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.

At London’s annual Call and Contact Centre Expo, the telecommunications company revealed how its customer-centric approach to limit consumer frustration has improved its overall loyalty.  

This solution is aligned with Vodafone’s strategy to transform customer experience. 

Customer experience results have been improved since the strategy launch in July 2025, with 9 out of 15 of its markets leading with this new strategy, resulting in a six percent reduction in company detraction after the first few months. 

When first researching customer experience strategies, Vodafone discovered that seamless interactions were a high priority for customers, with frequent causes of company detraction relating to negative customer experiences. 

These incidences included: holding for additional agents, transferring calls, repeated conversations, and a company’s failure to keep promises. 

In fact, they had discovered that customers who had experienced at least one bad experience from customer service were 4 times more likely to abandon the company. 

Melda Sofuoglu, Global Senior CX and Service Excellence Senior Manager at Vodafone, explained how the customer expectations have grown since the rise of AI in the CX space:

“We are operating in a rapidly changing industry – expectations have grown to 24/7 service.” 

However, research revealed that as long as customer interactions remained seamless, then customers would be more likely to remain with a company that avoided friction. 

And when companies failed to deliver on results, 46% of customers would research Google to find answers to their issues, driving enterprise intensity to produce better results as customers discover what sets the bar in the CX space. 

Just Ask Once

Vodafone has since researched some of the leading customer services spaces online, including Google and Octopus, to take elements and utilize the most productive strategy for resolving customer complaints. 

The strategy, known as ‘Just Ask Once’, began in 2024 to target pain points in the company’s Albania market, with the aim of resolving a customer issue after just one interaction. 

This strategy utilized generative AI omnichannel service to avoid customers re-explaining issues and call waiting times to resolve queries, whilst also keeping customers in the loop if issues cannot be solved immediately to avoid confusion or miscommunication via text. 

This is also done by deploying Vodafone’s suite of capabilities, such as Super-TOBi, a generative AI assistant that handles complex conversations and queries in comparison to the standatd TOBi chat bot. 

This strategy, however, is not designed to elminate human agents from the mix, but rather to place them at the center of this strategy with chat bots as a second option during traffic spikes, with many of these queries being completed through human agents rather than bots to avoid frustration. 

These bots simply allow agents to complete Vodafone’s vision for setting the new standard for customer experience centered around meaningful interactions and added loyalty, even when mistakes occur. 

This has involved significant investments in human agent training to keep them adapted and involved in the consistently changing state of customer experience, rather than eliminating their positions for AI-only service. 

However, Vodafone has experienced setbacks in this strategy amongst social media responses. 

Aimie Jago, Global Senior CX and Service Excellence Senior Manager at Vodafone, explained how the company are managing the influx from social media:

“In some markets we see some backlash on social media – its more about previous customer experience through chat bots.” 

Resolving past customer interactions remains challenging for Vodafone, arguing that to tackle this previous frustration into returned loyalty they need to experience this new transformation. 

This has affected the company’s ROI after significant investments; however, they are expecting to receive this once business case justification takes place. 

Furthermore, Vodafone’s continues to remain vulnerable, as this strategy depends primarily on technology, investment, and strong sponsorship, due to its small space within the CX space.  

Today, this strategy is being implemented by Vodafone’s global markets, aiming to champion the voice of the customer by creating a strong customer community, available to customers through the Vodafone app. 

Amidst this pressure, there are opportunities for AI to boost these metrics and help marketers to prove ROI.

For tech buyers in evaluation mode, it’s critical to recognize which marketing KPIs they’ll be judged on, and which tools can improve those metrics.

Follow the Money: Connecting Every Click to Revenue

What it is: This metric looks to measure the qualified business opportunities that have either been sourced or influenced by the work of the marketing team.

Teams measure this by tracking engaged potential customers and the touchpoints they’ve interacted with, whether its demos, landing pages, chatbots, or newsletters, for example.

With this information, CMOs and the board can assess which channels are the most valuable. This allows for resources to then be allocated appropriately.

How AI helps: With its capabilities for advanced data analysis, AI can stitch every meaningful touch back to the account. Whether it’s an ad click, webinar, email, or site visit.

In the past, attribution models prioritized a customer’s first touch (the first interaction they have with your brand) or the last touch (the final step before conversion).

However, AI software can now see the entire complex multi-touch journey of the modern customer. In practice, that means less spreadsheet wrangling and a defensible, CFO-ready number tied to the CRM.

Filtering only successful conversions helps eliminate vanity metrics and reward channels that genuinely drive results. And AI can keep all of this shared data clean automatically, reducing errors and letting sales reps sell instead of sorting through systems.

Predictive AI: Prioritizing the Prospects That Matter

What it is: Marketing teams are often measured on how well they combine with the downstream sales reps. This is usually done by looking at the quality of leads they pass to Sales, and the speed (Lead Velocity Rate) that they do this.

These metrics let decision-makers predict revenue and assess how efficiently demand turns into opportunities.

How AI helps: The first key step to improving lead generation is smarter prioritization of potential customers.

Predictive AI ranks prospects by behavior and intent, helping sales reps focus on high-quality leads faster.

AI can also take a conservational form to handle FAQs and automatically book meetings for high-intent visitors. This shortens the time from website visit to sales meeting, boosting the number of priority leads.

Smarter Spend, Sharper ROI

What it is: Beyond revenue considerations, marketers are also expected to deliver ROI in a financial sense.

One of the most common marketing KPIs is the customer acquisition cost. It measures whether marketing investments deliver strong returns.

These costs can include the spending on paid advertisements, creative production costs, conference travel budgets, and the costs of marketing tools being deployed.

How AI helps: Generative AI reduces the cost of creating marketing content. Marketing text, visuals, and landing pages can all be crafted by AI to suit unique target audiences with ease.

Furthermore, effective audience analytics reveal which channels customers use most. This will help an enterprise to decide whether they should pause or cut underperforming paid campaigns.

AI That Thinks Like a CMO

For buyers in evaluation mode, the mandate is clear: choose AI that does the boring work brilliantly – identity, attribution, prioritization, scheduling, and hygiene.

Whatever KPI they pursue, AI enables marketers to focus on high-value work like positioning, discovery, and closing.

Navigate the sales & marketing landscape

To discover how technology can transform workplace productivity in your marketing team, dive into CX Today’s Ultimate Guide to Sales & Marketing Technology.

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

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

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

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

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

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

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

He said: “Most companies can’t analyze interaction data at scale, leaving many common customer issues in the dark.” 

However, this acquisition provides enterprises the capabilities to view all customer conversations through unifying collected data. 

He added: 

“UJET’s acquisition of Spiral will provide businesses with a unified view of all customer conversations for more proactive, personalized service.”

This will also help enterprises locate blind spots in other areas of the business, such as product, other services, and the company itself. 

In conversation with CX Today, UJET VP Product Marketer, Matthew Clare, highlighted how other areas of companies can utilize this tool to understand their customers’ needs:

“This could be used by product teams to understand product and service issues – by marketing teams who want to understand what customers are saying about campaigns that are running.” 

Spiral’s AI Product 

Spiral is an AI startup specializing in conversational analytics to improve customer experience data. 

By leveraging AI, Spiral can be used to analyze customer interactions at scale to uncover pain points in customer experience, whilst also offering proactive recommendations to enterprises. 

The product can also be used to analyze various customer conversations across voice and chat channels, the internet, online reviews and surveys, and social media. 

Clare stated: “Anywhere customer conversations happen is a data source for this product.” 

Furthermore, this tool can be used to ask questions about customer churning and how enterprises can respond to these results through predictions to improve future customer experiences. 

“They are trying to solve the problems of customer conversations and customer feedback being spread across different teams and organizations,” he said. 

“How do you not only unify data but bring it together in a way that anyone in the organization can run deep research with a simple conversational AI agent?” 

This acquisition allows UJET to strengthen its status as a prominent CCaaS platform provider and offer customers an improved version of what is already available. 

Clare explained that the purchase will extend “UJET’s reach and gives us the ability to sell Conversational Analytics over the top of any Contact Center and CX software that may be in place, without having us need to position our end to end CCaaS platform.”

For Spiral, this acquisition will allow them to continue providing conversational intelligence alongside UJET’s AI service capabilities, rebranding as Spiral by UJET. 

Elena Zhizhimontova, Founder and CEO of Spiral, discussed how the acquisition will allow them to prioritize a customer-focused plan and continue to improve customer outcomes for a wider enterprise range. 

She said: “We built Spiral to take millions of customer conversations and turn them into clear, actionable insight,”  

“By combining Spiral’s AI with UJET’s cutting-edge CCaaS platform for modern-day customer service, Spiral by UJET will continue as the focused product our customers rely on, now with a more CX-driven roadmap and deeper integrations. 

“Together we can shine a brighter light on customer issues for more organizations worldwide, giving brands the clarity they need to spot issues sooner, address problems faster, and create better products, services, and experiences over the long term.”

Customer Feedback

This partnership will allow current and future customers of UJET to experience Spiral’s product integrally by improving its overall AI and product organization. 

Turo, a long-term customer of both UJET and Spiral, has reaped the benefits of both these companies’ approaches to solving customer issues, as well as having collaborated on a program with Spiral to improve its data collection method. 

Julie Weingardt, Chief Operations Officer at Turo, emphasized how both companies have enabled them to receive customer experience resolutions with reduced friction. 

She said: “Spiral’s AI transformed our approach and helped us build a Voice of the Customer program that is smart and strategic, by capturing structured feedback during the support journey.  

“Spiral AI’s platform allows us to analyze customer conversations and commentary, pinpointing areas where we can improve proactively. 

“We’ve used these insights to refine our self-service options, hone our knowledge base, and help better guide quality agent responses.”

Despite the acquisition, Spiral has confirmed that it will continue to work with its existing customers and products however with UJET integrations.

Spiral was acquired by UJET for an undisclosed amount.

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.

In its quarterly report on Wednesday, the technology company revealed several high-value investments in its AI products. 

In the earnings call, Cisco emphasized that this rapid growth in AI product adoption indicates a rising demand for secure networking. 

Customer-Centric Strategy 

Over the past year, Cisco’s quarterly results have demonstrated high levels of growth after several previous declines, and it is now reaping the benefits of its increased customer spending and investment. 

This has included various AI products and suites, as well as investments in data centers to support the demands for AI-driven workloads and cloud networking. 

However, the attention has turned towards its customers and their willingness to adopt these products into their workflows. 

Despite this growth in demand, a Cisco study revealed that only a third of companies are certain that their IT infrastructure can safely integrate their AI projects, which Cisco views as favorable for them. 

With its extensive networking portfolio, the company believes it is on track to deliver the critical infrastructure to its customer enterprises, enabling them to adapt to the AI era. 

Modernizing Customer Experience 

In response to the study, Cisco has acknowledged that many companies are still far off from where they’ve been expected to be with AI. 

Charles Robbins, CEO and Chairman at Cisco, recognized the readiness gap between planning and execution when it came to adopting AI. 

He said:

“We know many customers still have a lot of work to do to ensure they have the modern, scalable, secure networking infrastructure to support their AI goals.” 

Cisco has already begun its move toward a modernized customer experience through various upgrades and expansions, allowing for simpler large-scale AI deployments. 

This has included its global network and infrastructure upgrades, allowing Cisco to enhance its enterprise switching, routing, and Wi-Fi to conduct large-scale AI and data-intensive workloads with fast, scalable, and secure performance. 

From this, Cisco expects its enterprise customers to switch from legacy networking equipment to its newer systems, collectively spending billions as part of its multiyear, multibillion-dollar refresh opportunity. 

With global data expansion, Cisco has established numerous regional data centers worldwide, as well as a European customer-based sovereign critical infrastructure portfolio, focusing on a global scale-up with region-focused deployments. 

By supplying software and cloud-native transformation, customer enterprises can also receive automated network surveillance and deliver secure, scalable customer experiences. 

In addition, Cisco offers end-to-end security integrated into the network, supporting modernized infrastructure for reliable and capable traffic pattern management. 

Workloads with Agentic AI 

Cisco’s earnings call reported a surge in agentic AI activity, with the number of queries through agentic AI measuring at 25x higher in network traffic than chatbots. 

And demand for AI has increased with it, with Cisco expecting AI infrastructure to generate $3BN in revenue for fiscal year 2026. 

A contributing factor is the AI workloads needing the necessary models and infrastructure to process locally. To support this, Cisco announced the release of its Unified Edge last week, as part of its strategy to process AI at a speedier and secure level. 

This platform offers integration for compute, networking, and storage into one system, enabling enterprises to receive real-time predictions and decisions for secure AI management. 

Another recent release is the Cisco Data Fabric architecture, which allows for the unification and management of various machine data sources, enabling companies to create more innovative AI models, adding to Cisco’s value when it comes to technology investments. 

Cisco Webex Winter 2025

Cisco has also published its Webex Winter 2025 press release, detailing its recent updates in CX technologies. 

Some key results from the season include: AI translation capabilities now expanding to 120 languages for meeting summaries; its regional cloud infrastructure locations such as the UK, Saudi Arabia, South Africa, and the UAE; a 3D workspace designer for visual blueprints; and AI Assistant for Calling for live and post-meeting summaries. 

These help to enable higher productivity levels, improve global coverage, and drive flexible working systems, with Webex allowing customers to use meeting rooms, calls, and contact center through one platform. 

However, not all these features are available for deployment as of yet. 

In conversation with CX Today, Tim Banting, Head of Research at Techtelligence, discussed Cisco’s decision to strengthen its overall CX stack across AI, global scale, and device flexibility. 

He said, “The move aligns with current Techtelligence buying-intent data showing a 19% rise in enterprise research around UC productivity and automation, involving more than 29,000 companies actively investigating process and workflow automation in communications suites. 

“However, Cisco faces an execution challenge. Several key AI and automation capabilities remain in the “coming soon” category, creating a perception gap in a market that rewards immediacy and credibility. 

“Techtelligence data shows that buyers are rewarding vendors delivering deployable automation and measurable risk controls now – not future roadmap promises. 

He added: “For CX buyers, the practical value lies in features that are globally available and compliance-ready today. The platform consolidation trend is undeniable.  

“Cisco’s success will hinge on whether it can deliver AI responsibly, at scale, and ahead of rivals who are already reshaping perception around “secure AI collaboration.” 

Cisco Key Earnings Results

After enterprise customers’ strong demand for its AI products, Cisco has risen above estimates for the quarter. 

• Cisco’s revenue is up to $14.9BN, increasing 8% year-over-year  
• Its product orders are up 13% year-over-year, with growth across all markets and geographies 
• AI infrastructures currently stand at $1.3BN 
• Service revenue increased by approximately 2% 
• Product revenue increased by approximately 10%

Christian Draeger, who runs passenger experience there, talks about it in a way that’s surprisingly down-to-earth. “We’re not just starting at the airport door,” he says. “We’re already looking at customers, how they can get prepared for their travel, even days ahead of the actual travel plans.”

That’s a different way of thinking about travel, one where the airport is part of the journey, not a pause in it. Draeger’s rule is simple: “Put the passenger in the center.”

That idea is becoming more important. Around two-thirds of travelers now use AI tools to plan their trips, and most say they want services that adjust to them, not the other way around. Berlin’s answer is to mix technology with empathy, using automation to remove hassle, not humanity, and turn the everyday airport routine into something that actually works for people.

Understanding & Designing for the Modern Traveler

Christian Draeger has spent a lifetime around airports. More than thirty years in aviation have given him a deep sense of how people move, wait, and connect. During his time with Star Alliance, he helped shape what millions of passengers now recognize as the modern travel experience. When he joined Berlin Brandenburg Airport, he came in ready to rethink that experience from the ground up.

Berlin handles around 25 million passengers a year, so it’s big enough to be busy, but small enough to still care. “We also operate our premium services: two business-class lounges and an ultra-premium lounge where you get à la carte dining and a chauffeur service to the aircraft,” Draeger said.

That same care for detail extends to the parts of the journey most people barely notice. The airport also took control of its own security operations, because, as Draeger puts it, “We felt that the mandate of the federal police didn’t provide enough attention towards the passenger experience.”

Now there are 32 security lanes, 24 fitted with advanced CT scanners, so passengers can keep laptops in their bags and carry small amounts of liquid without delay. “It’s about having a consistent experience across the whole area of the airport,” he says.

Every choice is made with the passenger in mind. “It starts really by knowing our customers,” Draeger says. “If we have a family that’s traveling once a year on holiday, their prerogatives are different from a business-class customer focused on getting through as efficiently as possible.”

That balance, efficiency for some, discovery for others, is at the heart of personalization in travel, and it’s essential. A recent study found that 93 percent of travelers now expect some form of tailored service. Berlin’s approach proves those numbers translate into real-world design decisions: better security flow, less queuing, and even duty-free areas reimagined as “specialized marketplaces.”

Dual-Terminal Strategy: Two Philosophies, One Vision

A walk through Berlin Brandenburg Airport reveals something a bit different. Its two terminals don’t just separate airlines; they reflect two completely distinct types of travelers. One is designed for comfort, the other for speed. Together, they show how personalization in travel can be built into the physical space, not only into digital systems.

“The level of automation that you will find with low-cost carriers is more in focus than with a legacy carrier,” says Draeger. Terminal 2 is the efficient, minimalist one: smaller, sharper, and geared toward travelers who value simplicity and price over perks. “Terminal 2 is geared to simplicity and generating additional revenues through add-on services,” he explains.

Think self-service kiosks, intuitive wayfinding, and a layout that helps people move quickly from curb to gate. “The utilization of busses is less, you have more walk boardings,” he adds.

Terminal 1, meanwhile, is a different rhythm altogether. “It’s about efficiency and comfort, both guided by digital tools.” Business and frequent flyers pass through airport automation that’s designed to make the process seamless. Over a hundred self-service kiosks are spread across the terminal, complemented by digital signage and premium lounges.

It’s the physical version of a digital truth: no two passengers want the same thing. Some want to breeze through with a coffee and a boarding pass on their phone. Others want time, space, and a glass of something cold before they fly. Both deserve an experience that feels intentional.

That’s what Berlin is building, a new kind of airport customer experience where infrastructure itself becomes a form of personalization. Different terminals, different tools, same philosophy: know who’s traveling, and design accordingly.

AI and Automation Enhancing Personalization in Travel

Like most airports, Berlin once relied on a traditional call center. It worked, but just barely. “We were looking at our call center and we weren’t completely happy,” says Draeger. “It was limited, inconsistent, and expensive.”

That frustration turned into an opportunity. Berlin decided to replace its call center entirely with a generative AI-powered system. The result was “Berry”, Berlin’s always-on virtual assistant.

“Customers can call the AI hotline and have a conversation just like we’re having right now,” Draeger says. It took just six weeks to build and launch, and within a few months, the results were striking: satisfaction above 85 percent, costs down 65 percent, and service available 24/7.

The human element didn’t vanish; it just found a new home. Instead of waiting in phone queues, travelers get answers right away. Lost something? Need flight details? Berry, the airport’s AI agent, takes care of it and loops in a person if the question needs a human touch. It’s simple to use too: one phone number on Berlin Airport’s website connects straight to Berry.

Building the AI Layer with Berry

Behind the scenes, Berry learns fast. “After six to eight weeks we reached an acceptable level… then you could see week-to-week improvements as GenAI learned,” Draeger explains. His team fed the system with real passenger questions and prioritized the most urgent topics first, like the classic “I left my laptop on the aircraft.” “We prioritized major customer concerns to ensure correct routing from day one,” he says.

Now the airport is preparing for the next step, chat. “We want to also offer the ability to get in touch with our AI agent through chat functionality,” says Draeger. QR codes will soon appear throughout the terminals, linking passengers directly to Berry via chat, integrated into the website and app. “If you’re standing in the arrivals hall, we’ll know based on the QR code where you are, and tailor the information accordingly.”

The idea is to build truly contextual assistance: a passenger in departures might ask about gate directions or restaurants, while someone at baggage reclaim could get help locating transport or lost luggage. “Customers can switch between voice and chat depending on environment or age. My children would prefer to talk; someone in a crowded terminal might prefer to chat,” Draeger says.

Operational AI and the Quest for Seamlessness

A lot of what makes Berlin Brandenburg Airport work isn’t something you can see. It happens on the tarmac between the terminal and the runway, where planes turn around for their next flight, and timing is everything.

“We also have others more on the ramp side,” says Draeger, referring to a system the airport now uses to track ground operations in real time. Cameras watch every stage of the turnaround, feeding data to an AI that predicts how long the process will take and where it might go wrong. “They can predict turnaround durations and steer additional resources if required,” he explains. “If a baggage belt is missing upon arrival, they can autonomously act on that and resolve bottlenecks.”

This is the kind of work that truly shapes the airport customer experience. When flights leave as scheduled, lines move faster, and connections fall into place without drama. Most travelers never think about the coordination behind it all. Yet every new piece of technology adds a layer that must fit perfectly with the rest.

But every new layer of technology brings its own challenge. “We always want to have this seamless experience for our customers,” Draeger says. “As we introduce more technology, we’ll have the challenge of combining it with legacy systems.”

Airports, after all, are built to last, and that means old baggage systems, decades-old software, and miles of wiring that can’t just be swapped overnight. “Traffic is increasing significantly, and we have limited infrastructure,” he adds. “We need simpler processes and better technology to absorb growth.”

Behind the polished front end of any airport automation project lies a balancing act: new tools talking to old systems, innovation working around concrete and cables.

The Future for Personalization In Travel: Digital Handholding

When asked what he thinks the future of travel looks like, Christian Draeger doesn’t mention drones or driverless terminals. He talks about something far simpler: help that is steady, thoughtful, and personal. “We always like to call it digital handholding,” he says. “A digital entity that’s completely informed, taking the customer by the hand and guiding them through the journey.”

Many agree that this is exactly where AI in the travel industry is heading. Gartner predicts that more than 80% of all customer interactions will be AI-assisted by 2029. The difference now is how personal that assistance can become.

“In the future, we see customers having their own personalized digital agents,” Draeger says, “on mobile, VR glasses, or other interfaces.” Those agents will be able to do a lot. “They’ll be able to rebook flights, change hotels, handle issues,” he explains. “We’ll need to provide them with the knowledge base and interconnectivity so they can act.”

He describes a world where these personal assistants talk to each other. “We’ll see a marketplace developing for agent-to-agent interaction,” he says, a network where your digital travel companion can speak directly to an airline, a hotel, or even the airport itself to smooth out the details before you notice them.

Some of that is already visible in small ways. Berlin is already imagining using augmented reality to help people find their way through the terminal. “If you come to Berlin Airport, sometimes you’ll find too many information boards,” Draeger admits. “Imagine augmented reality guiding you through the airport.”

It’s easy to see where this leads: toward an airport customer experience that blends technology with intuition. The idea isn’t to overwhelm passengers with data, but to take away the stress of travel entirely.

Personalization in Travel and Airports as Experience Ecosystems

Christian Draeger talks about air travel the way some people talk about music, not as noise and movement, but as rhythm. Airports, he says, are meant to keep that rhythm steady. When they do, everything else feels effortless.

“It’s all about making travel easier,” he says. “Like when you take a train, you just arrive and go, that’s the overarching ambition.”

Mostly, Berlin Brandenburg Airport is just pushing for a calmer travel experience. From the moment a traveler checks in to the moment they leave the gate, the goal is to take away the small frictions that make airports stressful. Berry, the AI voice agent, is part of that. So are the self-service kiosks, the CT-scan security lanes, and the quiet bits of software that keep aircraft turning on time.

“It’s not about one technology: Gen AI, robotics, biometrics, or AR,” Draeger says. “It’s about combining them to make travel much simpler.”

That line sums up Berlin’s whole approach to personalization in travel. It isn’t about showing off what technology can do; it’s about how little the traveler has to notice it.

That’s the real future of airport customer experience: an ecosystem that looks complicated underneath but feels beautifully ordinary on the surface, the kind of simplicity only achieved when someone’s been obsessing over every detail on your behalf.

Most companies know customer experience (CX) remains more than a feel-good initiative, it’s central to strategy. Companies strong in CX enjoy up to 80 % faster revenue growth, 60% higher profits, and better retention rates. Workflow automation can amplify those returns.

Already, 60% of organizations report achieving ROI within 12 months of deployment of an AI deployment, alongside 25–30 % boosts in productivity, error reductions of 40–75%. Some even show 15–35 % increases in employee satisfaction when routine tasks are offloaded. It’s clear automation pays off, enterprises just need to know what to measure.

The Three Pillars of Workflow Automation ROI

There are hundreds of case studies sharing insights into potential automation ROI. Some focus on efficiency gains and reduced time to resolution, like Vonage, that reduced average response rates from four days to four hours. Others concentrate on new revenue, like Simba, which unlocked £600k of extra monthly revenue with AI automated sales processes.

It’s also worth noting how much can be lost by delay. NiCE’s AI Value Calculator, based on billions of anonymized customer interactions, makes it clear that the cost of not investing in automation could be higher than most companies realize.

Usually though, the biggest results fall into three categories:

• Customer ROI: Audience growth, loyalty, and satisfaction. Automation can be a multiplier for conversion, proactive engagement, and longer-lasting relationships.
• Efficiency ROI: Faster resolution, fewer repetitive tasks, and reallocated human capital are tangible sources of cost reduction and improved productivity.
• Risk ROI: Consistent standards, compliance, trust, and automation guardrails deliver powerful returns by preventing losses, fines, and reputational blowback.

Customer ROI: Growth, Retention, Satisfaction

When boardrooms ask whether automation is worth the spend, customer impact is the first test. Workflow automation ROI is most convincing when aligns with movement in satisfaction, loyalty, and revenue. Faster service, proactive engagement, and fewer friction points directly shape whether customers buy again, or leave.

Fortunately, there’s no shortage of evidence that automation positively impacts CX. Forrester’s Total Economic Impact study of Kustomer’s CX platform reported a 422% ROI over three years. The study found service costs fell by 88%, while agent productivity climbed 50%. Reduced churn and faster responses combined to boost satisfaction scores at scale.

Elsewhere, retail company Loop Earplugs used Ada’s AI and automation platform to achieve a 357% ROI, driven mostly by faster response times. First response rates improved by 194.52%, and the company’s average CSAT score grew to 80.

Even highly regulated sectors are seeing the effect. In banking, conversational AI deployments delivered 250–400% ROI over three years, with proactive outreach reducing inquiries by 43% and satisfaction scores rising 22%.

The ROI of automation from a customer experience perspective doesn’t just come from fast responses or preventing churn either. Solutions like NiCE’s proactive AI Agent, which reaches out to “silent customers” before they abandon purchases or subscriptions, helps to recover otherwise lost revenue.

Efficiency ROI: Opex, Speed, Employee Experience

While customer outcomes get headlines,the economics of automation are often where investment decisions are won. The workflow automation ROI story in efficiency terms is straightforward: lower operating costs, faster resolution times, and stronger employee performance without necessarily reducing headcount.

Consider the impact of orchestration at scale. A Total Economic Impact study of Camunda found a 408% return over three years, translating into more than $112 million in net savings for a composite enterprise. These savings came from streamlining complex workflows, cutting redundant processes, and enabling faster time-to-market for new services.

With Salesforce and Agentforce automation, the Formula One team increased first-call resolution rates to over 95%, so teams had more time to focus on high-value tasks. Frontier Airlines used Cognigy to automate 800k conversations a month, allowing for a 15-30% growth rate without investing in extra headcount.

The efficiency pillar extends to people too, the ROI from improved engagement and decreased churn. Capgemini’s 2025 customer service research found 73% of agents report fewer repetitive tasks after AI-based automation adoption, while 70% say their overall workload has decreased.

NICE highlights similar gains with its automation suite, where routine admin tasks are stripped from daily workloads, and scheduling platforms like Playvox enable global teams to manage shifts more effectively. MongoDB, for instance, abandoned spreadsheets in favor of NICE’s Playvox, improving scheduling accuracy and boosting morale across its 24/7 technical support teams.

Risk ROI: Compliance, Continuity, Governance

The third pillar of workflow automation ROI is less visible in day-to-day operations but no less critical. Risk reduction often shows its value in what doesn’t happen: fines avoided, crises prevented, and reputations preserved. Boards and compliance leaders see these outcomes as material returns, even if they appear in the “cost avoided” column.

The risks of not investing are serious. In 2025, scammers hijacked a United Airlines customer support line, tricking a passenger into transferring $17,000. Automating verification processes, and enabling real-time security checks might have prevented that.

AWS’s Verafin solution shows how AI can be deployed for compliance investigations, cutting review times from days to seconds. For financial institutions, that level of speed does more than increase efficiency, it reduces the window of exposure, ensuring fraud or error is intercepted before damage accumulates.

From a workflow TCO perspective, the ability to scale without scaling exposure is invaluable. Fines from GDPR violations can reach up to 4% of annual global turnover; data breaches in regulated sectors can cost millions per incident. Risk ROI reframes automation as an insurance policy – one that protects brand equity as much as it protects the balance sheet.

Another risk deflected by workflow automation is the loss of expertise. As turnover rates continue to skyrocket, automation reduces reliance on scarce skills, and frees up more human time. Just look at Elanco, it reduced time spent on routine tasks with Google Gemini, saved $2.3 million in the first year with process efficiencies, and reduced the demand for extra team members.

How to Calculate True Workflow Automation ROI

Boards rarely sign off on technology spend without a clear model for value. The challenge with automation is that benefits are distributed across departments and often accrue over time. A disciplined framework for calculating the ROI of automation is therefore essential.

• Establish the Baseline: Every calculation begins with today’s reality. Measure current volumes, handle times, first-contact resolution, containment rates, and cost per interaction. Without this baseline, improvements risk being anecdotal.
• Define Automation Scope: Not all processes deliver equal value when automated. High-volume, repetitive, or compliance-sensitive workflows are usually the best candidates. Generic automation isn’t the best way to achieve higher ROI.
• Model Efficiency Gains: Here, the focus is on operational savings: Reduced time-to-resolution (TTR), Lower average handle time (AHT), Fewer escalations requiring senior staff. For CFOs, these metrics can be translated into improved customer lifetime value and higher retention revenue.
• Include Employee Experience Savings: Attrition is an overlooked line item. If automation helps reduce repetitive tasks and improve employee experiences, it will have a direct impact on your operating costs.
• Compare Against Total Cost of Ownership (TCO): Every model must weigh benefits against the Workflow TCO: software licenses, integration, training, and change management. Automation vendors increasingly support these calculations with ROI tools, but validate assumptions.

Finally, place the numbers on a timeline. Forrester and BCG studies suggest most enterprises see payback within 12–18 months, with ROI accelerating as AI scales and learns.

Turning Automation ROI from Theory into Evidence

Boards are asking a simple question: Does automation pay? The answer is yes, when it’s measured the right way. The three pillars of workflow automation ROI: customer impact, efficiency, and risk reduction, show that value clearly. Faster service wins loyalty, streamlined processes lower costs, and guardrails reduce exposure.

The real cost lies in waiting. Every unresolved case or manual process represents money left on the table. The practical next step is simple: identify high-value workflows, run pilots, and track outcomes against workflow TCO. The organizations that prove value fastest will be the ones setting the pace in customer experience.

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.

Microsoft began emailing its subscribers in Australia today about a cheaper Classic version of its Microsoft 365 Personal and Family plans that do not include its AI assistant, Copilot. The company added Copilot to Microsoft 365 in October 2024 and automatically raised prices without telling subscribers that they could keep their existing plans at the original price.

In a statement, Microsoft admitted it “could have been clearer” about this alternative, as it only revealed the Classic plans to customers who began cancelling their subscriptions.

“In our email to subscribers, we expressed our regret for not being clearer about our subscription options, shared details about lower-priced alternatives that come without AI and offered a refund to eligible subscribers who wish to switch.”

The emails were tailored depending on whether customers subscribe to a Personal or Family plan, and whether they pay on a monthly or annual basis.

Microsoft offered eligible subscribers a refund of the difference in price between the plans, starting from their first renewal date after 30 November 2024, if they chose to switch back to the Classic plan.

But soon after, customers began reporting glitches, according to The Sydney Morning Herald. Some said that the refund link didn’t work properly, and others found they could only downgrade to a Personal Classic plan, rather than the Family Classic version they had previously.

“One would have hoped that Microsoft would have checked this before sending out the mass email, but here we are,” one customer told The Sydney Morning Herald. Another called the situation “an epic fail by Microsoft.”

A company spokesperson later admitted that “some subscribers eligible for the refund received an incorrect link,” apologizing and saying Microsoft was fixing the issue.

ACCC Legal Action Highlights the Risks of Poor Customer Communication

The refund confusion comes as the Australian Competition and Consumer Commission (ACCC) pursues Microsoft in the Federal Court. The regulator has alleged the company misled customers when it told them they had to accept a price increase to keep their Microsoft 365 subscriptions or cancel entirely, without mentioning the cheaper Classic option.

The consumer watchdog argues this prevented subscribers from making informed choices.

“The Microsoft Office apps included in 365 subscriptions are essential in many people’s lives and given there are limited substitutes to the bundled package, canceling the subscription is a decision many would not make lightly,” ACCC Chair Gina Cass-Gottlieb said.

“We’re concerned that Microsoft’s communications denied its customers the opportunity to make informed decisions about their subscription options, which included the possibility of retaining all the features of their existing plan without Copilot and at the lower price.”

“We believe many Microsoft 365 customers would have opted for the Classic plan had they been aware of all the available options,” Cass-Gottlieb added.

Following the Copilot integration in October, the annual subscription price of the Microsoft 365 Personal plan increased from A$109 to A$159, a rise of 45 percent. The cost of the Family plan increased by 29 percent from A$139 to A$179.

Ahead of the change, Microsoft sent two emails to subscribers and published a blog post about the price increase for the Copilot integration that would apply to customers’ next renewal.

“We allege that Microsoft’s two emails to existing subscribers and the blog post were false or misleading as they conveyed that consumers had to accept the more expensive Copilot-integrated plans, and that the only other option was to cancel,” Cass-Gottlieb said, adding:

“All businesses need to provide accurate information about their services and prices. Failure to do so risks breaching the Australian Consumer Law.”

The ACCC has sued Microsoft to seek orders including “penalties, injunctions, declarations, consumer redress, and costs.”

Rebuilding Trust After a CX Crisis: Microsoft’s Next Challenge

In its email to subscribers, Microsoft said: “Our relationship with our customers is based on trust and transparency and we apologize for falling short of our standards.”

It also highlighted its 40-year presence in Australia and added that it “will learn from this and improve.” For a brand as established as Microsoft, the issue is less about price and more about reputation. Customers expect clear, transparent communication, especially when price increases and AI integrations are involved.

The mishap exposes how easily a company can lose goodwill when communication feels misleading or incomplete. Even well-intentioned recovery efforts, like Microsoft’s refund offer, can backfire if they’re poorly executed.

As more tech companies incorporate AI assistants and other features into their subscription products, there is likely to be a growing tension between innovation, pricing and transparency. Customers want access to new tools, but not at the expense of choice or clarity.

The company’s apology and refund campaign were meant to rebuild trust. But Microsoft now faces the challenge of resolving its legal dispute with the ACCC and proving to consumers that it values transparency as much as it values adding bells and whistles to its products.