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.

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.

Looking for more thought leadership on CX strategy? Subscribe to CX Today and don’t miss our upcoming interviews with industry leaders shaping the future of experience.

Yet many organisations still rely on traditional automation systems that struggle to meet these demands.  

Rigid IVR flows, generic chatbot scripts and siloed customer data often create more frustration than value, leaving customers repeating themselves and brands losing control of the customer journey. 

According to Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, many companies suffer from what he calls the “customer journey black hole” – a gap where context and customer history fall through the cracks, resulting in broken experiences and unnecessary friction. 

“Think about a typical banking interaction,” Anand says.  

“A customer fills in a loan application online, then calls the contact centre for support, only to be asked to provide the same information again. It’s no surprise that customers become frustrated.  

The consequence isn’t just dissatisfaction – 92 percent of customers say they’ll leave a brand after two or more poor experiences.

The Limits of Traditional Automation 

Even as businesses invest in automation to manage scale, traditional systems are increasingly showing their age.  

Script-based chatbots struggle to interpret nuanced intent.  

IVR systems force customers into predefined paths that rarely reflect what they actually want.  

And behind the scenes, data remains fragmented across CRM systems, ticketing platforms, and communication channels. 

“Legacy automation solves tasks, not outcomes,” Anand explains. “It might complete a form or look up an account, but it doesn’t understand the end goal of the interaction. It doesn’t collaborate with other systems.  

“It doesn’t adapt when the customer deviates from the script. Ultimately, it can’t orchestrate a full journey.” 

As customer journeys become more complex and decentralised, these limitations are becoming untenable.  

Organisations are now looking for a more intelligent and adaptive approach that can engage customers in real time, maintain continuity, and drive tangible results. 

Agentic AI in Action 

This is where agentic AI comes into play.  

Unlike traditional automation, agentic AI is built around autonomous, outcome-driven agents that can reason, collaborate and take contextual decisions.  

These agents can be trained for specific use cases such as cart abandonment recovery, KYC completion, proactive service notifications or multi-step issue resolution. This helps brands transition from basic automation to autonomous actions and AI decisioning.  

“Agentic AI is purpose-built,” Anand says. “Each agent understands the goal it needs to achieve, but it also knows how to work with other agents throughout the journey.  

“So you may have one agent focused on customer onboarding, another handling verification, and another coordinating follow-ups – all sharing context in the background.” 

This type of orchestration is increasingly essential for large enterprises. In e-commerce, for example, an agentic AI flow can detect a customer abandoning a cart, trigger hyper-personalised reminders across SMS, WhatsApp or email, and follow up based on engagement. If the customer expresses confusion or dissatisfaction, the agent can switch channels or escalate to a human agent with full context. 

“You’re no longer relying on one-size-fits-all automation,” Anand adds.  

You’re creating a dynamic loop that adapts to each customer’s needs and behaviours.

Voice AI: Transforming Real-Time Interactions 

The rise of voice AI is taking things a step further.  

Advanced speech-to-speech models now enable natural, human-like interactions that go far beyond traditional voice bots.  

These systems can understand real intent, detect emotion, and respond conversationally – making voice channels significantly more efficient and engaging. 

“For many customers, voice is still the channel of choice,” Anand notes.  

“But the experience has often been painful because legacy IVR is so restrictive. With voice AI, customers can speak normally and get real-time problem solving without navigating menus or waiting for an agent.” 

Tata Communications is seeing growing demand for voice AI in sectors such as banking, utilities, retail and travel, where customers frequently need rapid support with complex queries.  

When combined with agentic AI, voice agents can collaborate with other AI systems, retrieve information, complete tasks and escalate with full context when human support is required. 

“The beauty of voice AI is that it doesn’t break the flow,” Anand says. “If an escalation is needed, the human agent gets the full transcript, sentiment analysis and journey history. The customer never has to start again.” 

A Unified Approach 

Tata Communications has integrated these capabilities into a unified platform that connects multiple AI agents, voice systems and human support teams through powerful APIs and data connectors.  

The goal is to create a single interaction fabric that ensures continuity across every channel. 

“When an AI agent hands over to a human, or vice versa, all context is preserved,” Anand explains.  

“This is critical. If customers have to repeat themselves, the customer feels unheard and the journey becomes painful. Our platform eliminates that friction by ensuring that every agent – human or AI – understands the full picture.” 

The company has already seen strong results.  

One electric vehicle brand achieved a 25 percent increase in customer follow-through after deploying agentic AI-driven outreach.  

A large e-commerce marketplace reduced return-to-origin orders by 45 percent following the introduction of AI-powered WhatsApp workflows. 

“These are not incremental improvements,” Anand highlights. “They are major operational gains driven by intelligent automation that understands the customer’s intent.” 

Human-First, Outcome-Driven CX 

Despite the advances in AI, Anand emphasises that human expertise remains essential.  

Tata Communications’ approach is intentionally hybrid – using AI to handle repetitive tasks, streamline journeys and provide real-time insights, but ensuring humans remain central to complex, high-empathy interactions. 

“The best CX strategy is human-first,” he says. “AI should enhance human capability, not replace it. When AI and humans collaborate, you deliver outcomes that are personalised, proactive and genuinely valuable. That’s the future of customer experience.” 

As enterprises look to modernise their digital engagement, agentic AI and voice AI are emerging as critical technologies that can close the customer journey black hole and deliver the seamless, context-aware experiences customers expect. 

To explore how your organization can overcome the customer journey black hole and create seamless, unified experiences, contact Tata Communications to learn more about their integrated CX platform capabilities.   

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.