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.” 

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.

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.” 

Leaders know it’s not sustainable. Deloitte’s 2025 research found that most health executives now rank operational efficiency and patient engagement among their top priorities. The intent is there, but outdated systems and staffing pressure keep getting in the way.

That’s where smart orchestration and AI routing in healthcare comes in. It works behind the scenes, analyzing what patients say, how urgent the request sounds, and where it should go next. When designed well, smart routing in healthcare makes care faster, smoother, and a little more human.

The Case for AI Routing in Healthcare

In most hospitals, the way calls and messages get routed hasn’t changed much in years. Patients still face long phone menus or wait in queues that treat every request the same. A billing question sits next to a medication concern; an anxious parent holds while a routine appointment request gets answered first. These systems weren’t built for the pace or complexity of modern care.

Now, AI routing in healthcare is starting to mend those seams. It listens as each interaction unfolds, catching tone, urgency, and emotion, then quietly figures out what the person actually needs. Sometimes that means a nurse. Sometimes it’s a digital answer or a specialist ready to step in. The process feels smooth: fast triage, fewer transfers, less pressure on teams already stretched thin.

For once, the technology bends toward the rhythm of care instead of forcing people to bend to it. And it’s not theoretical. Platforms like Genesys Cloud,  now weaving journey management tools directly into their routing engines, are turning that idea into practice. Orchestration and routing aren’t separate anymore; they’re starting to move as one.

The Value of Orchestration and AI Routing in Healthcare

Healthcare leaders are looking for real results from AI, not just automation for its own sake. The value of AI routing in healthcare comes into focus when it solves problems that patients and clinicians actually feel every day, challenges like:

Data Silos and Interoperability

Every hospital runs on data, but much of it lives in its own world. Appointment software, EHR systems, billing tools, and contact centers often act like distant relatives – related, but rarely on speaking terms. A patient might confirm an appointment online, call later to ask a question, and still get transferred three times because no one sees the full picture.

Smart routing in healthcare depends on connected context: identity, intent, and history coming together in one secure layer. That’s what orchestration platforms are built to deliver.

In the UK, the NHS is showing what’s possible. It’s connecting massive data sets inside secure, de-identified environments, proving that privacy and insight don’t have to be enemies. When handled right, patient data can be both protected and powerful.

Privacy-First Personalization

Healthcare runs on trust. Patients expect their personal details to stay protected, even as care becomes more digital. That’s why any move toward AI-driven routing has to start with privacy in mind.

Modern systems now analyze behavioral signals: the way people communicate or express urgency, instead of relying on demographic data. This approach keeps interactions compliant while still delivering a tailored experience. CareSource, using Microsoft tools, followed this model, matching patients with agents or AI bots based on their needs. The result was faster customer responses, better personalization, and fewer burned out agents.

This balance of empathy and oversight is what makes AI routing credible in regulated environments. It’s also where governance meets experience. Transparent orchestration builds trust for both patients and staff, a principle every health organization will need to embrace as AI becomes more embedded in care delivery.

Reducing Wait Times and Improving First-Contact Resolution

Few moments shape patient perception more than the wait. Whether it’s a phone queue, a delayed call-back, or an unanswered portal message, every minute feels longer when health concerns are involved. Traditional routing systems still treat each request as equal — but in healthcare, urgency varies widely.

AI routing in healthcare can now tell the difference between a quick scheduling question and a life-or-death call. It listens for tone, urgency, and context, then sends each interaction exactly where it belongs, the first time. Siemens Healthineers proved how powerful that can be. Using Genesys Cloud, it linked 2,200 experts across 35 countries, routing cases by urgency and need. The entire rollout took just eight months and ran with zero downtime.

Even within a single organization, intelligent routing can ease heavy workloads. Kaiser Permanente used natural language processing to triage millions of patient messages, automatically routing nearly a third to care teams before they reached a physician’s inbox. Wait times fell, and clinical staff had more space to focus on complex cases.

Tackling Staffing and Capacity Pressures

Behind every phone line and inbox is a care team trying to do too much with too little. Hiring more people helps for a while, but it doesn’t fix the math. Smarter systems do. AI routing and journey orchestration now balance workloads automatically, match tasks to skill, and hand off routine admin work to digital assistants.

Maxicare, one of the largest healthcare providers in its region, found itself at a crossroads: rising call volumes, higher expectations, and limited staff. By turning to NICE Enlighten AI Routing, it reimagined how every interaction moved through the system.

The results were impossible to ignore: over $11 million saved each year and a threefold return on investment, all while patients got faster, more personal service. In hospitals, that same idea holds: when systems take on the grunt work, people finally have time to care. At John Muir Health, AI charting tools helped clinicians spend less time on paperwork and more time with patients.

The firm cut 34 minutes of documentation per day per clinician and reduced physician turnover by 44 percent. It’s proof that intelligent automation doesn’t just lift productivity, it helps protect the people who deliver care.

Maintaining Empathy in Digital Channels

When care feels cold, trust cracks fast. Anxiety rises, and even simple problems start to feel personal. That’s why empathy has to be part of automation. Well-designed systems can read frustration in tone or phrasing and know when it’s time to pause, or hand the conversation to a real person.

By detecting emotion and urgency cues, AI routing can send distressed patients straight to a clinician while keeping routine requests within digital channels. The result is faster help without losing the warmth that builds confidence.

Dental Axess pulled this off when it unified phone, email, WhatsApp, chat, and social channels through Genesys Cloud. The company gained 24/7 responsiveness, better visibility for staff, and saved about 1.5 days of work each week, all while keeping 100% call-answer rates. It’s living proof that journey orchestration in healthcare can combine efficiency and empathy without compromise.

Getting Started with AI Routing in Healthcare

Every healthcare group talks about simplifying the patient journey. Doing it is another story. Moving from good intentions to measurable change takes discipline.

• Step 1: Build a Secure Data Foundation: Build a secure data base: Link EHRs, scheduling tools, billing systems, and contact centers within a single, protected orchestration layer.
• Step 2: Pick the pressure points: Fix what hurts first: triage queues, referral delays, or lab-result follow-ups. Small wins earn buy-in.
• Step 3: Choose the Right Platform: Real-time decisions and explainable logic are must-haves. The system should show why it acted, not just that it did.

Then track what matters: faster responses, better resolution rates, happier clinicians. Expand only after the data proves it’s working.

The Future of Smarter Healthcare Journeys

The next phase of digital healthcare won’t be defined by new tools but by connection. As patient interactions multiply across apps, calls, and portals, the systems behind them must keep up – learning, adapting, and adjusting on the fly. That’s where AI routing in healthcare is heading: toward orchestration that evolves continuously, improving with every touchpoint and interaction.

Already, AI is moving beyond static workflows into something more dynamic, systems that can rewrite their own logic as conditions change. These “agentic” models monitor outcomes and adjust automatically, learning what works best for different patient types or clinical contexts.

Plus, we’re seeing evidence that future healthcare journeys will run on signals. A spike in vitals, a new lab result, or an urgent message will instantly trigger a routing decision. Platforms like Genesys Cloud, and NiCE’s orchestration system already point in this direction.

At its best, AI routing in healthcare does more than manage calls or messages, it restores order to chaos. It helps patients reach the right care faster, eases the pressure on clinical teams, and rebuilds trust in digital health itself. That’s the future of smarter care.

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

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

What is sales automation (and why now)?

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

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

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

Where automation reduces repetitive workload

1. Automatic data capture and CRM hygiene

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

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

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

2. Smarter lead and account prioritisation

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

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

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

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

3. Guided outreach and content automation

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

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

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

Helping people do the work only people can do

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

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

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

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

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

The recent CX Today report on AI in Customer Experience found 46% of businesses now invest more in AI for customer service than they do for sales, marketing, or commerce. Additionally, more than 90% of CX teams are planning on centralizing customer-facing solutions to ensure AI applications can influence every part of the customer journey.

The tools are already everywhere from virtual assistants, AI copilots, auto-reply apps, to smart routing systems. But customer satisfaction levels still aren’t improving as much as expected.

In fact, Forrester’s Customer Experience Index hit a record low in 2024. So, while AI is certainly changing how things get done, it’s still unclear whether it’s making things better for the people on the other end of the call. So, what can CX leaders learn from all this, and what’s next?

The Current State of Generative AI in the Contact Center

It’s wild to think about how quickly generative AI in the contact center went from interesting concept to “already everywhere”. Just two years ago, most contact centers were still playing around with basic chatbots. Now, nearly 80% have implemented some form of generative AI, from agent copilots to auto-summarization tools.

For most companies, the early go-to was AI copilots, built to sit alongside agents during live interactions. These tools help with drafting replies, pulling up relevant articles, and summarizing calls afterward. They’re fast, low-risk, and popular. 82% of CX teams say they’re already using some kind of copilot, and three out of four say it’s delivering real value.

But the tech is moving fast. Many companies are now experimenting with autonomous AI agents, tools that don’t just assist agents but actually handle full conversations on their own.

Some can answer customer questions using internal documentation. Others can triage issues and escalate them if needed.

Despite some very public AI misfires (remember the delivery bot that started swearing at customers?), the confidence in these tools is surprisingly high. According to the same CX Today report, 79% of CX leaders say they’d trust an AI agent to talk to customers without any prior training.

That’s interesting, when 66% of businesses still admit their customers prefer talking to humans over bots. And 61% of industry pros believe the government should step in and guarantee people the right to speak to a human if they want to.

Core Use Cases of Generative AI in the Contact Center

So, what’s generative AI actually being used for in contact centers today? Quite a lot. Some of the most common use cases, as mentioned above, focus on helping agents move faster, avoid admin work, and make fewer mistakes. The CX Today report highlights a few popular options:

Writing replies for agents

This is the big one. Half of all contact centers are using generative AI to draft replies for agents. The AI figures out the customer’s intent, pulls relevant info from internal systems, and writes a suggested message. Agents can tweak it or send it as is. It saves time and helps teams stay consistent in tone and language.

Auto-QA and coaching

Quality assurance is another go-to. Around 45% of teams use AI to automatically review conversations, flag good and bad moments, and even generate coaching tips. It’s a lot faster than handling manual reviews, and it’s helping managers spot trends they might miss otherwise.

Creating knowledge articles on the fly

Tools like NICE Enlighten AI are excellent for this. Instead of waiting for someone to write up a new help article, the AI listens to real conversations and creates articles based on what agents are already doing. Around 39% of teams are using this to keep their knowledge bases fresh and useful.

Post-call summaries and CRM updates

Fewer teams are using AI here (about 38%), but demand is still high. Instead of spending a few minutes writing a call summary, the AI does it instead. It fills out CRM fields, updates tags, and gets the agent ready for the next call in seconds.

Virtual assistants and copilots

Over 80% of contact centers now use some kind of copilot. These tools help agents find answers, suggest next steps, or guide them through processes. They’re everywhere, and for good reason: most teams say they’re working well.

Full-service AI agents

This is where generative AI in the contact center is really starting to evolve. Some businesses are testing fully autonomous agents that don’t just assist, they act as digital team members. They can answer customer questions, escalate complex cases, and even improve themselves over time by learning from past interactions.

Additional Use Cases

Of course, lots of other generative AI use cases are beginning to emerge too, particularly as AI continues to be baked into endless tools and platforms. Companies are exploring:

• Real-time language translation for global support teams.
• AI that understands sentiment and escalates emotional or frustrated customers faster.
• Bots that talk to other bots as “machine customers” become more common.
• Compliance-checking tools that flag risky language or policy violations.
• Predictive tools that spot early signs of churn and trigger personalized retention efforts.

Lessons to Learn from Generative AI in the Contact Center

There’s no shortage of excitement around generative AI in the contact center. But companies aren’t just focusing on the hype anymore, either. They’re paying attention to the reality. Now that the tech is living in thousands of contact centers, patterns are starting to emerge, and they’re not all good.

We’re learning that:

More AI doesn’t automatically mean better CX

Despite all the investment, customer satisfaction scores aren’t going up. At least not consistently. In fact, Forrester’s CX Index hit its lowest level ever in 2024. Throwing AI at the problem without a clear strategy doesn’t guarantee positive results.

A Gartner study found 64% of customers would actually prefer it if companies didn’t use AI in their service strategy at all. That doesn’t necessarily mean AI doesn’t have a place in the contact center, but it might not always need to be customer-facing.

Another major issue is that AI and humans aren’t always working together effectively. Bot-to-human handoffs are still messy. Some people still have to repeat themselves when they get to a live agent. If a bot hands over a conversation, the agent needs the full backstory, otherwise the customer experience falls apart.

Companies Need to Build the Right Foundations

For all the talk about AI transforming customer service, many contact centers are still skipping the groundwork. One common issue is rushed investments, often driven more by C-suite pressure than by actual customer needs. When the goal is just to cut costs or ride the AI trend, the resulting deployments tend to underdeliver or backfire.

But even well-intentioned projects can stumble without a clear understanding of customer demand. Many teams still don’t fully grasp why customers are reaching out. Without good journey mapping or conversation analytics, there’s a real risk of automating the wrong things.

On top of that, governments are starting to enforce guardrails. Spain’s three-minute response rule and California’s AI Act are just the beginning. Brands will soon need to prove that their AI systems are fair, accessible, and respectful of customer rights. Future-proofing means thinking about compliance from the very beginning, not scrambling after a policy change.

Get the Data Right, or Everything Falls Apart

There’s a saying in tech: garbage in, garbage out. And that applies heavily to generative AI in the contact center. Even the best generative AI won’t deliver results if it’s working with bad or incomplete data.

Outdated knowledge articles, siloed customer histories, and missing context can all lead to poor recommendations or wrong answers. That erodes both trust and efficiency.

This is why centralizing and enriching customer data has become a top priority. The best-performing contact centers are investing heavily in unified CRMs, real-time data pipelines, and knowledge management tools that keep everything current. Because when AI has access to the right data at the right moment, its value skyrockets.

Containment is a Flawed KPI

Many companies still measure the success of their generative AI bots by measuring containment rates, how many users stay with a bot and never reach a human. But containment doesn’t always mean that an issue was solved.

Contact centers need to back up their AI strategy with clear, business-relevant metrics. Think resolution rates, average handle time, deflection to human, customer satisfaction (CSAT), and even Net Promoter Score (NPS).

While high containment rates or automation percentages might look good in a dashboard, they rarely tell the full story. What actually matters is whether the customer walked away with their problem solved, and whether they’d come back again next time.

AI Is a Living System, Not a One-Time Project

One of the biggest misconceptions about AI is that it’s plug-and-play. That’s particularly true now that there are so many pre-built bots and AI solutions that seem so easy to use.

Realistically, though, generative AI in the contact center needs to evolve alongside the business. That means tracking performance, reviewing what’s working (and what’s not), and constantly refining prompts, workflows, and escalation rules.

The contact centers seeing the most success place their AI tools in a continuous improvement loop. They create an engine that gets better over time with the right inputs. It’s not just about the tech either. Cross-functional collaboration is also essential.

When service, marketing, sales, and IT teams align on data, goals, and customer experience design, AI deployments become more consistent, scalable, and effective. In some cases, organizations are creating new roles, like Chief Experience Officers, to keep everyone focused on the full customer journey, not just isolated fixes.

The Rise of Agentic AI: What’s Next?

Interest in generative AI in the contact center hasn’t disappeared completely, but business leaders are shifting their attention. While GenAI usually focuses on content, like writing replies, summarizing calls, and generating knowledge, the new era of AI focuses on action.

Agentic AI is stepping into the spotlight. Major companies, from Salesforce, to IBM, Microsoft, Zoom, and even Adobe are investing in a new era of flexible agents.

These agents don’t rely on humans for constant handholding and prompts. They follow multi-step workflows autonomously, adjust dynamically, make decisions based on context, and even access connected tools. Real world examples include:

• AI agents that handles a customer issue from start to finish, triaging the request, checking the knowledge base, resolving the problem, and closing the case.
• Sales-focused AI agents that joins calls with new reps, offers real-time coaching, and updates the CRM automatically afterward.
• Marketing agent that audits landing pages, suggests stronger calls to action, and A/B tests messaging, without human input.

These tools are becoming just as accessible as generative AI, with kits like Salesforce’s Agentforce, Microsoft Copilot Studio, and even Zoom’s customizable AI Companion.

Agentic AI will undoubtedly unlock new value for contact centers, but it also raises the stakes. With more autonomy comes a bigger need for transparency, explainability, and trust.

Looking Ahead: Preparing for the Next Age of AI

For companies investing in generative AI in the contact center or forward-thinking brands exploring agentic AI, there’s still a lot of work to be done. Every organization will need to clean up its data strategy, adjust how it monitors metrics and KPIs, and think carefully about how it will manage human-AI collaboration going forward.

GenAI definitely brought both innovation and disruption to the contact center. Now, companies have numerous lessons to learn as they move forward into the next age of autonomous agents and intelligent growth. The businesses that thrive will be the ones that don’t just focus on reducing costs or speeding up tasks but use AI to enhance customer experiences.

As agentic AI takes hold, the expectations will rise. Customers will want fast, smart answers, but still expect empathy and control. Agents will rely on AI more heavily, but still need tools they can trust. And CX leaders will need to justify not just the cost of AI, but its long-term value.

NiCE’s $955 million acquisition of Cognigy is turning Enlighten Autopilot into a unified orchestration engine for AI-driven customer journeys.  Salesforce’s takeover of Bluebirds accelerates the “agentification” of enterprise apps, bringing low-code orchestration into the CRM core.

Thoma Bravo’s bid for Verint – a $2B+ portfolio expansion into WEM, voice-of-customer, and AI analytics- is another consolidation signal.

This is more than just M&A headline fodder; it’s reshaping what “AI consolidated” means to enterprise buyers and how they hold choice, pricing, and integration risk in the same tight grasp.

In the broader SaaS universe, this trend is already underway. A recent survey reports that 52% of SaaS companies now integrate AI, and by the end of 2025, 95% of organizations will use AI-powered SaaS solutions, yet, contradictorily, the number of apps per organization has actually shrunk by 18% between 2022–2024.

The Benefits of AI Consolidated with CX

For all the concern about tighter stacks and reduced vendor choice, AI adoption and SaaS consolidation bring clear benefits that can’t be ignored. Buyers are seeing more cohesive platforms, faster time to value, and fewer integration headaches.

Unified stacks, fewer silos

One of the clearest benefits of AI consolidated platforms is a reduction in complexity. Building an intelligent contact center meant buying orchestration from one vendor, analytics from another, and automation from a third. Then enterprises would pay systems integrators to stitch it all together. Every new layer introduced more risk, more time, and more cost.

Now, deals like NICE’s acquisition of Cognigy change the equation. By embedding Cognigy’s orchestration capabilities directly into Enlighten Autopilot, NICE can offer an end-to-end solution where customer intent detection, conversation design, and resolution tracking are all managed in the same stack. For buyers, that means fewer moving parts and less reliance on fragile connectors or middleware. Salesforce’s Bluebirds acquisition points in the same direction, baking low-code orchestration straight into CRM workflows.

When data, automation, and orchestration live in one place, outcomes become easier to measure, and upgrades roll out faster across the entire platform.

Improved AI maturity and adoption

Most organizations are still immature in their AI usage. McKinsey research finds that only around 1% think they’ve reached AI maturity, meaning they can deploy AI at scale with governance and accountability.

Consolidated platforms can close that gap by providing a packaged approach where compliance, observability, and orchestration are part of the core product. That matters because AI adoption has historically stalled when governance frameworks lag behind deployment goals.

With AI adoption and SaaS consolidation, CX leaders get predictable guardrails: dashboards for monitoring, pre-built integrations for data governance, and policy frameworks aligned to regulations like the EU AI Act.

For buyers under pressure from boards to accelerate rollout, this kind of ready-made governance can make the difference between a controlled expansion and an uncontrolled experiment.

Potential for Lower TCO (Total Cost of Ownership)

Running several vendors side by side is rarely efficient. Each one comes with its own license fees, connectors, and support contracts. In many projects, integration alone can eat up 25-35% of the total cost of AI, often costing more than the software itself. A consolidated platform trims that overhead by rolling functions into one package and cutting down on duplication.

Vendors are also experimenting with new pricing models. NICE, Genesys, and others are shifting from per-seat models toward usage- or outcome-based pricing, where companies only pay when an issue is successfully resolved. This approach mirrors trends in automation, where providers like Ada promote “resolution-based” economics. For CFOs, the promise is a clearer ROI story: predictable costs, lower integration fees, and pricing that aligns with actual business outcomes.

Scale and innovation at speed

Consolidated AI platforms can also drive scale. Big vendors often have larger research budgets, wider datasets to train on, and shorter development cycles. For buyers looking to move AI from pilot projects into live production, that muscle makes a difference.

The NICE–Cognigy deal shows how this plays out. Cognigy’s orchestration tools already had traction with global enterprises. Folded into NICE’s Enlighten AI, they become part of a wider platform that blends automation with analytics. That scale gives big vendors an edge in areas such as observability, compliance, and vertical add-ons.

A hospital can benefit from pre-built frameworks that support regulation, while a retailer might get plug-and-play modules for returns or warranty claims. In practice, these unified stacks act like AI factories, shipping features at a pace smaller vendors would struggle to match.

The Challenges of AI Consolidation

Consolidation makes life easier in some ways, but it also creates new problems that enterprise buyers can’t ignore. The same moves that simplify stacks can limit choice, raise costs, and expose organizations to bigger risks.

Fewer options, greater lock-in

Consolidation narrows the field. Vendors like NICE, Salesforce, and Microsoft are pulling automation, orchestration, and analytics into the same platforms. Once a company’s data and processes are tied in, breaking free is costly and disruptive. Smaller vendors, like Rasa, Kore.ai, and others, may still offer strong products, but it gets harder to justify the integration effort when the big players are bundling everything by default.

The pricing squeeze

At first, unified stacks often look cheaper. One bill, one vendor, fewer integration costs. But consolidation shifts leverage to suppliers, not customers. Once locked in, enterprises are at the mercy of new bundles, higher license tiers, and usage-based pricing that can quickly outpace forecasts. Some vendors are moving toward “resolution-based” pricing, where costs depend on outcomes, not licenses. That sounds attractive, but it shifts financial risk onto the buyer if volumes or recontacts rise.

Customization takes a hit

Broad platforms often miss the mark for niche requirements. Sectors such as healthcare, finance, or government run on strict workflows and heavy regulation. Generic, one-size-fits-all automation can erode those differences. CX leaders are already calling out the risks of “off-the-shelf” AI that scales well but fails under the weight of sector-specific complexity.

Innovation slows at the edges

While consolidation can accelerate mainstream development, it often leaves less room for experimentation. Cavell analysts have argued that NICE’s move for Cognigy will strengthen its position but could also reduce variety in the CX technology ecosystem. Smaller players are usually the ones pushing boundaries, and acquisitions often fold them into slower, corporate release cycles.

Higher stakes when things break

When fewer companies carry more of the stack, the stakes rise. The 2024 CrowdStrike outage is a clear reminder: a single error grounded flights, froze banks, and halted hospitals worldwide. AI adoption and SaaS consolidation can create similar vulnerabilities. If a major vendor’s automation platform goes down, the impact could ripple through entire industries overnight.

Preparing for the AI Consolidation Era

Consolidated AI is a growing trend, and CX leaders don’t have the luxury of waiting it out. The smart move now is to prepare, both technically and culturally, for a market where fewer vendors control more of the stack:

• Rethink vendor strategy: Some enterprises will commit fully to one ecosystem, while others keep their options open. A blended model is gaining traction, keep the core on a major platform, but leave space to connect smaller, specialist tools. It’s less tidy than going all-in, but it reduces dependence.
• Fix the data problem: Consolidation doesn’t fix poor data hygiene. Fragmented or inconsistent records still derail AI. Companies that invest in reliable pipelines now will see stronger performance regardless of the stack.
• Ask for transparency: Fewer suppliers mean more vendor power. Buyers should ask for straightforward pricing, clear product roadmaps, and monitoring tools that show how the AI is performing.
• Prepare the workforce: Automation changes roles more than it cuts them. Cavell expects contact center roles to rise over the next three years, though the focus will shift. Agents will handle complex or emotional tasks while AI takes the routine. Training and reskilling should be part of the plan.
• Keep regulators in mind: Rules are tightening. The EU AI Act and ISO 42001 set high standards for auditability and control. Gartner expects most enterprise AI systems to face audits by 2026. Big vendors may bundle compliance frameworks into their platforms, but that doesn’t let enterprises off the hook. Independent checks are still essential.

What’s Next in AI Consolidation

More deals are coming. Analysts expect another wave of mergers as SaaS and CX vendors try to scale AI faster. Data providers, orchestration platforms, and automation specialists are the most likely targets.

Genesys is already leaning into orchestration. Microsoft is expanding its intent-based agent frameworks. Google is pushing Gemini deeper into contact center workflows. Each move shows the same pattern: vendors want to own the entire experience, not just a piece of it.

Another shift is the rise of AI factories – studios where enterprises can design, test, and deploy their own agents at scale. NICE, Genesys, and Five9 have all released versions of this. These tools speed up development, but they also pull buyers further into a single vendor’s ecosystem.

AI is now built into almost every CX system. The critical question is who runs it and how it’s delivered. In an era of AI consolidated stacks, the firms that succeed will be the ones that prepare early and keep flexibility in reserve.