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.

For enterprises, that’s both a challenge and an opportunity. Handled properly, feedback reveals exactly where things are and aren’t working. It tells support teams which moments frustrate. It tells product teams what’s missing, and it tells the C-suite what customers value enough to fight for.

That’s the real job of customer feedback management, turning scattered input into structured insight, then routing it to the teams that can actually do something with it.

The best CFM systems don’t just capture data. They:

• Map feedback across the full journey, not just surveys
• Spot trends early, before they show up in churn
• Connect insight directly to actions: faster support, better products, clearer messaging

In short, modern customer feedback management platforms give enterprises a new kind of muscle: the ability to listen deeply, move early, and improve continuously

What is Customer Feedback Management?

Customer feedback management is the discipline of collecting, interpreting, and acting on customer sentiment – not just from surveys, but from every channel where customers leave a mark.

That might mean tracking a drop in CSAT after a product update, combing through live chat logs, or decoding a two-star review on Trustpilot. In most enterprise settings, it means building a feedback loop that crosses teams: product, marketing, service, and operations all relying on the same source of truth.

The best customer feedback management software doesn’t just store responses. It translates them into structured insight, surfacing trends, routing complaints, and pushing alerts to the right place, fast. It’s the glue between listening and resolution.

To work at scale, feedback systems typically include:

• Multichannel ingestion: Web forms, support tickets, NPS, app reviews, even social DMs. Every signal matters, even if it’s unstructured.
• Theme detection and prioritization: Tools flag repeat issues or keyword clusters before they become reputational risks.
• Workflow integration: A refund complaint can notify finance. A delivery bug can trigger a ticket in product ops.
• Dashboards and reporting: With the help of AI systems, leaders get a filtered view of real insights by product line, geography, or channel.

Leading companies aren’t collecting feedback in a vacuum. They’re wiring it directly into CRM systems, contact center tools frontline workflows, so the right people can act without delay. The tighter the integration, the faster teams can respond, fix what’s broken, and strengthen customer relationships that last.

Where Feedback Fits: Feedback Management, VoC, and EFM

Feedback is only useful if it leads somewhere. That’s where terminology starts to matter. Voice of the Customer (VoC), customer feedback management, and enterprise feedback management (EFM) are often used interchangeably. They shouldn’t be.

Customer feedback management is the engine room. It handles collection, sorting, tagging, and routing. Think of it as the operational layer that turns raw input from surveys, ratings, and comments into tasks and decisions. This is where data moves from inboxes and dashboards into action plans.

Voice of the Customer (VoC) goes broader. It doesn’t just listen to what customers say, it listens to how they feel, how they behave, and where they’re frustrated or delighted without necessarily saying it outright. A good VoC program blends direct feedback with behavioral signals and sentiment analysis. It’s about seeing the full picture.

Enterprise feedback management (EFM) stretches even further. It includes employee and partner insight, compliance triggers, internal process reviews, and often sits closer to risk management than CX. In highly regulated or distributed organizations, EFM is essential infrastructure.

At enterprise scale, feedback management isn’t just a support tool. It’s part of the system of record: connected to customer data platforms, CRMs, business intelligence tools, and employee engagement systems (WEM tools).

Each of these frameworks adds something. The most mature organizations use all three as parts of one loop: listen, understand, and act.

What is Customer Feedback Management? Feedback Types

Customer feedback isn’t always a form or a star rating. It’s often informal, unstructured, or buried in systems where no one’s looking. Recognizing the different types is the first step toward building something that works across departments and channels.

• Direct Feedback: The most visible kind. Surveys after support calls. CSAT and NPS prompts. Product reviews submitted through apps or portals. It’s usually structured, timestamped, and easy to analyze. But it’s also the most filtered. The people who answer tend to be at the emotional extremes, either thrilled or annoyed. Everyone else stays quiet.
• Indirect Feedback: This is what customers say when they’re not talking to you directly. Tweets. Public forum threads. Online reviews. Complaints posted to third-party sites. In many organizations, this insight slips through the cracks. But today’s customer feedback management platforms use NLP and sentiment tools to bring these comments into view before they become brand problems.
• Inferred Feedback: This is the feedback customers don’t say out loud, but show in what they do. Dropping out halfway through checkout. Asking the same question in three different places. Bouncing between help pages without finding what they need.

On their own, these signals can be easy to miss. But together, they reveal patterns of frustration that direct surveys might never surface.

Why Customer Feedback Management Matters

There’s no shortage of dashboards in a modern enterprise. But few of them speak with the voice of the customer. That’s what feedback management changes. It shifts insight from lagging reports to live reality, focusing on the real-time pulse of what customers need, want, and expect.

For enterprise leaders focused on customer experience, this isn’t a soft metric. It’s operational. According to Bain & Company, companies that excel at customer experience grow revenues 4%–8% above their market. But growth doesn’t come from tracking satisfaction scores alone. It comes from turning those scores into action.

Here’s where feedback becomes a business driver:

• Alignment Across Teams: Sales hears one thing. Support hears another. Product has a third backlog entirely. When feedback lives in separate systems, teams solve different problems. When it’s centralized, patterns emerge, and teams move in the same direction.
• Early Signal Detection: A broken link on a signup form. A billing process that’s confusing in one region. A surge in cancellation requests. Customer feedback management platforms surface these issues before they hit churn reports. The earlier the fix, the lower the cost.
• Smarter Roadmapping: Feedback isn’t just a support signal, it’s a product roadmap tool. Tracking customer insights, linking them to outcomes, and activating responses leads to strategic action. Teams can prioritize features that drive loyalty.
• Competitive Advantage: Every brand says it listens. Few can prove it. Companies that consistently close the loop visibly earn trust. In a market where switching costs are low, trust is often the only real moat.

The case for customer feedback management software isn’t just about efficiency. It’s about agility, spotting the next risk or opportunity while competitors are still guessing.

How to Build a Customer Feedback Management System That Works

Enterprises don’t lack feedback. They’re swimming in it. The challenge isn’t collection, but coordination. Scattered responses, siloed ownership, and no clear plan for what happens next. That’s where customer feedback management becomes a system, not just a task.

1. Start with What You Already Have

Before adding new tools or channels, map what’s in play. Most enterprise teams already gather feedback across:

• Post-interaction surveys
• Help desk conversations
• Social and review platforms
• Product feedback forms
• Sales and account notes

But it’s often fragmented, or locked in spreadsheets, CRM fields, and third-party platforms. Start by listing every touchpoint where customers leave a trace. Then identify who owns that data, how it’s reviewed, and whether it drives action.

2. Build a Shared System, Not Just a Repository

A true customer feedback management system isn’t just a bucket. It’s a hub. One place where cross-functional teams can view, analyze, and act on insights. That requires more than storage. It needs structure. Look for tools that:

• Integrate with your CRM system and CDP
• Tag feedback by source, product line, sentiment, urgency
• Offer role-specific dashboards for ops, product, CX, compliance
• Allow for routing, escalation, and response tracking

Consider other integrations that might be helpful too, such as connections to your ERP and business intelligence platforms, or workforce management tools.

3. Design a Feedback-to-Action Pathway

Without clear ownership, feedback dies in the backlog. Teams need to agree on what gets prioritized, who responds, and how it loops back into service design, training, or product fixes.

The strongest systems:

• Flag urgent or high-impact issues automatically
• Route insights to the right teams (with deadlines)
• Track outcomes, not just volume
• Communicate resolution back to the customer

When that loop works, feedback becomes part of how the business runs.

How to Use Feedback to Improve Business Results

Most companies collect feedback. Fewer actually do something meaningful with it. In mature organizations, feedback isn’t just a sentiment report, it’s a driver of change. Done right, it informs strategy, sharpens execution, and reduces churn.

• Prioritize patterns over outliers: It’s easy to get caught up in the latest complaint or viral review. But high-performing teams step back. They look for volume, frequency, and trends, not just anecdotes. That could mean mapping repeat issues to product features, or tracking common service pain points over time.
• Feed insight to the right systems: Don’t keep customer feedback on a CX dashboard. Use it to inform product roadmaps, workforce planning, pricing models, training strategies, and anything else that impacts the customer experience.
• Expand your metrics: Go beyond NPS and CSAT. Think about customer effort scores, overall retention rates and churn. Determine the KPIs you want to keep track of in advance, and make sure everyone is watching them, including the C-Suite.

Choosing Customer Feedback Management Software

Customer feedback is everywhere. What separates good companies from great ones is what they do with it. That’s where the right customer feedback management software comes in, to make insights actionable, accountable, and accessible across the enterprise.

Start With the Business, Not the Tool

Software selection should begin with the problems it’s meant to solve. Are customers dropping off after onboarding? Or are service complaints slipping through the cracks? Are product teams getting insight too late to act?

Clear goals tend to point to the right tool:

• Real-time alerts for contact center agents?
• Text analytics for unstructured NPS comments?
• Trend reporting to inform product roadmaps?

Once those use cases are clear, it becomes easier to separate the platforms built for scale from those that just tick boxes.

Integration Over Isolation

In a modern tech stack, no system should sit alone, especially not feedback.

Customer insights gain power when connected to:

• CRM platforms, where individual records tell a full customer story
• Contact center solutions, where timing and channel matter
• CDPs, which consolidate behavioral and transactional data
• BI tools, for deeper cross-functional reporting
• Broader ERP, WEM, and business management tools

Make sure your platforms feed the systems powering decisions.

Think Long-Term: Governance, Scalability, and Fit

Even the most powerful platform will struggle without strong foundations. For enterprise buyers, that means focusing on operational readiness:

• Can the system support multiple teams and regions with clear permissions?
• Are escalation workflows and approvals built in?
• Does the vendor offer strong uptime guarantees and compliance controls?
• Is the reporting flexible enough to satisfy both executive leadership and front-line teams?

Ease of use matters too. If agents, analysts, and leaders can’t find value in it quickly, feedback won’t flow where it’s needed most.

Discover the best customer feedback management solutions:

Customer Feedback Management Best Practices

Technology may capture customer sentiment, but it’s what companies do next that separates good intentions from real improvement. At the enterprise level, feedback shapes products, and defines brand reputation, retention, and revenue.

Here’s what the most effective teams get right.

• Track consistently: Feedback isn’t a file to review later. It’s a feed that’s active and ongoing. Companies need to review regularly, discuss in depth, and build around it.
• Make feedback cross functional: Operations needs visibility into service complaints, marketing needs to know where messaging misses, and HR should see how poor feedback is affecting teams. Get everyone involved.
• Close the loop: Replying to feedback, or acting on it, is crucial. Customers want to know their input mattered, and teams want confirmation their fix was felt. Ensure that your action is clear, powerful, and visible.
• Read between the lines: Surveys are useful, but raw behavior can say more. Combine behavioral insights, structured survey data, and conversational analytics for a comprehensive view of what customers really feel, not just what they say.
• Make it easy to act: Help teams fix issues quickly. Check if workflows are in place for feedback routing, and whether CX agents can escalate recurring problems. Give people the tools they need to act.

Customer Feedback Management Trends

Customer expectations haven’t just shifted, they’ve splintered. Channels have multiplied. Responses move faster. The tools used to manage it all are catching up. Here’s what’s defining feedback management right now:

The Rise of AI-Powered Analysis

Enterprise teams spent years circling AI as a concept. Now it’s operational. The strongest feedback systems today don’t just categorize responses, they break them down by tone, urgency, and underlying cause.

Platforms like Medallia, NICE, and Sprinklr are using natural language processing and conversational analytics to surface issues before they mutate. Instead of waiting for quarterly survey analysis, teams can spot sentiment drops and recurring themes as they happen.

Feedback Is Becoming Embedded

Feedback used to live in standalone forms: a survey here, a rating box there. That’s changing. Leading platforms now capture signals from everyday interactions: chat logs, call transcripts, even app usage.

Feedback is moving closer to the moment. A delivery delay triggers a quick prompt. A cancelled subscription opens the door to ask why. Systems are listening all the time, and they’re getting smarter about what to listen for.

Structured Feedback Loses Traction

It’s not just about ticking boxes. The most valuable insights often show up in open comments, social threads, or long-form email replies. That unstructured data used to be hard to sort. Now, it’s where the action is.

Enterprises are investing in platforms that can handle nuance: that can understand sarcasm, spot emotion, and cluster feedback without a human reading every line. Forrester calls this shift “human insight at scale”, and it’s showing up as a core capability in nearly every customer feedback management platform leader.

Everything Connects Or It Doesn’t Work

Feedback is most valuable when it flows. Into support platforms, product roadmaps, agent scripts, and CX dashboards. But that only happens when systems talk to each other.

Leading tools now integrate out-of-the-box with CRMs, contact center systems, VoC platforms, and enterprise resource planning (ERP) solutions. That allows customer concerns to influence decision-making across the business, not just in service.

Privacy Remains Crucial

The line between “listening” and “surveilling” is thin, and enterprise buyers know it. In a post-GDPR, opt-out-default world, customer feedback strategies need to include transparency.

That means clear consent prompts. Data handling disclosures. Anonymization features. Especially in regulated sectors, ethics now sit beside analytics in the buyer’s checklist.

What is Customer Feedback Management? The Voice of CX

Customer feedback management It affects product decisions, shapes brand reputation, and drives loyalty at scale.

Done well, it connects dots across departments, from support and sales to marketing and operations. It puts real-time customer truth in front of the people who can do something about it.

But it only works when the systems are connected, the insights are trusted, and the loop is truly closed. That’s why enterprise teams are investing in modern customer feedback management platforms to operationalize input.

For companies focused on loyalty, innovation, and experience, the question isn’t whether to invest in customer feedback tools. The only real question is: which one will help you act faster, and smarter? CX Today is here to help:

• Join the Community: Be part of a dynamic CX-focused network. Swap ideas with thought leaders and elevate your feedback strategy.
• Test the Tech: Discover the top-rated platforms, meet vendors, and explore trends at live and virtual events.
• Plan Your Next Investment: Use our CX Marketplace to explore top vendors in feedback, VoC, CDP, and contact center tech.

Or visit the ultimate CX guide for enterprise experience leaders, for insights into how to build a better CX strategy, one step at a time.

The timing matters. Shoppers expect speed and personalization in the same breath. Around 71% say they actually want AI built into the shopping journey. They’re not asking for gimmicks. They want better stock visibility, quicker service, and recommendations that actually fit. Miss those marks and loyalty drops fast.

Amazon has already shown where this is heading: robotics in its fulfilment centres have cut costs by roughly 25%, a sign that retail automation solutions can shift margins as well as customer experience.

Tech giants are moving quickly, too. Salesforce, Google, and Microsoft are building AI agents to automate frontline support and back-end operations alike. It’s the “agentification” of the enterprise – automation that doesn’t just support the business but runs through it.

Challenges Retailers Must Overcome

One of the reasons retail automation is gaining so much attention right now is that the right tools can genuinely solve real-world problems – the kind that hold brands back. Right now, retailers have a lot of issues to overcome. The systems they already have don’t connect. Processes run in silos. Customers fall through the gaps. The result is frustration on both sides of the checkout.

Automation has the potential to tackle issues like:

• Disconnected inventories: A shopper checks a website, sees an item listed as available, makes the trip, and finds nothing on the shelf. The reverse happens too: stock piling up in storerooms with no visibility online. Without automation tying together store systems, warehouses, and ecommerce data, managers are left to guess.
• Cart abandonment: More than seven out of ten online baskets are abandoned before payment, a persistent drain on digital sales. Some of that is down to clunky checkout flows. But much of it comes from poor timing: slow shipping updates, lack of payment options, or no personalized nudge to finish the order.
• Poor customer experience: Customer experience is another sore spot. Fragmented journeys cost U.S. businesses an estimated $136.8 billion a year in lost loyalty. It’s the same pattern every time: a customer starts with live chat, follows up by phone, then gets a completely different answer by email. Each handoff repeats the pain. Without retail automation solutions that unify data, every channel feels like a different company.

As Gartner warns, “limitless automation” is a myth. But the goal isn’t automating everything. It’s automating the right things, with the right guardrails, to fix broken journeys.

Retail Automation Use Cases and Benefits

The impact of retail automation shows up in the basics: how goods flow, how shelves stay full, how support teams respond. When it works, it links the back office to the customer in one thread. When it doesn’t, it becomes just another layer of friction.

The following use cases show where the biggest opportunities lie.

Supply Chain & Logistics

Retail supply chains face constant pressure. Surges in demand, shipping delays, and rising costs. The systems built years ago weren’t built for the pace of modern ecommerce. Automation is starting to bridge that gap. AI now forecasts demand spikes, reroutes deliveries, and even triggers restocks without human input. The payoff: fewer empty aisles, lower transport costs, less waste.

Analysts at NetSuite note that automation in logistics can trim lead times significantly while also cutting excess inventory. Amazon’s own network shows the effect at scale, using AI-driven workflows to manage thousands of sites, speed up decisions, and reduce overheads.

Inventory Management & Forecasting

Inventory has always been retail’s balancing act. Too much stock ties up cash and fills warehouses. Too little drives customers to competitors. The gap between online and in-store data only makes it harder.

Retail automation can close that gap. Machine learning models forecast demand more accurately, pulling signals from sales patterns, seasonality, and even local events. IoT sensors and ERP integration push updates in real time, so a store manager isn’t left guessing what’s on hand. One company, FLO, reduced lost sales by 12% just with AI-powered demand forecasting, allocation, and replenishment tools.

Elsewhere, by connecting systems and automating core workflows, ThredUp reduced manual bottlenecks and kept inventory moving efficiently through its marketplace. That meant quicker processing times, fewer errors, and a smoother experience for both sellers and buyers.

Smarter Customer Service

Customer service is often the first test of a retailer’s brand. It’s also one of the hardest to scale. Long queues, repeated questions, and inconsistent answers push customers away.

This is where retail automation has some of the clearest wins. Many firms now use AI agents to cover FAQs, returns, warranty requests, and basic order updates. That shortens queues and frees staff to focus on tougher cases.

Proactive outreach also helps cut down on cart abandonment and cancellations. At a deeper level, automation is reshaping the shopping experience itself. L’Oréal, for example, used Salesforce’s Agentforce to unify data and automate service interactions. Customers received consistent, personalised responses across every channel, turning routine contacts into relationship-building

Revenue Growth & Marketing

Automation goes beyond efficiency; it drives sales. Ecommerce automation tools are now used for predictive pricing, upselling, cross-selling, and tailored offers at scale. Customer Data Platforms bring scattered records into a single profile, enabling true personalisation. That data fuels real-time campaigns designed to anticipate customer needs and lift conversion rates.

By automating parts of its customer experience, marketing, and sales strategies, Simba Sleep generated more than £600,000 in additional monthly revenue. The company’s AI agent now does the work of 8 full-time employees, freeing human staff up for other work. The automation didn’t just cut costs. It created a direct and measurable growth impact.

Enhancing Employee Experience

Retail isn’t just about customers. Employee experience matters too. High turnover and burnout are expensive. Automating repetitive work helps keep staff engaged, while workforce scheduling tools ease pressure during peak demand.

For example, by automating key workforce processes, Lowe’s saved over $1 million in just eight months. The benefits went beyond the bottom line – supervisors reported higher satisfaction, and frontline staff were able to focus on more meaningful work.

Great Southern Bank also achieved similar results, watching attrition rates fall by 44% after building intelligent automation into workflows. This is clear evidence that automated retail tools don’t replace staff. They make jobs more rewarding by removing the least engaging parts of the day. That has a direct impact on retention.

Unlocking Business Insights

Retail runs on data. But in most organizations, that data is split. Marketing has one view. Ecommerce has another. Service teams work with something different again. By the time reports land on a desk, the moment to act has already passed.

Retail automation changes that. Automated systems connect the dots between platforms and feed AI models that can see patterns in real time. Which product lines are about to sell out? Which promotions will flop? Who looks ready to walk?

A single view of the customer makes the difference. That’s why retail automation solutions now often include Customer Data Platforms. When Vodafone brought its records together in one place, engagement rates jumped by nearly 30%, and teams were able to build more effective journeys without risking burnout.

The gains aren’t limited to revenue. Automation can also catch compliance issues, broken workflows, or supply chain weak spots before they turn into costly problems.

Best Practices for Retail Automation

The potential of retail automation is huge. But so are the risks. Without a clear plan, projects can misfire – frustrating customers, raising compliance concerns, and wasting money. The retailers that succeed tend to follow a few clear rules.

• Get the data foundation right: Automation is only as good as the information it runs on. If customer records are scattered, bots will give inconsistent answers and supply chains will make the wrong calls. That’s why many retailers are investing in Customer Data Platforms. A CDP pulls together records from marketing, sales, service, and ecommerce. One view. One source of truth. Without that, everything else is shaky.
• Set guardrails: Gartner has already warned about the danger of chasing “limitless automation”. Not every process should be automated. Not every customer interaction should be handed off to AI. The best deployments use escalation rules, monitoring, and clear ownership so nothing gets lost.
• Avoid generic automation: Customers spot it instantly. A one-size-fits-all chatbot that can’t see their order history does more harm than good. Graia has called out this problem in CX, showing that automation has to be tuned to the business and the customer journey, not just bolted on.
• Train the workforce: Automation changes jobs. It takes away repetitive tasks, but it also requires staff to know how to work with AI systems. The best companies invest in training and create “automation champions” on the front line. That reduces fear and speeds up adoption.
• Measure what matters: Metrics like call volume or handle time don’t show the true impact of automation. Smarter measures include containment quality, safe deflection, and revenue lift. Tools like Scorebuddy now track the performance of AI agents directly, adding oversight where it’s needed most.

Don’t jump in trying to automate everything. Automate carefully, with the right data, the right checks, and the right training.

The Future of Retail Automation: Growth, Loyalty, and Smarter Operations

The role of retail automation has shifted. It’s now about reshaping the sector end-to-end – supply chains, inventory, customer service, and marketing. When used well, automation and AI cut costs, trim waste, and improve both staff and customer experiences.

But there are risks too. Fragmented data, overuse of bots, and weak oversight can undermine trust faster than they deliver returns. Success depends on planning: build solid data foundations, set limits, train teams, and track outcomes that go beyond call times or ticket counts.

Automated retail is already here. The retailers that move carefully but with intent will be the ones winning the next decade, with leaner operations, more loyal customers, and stronger margins.

The problem usually isn’t the model. It’s the data behind it. When knowledge bases are out of date, fragmented, or inconsistent, even the smartest AI will confidently generate the wrong answer. This is why knowledge base integrity and RAG governance matter more than model size or speed.

The urgency is clear. McKinsey reports that almost all companies are using AI, but only 1% feel they’re at maturity. Many also admit that accuracy and trust are still major barriers. In customer experience, where loyalty is fragile, a single hallucination can trigger churn, compliance headaches, and reputational fallout.

Leading enterprises are starting to treat hallucinations as a governance problem, not a technical one. Without governed data, AI becomes a liability in CX. With it, organizations can build automation that actually strengthens trust.

What Are AI Hallucinations and What Causes Them?

When customer-facing AI goes off-script, it usually isn’t because the model suddenly turned unreliable. AI hallucinations in CX happen when the system fills gaps left by bad or missing data. Picture a bot telling a customer they qualify for same-day refunds when the actual policy is 30 days. That’s not creativity, it’s a broken knowledge base.

Hallucinations tend to creep in when:

• Knowledge bases are outdated or inconsistent, with different “truths” stored across systems.
• Context is missing, for example, an AI forgetting a customer’s purchase history mid-conversation.
• Validation checks are skipped, so the bot never confirms whether the answer is still correct.

The risks aren’t small. 80% of enterprises cite bias, explainability, or trust as barriers to using AI at scale. In CX, inaccuracy quickly turns into churn, complaints, or compliance headaches.

There are proven fixes. Enterprises just need to know what to implement before they go all-in on agentifying the contact center.

The Real-World Impact of AI Hallucinations in CX

The stakes around AI hallucinations in CX translate directly into lost revenue, churn, and regulatory risk. A bot that invents refund rules or misstates eligibility for a benefit doesn’t just frustrate a customer – it creates liability.

Some of the impacts seen across industries:

• Retail: Misleading warranty responses trigger unnecessary refunds and drive shoppers to competitors.
• Public sector: Incorrect entitlement checks leave citizens without services they qualify for.
• Travel: Fabricated policy details can mean denied boarding or stranded passengers.

The financial burden is real. Industry analysts estimate that bad data costs businesses trillions globally each year, and the average cost of a single data-driven error can run into millions once churn and remediation are factored in.

Case studies show the impact, too. Just look at all the stories about ChatGPT, creating fictitious documents for lawyers, or making up statements about teacher actions in education. Every hallucination is a reminder: without knowledge base integrity and RAG governance, automation introduces more risk than reward. With them, AI becomes a growth driver instead of a liability.

Why Hallucinations Are Really a Data Integrity Problem

It’s tempting to think of AI hallucinations in CX as model failures. In reality, they’re usually symptoms of poor data integrity. When the information feeding an AI is out of date, inconsistent, or fragmented, the system will confidently generate the wrong answer.

Knowledge base integrity means more than just storing information. It’s about ensuring accuracy, consistency, and governance across every touchpoint. Without that, CX automation is built on sand.

Common breakdowns include:

• Outdated articles: A policy change goes live, but the bot still cites the old rules.
• Conflicting records: Multiple “truths” for the same customer, leading to contradictory answers.
• Ungoverned logs: Data pulled in without privacy controls, creating compliance exposure.

Some organizations are already proving the value of treating hallucinations as governance problems. Adobe Population Health saved $800,000 annually by enforcing stronger data controls, ensuring agents and AI systems pulled only from validated knowledge sources.

Building the Foundation: Clean, Cohesive Knowledge

Solving AI hallucinations in CX starts with building a solid data foundation. No model, no matter how advanced, can perform reliably without knowledge base integrity. That means every system, from the CRM and contact center platform to the CDP – has to point to the same version of the truth.

A few steps make the difference:

• Unified profiles: Use CDP to connect IDs, preferences, and history across systems. Vodafone recently reported a 30% boost in engagement after investing in unified profiles and data quality.
• Agent-ready records: Golden IDs, schema alignment, and deduplication stop bots from improvising. Service accuracy depends on knowing which record is the right one.
• Data freshness: Expired knowledge is one of the fastest routes to hallucination. Setting SLAs for update frequency ensures AI doesn’t serve answers that are weeks, or years, out of date.
• Governance layers: Microsoft’s Purview DLP and DSPM frameworks, for example, help enforce privacy boundaries and ensure sensitive data is never exposed to customer-facing AI.

Clean, governed data is what allows automation to scale safely. In fact, Gartner notes that automation without unified data pipelines is one of the leading causes of failure in AI deployments.

The lesson is clear: AI only works if the underlying knowledge is accurate and consistent. RAG governance begins not at the model layer, but in how enterprises treat their data.

Choosing Your LLM Carefully: Size Isn’t Everything

When automating CX workflows, the assumption that “bigger means better” often backfires. In fact, purpose-built, smaller language models can outperform broad, heavyweight counterparts, especially when they’re trained for specific customer service tasks.

Here’s what’s working:

• Smaller, tailored models excel at soft-skill evaluations. In contact center hiring, they outperform general-purpose LLMs simply because they understand the nuances of human interaction better.
• Efficiency is a major advantage. Smaller models require fewer computational resources, process faster, and cost less to run, making them ideal for real-time CX workflows.
• They also tend to hallucinate less. Because they’re fine-tuned on targeted data, they stay focused on relevant knowledge and avoid the “overconfident bluffing” larger models can fall into.
• Distillation, teaching a smaller model to mimic a larger “teacher”, is now a common technique. It delivers much of the performance without the infrastructure cost.

Choosing the right model is a strategic decision: smaller, domain-specific models support RAG governance and knowledge base integrity more effectively, without blowing your budget or opening new risks.

RAG Governance: Why Retrieval Can Fail Without It

Retrieval-augmented generation (RAG) has become a go-to strategy for tackling AI hallucinations in CX. Companies like PolyAI are already using RAG to make voice agents check against validated knowledge before replying, cutting down hallucinations dramatically.

Instead of relying only on the model’s training data, RAG pulls answers from a knowledge base in real time. In theory, it keeps responses grounded. In practice, without proper RAG governance, it can still go wrong.

The risks are straightforward:

• If the knowledge base is outdated, RAG just retrieves the wrong answer faster.
• If content is unstructured, like PDFs, duplicate docs, or inconsistent schemas, the model struggles to pull reliable context.
• If version control is missing, customers may get different answers depending on which copy the system accessed.

That’s why knowledge base integrity is critical. Enterprises are starting to use semantic chunking, version-controlled KBs, and graph-RAG approaches to make sure AI agents retrieve the right data, in the right context, every time.

Vendors are also moving quickly. Google Vertex Agent Builder, Microsoft Copilot Studio’s RAG connectors, and open-source projects like Rasa’s extensions are designed to enforce cleaner retrieval pipelines. Companies like Ada are proving that governed RAG can cut down false answers in sensitive workflows like background checks.

RAG is powerful, but without governance, it risks becoming a faster way to spread bad information. Grounding AI in trusted, validated sources, through structured retrieval and strong RAG governance, is the difference between automation that builds trust and automation that erodes it.

The Model Context Protocol for reducing AI hallucination

Even with RAG governance, there’s still a missing piece: how the model itself connects to external tools and data. That’s where the Model Context Protocol (MCP) comes in. MCP is emerging as a standard that formalizes how AI systems request and consume knowledge, adding a layer of compliance and control that CX leaders have been waiting for.

Without MCP, connectors can still pull in unreliable or non-compliant data. With MCP, rules can be enforced before the model ever sees the input. That means:

• Version control: AI agents only access the latest, approved policies.
• Schema validation: Data must meet format and quality checks before it’s used.
• Integrity enforcement: Broken or incomplete records are automatically rejected.

This is particularly relevant in regulated industries. Financial services, healthcare, and the public sector can’t risk AI fabricating eligibility or compliance-related answers. MCP provides a structured way to prove governance at the system level.

Vendors are already moving in this direction. Salesforce’s Agentforce 3 announcement positioned governance and compliance as central to its next-generation agent framework. For CX leaders, MCP could become the difference between AI that “sounds right” and AI that is provably compliant.

Smarter Prompting: Designing Agents to Think in Steps

Even with clean data and strong RAG governance, AI hallucinations in CX can still happen if the model is prompted poorly. The someone asks a question shapes the quality of the answer. That’s where smarter prompting techniques come in.

One of the most effective is chain-of-thought reasoning. Instead of pushing the model to jump straight to an answer, prompts guide it to reason through the steps. For example, in a travel entitlement check, the AI might be told to:

• Confirm eligibility rules.
• Check dates against the customer record.
• Validate exceptions before giving a final response.

This structured approach reduces the chance of the AI skipping logic or inventing details to “sound confident.”

Other strategies include:

• Context restating: Have the model summarize customer inputs before answering, to avoid missing key details.
• Instruction layering: Embedding guard phrases like “If unsure, escalate” directly into prompts.

Better prompting changes how the AI reasons. Combined with knowledge base integrity and retrieval grounding, thoughtful prompt design is one of the simplest, most cost-effective ways to cut hallucinations before they ever reach a customer.

Keeping Humans in the Loop: Where Autonomy Should Stop

AI is getting better at handling customer requests, but it shouldn’t be left to run everything on its own. In CX, the cost of a wrong answer can be far bigger than a frustrated caller. A single AI hallucination in CX around something like a loan decision, a medical entitlement, or a refund policy can create compliance risks and damage trust.

That’s why most successful deployments still keep people in the loop. Routine questions like order status, password resets, and warranty lookups are safe to automate. But when the stakes rise, the system needs a clear off-ramp to a human; no company should try to aim for limitless automation.

There are simple ways to design for this:

• Flagging low-confidence answers so they’re routed to an agent.
• Escalating automatically when rules aren’t clear or when exceptions apply.
• Training models with reinforcement from human feedback so they learn when to stop guessing.

Real-world examples prove the value. Ada’s work with Life360 showed that giving AI responsibility for repetitive queries freed agents to focus on tougher cases. Customers got faster answers when it mattered most, without losing the reassurance of human judgment for sensitive issues.

The lesson is straightforward: automation should extend, not replace, human service.

Guardrail Systems: Preventing AI hallucination

AI can be fast, but it still needs limits. In customer service, those limits are guardrails. They stop automation from giving answers it shouldn’t, even when the data looks clean. Without them, AI hallucinations in CX can slip through and cause real damage.

Guardrails take different forms. Some block responses if the system isn’t confident enough. Others make sure refund rules, discounts, or eligibility checks stay within company policy. Many firms now add filters that catch bias or toxic language before it reaches a customer.

The goal isn’t perfection. It’s layers of protection. If one check misses an error, another is there to catch it. Tucan.ai showed how this works in practice. By adding guardrails to its contract analysis tools, it cut the risk of misinterpreted clauses while still saving clients time.

For CX teams, guardrails aren’t about slowing automation down. They’re about trust. Customers need to know that the answers they get are safe even when they come from a machine.

Testing, Monitoring, and Iterating

AI systems drift. Policies change, data updates, and customer expectations move quickly. Without regular checks, those shifts turn into AI hallucinations in CX.

Strong CX teams treat testing and monitoring as part of daily operations. That means:

• Running “red team” prompts to see how an agent handles edge cases.
• Tracking hallucination rates over time instead of waiting for customer complaints.
• Comparing different prompts or retrieval methods to see which reduces errors.

Enterprises are starting to put this discipline into place. Retell AI cut false positives by 70% through systematic testing and feedback loops. Microsoft and others now offer dashboards that log how models use data, making it easier to spot problems early.

The principle is straightforward. AI is not a one-off project. It’s a system that needs continuous oversight, just like a contact center workforce. Test it, measure it, refine it.

The Future of AI Hallucinations in CX

Customer experience is moving into a new phase. Contact centers are no longer testing basic chatbots. They are rolling out autonomous agents that can manage full interactions, from checking an order to triggering a refund. Microsoft’s Intent Agent, NICE’s CXone Mpower, and Genesys’ AI Studio are early examples of that shift.

The upside is clear: faster service, lower costs, and better coordination across systems. The risk is also higher. A single AI hallucination in CX could mean a compliance breach or a reputational hit that takes years to repair. Regulators are watching closely. The EU AI Act and ISO/IEC 42001 both push for stricter rules on governance, transparency, and accountability.

The market is responding. Salesforce’s move to acquire Convergence.ai and NiCE’s purchase of Cognigy show how major vendors are racing to build platforms where governance is built in, not added later. Enterprises want systems that are safe to scale, not pilots that collapse under risk.

The reality is that hallucinations won’t disappear. Companies will need to learn how to contain them. A strong knowledge base, integrity, tight RAG governance, and frameworks like MCP will differentiate brands that customers trust from those they don’t.

Eliminating AI Hallucinations in CX

The risk of AI hallucinations in CX is not going away. As enterprises scale automation, the cost of a wrong answer grows, whether that’s a compliance breach, a lost customer, or a dent in brand trust.

The good news is that hallucinations are not an unsolvable problem. They’re usually data problems. With a strong knowledge base, integrity, clear RAG governance, and frameworks like MCP to enforce compliance, organizations can keep automation reliable and safe. Guardrails, smarter prompting, and human oversight add further protection.

Together, these measures turn AI from a liability into an asset. Companies that treat governance as central will be able to roll out advanced agents with confidence. Those that don’t risk being left behind.

But customers? They’re not exactly throwing confetti about it.

In fact, Gartner found 64% of people would rather companies didn’t use AI for service, full stop. Plus, only 24% of customers trust AI with anything messy, like complaints, policy decisions, those emotionally-charged “your system charged me twice” moments. So there’s this weird split: companies rushing forward, customers dragging their heels, and everyone quietly hoping the bots behave.

That’s the real issue, honestly. AI doesn’t warn you it’s going wrong with an error code, at least not all the time. It goes sideways in behavior. A chatbot invents a refund rule. A voice assistant snaps at a vulnerable caller. A CRM-embedded agent quietly mislabels half your complaints as “general enquiries.”

This is why AI behavior monitoring and real AI governance oversight are really becoming the only guardrails between scaling CX AI, and watching it drift into places you really don’t want headlines about.

Why AI Governance Oversight Is Critical for CX Success

Most CX teams think they’re rolling out “smart automation,” but what they’re actually doing is handing decision-making power to systems they don’t fully understand yet. That’s just where the industry is right now. The tech moved faster than the manuals.

This is exactly why AI behavior monitoring, AI governance oversight, and all the messy parts of CX AI oversight are suddenly showing up in board conversations. It’s pattern recognition. The problems are becoming glaringly obvious. We’ve all seen a bot make a weird decision and thought, “Wait… why did it do that?”

Ultimately, we’re starting to bump against a very real trust ceiling with AI and automation in CX.

KPMG’s global study found 83% of people expect AI to deliver benefits, but more than half still don’t trust it, especially in markets that have seen its failures up close.

Unfortunately, business leaders aren’t making it easier to trust these systems either.

Here’s where things get dicey. PwC’s 2025 research shows only a small fraction of companies feel “very effective” at AI risk monitoring or maintaining an inventory of their AI systems. That’s not just making customers skeptical, it’s opening the door to countless problems with security, data governance, and even AI compliance.

What Off-the-Rails AI Looks like in CX

It’s funny, when people talk about AI risk, they usually imagine some Terminator-style meltdown. In reality, CX AI goes off the rails in more subtle ways:

Hallucinations & fabricated information

Hallucinations sound like this mystical AI thing, until your bot confidently invents a cancellation policy that’s never existed and suddenly, you’re handing out refunds like coupons.

2025 observability research keeps pointing to the same pattern: hallucinations usually come from messy or contradictory knowledge bases, not the model itself. A tiny change in wording, an outdated policy page, and suddenly the AI “helpfully” fills in the blanks.

This is where AI drift detection becomes so important. Hallucinations often creep in after small updates to data pipelines, not major system changes.

Tone errors, “cold automation” & empathy failures

Efficiency without empathy doesn’t win customers.

Brands aren’t losing customers because AI is wrong, they’re losing them because the AI feels cold. It encourages negative response. Research found 42% of Brits admit they’re ruder to chatbots than humans, and 40% would pay extra just to talk to a real person during a stressful moment.

Tone errors don’t even have to be outrageous, just off-beat. This is absolutely part of CX AI oversight, whether companies like it or not.

Misclassification & journey misrouting

Smart routing can absolutely transform CX. It might even be the secret to reducing handling times. But if your intent model falls apart:

• Complaints get tagged as “general enquiries.”
• Cancellation requests bounce between departments.
• High-risk customers get routed to low-priority queues.
• Agents spend half their time rewriting what the AI misread.

When companies adopt agentic systems inside CRMs or collaboration platforms (Salesforce, Teams, Slack), misclassification gets even harder to catch because the AI is now initiating actions, not just tagging them. Behavioral drift in these areas builds up subtly.

Bias & fairness issues

Bias is the slowest-moving train wreck in CX because nothing looks broken at first.

You only notice it in patterns:

• Certain accents triggering more escalations,
• Particular age groups receiving fewer goodwill gestures,
• Postcode clusters with mysteriously higher friction scores.

A survey last year found 63% of consumers are worried about AI bias influencing service decisions, and honestly, they’re not wrong to be. These systems learn from your historical data, and if your history isn’t spotless, neither is the AI.

Policy, privacy & security violations

This is the failure mode that’s getting more painful for business leaders:

• A bot accidentally quoting internal-only pricing.
• A Teams assistant pulling PII into a shared channel.
• A generative agent surfacing sensitive case notes in a CRM suggestion.

None of these will necessarily trigger a system alert. The AI is technically “working.” But behaviorally, it’s crossing lines that no compliance team would ever sign off on.

Drift & degradation over time

Here’s the thing almost nobody outside of data science talks about: AI drifts the same way that language, processes, or product portfolios drift. Gradually. Quietly.

Models don’t stay sharp without maintenance. Policies evolve. Customer context changes. And then you get:

• Rising recontact rates,
• Slowly dipping FCR scores,
• Sentiment trending down month over month.

Organizations that monitor drift proactively see significantly higher long-term ROI than those who “set and forget.” It’s that simple.

Behavior Monitoring Tips for AI Governance Oversight in CX

AI is making decisions, influencing outcomes, and shaping journeys, yet for some reason, companies still aren’t paying enough attention to what goes on behind the scenes. It takes more than a few policies to make AI governance oversight in CX work. You need:

A Multi-Layer Monitoring Model

With AI, problems rarely start where you’d think. If a bot is rude to a customer, it’s not a chat app that’s usually the problem, it’s something underneath. That’s why you need to monitor all the layers:

• Data layer: Here, you’re watching for data freshness, schema changes, versioning of your knowledge base, inconsistent tags across channels, and omni-data alignment across channels. Poor data quality costs companies billions a year, but unified data reduces service cost and churn.
• Model layer: At this level, useful metrics include things like intent accuracy, precision/recall, hallucination rate, and AI drift detection signals like confidence over time. Think of this as your AI’s cognitive health check.
• Behavior layer: Here, you’re looking at escalation rates, human override frequency, low-confidence responses, weird tool-call chains, anomaly scores on tone, sentiment, and word patterns.
• Business layer: This is where you see how AI activity correlates to results like CSAT/NPS scores, re-contact rate, churn levels, cost-per resolution, and so on.

The Right CX Behavior Metrics

If you forced me to pick the non-negotiables, it’d be these:

• Hallucination rate (and how often humans correct it)
• Empathy and politeness scores
• Sentiment swings inside a single conversation
• FCR delta pre- and post-AI deployment
• Human override and escalation rates
• Percentage of interactions where the AI breaks policy
• Cost-per-resolution

If you only track “containment” or “deflection,” you’re not monitoring AI properly.

A Holistic Approach to Observability

The teams doing this well have one thing in common: end-to-end traces that show the whole story.

A trace that looks like this: Prompt → Context → Retrieved documents → Tool calls → Model output → Actions → Customer response → Feedback signal

If you can’t replay an interaction like a black box recording, you can’t meaningfully audit it, and auditing is core to AI ethics and governance, especially with regulations tightening.

You also need:

• Replayable transcripts
• Decision graphs
• Versioned datasets
• Source attribution
• Logs that a regulator could read without laughing

If your logs only say “API call succeeded,” you’re not looking deep enough.

Alerting Design & Behavior SLOs

Most orgs have SLOs for uptime. Great. Now add SLOs for behavior, that’s where AI governance oversight grows up.

A few examples:

• “Fewer than 1 in 500 interactions require a formal apology due to an AI behavior issue.”
• “0 instances of PII in AI-generated responses.”
• “No more than X% of high-risk flows handled without human validation.”

Alerts should trigger on things like:

• Sharp drops in sentiment
• Spikes in human overrides
• Unusual tool-call behavior (especially in agentic systems)
• Data access that doesn’t match the pattern (teams/slack bots can be wild here)

Instrumentation by Design (CI/CD)

If your monitoring is an afterthought, your AI will behave like an afterthought.

Good teams bake behavior tests into CI/CD:

• Regression suites for prompts and RAG pipelines
• Sanity checks for tone and policy alignment
• Automatic drift tests
• Sandbox simulations (Salesforce’s “everse” idea is a great emerging model)
• And historical replay of real conversations

If you wouldn’t deploy a major code change without tests, why would you deploy an AI model that rewrites emails, updates CRM records, or nudges refund decisions?

AI Governance Oversight: Behavior Guardrails

Monitoring AI behavior is great, controlling it is better.

Behavior guardrails are a part of AI governance oversight that transform AI from a clever experiment into something you can trust in a live customer environment.

Let’s start with some obvious guardrail types:

• Prompt & reasoning guardrails: You’d be amazed how much chaos disappears when the system is told: “If unsure, escalate.” Or “When conflicted sources exist, ask for human review.”
• Policy guardrails Encode the rules that matter most: refunds, hardship cases, financial decisions, vulnerable customers. AI should never improvise here. Ever.
• Response filters: We’re talking toxicity, bias, PII detection, brand-voice checks, the things you hope you’ll never need, but you feel sick the moment you realize you didn’t set them up.
• Action limits Agentic AI is powerful, but it needs clear boundaries. Limits like maximum refund amounts or which CRM fields it can access matter. Microsoft, Salesforce, and Genesys all call this “structured autonomy”, so freedom in a very safe box.
• RAG governance guardrails: If you’re using retrieval-augmented generation, you have to govern the source material. Versioned KBs. Chunking rules. Off-limits documents.
  Use connectors (like Model Context Protocol-style tools) that enforce: “Use only verified, compliant content. Nothing else.”

The Automation / Autonomy Fit Matrix

The other part of the puzzle here (aside from setting up guardrails), is getting the human AI balance right. Before any AI touches anything customer-facing, map your flows into three buckets:

• Low-risk, high-volume: FAQs, order status, password resets, shipping updates. This is where automation should thrive.
• Medium-risk: Straightforward refunds, address changes, simple loyalty adjustments. Great fit for AI + guardrails + a human-on-the-loop to catch outliers.
• High-risk / irreversible: Hardship claims. Complaints with legal implications. Anything involving vulnerable customers. Here, AI is an assistant, not a decision-maker.

To keep these AI governance oversight boundaries solid, implement a kill-switch strategy that includes when to turn off an agent, pause a queue or workflow, or freeze updates to avoid further damage.

The Role of Humans in AI Governance Oversight

There’s still this strange myth floating around that the endgame of AI in CX is “no humans required.” I genuinely don’t know where that came from. Anyone who’s watched a real customer interaction knows exactly how naive that is. AI is remarkable at scale and speed, but when a conversation gets emotional or ambiguous or ethically tricky, it still just acts like software. That’s all it is.

AI governance oversight in CX still needs humans, specifically:

• Humans-in-the-loop (HITL): Any high-risk decision should get a human’s eyes first. Always. HITL isn’t slow. It’s safe. Good AI behavior monitoring will tell you exactly where HITL is mandatory: wherever the AI hesitates, contradicts itself, or hits a confidence threshold you wouldn’t bet your job on.
• Human-on-the-loop (HOTL): Here, the human doesn’t touch everything; they watch the system, the trends, and the anomalies. They’re basically the flight controller. HOTL teams look at anomaly clusters, rising override rates, sentiment dips, and the subtle cues that tell you drift is beginning. They’re the early-warning system that no model can replace.
• Hybrid CX models: We know now that the goal isn’t to replace humans. It’s to let humans handle the moments where trust is earned and let AI tidy up everything that doesn’t require emotional intelligence. Stop striving for an “automate everything” goal.

Another key thing? Training humans to supervise AI. You can build the best monitoring stack in the world, but if your agents and team leads don’t understand what the dashboards mean, it’s pointless.

Humans need training on:

• How to read drift signals
• How to flag bias or tone issues
• How to escalate a behavior problem
• How to give structured feedback
• And how to use collaboration-embedded ai assistants without assuming they’re always right

Embedding AI Governance Oversight into Continuous Improvement

AI behaves like a living system. It evolves, it picks up quirks, it develops strange habits based on whatever data you fed it last week. If you don’t check in regularly, it’ll wander off into the digital woods and start making decisions nobody signed off on.

That’s why continuous improvement isn’t a ceremony; it’s self-defense. Without it, AI governance oversight becomes a rear-view mirror instead of an early-warning system.

Commit to:

• Continuous testing & red-teaming: If you’ve never run a red-team session on your CX AI, you’re genuinely missing out on one of the fastest ways to uncover the weird stuff your model does when nobody’s watching. Red-teamers will shove borderline prompts at the system, try to inject malicious instructions, and stress-test policy boundaries, to show you gaps before they turn into real problems.
• Tying monitoring to predictive CX & customer feedback: If you want to know whether your AI changes are helping or quietly sabotaging the customer journey, connect them to your predictive KPIs. Watch what happens to CSAT, NPS, predicted churn scores, likelihood-to-repurchase, and customer effort.
• Knowledge base integrity review: 80% of hallucinations probably start in the knowledge base, not the model. One policy update slips through without review, or a well-meaning team member rewrites an FAQ with different wording, and suddenly your AI is making decisions based on contradictory inputs. Regular KB governance should become as normal as code review.
• Data quality & lineage checks: The model can only behave as well as the data it’s seeing, and CX data is notoriously chaotic: different teams, different taxonomies, different CRMs duct-taped together over several years. To keep AI honest: consolidate profiles into a CDP with one “golden record,” enforce schemas, and define lineage so you can actually answer, “Where did this value come from?”

The organizations doing this well treat AI like any other adaptable system. They run a full loop: Monitor → Detect → Diagnose → Fix → Test → Redeploy → Report. Simple as that.

AI Governance Oversight: The Only Way to Scale CX AI Responsibly

If there’s one thing that’s become clear while watching CX teams wrestle with AI over the past two years, it’s this: the technology isn’t the hard part. The model quality, the workflows, and the integrations all come with challenges, but they’re solvable.

What really decides whether AI becomes a competitive advantage or a reputational hazard is how well you understand its behavior once it’s loose in the world.

That’s why AI governance oversight, AI behavior monitoring, guardrails, kill switches, and human review models matter more than whatever amazing feature your vendor demoed last month. Those safeguards are what keep the AI aligned with your policies, your ethics, your brand personality, and, frankly, your customers’ tolerance levels.

You can’t prevent every wobble. CX is too complicated, and AI is too adaptive for that illusion. But you can design a system that tells you the moment your AI starts drifting, long before the customer feels the fallout.

CX is just going to keep evolving. Are you ready to reap the rewards without the risks? Read our guide to AI and Automation in Customer Experience.

They’re running on static journeys, and disconnected data. Context that should guide the next move gets trapped in silos. Customers end up repeating information to different agents, something more than 70% say businesses need to fix.

Delays are expensive. They make support lines longer, drive costs up, and quietly chip away at loyalty. It’s why real-time customer journey orchestration (RTJO) is moving to the heart of customer experience work. The idea isn’t complicated: watch what’s happening right now, match it with what you already know about the person, and act before the moment slips.

What Real-Time Customer Journey Orchestration Means

“Real-time” gets thrown around often, but in customer service it has a very specific meaning. It isn’t about answering a phone a little faster. It’s about noticing a customer signal the instant it happens, matching it to a live, unified profile, and deciding what to do before the customer has to ask.

Think of a failed card payment. A traditional system might flag it overnight, adding the customer to a recovery email list. Real-time journey orchestration (RTJO) does something very different: it sees the decline, checks recent interactions, weighs account value and risk, and can trigger an SMS with a retry link or route the next contact to an agent who already knows the issue. The action happens while the customer is still engaged.

That ability rests on three pillars:

• Unified identity and context: A customer data platform or connected CRM keeps every click, call and payment tied to one profile, even if the person has shifted from anonymous browsing to an authenticated account.
• Intelligent decisioning: Rules and AI models balance relevance with compliance and cost – choosing whether to push self-service, escalate to a skilled agent, or pause other messaging.
• Omnichannel activation. Whether it’s an email, app push, proactive chat, or direct hand-off to the contact centre, the response must travel through the right channel instantly – with full context for the human who picks it up.

For service teams, the change is dramatic. They’re no longer scrambling after a problem has exploded. They can spot it as it happens, adjust, and solve it while the chance to keep a customer happy, and avoid another expensive follow-up, is still alive.

Benefits of Real-Time Customer Journey Orchestration

When service teams can read what’s happening in real time and act on it, the rewards show up fast. Real-time customer journey orchestration cuts service costs, protects revenue, and keeps customers from abandoning a brand when frustration peaks.

The clearest way to see the impact is by looking at the “moments” where speed and context matter most. Each represents a chance to either save a relationship or lose it.

Rescue moments: failed payments, abandonments, and stuck self-service

Few situations create friction faster than a transaction failure or a dead-end in self-service. Traditional systems may capture the error but act too late, often following up hours later with an email that the customer ignores. Real-time journey orchestration (RTJO) turns those critical failures into a save opportunity.

When a payment declines, the platform can instantly attempt an alternate payment rail, trigger a push or SMS with a retry link, or, if the customer calls, route them to an agent who already sees the failure and possible fixes. In self-service channels, if a chatbot loop or authentication issue stalls progress, orchestration tools can escalate to a human before the customer abandons the journey.

For instance, HSBC implemented a real-time system, and cut abandonment rates by 48%, reduced average handle time by five minutes per interaction, and lowered transfers by 32%. Supervisors also gained about two extra hours each day thanks to live insights and routing improvements.

Disruption moments: travel changes, outages, and service incidents

Unplanned events like a flight delay, a broadband outage, or a medical service surge can overwhelm service channels if handled slowly. Batch notifications or static IVR menus simply can’t keep up when thousands of customers need help at once.

Real-time journey orchestration lets organizations push clear, timely updates and adapt routing rules as conditions change. Instead of customers flooding phone lines blind, they can get personalized alerts, self-service options, or direct access to specialized support. Some companies even use insights to stop issues before they happen.

IC24, a leading U.K. healthcare provider, once reviewed barely 2 percent of patient interactions by hand. Today, it analyzes every single one through a real-time analytics platform. That shift has meant faster, safer decisions during sudden demand spikes (including the intense waves of COVID) and slimmer IVR paths that get patients to the right care without delay.

Value moments: catching opportunity while it’s live

Some moments aren’t about fixing what’s broken – they’re about recognizing a chance to add value before it slips away. A customer lingering on a premium product page, an account edging toward a usage cap, a family planning a major purchase. These signals fade fast if a brand waits until the next scheduled campaign.

With real-time journey orchestration (RTJO), service and sales teams can react while interest is still warm. Decision engines weigh browsing behavior, account history, and risk markers, then trigger an action that feels helpful rather than pushy.

For example, at Ambuja Neotia, an Indian real-estate group, instant lead scoring and agent-assist tools mean the most engaged prospects go straight to the right rep. Hot-lead conversions jumped from 40% to 80%, doubling the impact of each marketing dollar.

Effort moments: smooth handoffs when automation stalls

Self-service has its limits. Voice systems mishear names, bots loop endlessly, and authentication can fail at the worst possible moment. What drives customers away isn’t automation itself – it’s having to start over once they finally reach a human.

Real-time journey orchestration keeps that from happening. The system watches for friction, then hands the case to a live agent with everything intact: menu selections, transcripts, account context. The customer moves forward instead of back to square one. Employees get guidance, too.

For instance, brokerage Angel One tied all service channels into one platform and gave agents guided workflows in real time. The payoff: first-call resolution climbed by 18–20% and average handle time dropped 30%, even as remote work reshaped its contact centers.

Experience moments: listening live and improving fast

Great service isn’t just about reacting to obvious events. It’s also about spotting friction before it turns into a complaint. Every digital tap, survey response, or call recording is a clue if it can be processed fast enough to drive change.

Real-time journey orchestration (RTJO) gives service leaders that ability. Feedback and behavioral signals flow in as they happen; analytics engines flag patterns; orchestration tools adjust messaging, routing, or self-service flows the same day instead of weeks later.

Example: Spanish bank ABANCA uses live feedback across contact centers and digital channels to spot pain points and act quickly. The approach has fueled higher acquisition conversion and sped up process improvements.

By treating every click and comment as a potential signal and closing the loop immediately, brands move from reactive fixes to continuous improvement. Agents benefit just as much. Broken workflows get fixed quickly instead of forcing customers to call again and again.

Implementing Real-Time Customer Journey Orchestration

Acting in the moment doesn’t happen by chance. It takes planning – linking identity, live events, decisioning, and every service channel into one fast, connected loop. For customer service teams, getting this right means fewer escalations, lower handle times, and a journey that actually feels connected.

The most important thing? The right architecture. Teams need building blocks for:

• Identity and consent. A customer data platform (CDP) or connected CRM becomes the single source of truth. It keeps track of who the customer is — even as they move from anonymous browsing to an authenticated session — while respecting consent rules.
• Event fabric. Systems need a live feed of signals: failed payments, app errors, delivery updates, usage spikes. Standardizing those feeds keeps triggers reliable.
• Rules and AI models decide what should happen next. They balance urgency, relevance, and compliance – for example, suppressing a marketing email while routing a payment failure to an agent.
• Once a decision is made, the action must happen instantly: an SMS, app push, proactive chat, or a fully contextual hand-off to the contact center. Modern CCaaS platforms increasingly build this natively for instance, check out Genesys Cloud’s journey management capabilities and NICE’s orchestration innovations
• The leaders in orchestration keep a close eye on first-contact resolution, transfer rates, abandonment, containment in self-service, and how much effort customers actually spend. They add voice-of-customer sentiment to see whether journeys feel easier.

Building this doesn’t require a massive, years-long overhaul. Many teams start small: tie together identity and event data, launch a few high-impact triggers, and grow once the results prove the value

The Future for Real-Time Customer Journey Orchestration

Real-time orchestration today is mostly about reacting well when something happens. The next wave will go further: predicting and preventing friction before the customer ever feels it.

One driver is agentic AI – systems that don’t just suggest next steps but quietly reshape journeys in the background. These tools will summarize interaction history, recommend compliant responses, and update rules when patterns shift. Instead of waiting for analysts to re-map journeys, the platform itself will fine-tune flows as new behaviors emerge.

Another change is predictive service. By combining journey analytics with machine learning, platforms can spot early signs of trouble – like unusual app activity or network data that hints at a looming outage – and trigger preemptive outreach. Customers might get a helpful notification or an alternative payment option before they even know there’s a risk.

Governance will matter more, too. As orchestration engines start to make proactive decisions in regulated industries such as banking, healthcare, and utilities, companies will need transparent audit trails and clear consent management. Decisioning can’t be a black box when compliance and trust are at stake.

For customer service leaders, this shift means fewer angry calls and lower costs, but it also means new skills: journey scientists who tune models, CX strategists who weigh risk and reward, and operations teams ready to roll out changes fast. The brands that build this muscle now will be ready when orchestration moves from reacting in seconds to preventing problems altogether.

Building an Engine for the Moments That Matter

People make up their minds about a brand in fast, fragile moments – when a payment fails, a call drops, or a service hiccup ruins the day. Real-time customer journey orchestration flips those points of friction into chances to help, keep revenue on the table, and avoid another round of costly support.

The approach is straightforward: stay tuned to live events, understand who they affect, and step in right then, while the moment still matters.

Ready to upgrade journey orchestration? Explore our guide to the power of generative AI in CJO, or discover how to scale safely, with this article on secure, scalable orchestration.

The problem is simple: systems don’t talk to each other. A customer starts a conversation on live chat, calls later to follow up, and then gets an email with conflicting information. Each hand-off forces them to repeat details, re-authenticate, or explain the issue all over again. Patience runs out quickly.

The cost isn’t hidden for long. U.S. companies lose an estimated $136.8 billion every year to avoidable churn. Customers leave when systems don’t connect, data is trapped in silos, workflows run in isolation, and departments push their own priorities instead of working toward the full journey.

Fixing that takes more than patches. It needs stronger journey orchestration, along with omnichannel workflow design and dependable CDP integration. The aim is for every channel to draw from the same source of truth, so the customer isn’t forced to start over at each step.

Fragmented Journeys: The Hidden Cost and Causes

The cost of fragmented customer journeys isn’t always obvious. Customers don’t usually complain about “systems not talking to each other.” They just get tired of repeating themselves, chasing updates, or being bounced between departments. Some walk away silently. Others switch to a competitor after one poor experience. That lost loyalty is expensive.

All the while, customers that get connected experiences are helping brands grow. Studies show customers who get “excellent” experiences spend about 140% more.

The Causes of Fragmented Customer Journeys

Why are fragmented customer journeys still getting worse? A big part of the answer lies in the systems. Older ERP platforms were built for accounting and operations. They store useful data, but they weren’t designed to share it across customer touchpoints.

On top of that, many firms still run sales, service, and fulfillment on different platforms. Each team shapes processes around its own system, so when customers move between departments, the context often gets lost.

Then there are issues created by:

• Multiple versions of the same customer: Without solid CDP integration, one person might exist in several databases under different IDs. That makes personalization, and even basic service, harder.
• Channels that don’t connect: Phone, email, chat, apps, and stores often sit on different platforms. Customers expect one conversation. Businesses deliver five.
• Processes that drift: Marketing offers a refund or discount, but the policy never makes it to the billing system. Customers get conflicting answers depending on who they ask.
• Automation in silos: Generic automation often backfires. A bot that can’t see the full journey adds more friction, not less.
• Slow-moving data: By the time an update syncs between systems, the customer has already called back.
• Compliance barriers: Privacy and security rules matter, but poor design can block the very context agents need to help.

Taken together, these gaps explain why customers feel let down. The business may see good metrics in one channel, but the overall journey tells another story. Until the foundation is fixed, journey orchestration and omnichannel workflow automation tools can only go so far.

Unifying Journeys: The Journey Orchestration Tech Teams Need

When customers say they feel like they’re dealing with “five different companies at once,” it’s rarely the fault of the service team. The problem sits in the systems. Fixing fragmented customer journeys means building a stack where data flows from the back office to the front line without friction.

Cloud ERP Integration

Most ERPs were built to balance books and manage inventory. They weren’t built to answer a customer who asks, “Where’s my order?” That’s why cloud ERP integration is now so important. When ERP data is connected directly to sales and service platforms, answers come back in seconds instead of days.

Cloud ERP changes that. By connecting ERP directly with CRM and service systems, data is available in real time. Smarter Furnishings made this upgrade with Microsoft Dynamics 365 and reduced quote turnaround times by 80 percent. That kind of improvement comes from eliminating the delays caused by disconnected back-office systems

CDP Integration

Most companies hold records with multiple versions of the same customer. A single person might appear in the marketing database, the CRM, and the billing platform under slightly different records. This duplication makes personalization impossible and creates obvious gaps in service.

A customer data platform (CDP) takes scattered records and pulls them into one profile. It updates as new information comes in, so teams aren’t working from old or conflicting data. That single view makes it possible to keep the journey consistent when a customer moves from one channel to another. With CDP integration, journey orchestration tools have a reliable record to draw on instead of piecing together fragments.

Combining Customer Journey Orchestration and AI Decisioning

The orchestration layer is where data turns into action. Journey orchestration engines like the industry-first solution from NiCE take context from CDPs, CRM systems, and ERPs, and use it to determine the next best step in the customer’s journey. That may mean routing a case to the right team, sending a proactive update, or triggering an RPA process in the background.

Qualtrics research shows that effective orchestration can boost revenue by 10–20 percent while reducing service costs by 15–25 percent At FedPoint, NiCE CXone drove similar results in practice: IVR containment increased from 28.5% to 33.9%, customer satisfaction rose to 98.35%, and average answer speed fell from 35 seconds to 15 seconds.

Omnichannel CCaaS

Customers don’t think in terms of “channels.” They expect one continuous conversation, whether they start with a phone call, follow up via chat, or receive an email confirmation later. Without a unified contact platform, those experiences quickly fracture.

That’s why omnichannel workflow through contact center as a service (CCaaS) is now a priority. BankUnited’s deployment with Talkdesk shows the results: self-service adoption increased by 16%, abandonment fell to 5.3%, and NPS more than doubled.

Automation and CRM Intelligence

Even with orchestration in play, journeys can stall if the back office is still running on manual tasks. That’s where RPA comes in. It takes on work like refunds, policy checks, and updates, jobs that would otherwise create delays and frustration.

On the front line, CRM automation does the heavy lifting for agents. AI creates summaries automatically, enriches profiles with data, and shares recommendations with agents in real time. The agent spends less time searching and more time solving. That combination speeds up resolution and helps ensure the journey doesn’t break in the final mile.

How to Start Reducing Journey Fragmentation

There isn’t a quick fix for fragmented customer journeys. The organizations that succeed usually take it step by step. They get the basics right, test in a few focused areas, and only then expand.

• Begin with the data: If core systems don’t share information, the journey will eventually break. That’s why so many CIOs are prioritizing ERP and CRM integration, or even tying in CDP solutions, before layering on orchestration.
• Create a single customer profile: A CDP integration pulls records together from sales, marketing, and service. It means every interaction draws from the same source of truth. Without that, different teams are still working off different stories.
• Pilot orchestration on high-value journeys: Trying to orchestrate everything at once rarely works. Pick a few critical touchpoints, like order tracking or benefit enrollment, and build orchestration around them. A Middle Eastern bank did this with Kore.ai, and eventually achieved 40% automation rates for workflows, as well as higher CSAT scores.
• Add omnichannel contact. Customers don’t think in terms of “phone” or “chat.” They want one continuous conversation. Moving to CCaaS platforms helps deliver that. Particularly when those systems can speak to ERP, CRM, and CDP solutions.
• Automate the back office. Journeys still fail when refunds or approvals sit in manual queues. RPA can process these instantly, while CRM automation gives agents context without the need to dig. Together, they prevent small delays from becoming big frustrations.

Also, measure what matters. Efficiency metrics only go so far. Average handle time may look good on a report but say little about customer loyalty. Outcome-based measures – resolution rates, effort scores, verified completions, tell you whether fragmentation is actually being reduced.

Journey Orchestration: From Fragmentation to Flow

Plenty of firms talk about improving customer experience. The real progress comes from those willing to confront fragmentation directly. They modernize their data foundations, connect ERP and CRM, put CDP integration in place, and then add journey orchestration and omnichannel workflows. Each layer builds on the last, creating a system that actually holds together.

The rewards are measurable. Containment improves without hurting satisfaction. Resolution times drop. Customers stop repeating themselves at every turn. Plus, loyalty grows stronger, the ultimate measure of success in competitive markets where switching costs are low and alternatives are one click away.

Journey orchestration is only going to matter more as AI takes on a bigger role in customer experience. But AI that runs on inconsistent data won’t deliver. Reducing fragmentation is the first step. Once that’s done, journeys become faster, cheaper to support, and more likely to end in loyalty instead of churn.

In addition to the annual SAP Sapphire, it has rebranded and expanded its SuccessConnect event, so practitioners, partners, and execs can keep their finger on the pulse.

At this year’s event, that pulse raced, with many massive announcements including AI agent advancements, a new engagement platform, and much more.

Celebrating the event’s many headlines, Muhammad Alam, Member of the Executive Board of SAP SE, SAP Product & Engineering, said:

Our announcements today demonstrate the power of SAP Business Suite, where AI, data, and applications come together in an experience to propel smarter decisions, faster execution, and scalable transformation.

That suite envelops the enterprise, yet its customer experience portfolio is a key cornerstone.

Given that, here’s a rundown of the event’s biggest news, with extra emphasis on CX.

1. SAP Introduces “Role-Aware” AI Assistants

SAP has announced the next step in its Joule and AI agent journey: role-aware AI assistants.

These assistants partner with a person in a specific business role to support them in fulfilling tasks across the SAP Business Suite.

In doing so, they spot tasks the employee needs to accomplish and tap AI agents to get the job done, configuring, orchestrating, and managing them.

SAP also unveiled an array of new Joule Agents to support these Assistants in getting those role-specific tasks done.

For instance, it gave the example of a People Manager Assistant, which may evoke a People Intelligence Agent to isolate and solve compensation anomalies and similar issues.

Critically, the assistants complete tasks that cross various SAP systems, breaking down silos.

The company’s next move is likely to have them operate across SAP and third-party systems that customers often integrate with.

In the future, the Assistants in Joule may even perform new tasks based on specific brand goals and engage in self-reflection. After all, that’s the future for AI agent technology.

2. SAP Launches Business Data Cloud Connect

The SAP Business Data Cloud (BDC) Connect is the next big launch from the event. It links with third-party, partner platforms to “enable a bidirectional flow of business-ready data products”.

In other words, the tech giant will work with adjacent tech providers to build bridges from their platforms to the SAP Business Data Cloud, enabling better data sharing and cross-platform AI agent applications.

With zero-copy sharing, SAO ensures data stays securely in its systems, yet is accessible from other tech solutions, to preserve business context with copies.

Databricks and Google Cloud are the first big-name brands to partner on SAP BDC Connect. However, the tech giant promises that more will soon follow.

3. SAP Engagement Cloud Is the Big Customer Experience News

The big customer experience news from SAP Connect 2025 is the debut of the SAP Engagement Cloud, which the company describes as a “unified system of engagement”.

Aligned with the SAP Business Data Cloud, it aims to unify data from customer-facing departments and orchestrate communications that cross marketing, sales, and service.

While it’s likely to sit in the marketing department, who may use it to run cross-channel campaigns, service teams may – for example – use it to trigger proactive messages based on data signals that indicate the customer has an issue. They may even employ the platform’s native Joule Agents to turn these insights into such actions.

In this sense, SAP aims to unify departments with the SAP Engagement Cloud, helping them share technologies, align CX initiatives, and think further beyond their functional domain.

Brands can now register to participate in a limited beta and get early access to the solution, which will become generally available in 2026.

4. SAP Customer Loyalty Management Expands Its CX Portfolio

SAP Customer Loyalty Management is another significant addition to the SAP CX portfolio, targeting retailers and consumer packaged goods (CPG) companies.

Teased in June, the solution centers on a “loyalty profile”, which empowers end-customers with a place to track rewards, view entitlements, and redeem personalized offers.

Businesses may run analytics initiatives across these profiles, with embedded metrics that allow them to track the performance of loyalty promotions, programs, and activities.

They may also create new loyalty initiatives on the platform, share gifts with customers across channels, and develop shared loyalty programs with partners.

Now, SAP does already offer a loyalty management solution: Emarsys Loyalty. Yet, by spinning up a new solution, SAP makes its capabilities more accessible to brands that don’t want all the bells and whistles of a full-scale customer experience platform.

The solution is set to reach general availability next month.

5. Another New Solution: SAP Supply Chain Orchestration

Before getting into the other major CX announcements, SAP notably announced yet another solution: SAP Supply Chain Orchestration.

With embedded Joule agents, working with a live knowledge graph, it detects real-time risks to supply chains and orchestrates an appropriate, coordinated response, with prioritized actions.

The solution takes data from SAP Business Network and the SAP Business Data Cloud to monitor “every tier of the supply chain”.

Indeed, it links closely to SAP Business Network to contextualize detected risks and notify the relevant parties – across planning, procurement, logistics, and manufacturing – which may be impacted.

Interestingly, Supply Chain Orchestration may also boost customer experience, helping increase visibility into stock and when goods may become available.

SAP expects the solution to become generally available in the first half of next year.

6. New Joule Agents for Service, Sales, and Commerce Teams

SAP has unleashed many new Joule agents across its portfolio. However, three of the most notable impact service and sales teams.

First is the Digital Service Agent. It offers conversational customer service across digital channels, business portals, and e-commerce platforms. It interacts by leveraging customer conversation context, history, and knowledge base data without needing to pre-configure intents.

Interestingly, it integrates with SAP workflows, which stretch beyond the front-office and into back- and middle-office SAP systems for more expansive resolution flows. That’s its differentiation.

For sales, a new Quote Creation Agent transforms email quote requests into “ready-to-send” quotes and orders, which sales and order management teams can review, edit, and pass on.

Finally, a Catalog Optimization Agent updates and optimizes product data to enhance the “accuracy and agility” of merchandising.

Other new Joule agents span finance, spend management, and HR.

7. SAP Unveils a Customer Self-Service Agent for Utilities

Alongside the Digital Service Agent, SAP has announced a Utilities Customer Self-Service Agent.

The solution integrates directly with SAP Cloud ERP Private solutions, so it utilizes data not only in a CRM or CDP, but broader customer context. That may include contract, tariff, product details, consumption data, etc.

Moreover, the Utilities Customer Self-Service Agent, which comes part-and-parcel with SAP for Utilities solutions, hopes to address specific industry pain points, including market regulations, prosumers, and beyond.

The Agent acts like a Joule agent – so can shift between systems – will reach general availability later this quarter.

8. SAP Integrates Its CX Portfolio with WalkMe

SAP proved ahead of the observability trend when it acquired WalkMe for $1.5MN last year.

The solution overlays an enterprise’s tech stack and offers visibility into how applications are performing and the workflows that run between them.

In doing so, it surfaces inefficiencies, suggests fixes, and recommends where businesses can build better workflows. On this last point, it may also spotlight opportunities for AI agent deployments.

Since the acquisition, SAP has integrated WalkMe with its broad suite. Its latest move is integrating it with SAP CX, allowing customer-facing teams to leverage the solution without IT involvement and disrupting current business flows.

Ultimately, SAP hopes to provide its CX customers with more guidance on using its solutions more effectively and automating new processes.

As such, they may recognize new opportunities to better automate support requests, accelerate onboarding, boost data accuracy, and more.

The integration will be generally available this quarter, and businesses can leverage an embedded version for free. However, a full, customizable WalkMe Premium solution will come at a cost.

9. SAP Jumps on the Revenue Intelligence Bandwagon

Many analytics platforms, like Gong, have shifted to become revenue intelligence solutions, emphasizing their capability to help decipher what drives a business’s revenue.

SAP has embraced this shift, announcing a Revenue Intelligence application in SAP Business Data Cloud, so that sales teams can better understand their sales pipeline and customer health. That comes not only in the form of static insight but also recommendations that help manage deal risk, accelerate the deal cycle, and uncover new opportunities.

This underscores a much more significant shift in business reporting. Analytics tools no longer surfacing insight; they’re prescribing actions on the back of those data points.

10. Meet the New-Look SAP Ariba

To finish, let’s step back from CX and consider SAP Ariba. It has a long history, dating back to the 1990s, yet SAP is rolling out the next generation of Ariba solutions for source-to-pay.

These solutions include many new capabilities, such as a simplified user interface with a central SAP Ariba launchpad that provides clear navigation, surfaces to-do items, and spotlights insight.

There are also new AI tools to assist users with tasks like reviewing contracts, analyzing bids, and generating supplier summaries.

Additionally, SAP Ariba now includes automated sourcing, enhanced 360-degree supplier profiles, and a new central intake management feature.

However, perhaps the most significant move is that SAP Ariba now sits on the SAP Business Technology Platform, enabling simpler integrations with SAP apps and third-party ERP systems.

What Else Did SAP Announce?

Onlookers outside the customer experience space may have added several other announcements to their top ten list.

For instance, there is a new skills-based hiring feature for SAP Fieldglass and a SAP Signavio Process Transformation Manager. That’s alongside all the other Joule agents and capabilities shared in the new SAP Cloud ERP Private release.

To learn more about all these, head over to the SAP website. However, for more on SAP’s CX journey and ambitions, check out our article: SAP Is Building a “Modern and Composable” Customer Experience Suite

Sean Taylor, CEO of Content Guru, was a judge and, as part of the evaluation, sat down with one of the finalists’ agents to watch how they worked.  

“They were taking a customer’s payment and had a stack of yellow Post-it notes on their desk,” said Taylor during a recent CX Today interview. “They typed the credit card details into one system, then quickly wrote them on a note.  

“When I asked why, they explained that the information also needed to go into another system, but they couldn’t do both at once.” 

Yet, imagine leaving the building with a pocket full of Post-it notes containing names, credit card numbers, expiry dates, and CVV codes… 

Needless to say, they didn’t win the competition. Nevertheless, the story illustrates just how difficult it is for many agents to switch between systems. 

For all the talk of a “single pane of glass,” a Deloitte study in the UK found that the average contact center agent still works with around 14 separate systems of record. 

Ultimately, that illustrates that there is no silver bullet. A closely considered omni-data strategy is instead the best path forward. 

What Is Omni-Data? 

Just as omnichannel communication brings together voice, email, RCS, WhatsApp, and video into one platform, omni-data brings all an organization’s data systems together. 

Whether it’s Salesforce, Microsoft Dynamics, or proprietary systems like Guidewire in insurance or Epic in healthcare, an omni-data strategy surfaces them in one place. 

Agents can push and pull information without resorting to Post-it notes or duplicate entries. 

Content Guru is leading the omni-data charge by converging its CCaaS solution with a first-party customer data platform (CDP), enabling that single data thread.    

“To me, that’s a no-brainer,” added Taylor. “Yet when we deliver it, customers often say, “Wow, that’s unique.”  

“Other vendors may have strong connectors into Salesforce or Dynamics, but they don’t address the full challenge of integrating data across systems.”  

Omni-Data and the Future of Customer Service  

Omni-data provides a single source of data for agents to push and pull from. Yet, that’s not only human agents; AI agents can also do so.  

Indeed, contact centers may orchestrate experiences where AI agents interact with customers while leveraging data from one a single layer, instead of pulling from a myriad of integrations, which require continuous, rigorous security checks. 

Yet, they won’t just drink from the hosepipe; they feed back into it. 

As such, contact centers can advance their conversation automation strategies, thinking not only about inbound but also outbound.  

After all, the future of customer service isn’t only reactive, it’s proactive, and even pre-emptive.  

With an omni-data strategy, contact centers can detect signals from various systems that indicate the customer is experiencing (or is about to experience) an issue. From there, an AI agent will trigger a corresponding resolution flow and communicate that with the affected customer.  

Here are four cross-industry examples of what that proactive flow may look like in practice. 

1. Proactive Servicing & Repairs (Automotive & Retail)

IoT devices can send signals into the omni-data layer from sensors hidden within products. That includes everything from transport to white goods. 

The devices help monitor product health, so if a car has low tire pressure or a washing machine is about to give up, they can trigger a flow that ends with an AI agent scheduling a repair with the affected customer. 

2. Proactive Pipe Management (Water) 

Don’t think of only first-party data sources as triggers for a proactive flow. Public sources can also feed into the omni-data layer.  

For instance, data from the weather forecast could filter into a water company’s omni-data layer. With that data, an AI agent can predict whether a water supply pipe will freeze and when it may unfreeze, rather than just telling the customer to schedule an engineer.

3. Proactive Network Updates (Telco)

Telcos can feed data from their Network Monitoring System (NMS) into the omni-data layer, with AI agents on alerts for an outage.  

When this occurs, they can proactively notify customers and send updates, keeping them in the loop, so affected customers don’t have to contact customer service. 

4. Proactive Journey Recoveries (Travel)

Plane and train delays often cause passengers to miss their connections. Yet, by plugging data from the Traffic Flow Management and CRM systems, companies can charge AI agents with rebooking customers on the next-best journey. 

As such, when the customer arrives or lands, they don’t have to queue for customer support. Instead, they receive an alert with their new booking or alternative options to consider.  

Get Ahead with Content Guru  

Content Guru entered the cloud contact center market in 2005, before “cloud software” was a term that many people had ever used.  

Its parent company, Redwood Technologies, subsidized the business for the first eight years because adoption was slow. But, Taylor and his team believed the cloud would matter. 

The same happened with AI. Content Guru invested years before the current boom, before COVID delayed adoption, but models like ChatGPT accelerated it again. 

“Our philosophy is to blend customer-driven innovation with long-term bets based on intuition and experience,” said Taylor. “Sometimes we’re early, which can be frustrating, but when the market catches up, we’re ready.” 

Its next bet is omni-data. Given its track record and status as a global CCaaS provider, who’d bet against the company this time around? 

For more on Content Guru’s contact center tech, visit:  www.contentguru.com  

Most, if not all, are banging the AI drum. In 2024, some thudded much harder than others. Yet, in 2025, AI integration is much deeper in the tech stack and more consistent across vendors.

The use cases aren’t surprising, including self-service, AI assistance, conversational intelligence… these were all big last year too, but now are more embedded and refined.

Still, some are ahead of others, but the gap in terms of capability is closing, and spotting what sets the vendors apart gets trickier.

Against this backdrop and the explosion in contact center options, the ISG Buyers Guide goes deeper into the changing market and the vendors leading from the front.

ISG’s Inclusion Criteria and Methodology

ISG prides itself on offering a broader scope of the contact center industry, evaluating 33 vendors, which all provide technologies that route customer interactions to human and AI agents.

All evaluated providers offer that base capability, agent desktops, and other “elements of yore”. Yet, ISG also honed in on the “expanded universe of tools that support and extend” the modern contact center platform.

Those include advanced analytics, conversational AI for self-service, knowledge management, and workforce optimization (WFO) tooling.

Either through first- or third-party systems, there are potentially hundreds of vendors that meet these requirements. So, ISG also specified that providers must have at least $50MN in annual or projected revenue, 50 employees, and 25 customers spread across two or more continents, alongside other criteria.

With its shortlist of vendors, ISG evaluated each across seven core categories. The first five relate to the product experience:

1. Adaptability
2. Capability
3. Manageability
4. Reliability
5. Usability

The final two relate to customer experience:

6. Validation
7. Total Cost of Ownership/Return on Investment (TCO/ROI)

In doing so, ISG sorted through product demos, documentation, and a lengthy questionnaire to score the participants, shedding light on how their offerings match up.

The matrix below unpacks how each provider performed, while ISG’s overall scores are also available in the table to the right.

Top Takeaways from the ISG Buyers Guide 2025

Alongside its overall scores, ISG provides more information in its report regarding each vendor’s strengths and weaknesses.

Yet, the additional industry insights it shares most differentiates the analyst’s evaluations, giving buyers greater context on which to consider vendors and the broader market.

The following seven top takeaway extracts some of that insight, with additional commentary from the report’s lead author, Keith Dawson, Director of Research for Customer Experience at ISG.

1. Contact Center Buyers Have More and More Options

Vendors with a background in CRM, UCaaS, and other adjacent fields are making a beeline for the CCaaS space. That has given buyers a much broader range of options, from companies with very different origins and approaches. This means buyers can orient their decisions differently.

Dawson noted: “Some may still start with routing engines, but others begin with data infrastructure, CRM, or CDP, then build outward. A big decision point now is whether to focus on voice or digital first. That choice is leaving some traditional vendors behind.”

To that point, while voice remains crucial, digital is becoming the dominant channel. “It’s the present as much as the future,” summarized the analyst. That shift is happening faster than many legacy brands seemingly anticipated.

2. CCaaS Decision Making Is Shifting

Contact centers used to have their own IT offshoots, focused primarily on telecoms. IT leaders would happily leave customer support in a silo because it meant dealing with voice and phone numbers, which was tricky and, frankly, mind-numbing.

Yet, enterprise IT and the CTO now play a central role in CCaaS buying decisions. They arbitrate across marketing, sales, and service to ensure tech stacks integrate properly. That means buying decisions are increasingly collaborative.

“You can’t just buy a contact center solution in isolation anymore. IT, marketing, and service all need to be involved, because compatibility and integration are critical,” said Dawson. “A poor choice can hold an organization back for years.”

3. CCaaS and ITSM Are Converging

With IT playing a greater role in buying decisions, they will likely lean into contact center platforms that closely align with their solutions. That trend may boost Genesys and Zoom, as CCaaS vendors move toward tightening the gap between customer service and IT service management (ITSM).

Yet, from a broader perspective, with IT taking a leading role, they can see technology overlaps between the contact center and other departments that the old telecom-focused IT groups didn’t care about.

“Now, enterprise IT is looking across the whole structure, including contact centers, and asking why ITSM applications can’t also be applied in customer environments,” said Dawson. “The natural linkages are much clearer.”

4. The Standout Performers Have Embraced Industry Consolidation

Most vendors ISG evaluated can deliver a strong, basic contact center system. The gap there isn’t huge. The speed at which they’ve consolidated new technologies into their platforms separates the standouts from the middle of the pack, per Dawson.

“AI is the big one, but also advanced analytics and openness to integrations, especially with back-office systems,” he said. NiCE is perhaps the best example of a business building out those back-office integrations, with many big announcements so far in 2025.

Meanwhile, CCaaS solutions built on other platforms, most often Microsoft Teams, lag a little in the matrix, due to their reliance on external systems and related innovation limitations. However, Dawson stresses that they still may be the best for mid-sized buyers looking to converge systems.

5. CCaaS Leaders Recognize an Opportunity for Deeper Analytics

The likes of AWS, Microsoft, and Salesforce are increasing their presence across the CCaaS space. Notably, these all have powerful business intelligence (BI) tools.

Recognizing this, Dawson said: “If BI tools evolve into customer interaction analytics, the whole market could shift dramatically.”

Indeed, BI solutions could bring powerful new capabilities to contact center platforms. Just consider what is on the roadmap for platforms like Microsoft Power BI and Tableau; they’re going agentic. That means they are creating AI agents that spin up dashboards on command, even without predefined parameters.

As these capabilities drift into the contact center, Dawson stresses that it will become easier for business leaders to extract insights from data they didn’t use before.

Yet, the analyst suggests that the future of contact center analytics won’t be about just presenting insights but corresponding actions, too.

6. Agent Assist Adoption Is Lagging Expectations

From the ISG evaluation, vendors are innovating significantly around agent experience. Yet, according to Dawson, the take-up of agent-assist tools seems to be trailing expectations.

“Agent assist requires a strong knowledge management infrastructure and a data strategy, which may be slowing adoption,” he said. “Not every organization is ready for that investment yet.”

The analyst also cites an overriding emphasis on customer-facing automation and the need for extensive change management as possible adoption blockers.

“There are also questions about whether the incremental improvement justifies the disruption and costs,” added Dawson. “Some organizations will move faster than others.”

7. Questions Linger Over Contact Center AI

The next 12 months will ask CCaaS vendors many questions about whether they can deliver on their promises, especially those around cost savings and containment.

Essentially, does it reduce headcount, and if so, how does that impact customer experience?

As those questions start receiving answers, Dawson hopes that the mentality of many contact center buyers will shift away from cutting costs and that they’ll start asking more strategic questions about revenue, loyalty, and the contact center’s role in customer experience.

“Modern tools can enable that shift, but it’s up to leaders to recognize and act on the opportunity,” said Dawson.

Eager to learn more from Dawson and the ISG team? If so, check out CX Today’s top takeaways from the ISG Customer Experience Management Advanced Buyers Guide 2025