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.

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

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.   

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

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

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

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

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

The Rise of the Super Agent  

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

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

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

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

Instead of switching between multiple systems or digging through outdated documentation, agents receive live prompts from a connected knowledge base that continuously suggests next best steps.  

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

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

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

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

For Penn, CoPilot delivers the best of both worlds:  

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

Efficiency with Integrity  

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

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

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

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

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

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

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

Case Study: Insurance Company  

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

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

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

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

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

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

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

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

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

Empathy Through Enablement  

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

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

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

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

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

Enterprise Takeaway: What Leaders Should Do Now  

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

To realize that value, the Puzzel man recommends:  

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

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

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

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

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

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

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

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

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

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

Today’s contact centers are interacting with customers that stretch from the Silent Generation all the way to Gen Alpha in a day.

As Garry Gormley, Founder of FAB Solutions, put it, “We’re at a strange impasse.”

There’s the Silents and Boomers who still value voice contact with human agents; Gen X and Millennials who mix digital self-service with human support; Gen Z, who jump between apps and channels; and the emerging Gen Alphas, who are growing up expecting hyper-personalized, predictive experiences.

Put all six generations together, and it’s easy to see why no single communication style or service model can cover everyone.

Managing by Generation Isn’t Just an HR Strategy

There’s an economic case for taking generational differences seriously. Data from the World Economic Forum shows that countries can increase their GDP by 19% over 10 years by managing their workforce based on generational groups, and that extrapolates itself out to customers, noted Katy Forsyth, Managing Director of Red Recruitment.

Every generation wants speed, multichannel and intuitive service, but the defining difference between the generations is that younger consumers want personalization, Forsyth said.

“[Gen Z] want everything addressed to them personally. They want an emotional connection with their service… And then when we get the Alphas coming along, they’re even more hyper-personalized.”

These consumers expect brands to use predictive analysis to make recommendations based on the activity on their phones.

Much of this comes down to economic pressure. Gen Z’s spending habits are different because their financial realities are different. Forsyth highlighted that in the UK, “it costs Gen Z six times their salary to get a house deposit. For an Xer in 1995, it was a third of one year’s salary.”

It’s partly because of that smaller buying power that when Gen Z consumers do choose to spend, they want meaning behind the transaction. “The personalization is super important,” Forsyth said, noting that on Black Friday, Gen Z consumers will spend an average of £255 on purchases, which is almost double the £155 that Gen Xers are expected to spend.

That makes Gen Z customers an important demographic for brands to address and they need to understand how to appeal to them. Younger consumers are the mostly likely to use GenAI and AI assistants in their holiday shopping, with Gen Z accounting for two-thirds of shoppers turning to ChatGPT for gift inspiration, according to research from Bread Financial.

Despite narratives around younger generations’ aversion to picking up the phone, Forsyth warned against oversimplification when it comes to providing voice channels:

“Even though they might want to self-serve, you’ll be surprised… as soon as it gets uncomfortable, they want voice. Do not believe the headlines.”

Data shows that when dealing with emotional issues Gen Z wants to speak to a service agent, indicating that companies need to tread carefully and avoid dealing with customers on the basis of assumptions.

Matching Service to the Customer You Actually Serve

For contact centers, getting to grips with practical ways to stay authentic as they juggle all the different ways generations communicate is not an easy task. Beyond managing multiple channels, it’s about making sure every interaction feels honest and human, whether a customer reaches out on the phone, chat or through social.

That means giving contact center agents the freedom and tools to adjust their tone to the customer, using technology to support real connection.

While older generations may still prefer a softened message, younger generations will not tolerate spin. Forsyth said of Gen Z:

“They do not trust you as businesses, whether you’re employing them or selling to them… They will take bad news… but you need to tell them the truth. Do not flower it up… Forget the good news, just go for the jugular, and they’ll respect you a whole lot more. Tell them it’s expensive, but why?”

Trust was a recurring theme during the discussion, as well as the ways in which it spans across generations.

Marco Ndrecaj, Director of Customer Experience Management, Shared Services Connected, said the biggest threat to customer experience isn’t channel fragmentation, it’s eroding trust:

“We need to make sure that we are demonstrating trust in the right way, through communicating honestly and openly about the engagements that we have, either through a bot, or through an AI agent, or through a live person and being really clear on the distinction between those and servicing that to the right people.”

Ndrecaj highlighted a sentiment from one of his contact center advisors: “Customers are being overwhelmed with information… technology on its own doesn’t build trust. People do… What matters is how we use technology to enhance the customer connection.”

The balance between human empathy and AI capability is the foundation on which to build credibility, increasing trust rather than eroding it.

“Humans bring empathy and judgment, while AI provides skill and insight. And when brands get this balance right, that’s when the magic happens.”

Brands are challenged with producing content to appeal to the TikTok generation, which gravitates toward fast-paced, video-led storytelling, while remaining relevant to older audiences that engage in different ways. “How do we think about how we adapt and create that video first experience for the consumers of tomorrow?” Ndrecaj said.

But Ndrecaj also urged brands not to confuse channels with meaning: “I don’t think it’s the actual medium. It’s more around how you make them feel. Gen Z and Gen Alpha think very differently. It’s not about video content or… collecting points. It’s about them feeling a sense of purpose. It’s about organizations that actually have shared values.”

Ndrecaj pointed to brands like Nike and Lego as examples, noting: “They actually invite their customers to co-create products. And that is a feeling that you can’t buy through TikTok or Instagram.” Forsyth, too, cited Nike as brand that is connecting well with customer service for Gen Alpha.

Brands also need to strike a balance between acknowledging the differences between generations and making assumptions about what customers want.

Beyond Stereotypes: Reading the Real Customer Need

Sandrea Morgan, Head of Customer Support at Adanola, warned against treating generational traits as blanket truths. “It depends on where you are as a business and what type of customer you’re interacting with…. because what a customer expects depends on the experience they want to have. What am I trying to purchase. Is it something for the home? Is it something for you personally? That does change what you expect no matter what age you are.”

Morgan contrasted the customer expectations of two different types of businesses.

“In my current role [with a] younger Gen Z customer, the majority of what they want is [for interactions] to be simple, quick, on brand, but pretty efficient and professional. I was in a role a year ago, [with] a slightly older customer. The product was a bit more expensive. There was more of a luxury feel to it. What they wanted from us was very, very different, and the tone of voice that the advisor had was very, very different.”

Understanding the customer makes it easier for brands to move beyond generic customer service design and give their contact center employees the tools and training they need to connect with customers in the most appropriate way for the service they expect.

“Sometimes that’s a piece of technology that you can give them, and sometimes it’s about the training that you give them to be the best in their job every day,” Morgan added, stressing the importance of aligning agents’ skills to the customers they serve.

And to complicate things even further, figuring out what customers want isn’t straightforward, because while they might say they make purchases based on their values, their actual choices can tell a different story.

For Gen Z, for example, their values matter, but they are also under strain from the limits to spending power. As Forsyth pointed out:

“Their values are really critical, but we are in a cost-of-living crisis that is affecting the Zs, and they’re having their values pushed as a result of that.”

Businesses need to be prepared for that to change over the next three to five years and make sustainability more cost-effective to deliver. “Then we keep every generation happy, but particularly the Alphas, who will just be hitting with the spending power,” Forsyth said, as they transition to becoming a larger share of retail spend.

Ultimately, serving customers spanning six generations is about listening closely and building the kind of service that can flex as customers’ needs shift.

This feature is designed to further the Amazon Connect Email platform, utilizing built-in capabilities that enable agents to deliver quicker response times. 

The workflow tool is the latest addition to AWS’s email and contact center capabilities. 

Released in November 2024, Amazon Connect Email is an omnichannel support feature that enables customer service agents to respond to and divert customer emails all within the same system as voice and chat, allowing agents to handle customer queries in a single space. 

The AI-enhanced email workflow tool enhances Amazon Connect Email by allowing service agents to expedite email customer service through automation capabilities. 

By utilizing large language models (LLMs), these AI-powered workflows can allow Amazon Connect to analyze emails, detect customer intentions, assess potential risks or complexity from the interaction, and evaluate next steps. 

After analyzing the email, the tool will provide a summary of the customer’s profile and any previous activity with the enterprise, the determined query category, and a brief rundown of the email to help tailor the response accurately. 

This tool also grades the received email based on how confidently it understands the message with Amazon Bedrock API and Claude AI, factoring in clarity, tone, topics, risk assessment, and time sensitivity, while also considering whether the customer is part of any premium packages by retrieving the user’s profile. 

This profile retrieval will include the customer’s current credit score, service level, and contact history to help the tool further evaluate the email score. 

After this, the tool implements a two-step process, where the LLM produces binary outputs for each negative factor it recognizes, using an embedded mathematical function to ensure the calculations align with the scoring evaluation. 

Enterprises can also personalize the scoring framework to fit their needs and determine the number of emails routed to an agent. 

Once a score has been determined, the email will receive either a generated response (if the score is 80 or higher) or be routed to an agent’s inbox for a personalized response (if the score is lower than 80), whilst also providing an explanation detailing how the tool determined the score. 

To simplify future interactions, the tool can automatically generate a case detailing the email and information previously gathered, allowing agents to view conversation history from a single place where email handling has occurred with generative AI.  

Agents can also personalize AI-generated responses to keep human intelligence in the loop and can add AI-powered workflows to Amazon Connect Email via Amazon Bedrock. 

What This Tool Means For Customer Experience 

The Amazon Connect AI-enhanced email workflows allow enterprises to bridge the gap between customer demand and agent availability through automating repetitive email tasks and filtering complex queries to agents, providing customers with human responses and agents with enhanced productivity where needed. 

These AI-powered workflows outrun traditional automation systems by understanding context clues behind interactions, including emotion and characteristic human responses by analyzing a customer’s profile. 

This avoids agents from partaking in repetitive research tasks and instead solving complex, human-based problems to deliver solutions that an LLM cannot solve, resulting in higher levels of meaningful work for the agent. 

This also resolves issues with email backlogs, customer survey responses, and agent exhaustion, driving improvements in customer service. 

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

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

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

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

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

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

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

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

From Analytics to Agility  

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

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

The enterprise equivalent of this is data and analytics.   

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

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

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

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

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

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

Cloud, But on Your Terms  

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

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

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

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

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

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

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

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

The Quiet Advantage of Going Slow  

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

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

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

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

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

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

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

A Future Defined by Flexibility  

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

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

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

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

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

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

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

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

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

Modernization That Fits the Business  

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

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

As Marcos puts it:  

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

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

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

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

The IRS will use Agentforce across the Office of Chief Counsel, Taxpayer Advocate Services and the Office of Appeals, Paul Tatum, Executive Vice President of Global Public Sector Solutions at Salesforce, told Axios.

The move follows major staffing cuts at the agency implemented by the Department of Government Efficiency (DOGE), which has since been quietly disbanded according to reports, as well as furloughs during the recent government shutdown. The IRS lost around 25 percent of its staff between January and May, shrinking from roughly 103,000 employees to about 77,000, according to a report from the Treasury Inspector General for Tax Administration (TIGTA).

The US Congress has reduced the IRS’s funding under the Inflation Reduction Act (IRA) from $80BN over a 10-year period to $37.6BN. And proposed budgets for the 2026 financial year would reduce the agency’s annual funding by around 20 percent.

“Completing IT modernization projects, providing quality service to taxpayers, and enforcing tax laws with a reduced workforce and budget will be challenging for the IRS,” the TIGTA said.

That’s where Salesforce comes in. The vendor received FedRAMP High Authorization back in June for Agentforce alongside its Data Cloud, Marketing Cloud, and Tableau Next offerings. It launched its Agentforce for Public Sector edition in August, which provides government agencies and local authorities with tailored, pre-built and AI agents.

Salesforce has already been working with the IRS to modernize some basic technology in those departments and will now add AI agents to handle tasks like generating case summaries and searching data to reduce the time it takes to handle customer interactions.

The aim is to help employees process cases so that taxpayers get the information they need faster, rather than to replace them, Tatum said.

The TIGTA pointed out that the agency still needs human staff to manage its complex remit:

“Despite numerous ongoing automation projects, the IRS still needs skilled and experienced employees to interpret tax law changes, investigate criminal activity, prevent fraudulent refunds, and implement complex coding changes for its information systems.”

That puts pressure on employees to deliver all this while providing a satisfactory service to taxpayers.

According to a separate TIGTA report in October, the Inspector General received nearly 250 complaints from taxpayers during the 2024 filing season, from January through May 2024, about interactions they had with IRS representatives.

The TIGTA found that IRS contact center representatives were typically courteous and professional in their interactions with taxpayers. But it identified “some instances where improvements are needed”. In 11 percent of call recordings, taxpayers received poor customer service, such as unprofessional behavior from IRS representatives, long wait times on hold or disruptive background noise. Another 15 percent of calls were either dropped or disconnected.

Why Automated Platforms Are Emerging as a Fix for Government Contact Center Shortages

AI platforms like Agentforce could provide a solution for government organizations like tax agencies that are facing a challenge in staffing contact centers to provide service to millions of taxpayers, especially during peak seasons.

Efforts to boost contact center productivity eventually hit a natural limit, because employers face a challenge in maintaining full staffing. “There’s a ceiling. You can’t make people give 110%. We work with governmental organizations who are losing employees and haven’t got the power or the money to fire into that space,” James Mackay, Regional Sales Manager at conversational AI firm Rasa, told CX Today in a recent interview.

Faced with thinning ranks from retirements, budget cuts or hiring freezes, these agencies are turning to AI to help frontline employees manage the workload. Mackay laid out the scope of the challenge ahead:

“Not reimagining how it works is an existential threat. It’s not like they need to just pay more for people; they can’t get the people. So now they have to figure out how they rationalize that ability to serve these customers. You’ve got a government who can’t afford the contact center, who legally have to provide that service, how are they going to do it?”

For government organizations, changes in service approaches such as scaling back contact center operations are not simply a business decision.

In the UK, HMRC was forced to backtrack almost immediately last year on plans to reduce its customer helplines in an attempt to funnel taxpayers towards its digital services such as chatbots and online forms. The agency stated that was “halting its plans in response to the feedback” from the public, business groups and politicians.

The Canada Revenue Agency (CRA) has also come under scrutiny in recent months following layoffs earlier this year, given ongoing taxpayer complaints around long wait times, unanswered calls, and contact center agents providing inaccurate tax information. The CRA has hired back some staff, as the issue has been escalated to Parliament and the Auditor General. An assistant commissioner at the CRA, Melanie Serjak, told MPs in a standing committee in October that the agency is looking at AI, among other technology tools, to assist agents in providing accurate responses.

While AI is often touted as a solution that “makes everything better,” supporting staff in government agency contact centers is one area where there is true potential, Mackay added.

“Is AI the answer to that? It might not be. There might be other ways… like outsourcing has been a favorite way of dealing with that. But AI has a real chance, if used well, to help augment and get rid of—to some degree—some of the requirements for bums on seats.”

For an agency that fields millions of questions, complaints and appeals each year, even small improvements in case handling can translate into noticeable gains in service experience for the public.

Summarizing long case files, retrieving policies instantly, drafting communications, and pulling up relevant data are the kinds of small efficiencies that add up to noticeable real-time savings in understaffed environments.

However, the challenge will be using AI solutions appropriately and avoiding the risk of hallucinations related to tax filing or collection, which could have serious consequences for taxpayers.

Organizations will need to establish clear guardrails around how automated systems handle sensitive financial information, making sure that every recommendation or calculation they make is grounded in verified data. Human oversight will remain critical, particularly if AI is tasked with interpreting complex regulations or communicating with taxpayers.

Salesforce, for its part, recommends deploying AI agents for non-critical tasks and where human experts can easily intervene.

“Salesforce doesn’t advocate for a blind AI processing tax returns without a human being involved in reviewing and supplementing it,” Tatum told Axios. “When the agents are built, there’s a lot of guardrails put in … [they’re not] allowed to make final decisions, they’re not allowed to disperse funds.”

For other government organizations, the IRS’s move offers a case study into how AI might fit into their own service delivery approaches, aimed squarely at relieving pressure on overworked teams.

Yet, many enterprises are struggling to deliver on this promise, falling victim to what experts call the “customer journey black hole.”  

This phenomenon occurs when customer data, context, or information are lost between interactions, creating fragmented and frustrating experiences that can cost brands both loyalty and revenue. 

Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, describes the black hole as the exact opposite of what brands intend to deliver.  

“Your data – whether it’s for an inquiry, a product question, or a complaint – is going in one channel and then getting lost. That’s the black hole side of it,” he explains.  

And the impact of this journey black hole is severe. It’s not just that the customer is unhappy. Your actual business numbers are getting impacted. 

Why It Happens: Silos, Channels, and Technology Gaps 

The consequences of a fragmented customer journey are stark. A study by PwC found that 92% of customers would abandon a brand after two or more negative interactions. 

In other words, every dropped call, unresolved query, or repeated customer statement represents a potential lost sale, diminished brand trust, and decreased customer lifetime value. 

The causes of the black hole are multifaceted. Enterprises often operate in silos, with marketing, product, billing, and support teams holding pieces of the customer puzzle in separate systems that do not communicate with each other.  

Misaligned channels exacerbate the problem, as customer preferences differ by demographic, time of the day, their stage in the journey etc. For example a younger consumer may prefer messaging apps, while older generations often prefer phone calls. 

Technology gaps, such as disconnected AI solutions and multiple vendor systems, along with operational inefficiencies, further worsen the situation.  

And in today’s world, if the hand-off from AI to humans loses context and customers have to repeat themselves, then this black hole can be pretty deep.  

The Cost of Ignoring the Black Hole 

The impact extends beyond customer experience metrics. Anand points out that “closing these gaps is just not about improving customer experience. It’s about protecting the brand value and the value of the businesses that these brands are in.”  

Fragmented customer interactions can cost enterprises significantly in lost sales, operational inefficiencies, and increased churn, underscoring why addressing the black hole is not just a CX priority, but a business imperative. 

When customer data is lost or mismanaged, brands risk reduced sales, operational inefficiency, and reputational damage, as negative experiences can spread quickly in the age of social media. 

Compliance and privacy requirements can also unintentionally introduce friction, making it more challenging to provide smooth, personalized interactions. Ignoring the black hole may result in a slow erosion of customer trust, which competitors can capitalize on. 

Closing the Gap: Unified CX and AI Solutions 

Addressing the black hole requires a holistic approach.  

Enterprises need to unify their data and orchestrate interactions across all touchpoints to streamline operations. This includes ensuring that every channel communicates with the others, maintaining context between interactions, and empowering human agents with the right information at the right time.  

AI can help, but only if fed with contextual & comprehensive data and deployed thoughtfully, maintaining continuity between automated and human-led experiences. 

The payoff is significant. Brands that achieve seamless, data-driven experiences can reduce churn, increase engagement, and extract actionable insights from end-to-end journey analytics. In a competitive marketplace, providing frictionless experiences is no longer optional – it’s a differentiator.  

As Anand warns, “It sounds simple, but the customer has high expectations. And if their expectations aren’t met, they will leave. The black hole is real, and brands cannot afford to ignore it.” 

As more enterprises confront the customer journey black hole, many are turning to unified CX strategies that bring together communications, data, and AI.  

Companies like Tata Communications are helping organizations reimagine this space – integrating CPaaS, CCaaS and CXP technologies to create more connected, context-rich interactions.  

It’s a shift from managing channels to orchestrating journeys – a move that could define the next era of customer experience. 

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.

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

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

The traditional model simply wasn’t built for that.  

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

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

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

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

The Shift: From Passive Scoring to Meaningful Discovery  

AI and modern analytics are rapidly changing the QA equation.  

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

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

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

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

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

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

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

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

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

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

Spotlight: When QA Data Reveals What No One Else Saw  

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

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

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

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

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

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

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

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

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

Building the QA–VoC Insights Loop  

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

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

This integrated view is exactly where Corcoran sees QA heading:  

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

From Problems to Priorities  

Scorebuddy’s evolution mirrors this broader industry shift.  

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

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

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

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

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