Today’s consumers expect every interaction with a business to be convenient, impactful, and tailored to their needs. A customer data platform goes beyond the basics of a standard “Customer Relationship Management” tool to help businesses build comprehensive customer profiles they can use to personalize every interaction.

With these tools, companies can take a unique, data-driven approach to optimize every stage of the customer journey, increasing loyalty, retention, and conversions. Here’s why you should be investing in a CDP to turbocharge your customer experience strategy this year.

1.    More Comprehensive, Unified Customer Profiles

CDPs don’t just “store” data about your customers. These tools collect and combine information from countless different environments, unifying it into in-depth customer profiles (or single customer views). That’s crucial when customer journeys are becoming more complex and multifaceted.

Companies can’t rely on analyzing just one or two customer interactions and touchpoints to get a complete view of their target audience anymore. They must bridge the gaps between hundreds of “data footprints” left across numerous applications. CDP solutions wrangle all the data you need to truly understand your customers, their needs, pain points, and even areas of “friction” in their journey.

This leads to the creation of profiles that give you a more comprehensive view of customer behavior, thoughts, feelings, and actions. With a CDP, you can see everything that impacts your customers’ experience with your brand, from the point when they first encounter your company to the stage when they renew subscriptions or make additional purchases.

2.    Customer Data Platforms Break Down Data Silos

The responsibility to deliver great customer experiences doesn’t lie only with traditional contact center agents anymore. For companies to compete in an experience-driven landscape, they must unify every customer-facing employee with the same data and insights.

Everyone, from traditional agents to “informal contact center employees”, like sales teams and product developers, must be on the same page. Unfortunately, without customer data platforms, it’s easy for data silos to emerge. One team might have information on a customer’s purchasing history, while another knows about their previous issues and support requests.

When all of that data is fragmented, it’s difficult for teams to collaborate on delivering a consistent user experience. Plus, it’s much harder to understand the customer journey fully. CDPs bring teams and data together, enhancing collaboration and customer service decisions.

3.    Unlock Valuable Insights for Growth

The only way to deliver the highly personalized experiences 71% of customers say they expect from brands today, is to gather as much data as you can about your audience. Unfortunately, collecting data today is much more complicated than it seems. Google is terminating third-party cookies that marketers previously relied on to understand customer journeys.

Additionally, buyers are becoming more discerning about the type of information they share. Customer Data Platforms help companies to overcome data shortages. They ensure you can collect larger volumes of the data your customers willingly share through different channels and platforms.

This means you can unify more “first-party” data, to adhere to evolving privacy laws, and you can maintain more control over that data too (which improves the accuracy of your insights). On top of that, you can use the data you collect to develop more in-depth insights. For instance, many CDP solutions allow companies to use their historical data for “predictive analytics” tasks, allowing them to predict changes in behavior, buyer trends, and preferences in advance.

4.    Customer Data Platforms Improve CX

The biggest benefit of investing in customer data platforms is that they actively improve the customer experience. Your customers want to feel like you truly understand them and are ready to put their needs first and deliver a consistent experience across every channel.

CDP technology helps companies to enhance the customer experience in various ways. It ensures your team can:

• Personalize interactions: With access to in-depth customer profiles and insights into previous customer behaviors, it’s much easier for employees to personalize customer interactions. They can triage support requests based on customer concerns and preferences, suggest relevant products to buyers based on previous purchases, and even share relevant content with customers based on their interests.
• Enhance omnichannel interactions: When customer data lives in multiple places, it’s hard for sales, service, and marketing teams to deliver a consistent experience. Alternatively, if everyone on your team has access to the same data when they interact with customers on different channels or create content for different platforms, this leads to a higher level of consistency.
• Accelerate problem solving: When support agents can instantly access everything they need to know about a customer, they can solve problems much faster. CDP platforms can help accelerate resolution rates in the contact center. Some tools even have AI solutions to generate “custom” responses to customer concerns in seconds.

5.    Improve Data Privacy and Compliance

Companies need customer data to deliver personalized experiences. Unfortunately, the more data they collect (particularly across different channels), the more they expose themselves to security and compliance issues. It’s much harder to track sensitive data when distributed across multiple apps and tools.

Customer data platforms ensure you can access a secure, central location for housing data from various sources. This means you only have one environment to monitor for signs of threats and risks (rather than dozens). Plus, it ensures that whenever a customer asks for their information to be “removed” from a system, you can easily find and eliminate that data.

Most CDP platforms have robust data protection tools, such as end-to-end encryption options, multi-factor authentication, and protected data vaults. These can all help to protect you from breaches, damage to your reputation, and regulatory fines.

6.    Customer Data Platforms Can Boost Revenue

Customer Data Platforms allow companies to use the data they gather about their customers more effectively and thoughtfully. When you understand everything there is to know about your audience, it’s much easier to create the personalized experiences that McKinsey says can boost revenues by up to 15%. The more personalized your interactions with customers are, the more likely you will earn their loyalty and trust.

Plus, you can discover new ways to reduce churn and increase conversions by understanding the customer journey and the triggers that influence buying behavior. You’ll be able to identify which factors might cause a customer to abandon your business (like long wait times for service). Plus, you can use AI to detect opportunities to upsell and cross-sell customers at different times.

You can even leverage AI tools to proactively reach customers with offers, deals, and product suggestions relevant to their needs, further increasing conversion rates. This means customer data platforms give you new opportunities to increase profits, and retention.

7.    Increase Efficiency and Productivity

Finally, customer data platforms can significantly improve efficiency and productivity. CDPs can automatically import data from your contact center, CRM, survey tools, and more. This reduces the repetitive “data entry” processes your team needs to commit to.

Plus, when employees only have to use one platform to access all the information they need for marketing, sales, and customer service, they spend less time jumping between tools. This means staff members can complete all kinds of tasks much faster and more efficiently.

Thanks to AI, CDPs will become more advanced, giving employees even more ways to save time and energy. For instance, tools already exist that can instantly transcribe and summarize conversations in the contact center, making it easier to update profiles in real-time.

Discover the Benefits of Customer Data Platforms

Investing in customer data platforms isn’t just a good way of making managing customer data easier for your company. These platforms give you the tools to design stronger customer journeys and build deeper customer relationships.

Through in-depth insights and unified views of your customer’s journey, CDPs can empower you to personalize, optimize, and enhance every interaction with a customer. This leads to greater customer lifetime value, higher revenue, increased retention, and better workplace efficiency.

This comes after its annual report revealed that AI implementation had increased by 282% since 2024. 

These tools enable enterprises to deploy AI agents without worrying about the reliability and safety of their performance within a system. 

Salesforce’s observability tools provide AI agents with the capabilities to analyze performance, optimize interactions, and monetize stability. 

Agent Analytics

This capability allows enterprises to view how well an AI agent is operating through monitoring its movements, how it’s improving/declining, and where these pain points are coming from. 

This can be turned into performance data, trends, and insights to understand how efficiently these agents are performing and take actionable steps to improve their usage. 

This can also be done across all implemented agents, allowing enterprises to view their agents’ overall effectiveness on customer interaction and support their continuous improvement. 

Agent Optimization

As a key observable capability, Optimization offers customer enterprises full transparency with each agent interaction. 

Customers can uncover how agents make decisions and what led them to make those choices, highlighting performance gaps and session flows to diagnose any issues and deduce the steps needed to improve its performance. 

This can include prompt, rule, or data source adjustments to solve misinterpreted information, inconsistent results or agent hesitation. 

Salesforce provides access to end-to-end visibility for customers to view each agent’s response and action, even with larger, complicated action chains. 

For less varied issues, similar requests can be accumulated to uncover larger problems in patterns or trends. 

Customers can also identify an agent’s configuration issues to pinpoint how an agent’s behaviour is affecting its operation and uncover which areas need to be retrained or personalized further for improved performance. 

Agent Health Monitoring 

This capability can monitor an AI agent’s reliability and safety level to ensure that it is running as expected. 

It provides almost real-time visibility and alerts when the agent is performing unpredictably, notifying the company before any significant damage takes hold. 

It measures an agent’s ability to handle requests, time taken to respond, and tracks incidents such as failures, breaks in activity, or invalid responses. 

By leveraging the capability, teams can speedily detect and resolve issues to minimize agent downtime and continue productivity. 

This tool is formed by two of Agentforce’s components, acting as the foundation for the observability tool by supplying the data and governance structure needed to monitor agents: 

• Session Tracing Data Model: By logging every agent interaction, the data model can store all its data in Data 360 and provide the observability tool the means to generate reliable analytics, error identifiers, and support optimization for unified visibility.
• MuleSoft Agent Fabric: This enables enterprises to control, register, and review agents to justify how they function and interact. 

AI Implementation Report 

In a report published in November, Salesforce announced that AI implementations had increased to 282% since last year. 

This data reveals that companies are now at a far better position to deploy pilot projects at scale rather than risk the threat of experimentation. 

Despite this, data governance, security, and trust remain high priorities, requiring risk management across workflows. 

This means that more companies are going to require higher visibility and control across large-scale AI deployments, which is where Salesforce’s observability tools come in. 

By supporting enterprises with agent interactions, Salesforce’s observability tools can decrease operational risk by allowing teams to keep up to date with agent visibility and analytics to keep agent deployments stable. 

Reddit, a customer of Salesforce, highlighted how Salesforce has allowed the customer enterprise to scale agents securely through consistent visibility. 

John Thompson, VP of Sales Strategy and Operations at Reddit, stated: “By observing every Agentforce interaction, we can understand exactly how our AI navigates advertisers through even the most complex tools.  

“This insight helps us understand not just whether issues are resolved, but how decisions are made along the way. 

“Observability gives us the confidence to scale these agents, continuously monitor performance, and make improvements as we learn from their interactions.”

Will Salesforce’s Informatica Acquisition Make Agentforce Unstoppable?

Salesforce has announced the acquisition of Informatica.

First reported back in May, the purchase has now officially been confirmed for approximately $8BN.

The deal will see Salesforce leverage Informatica’s AI-powered cloud data management capabilities to improve its agentic AI offerings – most notably, the Agentforce platform.

Alongside the data catalog, Salesforce will also gain access to Informatica’s integration, governance, quality and privacy, metadata management, and Master Data Management (MDM) services.

The end goal is to use these capabilities to build a unified data foundation for agentic AI, enabling safe, responsible, and scalable AI agents across the enterprise.

Indeed, in discussing the news, Salesforce Chair and CEO Marc Benioff described data and context as the “true fuel of Agentforce.”

“When companies get their data right, they get their AI right, and Agentforce becomes unstoppable.”

In terms of the specifics, Salesforce also detailed how Informatica’s capabilities will sharpen its Data 360 feature… (Read more).

Cloudflare Outage Disrupts Major Platforms, Payments, and Black Friday Plans

It’s becoming a familiar story: A technical glitch at Cloudflare, one of the biggest internet infrastructure providers, knocked a number of websites and services offline for a few hours on November 18, disrupting customer access and merchant payments.

X (formerly Twitter), ChatGPT, Claude, Perplexity, Spotify and payment giant Square were among those caught up in the fallout.

The trouble began just before 11:48 GMT, when Cloudflare posted that it was dealing with an “internal service degradation” causing intermittent outages across its service network. Users saw error pages, stalled logins, broken APIs, and sites claiming connections were blocked. There were a few conflicting signals about the restoration progress, as at one stage the company reported that services were beginning to recover, but then around 15 minutes later reverted to “continuing to investigate this issue.”

By 13:04 GMT, Cloudflare admitted that one of its fixes involved disabling WARP access in London entirely, temporarily cutting off users from its WARP performance-boosting and VPN service that helps secure and accelerate internet connections:

“During our attempts to remediate, we have disabled WARP access in London. Users in London trying to access the Internet via WARP will see a failure to connect.”

Cloudflare announced a fix five minutes later, but continued to receive “reports of intermittent errors” until close to 17:00 GMT… (Read more).

Five9 Targets CX Inefficiencies with New Genius AI Upgrades

Five9 has introduced a fresh wave of Genius AI updates designed to push the company’s “Agentic CX” vision further into the contact center core.

Announced at the company’s CX Summit in Nashville, the new capabilities span routing, quality management, analytics, and digital engagement, tying them more closely together to help organizations extract greater value from AI at scale.

As many enterprises attempt to take AI from pilot projects into day-to-day operations, fragmentation continues to slow progress.

Disconnected data, inconsistent reporting, and standalone AI experiments often make it difficult to achieve the continuous improvement leaders expect.

Five9’s latest releases aim to combat these challenges by treating AI not as an add-on but as the connective layer running across the environment.

Five9 Chief Product Officer Ajay Awatramani framed the shift as a more fundamental rethinking of how AI should function inside the contact center:

“Our Agentic CX vision is about creating systems that don’t just respond but also help teams better understand and anticipate customer needs.”

So, let’s take a closer look at Five9’s newest features… (Read more).

UJET Acquires Spiral to Address Customer Data Analysis Roadblocks

UJET has announced its acquisition of Spiral to bolster its AI capabilities.

The AI startup will allow UJET to continue its AI roadmap for enhanced customer service solutions.

This partnership will also address customer data analysis issues for UJET’s enterprise customers.

This acquisition is set to further UJET’s AI roadmap vision by bolstering the company’s AI capabilities and addressing customer experience concerns.

By highlighting these issues of visibility between customer and leader, organizations will be able to improve their customer issues before they reach escalation.

In fact, UJET has reported that organizations that are unaware of these individual customer problems are losing approximately $5MN-$30MN in customer churn revenue.

This can be linked to ignored or forgotten negative customer experience complaints, with organizations reportedly gathering only five percent of reported customer issues.

According to UJET CEO, Vasili Triant, customer churn remains a blind spot for many enterprises, arguing that customer interaction analysis is not done effectively… (Read more).

Spoiler: just telling them to “calm down” doesn’t work. That phrase (while innocuous) often triggers the “reactance effect” – an instinctual resistance people feel when their sense of control is threatened or their concerns seem to be dismissed.

Over time, calls escalate more often. Escalations mean longer resolutions and lower satisfaction scores. Anxiety spills over into the workforce because agents feel cornered, and emotion hijacks logic.

So, what’s the right way to speak to a customer who’s far from happy?

What to Say to Angry Customers: 7 Defusing Phrases

So, “calm down” won’t work – but what will? Basically, anything that makes them feel heard, respected, and understood. Here are some practical phrases to embed into the de-escalation techniques customer service teams might already use:

1.      “That sounds really frustrating.”

Opening with recognition helps. Callers feel acknowledged. That simple nod shifts the tone. Callers don’t feel like their emotions are being belittled or ignored. The agent is actively validating them. Customers can start to think more logically, because they’ve made their point emotionally.

Alternative (but similar phrases include):

• I’d feel the same in your position
• I can see your point there
• I understand why you’re upset about this

2.      “We want to fix this just as much as you do.”

Sometimes, knowing what to say to angry customers means reinforcing a sense of collaboration – like this phrase does. It shifts blame and drama toward alignment. It also shows that the customer service agent isn’t trying to pass the buck to someone else, or imply it’s not their responsibility to deal with the issue.

Other ways to say this include:

• Thanks for letting us know about that, we definitely need to fix it
• We’re sorry about that, let’s try and get this issue solved

3.      “Here’s what we know, what we’ve done, and what we can do…”

Agitated customers want clarity, about what happened, why it happened, and what’s being done to resolve it. The best customer service professionals explain in three steps:

• What we know: We see your systems went down at 3 p.m. due to a technical fault with our server, and you’ve been experiencing issues since.
• What we’ve done: We’ve already got a technician working on the problem, and the server is now functional, but we’re still troubleshooting issues.
• What we will do: We’ll make a note on your file and run some additional checks. Our team will inform you immediately when the issue is resolved.

4.      “I understand, other customers have had a similar problem, here’s what they found worked…”

This “Feel, Felt, Found” approach builds empathy and a sense of community. It shows customers that they’re not alone in facing an issue, and helps them to see that the company they’re contacting has already helped others resolve the problem.

An example:

• Feel: I see how you feel. I hate it when I can’t log into my account.
• Felt: Some of our other customers have reported login issues, too.
• Found: They found that restarting the app helped solve the problem. Have you tried that?

5.      “Here’s what I can do…”

Confidence is key in knowing what to say to angry customers. Saying “the best I can do is get a technician to call you back,” sounds hesitant and invites escalation. Customer service agents should say exactly what they can do, and how it’s going to help.

For example “What I can do for you right now is pass this issue over to our technical team, they’ll be in touch within the next hour with an update. In the meantime, I’ll add a credit to your account for the inconvenience.”

6.      “Are you able to…”

Often, a customer will need to do something on their side for a problem to be fixed, like sharing their serial number, account details, or refreshing an application. Rather than using direct statements like “I need you to give me your…” it’s helpful to switch to polite questions like:

• Are you able to grab your serial number for me?
• Could you just switch your computer off and on again for a moment?
• Would you be able to give me a moment to check on that?

7.      “Could I try…”

When customers feel powerless, giving them options restores a sense of control. These kinds of phrases are particularly helpful when “troubleshooting” an issue that doesn’t have a clear solution:

• Could I try speaking to my colleague, they may have another idea?
• I have another option we could try; would you be open to that?
• I could do [action] but it might take a moment, would you mind waiting a moment, or would you like a call back?

Even if the options are limited, demonstrating effort helps customers calm down and feel supported.

Best Practices for De-escalating Angry Customers

Knowing what to say to angry customers is just the first step. Customer support agents also need to know what to do to help de-escalate the issue. Often that means using active listening techniques, empathy, and emotional control.

Stay Calm and Practice Active Listening

Remaining composed sets the tone. It’s easy for agents to get upset when they’re being yelled at, but responding defensively usually makes the issue worse. Train employees to take a couple of deep breaths if they’re feeling attacked, and tell them to focus on active listening. They should let callers speak uninterrupted, using occasional cues like “I see” and “that makes sense.”

Track Tone, Then Pivot to Solutions

Agents should be experts at tuning into tone, intent, and sentiment, even if they occasionally need help from analytical tools. Responding to tone doesn’t mean showing anger back to an angry customer, it means demonstrating empathy, then guiding the conversation towards resolution.

Simple framing tweaks can help:

• Use positive, reassuring language: “Absolutely” instead of “No problem.”
• Avoid negative statements like “That’s against policy.” Instead, say: “What I can do is…”

Use Templates, but Personalize Them

Templates speed up responses, but they only work when they’re used thoughtfully. Customers still want personalized service, even when they’re making a complaint or reporting an issue.

Agents should tailor templates with names, issue specifics, and a human touch. Give them access to useful insights from CRM tools to help tailor each interaction.

Think Critically

Agents should understand that an angry customer isn’t necessarily angry at “them” – they’re frustrated by the situation. Their focus shouldn’t be on protecting themselves, but figuring out what the customer actually needs.

Do they want a refund, or do they just want someone to validate their experience? A good way to find out a customer’s motivation is to restate what they said and what they might mean by it: “I understand you’ve been unable to use your subscription, would a refund help with this?”

Leverage Analytics and Voice of the Customer (VoC) Data

Not sure which frustrated issues crop up most? VoC analytics pull in feedback from surveys, chat, calls, social, and help center tickets.

AI-powered sentiment analysis flags negative experiences automatically. That means agents and supervisors can proactively address hot spots before they escalate. Using data is a good way to improve self-help resources and identify recurring problems, so they’re less likely to happen again in future. A more proactive approach means less time teaching agents what to say to angry customers.

Turning Angry Customers Into Long-Term Advocates

Customers won’t magically calm down. What works is a conversation built on validation and structure, packaged in calm, purposeful phrases and backed by smart tools.

Train teams on what to say to angry customers, but also show them how to manage their own emotions, stay calm, listen actively, and personalize the experience. At the same time, take advantage of the tools that can help businesses stay one step ahead of recurring problems.

Every company will deal with angry customers at some point. The key is knowing how to de-escalate the situation and turn an irate caller into a long-term advocate.

At the Call and Contact Centre Expo 2025, the primary focus of many sessions was centered around AI, trends, and how to join the never-ending CX competition.

Fola Olafare, Senior Contact Center Delivery Manager at Transport for London, highlighted TFL’s approach to differentiate from the mainstream AI-focused contact space toward a more customer-centric one.

The event, ‘Mind The Gap: How TFL’s CX Strategy is Transforming Customer Experience’, emphasized how organizations can still function in the modern day meaningfully without turning straight to AI for results.

Olafare highlighted what he sees as the current CX trend issue:

“You’ll just see AI laced everywhere – it’s getting to the point of bringing AI for the sake of bringing AI.”

Less AI, More Human

However, with customer loyalty being a valuable currency in the CX space, the customer-centric trend has allowed TFL to thrive, having simplified their customer service approach drastically in the last decade.

By focusing less on AI integration, customer service can continue the traditional approach to drive meaningful human experiences to each individual customer.

He highlighted TFL’s current “Core ethos about listening to our customers and demonstrating that we care.”

Previously, TFL saw frequent barriers between company and customer.

“From a customer point of view it wasn’t great, we had 15 different phone numbers and 35 different emails.

“Maybe 1o, 12 years ago we didn’t have that influence.”

Today, customer’s transportation experiences are leaps and bounds ahead.

He said, “From a contact center point of view, we’ve come a long way representationally,”

“We have a really nuanced support for our visitors – allowing us to provide a dedicated care service for all our customers,

“Telephone is a great starting ground for us – now we have one phone number for customers.”

Customer’s can go to customer support to receive advice on travel planning, how to handle complaints, and discuss transportation issues within their area.

This has also included showing support for customers after negative or traumatizing transportation experiences.

“The aftercare support to the victims of life changing incidents that happen on our networks – providing them that support for a good duration.”

This approach has shown in its results, with 2024 seeing 30 million daily journeys, 9,000 buses, and over 1,400 e bikes around the city, whilst also managing 25% of London’s roads, improving overall transportation for residents and visitors.

In fact, this has prompted the London mayor to set a goal to see more people using public transport, hoping that “by 2041, 80% of customer journeys are being taken by customer transport.”

Automation is Still Key

Despite the focus on human experiences, automation continues to play a role in TFL’s customer experience, utilizing that tool through analyzing real customer experiences to provide its customer’s with a system they actually need.

He said, “We do some much from an automation point of view – we have a lot of strong algorithms working in the background to work out different anomalies.

“We’ve now ensured that intention is available for customers from a journey point of view by enhancing them with our alternative journey messages.” 

And whilst TFL does implement automation in the organization, it recognizes that setbacks will occur with this tool.

“For every automated journey we have, we acknowledge it may not work as well.”

However, in return, “You can have really great conversations for resolving that issue in a really caring way.”

Despite this approach, TFL are conscious about keeping up with customer demands and circumstances.

“We also want to make sure we’re adapting to customer circumstances, to have a really good strong set of customer values – we want to be open and transparent about what we’re doing.

“From a TFL point of view, what excited me is there is so much opportunity to make a lot of people’s lives better in an integrated way.”

Speakers pointed to the widening gap between aspiration and execution, especially as AI exerts increasing pressure to demonstrate innovation.

As Natalie Higgins, Solutions Sales Executive at NiCE put it:

“There’s an overload of data and a very short amount of time… frontline team managers, supervisors, executives are spending hours trying to mix all of this information together, but by the time they’ve done that analysis the opportunity has passed.”

The manual approach of the past no longer matches the pace of the environment. “The speed to deliver has changed,” Higgins said, “Using the manual, time consuming process does not make it easy for you to drive continuous optimization.”

Too Much Data, Too Little Clarity

Speakers acknowledged a repeating cycle. Enterprises face the same issues year after year but struggle to move forward. As one speaker put it, “Even though we’ve had the same challenges and outcomes for the last 10 plus years, organizations are stuck,”

“And why is that? Because there’s too much data that we don’t make enough sense of.”

Describing the challenge as “super hard”, Higgins noted that “demands keep increasing and the challenges are rising… the speed to deliver has changed, and that’s what makes it difficult.”

In the rush to respond quickly, teams confront a wall of questions: “Where is my data? What data do I have? And how on earth do I go about it for any particular solution?” Higgins said.

The issue is not the lack of data but the lack of time, capability, and coherence to make it usable.

“There is a massive disconnect between the strategic direction that a business wants a company to go in and actually, how do you get to that insight?” said Andrew Tucker, Solution Engineer at NiCE.

“You know that the data exists for you to be able to make the decisions and roadmap these initiatives.”

Data silos make it difficult to move with agility. “There is a massive time lag between ‘I want to achieve a certain thing,’ and ‘here is a roadmap for how you achieve that,’” Tucker added.

To make matters worse, “we’re layering on an extra level of complexity… caused by every single vendor out there… pushing the AI narrative.”

Data Connection, Not Just Collection

When it comes to data management, teams often lack the ability to translate it into meaningful action. “How do we make a data-driven decision… to be able to influence that?” Tucker asked, describing organizations that spend “weeks and months worth of work… to say what is happening, why it’s happening, and how do I forecast outcomes?”

Building bigger datasets won’t solve the problem. What matters is linking information in a way that reveals what’s really happening.

“This isn’t about data collection. This is about data connection,” Higgins said.

There is much debate in the industry about the level of data organization needed to be able to roll out AI-based applications.

Kim Baker, Head of Operational Support Services at UK Housing Association L&Q warned: “There’s no point launching AI if your data is not right in the first place, because where’s it going to look to answer these questions?”

When enterprises can get a handle on their data, they can draw out actionable insights, and AI can be a tool to help manage and extract data, making it AI-ready.

“Data is becoming more readily available. It’s more insightful. We can make better data driven decisions as organizations,” said Chris Rainsforth, Director of Learning and Innovation at The Forum.

“Some people haven’t caught up with that data yet and are still sitting back in that old way of thinking. That change will continue, and it’ll continue to drive us.

Marco Ndrecaj, Director of Customer Experience Management at Shared Services Connected, described large-scale operations facing overwhelming customer segments and datasets: “You’re like, oh my god, what am I looking at first? And I think that’s where the strength of AI comes to play.”

“Let AI take care of the data, take unstructured data, the structured data, and then turn it into insight. Once that’s done… let the human with empathy and with care to take it from there.”

As the conversations made clear, it’s imperative that enterprises turn data “overload into clarity,” and strike the right balance between automation and human judgement.

First reported back in May, the purchase has now officially been confirmed for approximately $8BN.

The deal will see Salesforce leverage Informatica’s AI-powered cloud data management capabilities to improve its agentic AI offerings – most notably, the Agentforce platform.

Alongside the data catalog, Salesforce will also gain access to Informatica’s integration, governance, quality and privacy, metadata management, and Master Data Management (MDM) services.

The end goal is to use these capabilities to build a unified data foundation for agentic AI, enabling safe, responsible, and scalable AI agents across the enterprise.

Indeed, in discussing the news, Salesforce Chair and CEO Marc Benioff described data and context as the “true fuel of Agentforce.”

“And without clean, connected, trusted data there is no intelligence – only hallucination. Informatica is the trusted platform that turns fragmented enterprise data into context, so every agent can reason, act, and deliver outcomes with precision.

“When companies get their data right, they get their AI right, and Agentforce becomes unstoppable.”

In terms of the specifics, Salesforce also detailed how Informatica’s capabilities will sharpen its Data 360 feature by delivering cleaner, more reliable data across the business, while its integration strengths pair with MuleSoft to create a more complete, enterprise-wide connectivity layer.

That stronger foundation directly benefits Agentforce, giving autonomous agents the trusted data they need to act with confidence.

Tableau also gains from the deal, with clearer, richer context feeding into analytics and lifting the quality of insights across the board.

Making Agentforce “Unstoppable”

Benioff’s remarks around the Informatica deal helping to make Agentforce “unstoppable,” might sound like the usual vendor bravado, but they do speak to a genuine Agentforce concern: so far, the platform has been fairly stoppable.

While Agentforce has turned plenty of heads, its early momentum hasn’t quite matched the noise surrounding it.

Salesforce’s own fanfare helped create expectations that were always going to be difficult to meet, and many customers arrived at pilot projects only to realize they weren’t fully prepared for what the technology demands.

Some of that stems from implementation readiness. Consultancies and end users alike have found themselves wrestling with half-built agents, legacy processes, and a level of technical debt that makes it hard to get consistent outcomes.

The common thread is data. Agentforce can only be as sharp as the information it draws on, and too many organizations are discovering that their underlying data simply isn’t mature enough to support reliable agentic behavior.

Salesforce has offered guidance on best practices, but many customers underestimated just how much groundwork is required before Agentforce can deliver at scale.

When the data feeding the system is inconsistent or siloed, results can feel underwhelming.

Add to that a pricing model that initially confused buyers – particularly around how “conversations” were counted – and it’s clear why enthusiasm occasionally softened once projects moved from demos to delivery.

Salesforce has already taken steps to address some of those sticking points, including replacing the old per-conversation model with the more transparent Flex Credits approach.

Yet the larger barrier remains the data plumbing underneath the platform. And this is where the Informatica acquisition becomes far more than a simple expansion of Salesforce’s portfolio.

Informatica gives Salesforce a proven set of tools for tackling the data preparation issues that have slowed Agentforce deployments.

Its integration fabric, data governance controls, and MDM capabilities reach well beyond the front office, allowing organizations to clean, connect, and align information across the entire enterprise – not just whatever happens to sit inside Salesforce already.

That wider foundation is exactly what agentic systems need if they’re going to reason properly and take reliable action.

By bringing Informatica into the fold, Salesforce can help customers close the readiness gap that has held Agentforce back.

Cleaner inputs mean stronger outputs. Stronger outputs mean more trust in AI-driven automation. And with that trust, the platform finally has the conditions it needs to gain the momentum the early hype promised.

Across the customer service and experience space, the technology is being used to answer questions, guide purchases, and increasingly handle conversations once reserved for human agents.

Yet a familiar problem keeps surfacing: these systems still behave unpredictably.

They can be fluent and insightful one moment, then miss something basic the next. And when that happens, the customer experience suffers.

Salesforce’s latest releases take direct aim at this reliability gap.

Across service, commerce, and AI research, the company is seemingly placing accuracy, consistency, and predictable behavior at the center of its AI system design.

And the CRM powerhouse isn’t tiptoeing around the issue.

This is evident with the launch of eVerse – the vendor’s new simulation environment built by its AI Research department to uncover failures before they ever reach a live customer.

In discussing the motivation behind eVerse, Salesforce pointed to what it refers to as “Jagged Intelligence”, instances where AI excels at complex tasks but struggles with simple ones.

The vendor believes that this phenomenon “creates unacceptable business risk” and that eVerse “directly addresses” the issue.

A Reliability Problem Hiding in Plain Sight

Through its Agentforce platform, vendors like Salesforce have been at the forefront of expanding the capabilities and usage of AI agents in the customer service and experience space.

These tools are moving beyond basic interactions to acting autonomously in key moments of the customer journey – highlighting just how prevalent AI now is within the sector.

As Kishan Chetan, EVP and GM of Salesforce Service Cloud, puts it:

“AI agents go beyond predictions and automation; they can understand context, take action, make decisions, and adapt in real time.”

Indeed, Salesforce’s 2025 State of Service report claims that 30% of service cases are currently handled by AI, with this percentage expected to rise to 50% by 2027.

Given the sometimes volatile nature of AI, this shift has the potential to drastically increase instances of unpredictability.

In an environment where one incorrect decision or confused response can alienate a customer and damage a brand’s reputation, there is plenty of risk accompanying the reward of further automation.

Salesforce appears to be attempting to combat the unreliability of AI so that the technology can become safer while its popularity continues to soar.

Breaking AI Before Customers Do

In a nutshell, eVerse is designed to break AI before customers do.

The training environment is unforgiving. It throws noise, accents, unpredictable behavior, and messy real-world scenarios at AI models to see how they cope.

Salesforce AI COO Madhav Thattai explained how Agentforce Voice was one of the eVerse guineapigs:

“With eVerse, we were able to explore many nuances of human conversation before Agentforce Voice reached production.”

“This type of rigor is what turns breakthrough research into scalable products and dependable customer experiences.

“It’s how we’re expanding that same level of responsiveness and consistency across the full observability stack to solve our customers’ most complex needs.”

UCSF Health is one of the companies currently involved in the pilot phase of eVerse.

Collaborating with clinical experts, the Salesforce team is training AI agents to handle healthcare billing, where precision is essential.

Early tests showed agents reaching up to 88% coverage across routine and complex tasks, with reinforcement learning and clinical oversight shaping the results.

Sara Murray, MD, MAS, VP & Chief Health AI Officer at UCSF Health, summarized the goal:

“When used responsibly, we believe AI can help our teams simplify one of the most complex parts of healthcare.”

Salesforce believes that the continuous trial-and-error training loop “transforms agents from generic language models into enterprise-specialized systems ready for production deployment.”

Retail Has the Same Problem – With a Commercial Twist

Salesforce’s reliability push stretches into commerce too, where a new set of challenges is emerging.

Traffic from consumer AI channels, including upcoming integrations with ChatGPT, is rising sharply. Salesforce forecasts that during Cyber Week, intelligent agents will influence 22% of global orders.

This represents another potential risk if the AI representing your brand gives inconsistent answers.

To combat this, Salesforce has announced fresh capabilities for Agentforce Commerce.

The new features allow retailers to syndicate product data into AI channels with tight control over accuracy, pricing, and brand voice.

Self-described as the “first platform designed to give retailers full control over the entire agentic shopping journey,” Agentforce Commerce lets brands push product catalogues into AI platforms like ChatGPT while delivering personalised, agent-led journeys on their own sites to drive loyalty and higher lifetime value.

One organization currently using the tool is Pandora. The company’s Chief Digital & Technology Officer, David Walmsley, discussed the importance of being able to deliver dependable AI, stating:

“This is more than just commerce; it’s about using trusted AI and unified data to guide every customer, making them feel understood, supported, and valued.”

It is clear from Walmsley’s comments that when AI is steering purchasing decisions, that trust has the potential to become a direct revenue lever.

Fixing the Problem Long Before It Shows Up

Viewed together, Salesforce’s recent updates follow a clear logic: push reliability upstream.

The vendor is building it into the training data, the simulation environment, the risk checks, the decision-making logic, and the human oversight loops.

The logic appears to be: if reliability becomes an afterthought, AI remains a gamble; if it becomes structural, the technology scales far faster.

Salesforce’s State of Service report hints at the same dynamic. AI is already reshaping frontline roles. But adoption still hinges on trust. Service teams want tools they can rely on, not systems that feel experimental.

Security leaders echo this. While 51% report delayed rollouts due to risk concerns, Salesforce’s State of IT: Security report finds unanimous agreement that AI agents can strengthen security posture – once reliability and safety controls are in place.

In other words, the enthusiasm is there, but the confidence is catching up.

Maybe reliability will prove to be the missing piece.

A Line in the Sand for AI in CX

As AI agents step further into direct customer interactions, the industry is hitting a turning point.

These systems are no longer side tools; they’re the first point of contact in many journeys. That places reliability above convenience, above cost savings, and above speed.

Silvio Savarese, Chief AI Scientist at Salesforce, linked the research to real-world outcomes:

“Our partnership with UCSF Health demonstrates how applied science translates directly to customer value, proving that when you train agents in environments that mirror real-world complexity, they perform reliably when it matters most.”

That’s the crux of Salesforce’s message. In the agentic era, reliability must be foundational; not an afterthought.

Teams that recognize this early – and harden their AI accordingly – will be the ones who are best placed to fully maximize the potential of the technology.

The division brings together the company’s Partners, CX Customer Success & Services, Marketing, Global Business Operations, IT, and Corporate Security departments under a single organizational umbrella.

According to NiCE, the new structure is designed to enhance its operations while accelerating its AI-first approach to customer experience. By integrating technology, process, and customer-facing teams, NiCE aims to create a more seamless and responsive experience for its clients.

“NiCE continues to advance its mission to redefine customer experience through AI-orchestrated business outcomes, uniting human empathy and intelligent automation at scale,” the company said in announcing the new structure.

Arun Chandra is joining NiCE in the newly created role of Chief Operating Officer to lead the Global Customer Operations division, reporting to CEO Scott Russell. Chandra brings leadership experience from Disney, Meta, and Hewlett Packard Enterprise, where he built a track record of driving operational innovation and customer-centric growth, NiCE said. At Disney, Chandra modernized the $24 billion streaming business’s customer experience for over 195 million subscribers, using AI and automation to enhance engagement and reduce friction.

“I have a deep understanding of the significant impact NiCE delivers for leading organizations, and I’m excited to build on that foundation, scaling global operations, deepening partnerships, and ensuring every customer interaction reflects the innovation and integrity that define NiCE.”

NiCE’s new customer-focused division fits within a broader trend in which IT, security, and business operations are increasingly intertwined with customer service functions. Companies are investing in technology-driven customer experience strategies that rely on data, automation, and AI, while still emphasizing the human element in service interactions.

Supporting Integrated Customer Operations

IT and security teams are increasingly tasked with supporting customer service operations, making sure that AI tools and a patchwork of third-party apps and integrations run securely and reliably.

That requires a more integrated organizational approach, where teams that were previously siloed have to work together more closely. In turn, those teams need unified platforms and processes that allow data and insights to move across departments more freely, enabling proactive issue resolution and faster decision-making.

While few CX software vendors have explicitly launched a global customer operations division in the same way NiCE has, several are taking similar steps to integrate their customer success, service, IT, and operational functions.

For example, ServiceNow has been aligning its customer service management and front-office operations with its IT workflows, creating a more unified, AI-driven platform for service delivery and operational efficiency. Cisco recently merged its CCaaS and CPaaS teams into a single Webex Customer Experience organization, bringing together engineering, operations, and customer-facing teams with a view to accelerating innovation and streamlining AI-powered service. Meanwhile, SAP’s CX suite consolidates marketing, commerce, sales, service, and customer data with back-end systems, allowing cross-functional teams to collaborate more seamlessly.

By bringing together CX, IT, operations, and security under a single division, NiCE is positioning itself to make use of automation and AI more effectively across all touchpoints, while still preserving the human empathy central to customer interactions. The new structure should make it easier for the company to turn data and intelligence into actionable insights that drive operational efficiency and more personalized, responsive customer experiences.

Such integrated approaches could become a standard model for enterprise customer operations, as organizations increasingly blend technology, data, and human expertise to meet the demands of modern customers.

The update puts unification at the forefront. Addressing the fact that many enterprise teams have to toggle between standalone tools, Zoho aims to deliver something closer to a true operating system.

A Connected Workspace Designed for Frontline Service Teams

The most immediate impact comes from the redesigned interface. Zoho One’s new “Spaces” structure organizes tools by context, whether for personal productivity, company-wide collaboration, or functional areas like marketing, sales, or finance. The value proposition here is to reduce friction.

The Action Panel and unified Approvals take that concept further, pulling tasks, sign-offs, and action items from across the stack into a single view. Customer-facing roles, especially those that handle service escalations and sales cycles, often have to pull data from multiple apps. This approach aims to reverse that dynamic by pushing relevant information to the user instead of the other way around.

Dashboards and the new Boards framework also help consolidate operational and customer-related data. Because Boards can be assembled from Zoho’s own analytics or third-party dashboards brought in through single sign-on, customer support and sales teams can combine metrics, from ticket backlog to deal progression to customer sentiment, within one context.

Unified Integrations for Consistent CX Across Systems

Customer-facing operations typically rely on diverse systems covering ticketing, CRM, commerce platforms, payment portals, and engagement tools. Zoho has addressed this with a stronger emphasis on native integrations and an expanded model for third-party connectivity.

The unified integration panel gives administrators full visibility into Zoho-to-Zoho and Zoho-to-third-party connections, including recommendations for additional integrations.

Raju Vegesna, Chief Evangelist at Zoho, said during a media briefing that limits on integrations are typically dictated by outside vendors:

“There are limits in terms of capability and the exposure of their API… technically, as long as they support some of the standard protocols, it’s fairly straightforward.”

The introduction of a unified portal may have the most impact on customer experience operations. Instead of customers juggling multiple logins for CRM updates, support tickets, commerce orders, payments, and more, organizations can merge all their portals, including those from non-Zoho systems. For large enterprises especially, where siloed customer portals are a known pain point, this consolidation could help improve customer effort scores.

Domain verification, authentication records, and other behind-the-scenes tasks can also now be handled centrally. This includes new support for GoDaddy users, who can authorize automatic updates to DNS records, which is a useful capability for customer service and marketing teams that previously relied on IT intervention.

AI Steps Into a More Contextual Role

AI is deeply embedded in the release through Zia, Zoho’s AI assistant, which has an expanded footprint, and new intelligence hubs. Because Zoho One unifies data from more than 50 apps, Zia can take on tasks that span categories, for example, pulling HR, CRM and support information into a single query.

For example, it can help leaders understand how much time each employee spent with a particular account, Vegesna said, which requires cross-system reasoning not typically possible in isolated applications.

Zia Hubs, now promoted to a standalone application, automatically collects content such as signed contracts or recorded meetings into dedicated hubs. Users can then query that information directly. Vegesna explained:

“All the documents that you put in that hub… you can say, ‘hey, here are the same documents. Show me only the documents that auto renew within the next three months.’”

This could streamline contract renewals, customer onboarding, or issue-resolution workflows by exposing the right information without manual digging.

AI also assists in product navigation. Zia is trained on all 55+ Zoho applications, which means a user can ask how to run an Instagram campaign and be guided directly to the relevant tool, in this case, Zoho Social, and receive recommendations on how to use it, Vegesna noted.

Zia is coming into the status bar of Zoho One. Zia will be able to create and plug in widgets because the data is connected to the back end and powered by analytics. Users will be able to select from preset reports and dashboards and they are organized, whether they are CRM, HR or support related, such as a helpdesk overview, and pinned to the dashboard, Vegesna said.

“Because we have a broad suite of applications that are deeply integrated on our stack, and have the context that enables the intelligence… we are basically embedding [AI] contextually so that the user does not even know that they’re using AI here.”

Ensuring Data Sovereignty and Enterprise Control

Security and data sovereignty are becoming an increasing concern for enterprises as they pay more attention to where AI systems source and store data. Zoho made clear that its ability to operate its own full stack from infrastructure to applications enables deployment models that many competitors can’t match. As Vegesna noted:

“In sovereignty… we are doing these on-premise deployments in some countries where your data center has to be set up in that country, because we own … the entire stack … we are able to do it particularly when dealing with governments.”

The demand for national or regional control is growing, especially in markets where critical communication systems must remain within the country’s borders to comply with data privacy regulations. “They want to own some key aspects. For example, communication … I don’t want someone else to … pull the plug on my communication systems, so I want it on my data center within my country … those have come up a lot, and we are doing those deployments as well.”

Encryption and data governance were also focal points. Zoho confirmed that customers can now bring their own encryption keys and assign them at the application level. “On the security side … customers ask, ‘Can I bring in my own encryption keys and encrypt my data within Zoho?’ And now we are enabling that,” Vegesna said. “They can now select which applications can use what encryption key, and they can define encryption keys based on their specific set of applications, like their documents [and] emails.”

Zoho argues that AI governance, especially around permission systems, has been overlooked in broader industry discussions. Vegesna said:

“B2B LLMs are different from B2C LLMs, because the permission layer plays a very critical role. I think nobody can do a solid AI strategy and implementation if you do not have a directory system in there. And we have been saying it for years, and we are one of the very few vendors who do directly in Zoho, because that has the entire permissions structure in place.”

Cloud directory integrations that enable permissions are a granular level should be at the core of enterprise AI.

“If we do not have a directory system, we cannot say we can do good AI that is well protected, and you should not have access to some data that AI just memorized, because that all has to be under a firewall and the permission system.”

“We own the triple A: authentication, authorization and access control,” Vegesna added.

Together, these capabilities reinforce Zoho’s pitch to enterprises, that unified architecture and control over its infrastructure allow for tighter data governance and deployment models that meet stringent regulatory or geopolitical requirements.

The through-line across the release is the shift away from app-centric work toward context-centric work. This is a change that resonates strongly in customer-facing environments where speed, accuracy, and personalization depend on smooth access to data.

By pulling together dashboards, workflows, approvals, and AI-generated insights from across the business, Zoho One offers a way to reduce employee and customer effort. Functions that are traditionally fragmented, including billing, support, onboarding, and renewals, can be unified at the workflow and data level rather than treated as separate systems stitched together by manual processes.

For enterprises looking to consolidate their customer-facing tech stack without compromising on data control or the breadth of tools, Zoho is positioning the new release as a platform that can serve both operational needs and experience-driven outcomes.