, avoiding lock-in and enabling flexibility as their needs evolve.” 

Furthermore, this approach can maintain independence whilst also retaining knowledge and context it received from travelling across platforms, allowing for seamless assistance and optimal agent usage. 

With reduced deployment complexity and expanding human capability, this also helps accelerate integration and ROI even when changes are made to any of the adopted systems. 

He said, “These tangible outcomes directly impact cost-to-serve and customer satisfaction.” 

Microsoft Partnership  

Utilizing its long-standing relationship with Microsoft, AnywhereNow has utilized the CCaaS giant’s Copilot tool in its platform-agnostic customer experience approach, allowing it to deepen its partnership with Microsoft. 

The SaaS provider has leveraged Copilot AI as the engine in its approach, while also still enabling seamless deployment for systems other than Microsoft. 

This utilization only improves willingness to adopt this approach since many organizations already adopt Microsoft tools in their systems. 

Quayle added, “It also provides a trust advantage as governance, security, and compliance conversations have already happened. 

“This gives customers the freedom to choose the tools that work best for them, while still leveraging the benefits of Microsoft.” 

The Challenges

Despite the approach offering seamless flexibility with AI, Deepdesk finds difficulties in ensuring full compatibility at scale. 

This can include issues with enterprises choosing to locate their data sporadically across multiple CX systems, resulting in data gaps for the AI. 

In fact, “The real challenge lies in orchestrating data flows between disparate systems, ensuring that AI can support agents in real time without introducing latency or inconsistency.” 

This can cause havoc if the approach is blocked or not incorporated in all necessary systems, stopping the AI from moving across and accessing the required data to generate responses for agents. 

This may also prove challenging when systems need to be updated or thrown out without including the platform-agnostic model, likely leading to lost context or creating silos. 

The AI layer can only function once it receives a level of understanding and information at a consistent rate, no matter what types of varied technology are being used. 

This requires enterprises that implement this approach to expose the AI to all necessary systems, enabling its agents to receive the best possible responses. 

“Enabling platform-agnostic, hybrid agent journeys isn’t about plugging into existing systems, it’s about designing AI that can operate across fragmented architectures while maintaining continuity, context, and control.  

He added, “Our approach is composable by design, allowing us to adapt to different environments, evolve with changing tech stacks, and preserve the AI investment even as platforms shift.” 

On November 9, 2025, Mixpanel notified OpenAI that an attacker had gained unauthorized access to part of its systems and exported a dataset containing some customer information and analytics data related to the API. Mixpanel shared the affected dataset with OpenAI on November 25, the company stated in a blog post.

The breach occurred within Mixpanel’s systems and there was no unauthorized access to OpenAI’s infrastructure and systems. ChatGPT and other OpenAI products were not affected. “No chat, API requests, API usage data, passwords, credentials, API keys, payment details, or government IDs were compromised or exposed,” Open AI stated. It also confirmed that session tokens, authentication tokens, and other sensitive details for OpenAI services were not involved.

But Mixpanel’s systems had access to user profile information from platform.openai.com⁠. According to OpenAI, the information that may have been affected included:

• Users’ name and email address
• Operating system, browser and location (city, state, country) used to access the API account
• Referring websites
• Organization or User IDs associated with the account

OpenAI has removed Mixpanel from its production services and said it is working with the company as well as other partners to gauge the scope of the incident and determine whether any further response actions are needed. It is in the process of directly notifying the organizations, admins and users that were affected by email.

“While we have found no evidence of any effect on systems or data outside Mixpanel’s environment, we continue to monitor closely for any signs of misuse,” the post stated.

The incident is a reminder that exposure of non-critical metadata can introduce security risks, and sharing identifiable customer information with third parties should be avoided. As Ron Zayas, Founder and CEO of Ironwall by Incogni, told CX Today in a recent interview:

“The smart play is to learn how to sanitize your data. You don’t have to share 100 pieces of information on one of your customers with an outside company. It’s stupid. Why are you sharing all that customer information?”

Enterprises often underestimate the value of metadata to attackers, as it doesn’t contain critical information like customers’ login credentials or payment details. But malicious actors use the information to create credible phishing or impersonation campaigns, which are becoming an effective way to deploy ransomware attacks through social engineering.  Having a person’s real name, actual email address, location, and confirmation that they use OpenAI’s API makes malicious messages look far more convincing.

OpenAI acknowledged this in the blog post, advising its API users:

“Since names, email addresses, and OpenAI API metadata (e.g., user IDs)  were included, we encourage you to remain vigilant for credible-looking phishing attempts or spam.”

Users should “[t]reat unexpected emails or messages with caution, especially if they include links or attachments. Double-check that any message claiming to be from OpenAI is sent from an official OpenAI domain,” the post added. It also encouraged users to protect their account by enabling multi-factor authentication “as a best practice security control” and noted that OpenAI doesn’t request credentials such as passwords, API keys or verification codes through email, text or chat.

Complex AI Stacks Open More Ways In for Attackers

As with recent cyberattacks exploiting third-party platforms, the incident serves as a reminder that API-based architectures will only become more vulnerable with the use of AI in enterprises. AI systems are too complex for most companies to develop in-house, so they build stacks of third-party tools using APIs, all of which collect operational metadata and open up more attack vectors.

While vendors and enterprises are tempted to collect as much customer information as possible to train AI models as well as deliver personalization, they need to be judicious in the types of information they collect and store, Zayas said, as the risk of data breaches in the AI era will become “much more significant.”

“Companies are opening up all of their data and feeding it to an AI engine. And how secure are the AI agents? They’re led by big companies, but big companies get breached all the time.”

Zayas warned that the major AI and cloud providers like OpenAI, Google and AWS will become increasingly vulnerable as hackers target them for their wealth of data:

“When your data is sitting there, you’re going to get attacked. If I can pull out information… from an AI provider, I am going to get so much rich data that I don’t have to worry about attacking a lot of companies… That’s where companies and criminals are putting all their time and effort—going to the big ones. If you’re giving them data, you are much more of a target.”

Enterprises need to get smarter about the data they share with AI tools to get the outcomes they need. Customers’ personally identifiable information can often be removed to anonymize the data without affecting how the tools work, Zayas noted.

“You’re going to see the breaches being more and more related to the amount of information that’s coming out with AI, the amount of information that’s being enriched, and companies are going to suffer from this.”

Enterprises also have to train employees to avoid carelessly uploading spreadsheets and other files to chatbots like ChatGPT, because even if a company’s systems aren’t hacked, malicious actors may be able to extract customer information using certain prompts.

As the adoption of AI tools accelerates, enterprises should treat every handoff to an AI provider as a potential point of exposure of their customer data. Limiting the amount and sensitivity of information sent to these systems and designing workflows that avoid unnecessary data transfer can reduce the impact of a breach, protecting customers as well as the company’s reputation.

Rob Scott sits down with Josie Gaeckle, Senior Vice President of Client Insights at SMG, to break down how Unified Experience Management is turning data into revenue, retention, and ROI.

How does improving customer experience actually impact revenue? Josie reveals why many organizations struggle to show ROI: siloed data, lack of analytical maturity, and disjointed ownership of customer, employee, and brand experiences. But it doesn’t have to stay that way.

SMG’s approach to Unified Experience Management—bringing together customer, employee, and brand feedback into a single data ecosystem—helps organizations connect the dots between action and outcomes.

Discover why experience metrics aren’t enough without linking them to business metrics. Learn how predictive analytics and AI are reshaping the ROI conversation. Hear a real-world case study from Sally Beauty that ties associate behavior to average spend. Explore how cross-functional alignment with finance and operations is the secret to scaling success. Josie also offers a future-focused look at how prescriptive AI and real-time journey analytics are redefining what’s possible for CX leaders in 2026 and beyond.

NEXT STEPS: Want to see how SMG’s Ignite® platform helps brands drive measurable experience improvements? Visit smg.com to learn more.

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