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

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

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

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

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

The “Context Gap” Holding AI Back

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

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

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

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

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

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

What Cobrowse AI Actually Brings to Virtual Agents

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

Real-Time Visibility into UI State

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

Situation-Aware Guidance

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

Intelligent Analysis of Friction

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

Seamless Escalation

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

Enterprise-Grade Safeguards

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

Scalzi described the solution’s ambition clearly, stating:

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

Why This Release Matters for the Broader CX Landscape

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

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

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

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

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

Expected Outcomes for Support Organizations

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

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

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

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

A Step Toward AI That Truly Understands Customers

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

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

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

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

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

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

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

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

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

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

Agents Get Instant Access to Enterprise Knowledge

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

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

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

Amazon Connect Just Made Proactive Outreach Easier for Service Teams

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

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

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

Amazon Connect Delivers Complete Visibility for Teams Trusting Autonomous AI

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

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

Global Brands Get Genuinely Human Voice Interactions

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

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

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

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

Amazon Connect Removes Analytics Headaches for Managers with Natural Language Queries

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

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

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

What’s Next?

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

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

To kick things off, there are six predictions that all examine what might be the most tangible shift taking shape in customer experience: agentic AI moving from underwhelming chatbots into systems that can actually handle real work.

After years of disappointing automation projects, the technology has reached a point where self-service might finally live up to its billing.

Meet the experts:

• Simon Thorpe, Director of Global Product Marketing for Customer Service & Sales Automation at Pegasystems
• Kishan Chetan, EVP and GM of Agentforce Service at Salesforce
• Matt Price, CEO of Crescendo
• Hakob Astabatsyan, Co-Founder & CEO of Synthflow AI
• Matthias Goehler, CTO in the Europe region for Zendesk
• Zeus Kerravala, Principal Analyst at ZK Research

The Chatbot Problem Nobody Wants to Talk About

Simon Thorpe, Director of Global Product Marketing for Customer Service & Sales Automation at Pegasystems, isn’t mincing words about where self-service has been.

“Look, everyone is talking about AI right now. And for good reason,” he says.

“But the thing that I’m really excited about is the fact that we can finally deliver self-service that actually works for our customers. You know, self-service that can get real work done. It’s able to resolve issues, complete tasks, deflect work from our centers and it’s self-service that our customers are actively going to want to use.”

Customers actively wanting to use self-service has been the unicorn of CX for the better part of two decades. Chatbots and IVRs promised a lot but mostly delivered frustration.

Simple queries? Sure. Anything remotely complicated? Straight back to the queue.

Thorpe sees agentic AI changing that dynamic because it can reason, adapt, and understand natural language in ways that rigid scripting never could.

He explains “What once took months is now going to take weeks, which is tremendously exciting.”

But there’s a catch. Speed without structure creates chaos, particularly in regulated industries where processes can’t just be improvised by an AI agent with good intentions.

“Without governance and workflow or workflow backbone, AI agents can go rogue. They can ignore processes. They can introduce risks.”

His solution is pairing agentic AI with enterprise-grade workflows that act as guardrails, ensuring “your rules, your regulations, your standards are consistently applied every single time.”

AI Agents Move from Pilot Projects to Production

Kishan Chetan, Salesforce’s EVP and GM of Agentforce Service, believes 2026 is when AI agents move away from experimentation toward becoming infrastructure.

“For me, the CX prediction for next year, the biggest one, is far more mainstream of AI agents,” he says.

“Companies across the board will use AI agents in their customer experience, and they’ll use that for different processes, and that’ll work seamlessly with their human service reps.”

The emphasis on working alongside humans rather than replacing them reflects how the conversation around AI has matured. Early hype suggested automation would eliminate jobs.

The reality is messier and more interesting: AI handles volume and repetition, humans manage complexity and judgment.

When AI Outperforms the Average Agent

Matt Price, CEO of Crescendo, makes a prediction that’s bound to spark debate: AI agents will become more empathetic and more efficient than humans in 2026.

“On average across all of the interactions between service agents and AI, AI will perform better because on average, AI assistants are able now to have great language, detect tone and respond appropriately to customers in the moment and have full access to all of the information that they need in order to give customers what they want, which is an answer.”

Price isn’t suggesting every AI interaction will beat every human one. Top-tier agents will still outperform AI. But AI doesn’t have bad days, doesn’t forget context, and doesn’t struggle with tone on the 200th repetitive call of the day. That consistency matters.

There’s also a perception angle here, as Price notes that “a lot of the time for clients, it’s not necessarily just how well you serve them, but how much effort you put in.

“And there’s nothing better than showing the amount of effort that’s been put in, than putting a human in the loop rather than an AI agent.”

So even as AI gains emotional intelligence, there will still be moments where customers want to know a person is involved.

The Innovation Slowdown (That’s Actually Good News)

Hakob Astabatsyan, Co-Founder & CEO at Synthflow AI, predicts 2026 will see a decline in forward-looking innovation and instead focus on making AI work at scale.

“My prediction for 2026 is that we will be seeing less groundbreaking innovation that we have experienced in the last two years and more ROE and value delivery to the customers, to enterprises.

“What I mean by that is more scalable, more reliable platforms that allow the enterprises to go into production and deploy agents, voice agents, but also chat, omnichannel chat and text agents into production and scale them to millions of calls.”

That might sound boring compared to the breathless pace of the last couple of years, but it’s what enterprises actually need. Over-the-top innovations don’t matter if the technology can’t handle production traffic without breaking.

From Reactive to Proactive

Matthias Goehler, CTO in the Europe region for Zendesk, sees AI shifting from solving problems to preventing them before customers even notice.

“My biggest prediction for 26 when it comes to CX is that AI will move from automation to anticipation,” he says. “Instant resolution still remains the biggest expectation of customers. But on top of that, customers also more and more expect personalized engagement.

“And then even on top of that, if companies could start to become more proactive and reach out to customers instead of customers having always to reach out to companies, I think then we’re really talking about the gold standard in service.”

That’s a higher bar than most organizations have reached, but the technology to get there has begun its early stages.

Customers Will Actually Prefer Virtual Agents (For Simple Tasks)

Zeus Kerravala, Principal Analyst at ZK Research, predicts that for straightforward requests, customers will start choosing virtual agents over humans.

“My CX prediction for 2026 is that virtual agents get so good that for simple requests, people start to prefer the virtual agent over humans,” he says.

“And you might think that this is contrary to everything we believe, but if you look back at the early days of online banking and restaurant reservations online, people said that back then that no one would prefer a computer over a person. And in both cases that certainly wasn’t true.”

Kerravala draws a parallel to other initiatives that originally faced skepticism but eventually became preferred options once they proved faster and more reliable.

“Virtual agents can do things faster and more accurately than people now for complicated tasks. We’re still going to do prefer to a human, but in 2026 the quality of virtual agents will get so good that for simple tasks we’re going to prefer machines over people.”

The prediction doesn’t suggest humans become obsolete. It suggests customer preferences will align with the strengths of each channel.

What This Means for 2026

The common thread across these predictions is straightforward: AI agents are maturing from disappointing novelties into reliable tools that can handle real customer service work.

Self-service that actually works, agents that operate alongside humans without replacing them, and systems that anticipate problems rather than just reacting to them.

These aren’t abstract possibilities anymore. They’re becoming baseline expectations.

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.

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.   

They’re also a must-have in an era where first-party data has become crucial. In retail and eCommerce, CDPs now decide whether personalization feels seamless or invasive. For healthcare teams, they determine if data moves fast enough between portals, contact centres, and clinics to help patients instead of slowing them down. In finance, they’re becoming a line of defence -unifying records, flagging anomalies, and proving compliance to regulators.

What Is a CDP and Why It’s Not Just a CRM Upgrade

For years, the CRM has been treated as the central hub for customer data. But its design is narrow. It logs contacts, tracks deals, and manages service cases.

What it doesn’t do is unify every fragment of data a company holds – website visits, app behaviour, consent records, transactions, service interactions, marketing touches – into one, live profile that can be activated anywhere. That is the role of the Customer Data Platform.

A Customer Data Platform pulls first-party data from every corner – websites, apps, call logs, sales systems – and brings it into one place. The mess gets cleaned, duplicates are resolved, and what’s left is a living record of the customer that keeps updating. From there, it can do a lot: fuel AI models, trigger automated steps in the background, or simply give a frontline agent the context they usually waste time piecing together.

As Salesforce describes it, the platform moves data “from disconnected silos to real-time engagement.” Adobe takes it further, positioning CDPs as the only way to deliver “personalization at scale, powered by first-party data.”

For many CX teams, CDPs are now the foundation of any strategy for future growth. That’s part of why the market is growing so fast, at a rate of around 39.9% CAGR.

Measurable Customer Data Platform Benefits and ROI

Technology investments rise and fall on one question: do they pay back? In the case of Customer Data Platforms, the answer is increasingly clear. CDP benefits are measurable, and enterprises are using the data to justify spending at the board level.

The financial case is strong. A Forrester Total Economic Impact study of Oracle Unity found that companies achieved an ROI of 158% with payback in just seven months. The study pointed to efficiency gains in segmentation, campaign orchestration, and service response times.

The pattern repeats across industries.

• Telecoms: Spark NZ, working with Tealium and Snowflake, cut campaign delivery time by 80%. That speed translated into millions saved in media spend and higher response rates.
• Membership services: At AAA Washington, Salesforce Data Cloud unified data across channels to power faster, more relevant roadside support. The result was a jump in satisfaction scores and measurable cost savings as manual lookups disappeared.
• Retail: Klaviyo’s customers often cite faster payback than expected. At DKNY, Klaviyo helped to increase click rates and improve conversions by enabling end-to-end personalization.

But the benefits of a CDP often go further than expected, particularly in the age of AI and automation.

Customer Data Platforms as the Foundation for AI, Automation and Orchestration

Artificial intelligence and automation are boardroom priorities, but both depend on the quality of the data underneath. Without a trusted foundation, even the smartest models deliver the wrong outcomes. That is why CDP benefits now reach beyond marketing: they are powering AI decisioning and workflow orchestration across the enterprise.

Salesforce describes its Data Cloud as a “zero-copy CDP,” able to unify data across systems and activate it in real time. In practice, that means generative AI assistants like Einstein or Agentforce can draft responses, recommend actions, and even guide self-service troubleshooting – because the context is accurate. Adobe has taken a similar line, stressing that a CDP is the only way to make personalisation “real-time and scalable.”

Case studies bring this to life. Vodafone used Tealium to launch what it called an “AI-powered customer revolution”. By unifying subscriber data, the telecom giant improved cross-channel engagement by 30% and created new opportunities for automated retention campaigns.

Castore, a fast-growing sportswear brand, turned to Klaviyo’s CDP to scale its customer journeys and marketing campaigns, branching into four new SMS territories, and increasing click-through rates for AI-powered, automated campaigns.

Trying to automate on top of scattered data just makes the noise louder. With a CDP in place, orchestration feels sharper: actions run faster, responses land with more relevance, and journeys stay consistent. The same profile sits underneath everything – whether it’s an AI copilot, a bot handling routine work, or a self-service flow guiding a customer through a fix.

Customer Data Platform Benefits for Compliance, Security and Trust

Data is now under the same scrutiny as financial records once were. A misstep doesn’t just create headlines; it creates fines that hurt. Since GDPR came into force, regulators have handed out more than €5.6 billion in penalties, and the pace has only picked up in the last two years. For banks, insurers, and healthcare providers, fragmented data is a liability.

This is where a Customer Data Platform earns its keep, allowing enterprises to build compliance into the data layer itself. Every profile carries its own consent state. Every interaction can be logged against that consent. Sensitive fields can be masked automatically before they ever reach a campaign or a dashboard. The result: marketers can move quickly, while legal and risk teams know the audit trail is intact.

There are practical examples. Legal & General uses its CDP to improve real-time engagement, but compliance was part of the business case from the start. Data policies are enforced centrally, so every department works from the same set of rules.

A CDP doesn’t guarantee compliance on its own, but it gives organisations the framework they need to protect customers, and themselves.

Improved CX: Personalization and Omnichannel

Most people don’t notice a database. What they do notice is the experience: the email that lands with the wrong name, the app that asks them to log in again, the service agent who doesn’t know what happened last week. A Customer Data Platform exists to stop those seams from showing. That’s where some of the clearest CDP benefits come through.

The promise is simple enough: take signals from across the business, stitch them into a live profile, and feed them back into every channel. In practice, it means fewer repeated questions, less wasted spend, and more relevant offers. Analysts at Aberdeen have noted that organisations with a CDP in place see campaign response rates more than double.

An example? L’Oréal turned to Tealium to power personalization and improve media efficiency worldwide. The CDP became the bridge between consented first-party data and campaigns running in dozens of markets, ensuring local teams could tailor messages without compromising privacy.

Fisher and Paykel used the CDP from Salesforce to improve customer experience in another way – by giving clients a more efficient self-service experience. Agentforce now handles 65% of the company’s interactions, and 45% of customers book directly through AI. The company also benefits from a 50% reduction in call handling times, that means agents have more time to focus on other tasks.

CDP Benefits for Employees and Service Teams

Customer Data Platforms are usually sold on their marketing value, but the upside for employees is just as important. When data is scattered, staff spend time chasing records, re-entering details, or trying to match information between systems. It drags down productivity and morale. Unifying that data is one of the simpler benefits of a CDP, yet it can be the most visible inside the organisation.

For agents on the service desk, a unified profile means they no longer need to piece together a caller’s history from multiple screens. Instead, they see the journey in context – recent transactions, open tickets, even preferences collected elsewhere. Handle times drop, but so does frustration on both sides of the line.

There are clear examples. AAA Washington turned to Salesforce Data Cloud to give service agents a single view of member profiles. The organization described how AI agents could act on those profiles to dispatch roadside help faster and more accurately, without the risk of burnout.

Customer Data Platform Benefits for Business Leaders: Unified Insights and ROI

For executives, the challenge is rarely a lack of reports. It’s the opposite. Every department has its own dashboards, each with different numbers, often telling different stories. Decisions slow down because no one is sure which version of the truth to trust. This is where one of the more overlooked benefits of a CDP comes in: a single, reliable view that leadership can use with confidence.

There’s a strategy angle too. Once unified profiles start feeding into AI, executives can pick up churn risks before they hit the numbers, forecast with more certainty, and shift spending based on what customers are actually doing right now. It cuts down the boardroom surprises – and avoids the frantic catch-up when forecasts and reality don’t match.

The Adecco Group is a case in point. By implementing Salesforce Data Cloud across its global workforce solutions business, the company gave executives access to a 360-degree view of both clients and candidates. Its Chief Digital & Information Officer explained that AI now powers recommendations across the business, backed by trusted data. The CDP benefits in this case weren’t limited to marketing metrics; they reshaped how Adecco planned and delivered strategy.

The Customer Data Platform Benefits Enterprises Can’t Overlook

Much of the discussion around CDP benefits centers on personalization or marketing efficiency. Important, but not the whole story. Some of the biggest advantages are the ones that don’t always make it into vendor slides.

One is data quality for AI. Models are only as strong as the information they’re trained on. A Customer Data Platform acts as a safeguard, filtering personal data where it shouldn’t be used and ensuring inputs are consistent. That makes AI outputs more reliable – and less risky when it comes to privacy or bias.

Another is collaboration. Increasingly, organizations are using data clean rooms – neutral environments where they can share insights with partners without exposing raw records. CDPs can plug directly into these environments, allowing a retailer and a brand, or a hospital and a research partner, to coordinate using anonymised datasets.

Governance often gets left until last, but a CDP brings it right into the data layer. Consent tags travel with the customer wherever their information is used. If someone withdraws permission, that state follows them, blocking future use automatically.

These Customer Data Platform benefits aren’t flashy like a dashboard or a campaign, but they carry more weight. They shape whether AI scales responsibly, whether data partnerships work, and whether trust with customers holds firm.

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

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

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

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

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

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

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

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

From Analytics to Agility  

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

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

The enterprise equivalent of this is data and analytics.   

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

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

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

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

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

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

Cloud, But on Your Terms  

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

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

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

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

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

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

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

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

The Quiet Advantage of Going Slow  

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

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

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

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

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

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

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

A Future Defined by Flexibility  

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

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

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

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

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

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

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

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

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

Modernization That Fits the Business  

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

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

As Marcos puts it:  

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

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

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

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

For this writer, as much as it pains me to admit, it was the trademark side-swept fringe and uncomfortably tight jeans of an emo teenager.   

For younger readers, it might be the current, inexplicable obsession with Labubus, which one day you’ll look back on with confused nostalgia.   

Whatever your vice, the good news is that some mortifyingly embarrassing photos and a small amount of wasted cash are probably all you have to worry about.   

Unfortunately, for major enterprises delivering customer experiences that matter, deciding to hitch their wagon to the wrong trend can have far more damaging results.  

Right now, there is no bigger CX trend than AI. Be it chatbots, agent-assist tools, or QA, enterprises are experimenting with AI wherever and however they can.  

Of course, this isn’t to say that AI should be ignored; the technology’s potential to drastically alter and enhance CX is undeniable. But despite the hype, not every AI deployment delivers the results businesses expect.  

For Joseph Kelly, Solutions Architect at Miratech, part of the issue is the ubiquitous nature of the tech, as he explains:  

Everything is AI. But is it just AI for AI’s sake?  

“Customers really need to hone in on the right strategy to start with. In the CCaaS space, in customer experience and employee experience, first getting strategy right will help cut through a lot of the clutter and get to the heart of how AI can really help.”   

Kelly’s point hits at a real challenge: how to separate genuine AI value from marketing spin. 

Vendors are quick to slap ‘AI-powered’ on everything, from natural language understanding to speech recognition; the trick is knowing what will actually move the needle.  

The Hype vs. Reality  

When organizations are hype-driven, they run the risk of deploying technology without a defined goal, which often results in overspending.   

Kelly notes that even established tools like NLU IVRs have been ‘AI-powered’ in marketing terms for years, without fundamentally improving the experience.  

“It’s about cutting through the marketing and sales speak on what is really AI, and what’s not,” he says.  

“Then, you look at where you want to start to make real change. Are you looking to enhance your customer experience with AI? Or your agent experience with AI? That’ll help guide you where you’re trying to get to.”  

Enterprises that clarify their objectives – whether it’s reducing call volumes, boosting first-contact resolution, or improving agent workflow – are far more likely to see tangible benefits from their AI deployments.   

Start Small, Solve Real Problems  

For organizations just starting with AI, Kelly believes the best approach is to take things step-by-step. 

For example, he highlights practical pilots like agent-assist, smarter routing, and call deflection as good examples of “seeing how the technology can help agents provide more informed and efficient answers to customer inquiries.”  

Small-scale projects reduce risk and can produce immediate wins against clear goals to build on. Routing customers correctly the first time reduces wait times; agent-assist tools speed up complex resolutions. These early wins build momentum and justify wider adoption.  

However, in order for these pilots to be successful, he emphasizes the need for “good, accurate data that the AI can access.”  

Once pilot projects show value, scaling AI requires alignment with broader business goals. Efficiency, personalization, and agent experience must stay front and center. But again, data is at the heart of it all.  

“Where am I going to house all of this information?” Kelly asks.  

“Does it have to be in the CCaaS vendor’s platform? Do I need a way to connect these things so a change in one system propagates to another?”  

Kelly also cautions that adding AI won’t fix a weak foundation.  

 If you don’t have a really stellar customer experience today, adding AI is not going to provide the benefits you’re probably thinking it can. 

Consolidating data, optimizing knowledge management, and improving processes must come first.  

Avoiding AI for AI’s Sake  

For Kelly, the major contributors to AI project failures are vanity projects, poor integration, and a lack of adoption by agents and customers.   

To combat this, change management is critical.  

Agents need confidence in new tools, and customers must feel automation improves – not hinders – the experience. Without this, even advanced AI can underperform.  

This is where Miratech can help. By grounding AI projects in business needs and guiding enterprises through data strategy, integration, and adoption, the company turns AI investments into tangible, measurable business outcomes. 

This all means AI doesn’t have to be just a buzzword or trend. When used with clear goals, it can truly transform customer experience – improving efficiency, personalization, and agent empowerment.  

The key is to have a purpose: and then start small, scale strategically, and let AI serve the business, not the other way around.  

You can learn more from Joseph Kelly on how to maximize your CCaaS migration by checking out this article.

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

A system that mishandles a refund or sends a tone-deaf promotion doesn’t just create a bad interaction; it creates headlines. When mistakes are amplified across social media, the damage spreads faster than any brand can control.

The recent record is full of warnings. Google saw more than $100 billion wiped off its value after a Bard demo went wrong. KFC Germany was forced to apologise worldwide after its automated campaign promoted chicken on the anniversary of Kristallnacht. Australia’s Commonwealth Bank had to abandon AI-related job cuts when public anger boiled over.

Consumers are not patient. Research shows that a third of them will walk away after a single poor experience. That leaves businesses scaling automation into a trust gap big enough to swallow years of brand equity.

The real question isn’t how much can be automated, but how much should be. Cross the wrong line, and efficiency gains quickly become AI brand risk and lasting AI reputational risk.

AI Reputational Risks: Beyond Cybersecurity and Privacy

Talk of AI risks usually circles around security or compliance. Important, yes. But those are risks companies already know how to manage. The one that keeps catching brands off guard is reputational fallout. When automation goes wrong, it breaks trust. Once customers stop trusting a brand, the damage spreads faster than any IT team can contain.

Damaged Consumer Trust & Brand Loyalty

Loyalty is fragile. A single poor interaction can be enough to push customers away. Unfortunately, customers are already wary of AI – most don’t trust bots to begin with. Any evidence that this mistrust is justified is enough to drive people away.

Just look at Google, when its Bard chatbot shared one single incorrect fact during a demo, the company lost over $100 billion in market value overnight.

CNET had to correct 41 out of 77 AI-generated finance articles after readers uncovered plagiarism and factual mistakes. Cursor AI, a coding tool, hallucinated answers so often that paying customers canceled in frustration.

For contact centers, the risk is magnified. Automation often greets the customer first, which means the brand’s reputation is in the bot’s hands. That’s why AI maturity, the ability to run automation on reliable, well-governed data, is now the hidden differentiator. Without it, brands risk handing their most valuable asset, customer trust, to systems that aren’t ready.

Public Backlash & Social Media Amplification

One mistake can live forever online. With social platforms acting as megaphones, AI reputational risks don’t stay contained. A misfired campaign or a bot that behaves badly quickly becomes a trending story, with hashtags turning into boycotts.

KFC Germany learned this the hard way. An automated system sent customers a push notification urging them to celebrate Kristallnacht, the anniversary of a Nazi pogrom, with fried chicken. The backlash was immediate and global.

DPD’s chatbot similarly went viral for all the wrong reasons, insulting users and even writing a poem about how bad the company’s service was.

In Australia, Commonwealth Bank’s attempt to link AI to large-scale job cuts collapsed under public pressure. The bank was forced into a public reversal after customers and employees slammed the move. These incidents highlight how AI reputational risk multiplies once social media takes over. A local error can turn into a global crisis in hours.

Regulatory & Legal Risks

AI reputational risk doesn’t stop with angry customers. Regulators are watching closely, and governments are setting stricter rules. The EU AI Act, GDPR, and California’s CCPA all put sharp limits on how data can be used. Slip up, and the penalties include both fines and headlines.

Air Canada’s chatbot misled a grieving passenger about bereavement fares. When the case reached a tribunal, the airline argued the bot was responsible for the error. The tribunal disagreed, ruling the company was on the hook.

New York City’s MyCity AI assistant told entrepreneurs it was legal to withhold tips from workers and discriminate against tenants, both false and illegal.

Hiring software at iTutor automatically rejected older applicants, a clear violation of employment law. The company settled with the U.S. Equal Employment Opportunity Commission for $365,000.

Biased Algorithms & Discrimination

Bias is one of the most dangerous AI risks, because it strikes at values as much as outcomes. An algorithm that skews hiring, pricing, or recommendations signals that a brand is unfair. That reputational damage spreads quickly.

Amazon’s recruiting AI famously downgraded résumés from women, effectively automating bias in hiring. The project was scrapped after public backlash. Watson Oncology, once pitched as a revolution in cancer care, recommended unsafe treatments in part because its training data reflected narrow patient populations.

For brands, bias creates headlines about discrimination, a label that is hard to shake. Regular bias audits and transparency in how algorithms make decisions are now non-negotiable if companies want to avoid AI brand risk.

Lost Market Share Due to Ethical Misalignment

Ethics and values now carry direct commercial weight. Research shows that 62% of consumers prefer to buy from brands they see as values-aligned. That makes ethical missteps in AI more than a PR problem – they are a revenue problem.

When AI choices seem to put profit ahead of fairness or care, customers don’t wait around,they switch to rivals. That’s when AI reputation risk bites hardest: brands lose not only goodwill but also market share. The only real safeguard is building governance that ties AI use back to the company’s core values and ethics.

How to Reduce AI Reputational Risk: Practical Steps

The fallout from automation mistakes shows up on balance sheets, in lost customers, and in the morale of the workforce asked to pick up the pieces. When AI fails in public, the costs extend far beyond fixing the system.

Zillow’s Zestimate model forced the company to take a $304 million write-down when its automated valuations collapsed the housing business it had built. Legal hallucinations from ChatGPT landed a New York lawyer with a $5,000 fine after fake case citations were submitted in court.

Failures don’t just frustrate customers. They also hit employees. When McDonald’s tested AI at its drive-thrus, the system repeatedly added phantom items, sometimes hundreds of nuggets, forcing staff to override orders and frustrating customers.

So, how do companies minimize reputational risk?

1. Put Data Integrity First

Automation is only as reliable as the data it runs on. Flawed, incomplete, or biased data feeds lead directly to reputational mistakes. SAP estimates poor data quality costs companies $3.1 trillion annually. Forbes highlights it as one of the biggest hidden costs behind AI ethics failures.

Without “agent-ready” data, AI agents are prone to hallucinations – generating wrong answers that erode trust. Strong governance, golden records, and freshness checks are crucial for brand protection.

2. Set Guardrails and Boundaries

Don’t trust machines to do everything. The most resilient companies define clear boundaries for what AI can and can’t handle.

• Low-risk, reversible tasks (simple FAQs, order tracking) are good candidates.
• High-risk or sensitive issues (legal advice, medical guidance, refunds tied to customer hardship) require a human in the loop.

The vendor race to launch AI agent studios (from NICE, Genesys, Five9, Salesforce, Microsoft) is pushing many brands to over-automate before they’re ready. Without boundaries, businesses risk turning efficiency gains into AI brand risk.

3. Audit for Bias and Measure Ethics

Unchecked bias is a reputational hazard. Regular reviews are needed to catch unfair patterns in hiring, pricing, or customer service. Leaders should track ethics alongside business results – monitoring fairness scores, transparency ratings, and compliance checks.

Companies that share what they find, or at least explain how they address bias, often gain more trust. These reviews can’t be a one-time fix; bias audits should sit on the calendar with the same weight as quarterly financial audits.

4. Communicate Transparently

Customers don’t like to feel deceived. Making it clear when they are interacting with automation, and why, can actually build trust. Often, brands with open communication about AI use are far less likely to suffer backlash when mistakes happen.

Data minimization is one effective step: only collecting the data needed, not every available detail. This cuts regulatory exposure and signals respect for customer privacy.

Being clear from the start prevents the impression that something is being deliberately concealed, and that suspicion can do more harm than the original mistake.

5. Keep Humans in the Loop

Not all decisions should be left to machines. Customers expect empathy when something serious goes wrong.

Air Canada’s chatbot failed because it provided misinformation without any human safety net. The tribunal ruling made clear: accountability rests with the company, not the bot.

Retailers often limit bots to handling small refunds automatically, but escalate larger or more emotional cases to a live agent. Keeping people in the loop stops automation from crossing into areas where mistakes can’t be reversed.

6. Monitor Continuously and Govern Proactively

AI systems change over time. A model that works well today can drift off course tomorrow if left unchecked. Strong oversight is essential. Many firms are adopting dashboards to track error rates, bias issues, and customer sentiment in real time. Kill switches and escalation paths provide circuit breakers if a system begins producing harmful results.

Regular “red team” testing, where systems are deliberately stressed to find weak spots, is fast becoming a best practice.

7. Manage the Workforce and Culture

A Duke Fuqua study shows another layer of AI reputational risk: inside the workplace. Employees who use AI are often seen as less competent, creating stigma that slows adoption. Managers who don’t use AI themselves are more likely to penalize candidates who admit to using it.

To avoid these pitfalls, companies need to:

• Train “automation champions” who can demonstrate AI’s value to peers.
• Reframe metrics, focusing on containment, accuracy, and customer trust rather than just speed.
• Create a safe environment for staff to disclose and discuss AI use.

This is about protecting the company’s reputation as an employer and building a culture that sees AI as augmentation, not replacement.

Protecting Against AI Reputational Risk While Scaling

AI isn’t going away. Companies will keep leaning on it to cut costs and speed up service. The risk comes when they hand over too much, too quickly.

Customers don’t forgive easily. Many walk away after a single poor exchange. Regulators are less forgiving still. That puts brand reputation on the line every time an automated system speaks for a company.

The answer isn’t to avoid automation. It’s to draw clear lines around what should be automated and what shouldn’t. Keep data clean. Be transparent. Let humans handle the moments that matter.

The communications platform disclosed its Q3 earnings on Monday, highlighting a strong growth from its customer experience portfolio. 

Zoom has also revealed its plans to grow product revenue further by enhancing its existing products with additional AI capabilities to drive AI-first customer experiences. 

During the earnings call, Zoom announced that the platform would be evolving from its traditional customer experience platform to an AI-focused one, aiming to drive productivity and relationships. 

Eric Yuan, CEO and Founder of Zoom, revealed that after its strong quarterly results, Zoom would be able to move forward with this vision. 

He said: “This performance reflects the durability of our business driven by the growing value we are delivering for customers as we evolve from a communications leader to an AI-first platform for work and customer experience. 

“Our vision is to be the AI-first work platform for human connection.” 

Zoom expects to accomplish this transformation by following its three strategic priorities: enhancing its existing products with AI, driving growth in AI products, and scaling AI-first customer experiences. 

Enhancing Existing Products 

During Zoomtopia 2025, the communications platform unveiled AI Companion 3.0, an updated version of AI Companion that utilizes agentic AI not only to respond, but also to act, advising on tasks such as meeting preparations, freeing up time, and call follow-ups. 

Zoom has embedded various AI capabilities and tools, including AI Companion, across its platform foundation, including: 

• Zoom Meetings: Zoom’s AI Companion, a proactive AI assistant tool, offers meeting summaries, follow-ups for next steps, and drives work forward. 
• Team Chat: Rising by 20% in active monthly users year over year, AI Companion supports the messaging product by providing customers with chat summaries, composition tools, and simplified search options for higher productivity. 
• Zoom Phone: This tool now offers Voice Intelligence for call transcription, summaries, noise cancellation, call routing, and analytics and insights for customer data collection, with over 10 million users now paying for Zoom Phone as of early Q3. 
• Zoom Contact Center: Working as Zoom’s cloud-based contact center solution, this platform has adopted AI tools such as Virtual Agent, an agentic AI chatbot offering complex tasks and responses for customers, and AI Expert Assist, allowing agents to utilize AI support in real-time with summaries and translations and offer possible agent responses during customer interactions. 

In fact, AI Companion usage has grown four times year-on-year, revealing that these AI features are seeing value from user activity, resulting in rapid adoption. 

By adding AI to these already-established products, customers are more likely to accept these capabilities once they’ve been integrated into the software. 

Driving Growth in AI Products 

By moving beyond its core communication tools and investing in greater agentic abilities, Zoom offers its customers further access to its AI tools to personalize them to their needs. 

This allows Zoom the chance to drive AI product revenue with product monetization, generating financial growth rather than just adding tools to products. 

In fact, 90% of Zoom’s top CX deals involve paid AI features to contribute to product revenue, offering both subscription and consumption models to suit the customer. 

This includes the development of AI tools such as Custom AI Companion, a paid version of the standard AI Companion model targeted towards enterprise-tier customers, allowing businesses to customize the tool to meet specific demands and policies. 

This also includes similar products such as Virtual Agent and AI Expert Assist, as well as Zoom’s recent acquisition of BrightHire. 

Scaling AI-First Customer Experiences 

Through utilizing tools such as Virtual Agent and AI Expert Assist, Zoom is using AI to transform interactions between customers and enterprises by expanding these products across the platform for automated workflows. 

These tools will involve automating routine requests and advise agents during workflow automation, voice, chat, and video calls for faster results. 

Zoom has also implemented a feature that allows enterprises to install either Zoom’s or a third-party’s AI tool, encouraging them to become familiar with AI usage while tailoring it to their needs. 

This strategy will also involve Zoom working with its largest customers to move AI agents into deployment; however, this may prove difficult. 

During the earnings call, Zoom noted that despite this upsurge in AI tool adoption, its net dollar expansion rate stayed at 98%, 2% lower than expected, likely suggesting that large customers had not been spending as much as hoped on Zoom’s products, with renewals on larger accounts proving difficult to resume. 

Zoom Key Earnings Results 

Zoom’s earnings results showed some strong areas of performance across enterprise and cashflow revenue results 

• Zoom’s total revenue reached $1.23BN, up 4.4% year-on-year 

• Its enterprise revenue grew 6.1%, totalling 60% of Zoom’s total revenue 

• Average monthly churn increased by 2.7%, similar to Q3 2024 

• Its operating cash flow increased to $629MN, up 30% year-on-year