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.

A loyal customer isn’t just someone who comes back. They spend more. Stay longer. Recommend faster. They open emails, tolerate hiccups, and ignore your competitors’ ads. They’re also far cheaper to retain than any lead your sales team is chasing right now.

Loyalty isn’t a lucky break. It’s the outcome of moments that go right consistently, and often quietly. A first experience that flows without friction. A support interaction that resolves more than just the issue. A product that keeps its promise. Each of these moments builds equity in the relationship.

When those touchpoints connect  across teams, systems, and time something stronger than repeat business takes shape. Customers begin to trust. They stick around, not because it’s the easiest option, but because the experience earns it.

What is Customer Loyalty?

Customer loyalty reflects a decision: the conscious choice to stay with a brand when alternatives are just a click away. It’s not just about satisfaction, plenty of satisfied customers churn. Loyalty runs deeper. It’s emotional, earned through consistency, value, and trust built over time.

In practical terms, loyalty shows when customers return after a poor experience, because they believe it’s the exception, not the norm. It shines when existing buyers refer peers, opt into updates, or upgrade without needing a discount.

But for enterprises, this isn’t a soft metric. It’s measurable, in retention rates, customer lifetime value, and referral growth. In fact, increasing customer retention by just 5% can boost profits by 25% to 95% depending on the industry. Loyalty doesn’t just pay off; it compounds.

Now, it matters more than ever. With CX as a key battleground, loyalty becomes a lead indicator of business resilience, and a hedge against rising acquisition costs.

The ROI of Customer Loyalty

Customer loyalty used to be a feel-good metric. Now it’s a board-level priority.

Retaining a customer isn’t just cheaper than winning a new one, it’s smarter. The cost of acquisition has spiked over 60% in the last five years, especially across digital channels. Meanwhile, repeat customers spend more, refer faster, and support brands longer, even when things go wrong.

The return is measurable:

• CAC Down, Margins Up: Brands with strong loyalty programs don’t need to outspend rivals on ads. Their customers come back organically. Acquisition costs are up to 7x higher than retention costs, and rising. Loyalty brings those numbers down.
• Predictable Revenue: Returning customers are more consistent. They know the product, trust the brand, and often skip the comparison stage altogether. That makes forecasting easier, pipelines more stable, and marketing spend more efficient.
• Loyalty = Resilience: In downturns, loyal customers stick. They’re more forgiving of glitches and slower to churn. A loyalty strategy isn’t just about growth, it’s about survival when market headwinds hit.
• Better Intelligence: Good loyalty tools are also listening tools. They track not just transactions, but behavior: redemptions, preferences, referrals, and feedback. That kind of data can feed customer journey strategies and help pinpoint why loyalty is rising or falling.
• Cross-Functional Buy-In: Loyalty isn’t a marketing-only game anymore. When programs sync with CRMs and support channels, they empower every team that touches the customer and help break down the silos that usually hurt CX.

What is Customer Loyalty Management?

Loyalty isn’t a byproduct of good service; it’s the result of managing relationships with intent. For enterprises, customer loyalty management is the discipline of designing and maintaining systems that keep the right customers coming back, staying longer, and contributing more value over time.

Loyalty doesn’t come from running rewards programs on cruise control. It starts with clarity; knowing who your most valuable customers are, what keeps them engaged, and how to stand out even when competitors promise more for less.

The best loyalty strategies don’t operate in a silo. They’re part of the broader customer experience engine, connected to feedback, support, product usage, and behavioural cues. Managed well, these strategies turn loyalty into a dynamic input, not just a passive output. It’s not a metric at the end of a funnel, it’s something built and reinforced at every stage of the journey.

Loyalty Management Tools and Platforms

The strongest tools today aren’t just managing point balances or sending birthday emails. They’re helping organizations understand loyalty as a behavior, not a program.

At a basic level, these platforms centralize loyalty data: engagement patterns, redemption activity, repeat purchase signals, and more. But the more advanced systems go further. They apply machine learning to spot early signs of churn, flag disengaged segments, and recommend next-best actions in real time.

What sets the leading loyalty management platforms apart is their ability to fit inside a broader CX tech stack. That means:

• Integrating with CRM to unify customer context
• Connecting to feedback loops for real-time insight
• Embedding in messaging infrastructure like CPaaS to deliver hyper-personalized moments that actually land

Many also support predictive analytics, using behavioral data to calculate loyalty risk scores, tailor rewards dynamically, or prompt human intervention when relationships are at risk.

How to Measure Customer Loyalty

Loyalty isn’t a single number. It’s a pattern, and like most patterns in enterprise CX, it takes a mix of metrics to see the full picture.

Behavioral signals still lead the pack. Metrics like repeat purchase rate, frequency of interaction, average order value, and churn give a direct read on what customers are doing, and where that behavior changes over time.

Behavioural signals often say more than surveys. A customer who slows their spending, skips repeat purchases, or stops logging in is sending a message. Something has shifted, in the experience, the product fit, or the perceived value.

Behaviour tells you what happened. But it won’t tell you why. That’s where customer sentiment comes into play.

Tools like Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) dig beneath the surface, giving teams a clearer sense of how customers actually feel about their experience. When behavioural dips show up, they offer the context needed to act fast, and fix the root cause before it costs more.

For many organizations, this layer is captured across touchpoints with VoC tools, then analyzed over time to correlate sentiment with spend or attrition.

What’s changing now is the rise of emotional loyalty metrics. These tools look beyond direct feedback, using conversational analysis, sentiment trends, and inferred emotional cues to understand attachment, not just satisfaction. It’s especially useful for brands competing on experience, not price.

Taken together, these data points create a more reliable model. Not just who’s loyal today, but who’s likely to stay, spend, and advocate tomorrow.

How to Choose Loyalty Management Software

The wrong loyalty platform won’t break a business, but it will stall progress. What looks slick in a demo can crumble under pressure if it can’t sync with existing systems, surface usable insights, or grow with you.

Enterprise teams evaluating loyalty management software need more than a feature checklist. They need to know how the tool will hold up six months in, with multiple departments relying on it.

Here’s what separates the useful from the disruptive:

True Integration

No platform works in isolation. If loyalty data sits in a separate bucket from customer service, CRM, or analytics tools, there’s a problem.

That means:

• Real-time syncing with CRM systems
• Alignment with VoC platforms
• Integrations with Business intelligence solutions

Most loyalty management platforms also seamlessly connect with CCaaS platforms, conversational analytics tools, and ERP software.

Dashboards That Get Used

Too many platforms surface metrics. Fewer tell you what they mean.

The strongest systems flag what matters: declining engagement from a once-loyal segment, a regional drop in redemption rates, churn triggers hiding in feedback. Ideally, these insights feed into broader customer intelligence tools.

Ask the vendor: When loyalty starts to dip, how will your platform show it, and who will know?

Scalability

Will it handle loyalty across multiple brands? Markets? Languages? Can it adapt to tiered models, emotional loyalty, partner programs?

Look for:

• Configurable logic, not hard-coded structures
• Clean admin interfaces for rule management
• Role-based controls that keep compliance teams comfortable

If it takes a developer to adjust a points rule, it’s not enterprise-ready.

Discover who’s driving results in the loyalty management software market here:

Best Practices for Improving Customer Loyalty

Loyalty doesn’t just emerge from a points program or a fun campaign. For enterprises, it’s a byproduct of consistent, intentional experience design, built into service flows, product strategy, data models, and frontline decision-making.

Build Feedback Loops That Actually Close

The fastest way to erode loyalty? Ignoring input – or worse, asking for it and doing nothing.

Instead of measuring feedback volume, measure action: How many product updates were driven by complaints? How often are support teams looped in to resolve themes emerging from surveys? Connect your loyalty program to customer feedback management tools that can drive real changes, not just reporting.

Use Tiering: But Don’t Let It Turn Transactional

Tiered loyalty still has its place, but only when it’s designed with purpose. Value shouldn’t just reflect spend. It should acknowledge engagement in all its forms. Early adopters, advocates, testers, even those who provide consistent feedback – they’re all part of the loyalty equation.

In B2B especially, tiers work best when they reflect mutual success. Think retention milestones, shared KPIs, or collaborative innovation, not just contract size.

Let AI Do More Than Segment

Yes, AI can slice customer cohorts faster. But real value comes when it flags what’s slipping before it shows up in churn.

Modern loyalty management tools increasingly come with predictive features: surfacing customers at risk of disengagement, nudging reps to check in, or adjusting loyalty offers based on sentiment and behavior patterns. Don’t just use AI to automate, use it to alert.

Tie Service Quality to Loyalty Outcomes

When loyalty starts to dip, it’s often not marketing’s fault, it’s a missed service expectation, or a support gap that never got escalated.

Bring loyalty and service metrics closer together. Track whether NPS dips after a long resolution time. Monitor whether loyalty program members get faster assistance, and whether that’s noticed.

Reward the Behavior You Want More Of

Discounts create habits, and not always good ones. If you reward spend alone, you build deal-seekers, not advocates.

Instead, reward the moments that drive growth:

• Referrals
• Feedback submitted
• Community contributions
• Self-service engagement
• Event participation

Loyalty isn’t a transaction, it’s a signal. Recognize the signals that drive real business value.

Localize Where It Matters

For multinational brands, loyalty can’t be global by default. Preferences shift by market, so should campaigns.

Consider:

• Local holiday-based promotions
• Regional tier naming conventions
• Local influencers or ambassadors

Global strategy. Local flavor. That balance keeps loyalty human.

Customer Loyalty Management + Service: The Critical Link

Loyalty doesn’t just live in a dashboard or a rewards app. It’s won or lost in moments that often feel small: a delivery delay, a billing dispute, a misunderstood policy. The way a brand responds in these moments is often more influential than any discount or points tier.

And that makes customer service a cornerstone of customer loyalty management.

When Service Is Seamless, Loyalty Feels Earned

Customers don’t demand flawlessness. But they do expect clarity, speed, and respect when things go wrong. Loyalty isn’t tested during moments of delight, it’s tested when something breaks. Support teams who can see a customer’s history, loyalty status, and previous interactions don’t just fix problems faster. They solve them with more context, more care, and often, more impact.

This is where integration matters:

• CRM systems should surface loyalty data
• CPaaS platforms can enable proactive outreach
• Ticketing systems can reflect VIP status or churn risk

Proactive Service = Preventative Loyalty Loss

The best loyalty moves aren’t reactive. They’re invisible, because the problem was handled before the customer noticed.

For example:

• Flagging shipping delays and sending apologies before the complaint
• Alerting high-value customers when products they love are low in stock
• Following up after negative sentiment is detected in chatbot interactions

This requires orchestration. But the payoff is reduced escalation volume, increased trust, and loyalty built on more than transactions.

Empower Agents Like They’re Brand Ambassadors

Loyalty lives or dies with the agent experience. If the frontline team feels unsupported, overworked, or stuck with legacy tools, they can’t deliver the kind of service that loyalty depends on.

Modern workforce engagement platforms are helping here, giving agents better training, clearer knowledge bases, and visibility into customer journeys. This isn’t just an ops upgrade, it’s a loyalty investment.

Customer Loyalty Management Trends to Watch

Enterprise loyalty strategies evolve with the customer, and the customer continues to change.

Over the past two years, loyalty has shifted from tactical marketing add-on to boardroom-level priority. Why? Because retention has become the fastest route to stable revenue.

Here’s what’s changing right now.

• Loyalty Is Getting Smarter: Rather than shouting about rewards, top brands are building invisible loyalty, systems that work behind the scenes, adjusting experiences based on behavior, purchase history, and product use. The loyalty isn’t in the point balance. It’s in the recognition. AI and predictive analytics are playing a bigger role here, helping teams act on churn signals before the customer ever says a word.
• Emotional Loyalty Takes the Lead: Price cuts don’t build loyalty. They build expectations. Enterprise buyers are shifting from transactional incentives to emotional loyalty strategies, things like exclusive experiences, consistent service, and values-based alignment. In B2B markets, that might look like strategic co-development, VIP access to product roadmaps, or account-based reward systems.
• Loyalty Hardwired Into CX: The strongest loyalty programs don’t operate in isolation. They’re woven into the wider customer experience stack, touching CRM, CPaaS, contact center platforms, and data systems. This allows brands to reward customers in real time, based on meaningful actions, not just spend.
• Consent-First Design: The days of collecting data “because we can” are over. Modern loyalty programs are being rebuilt around trust and transparency. That means clear value exchanges, upfront permissions, and control for the customer. Loyalty is no longer about how much data you can gather, it’s about how responsibly you use what you have.

Customer Loyalty Management Beyond the Transaction

Customer loyalty isn’t a finish line. It’s an ongoing, intentional outcome earned across every interaction, reinforced with every decision, and protected by every system put in place.

For enterprise teams, managing that loyalty means more than launching a rewards program. Managing loyalty well means making it easier for customers to stay than to leave. That’s not about discounts or perks, it’s about designing experiences that feel effortless, relevant, and personal.

Whether the goal is improving retention, boosting lifetime value, or gaining a clearer view of customer behaviour, the right strategy starts with the right tools, and the right insights.

CX Today offers a range of resources to help enterprise teams build loyalty systems that actually move the needle:

• Explore the Marketplace: Compare top loyalty management vendors with features tailored for growth, data integration, and security at scale.
• Join the Community: Learn how CX and marketing leaders across industries are evolving loyalty strategies in the CX Community.
• Track What’s Changing: Follow new developments in AI-powered loyalty, cross-channel experience design, and customer journey intelligence with research reports.

See how loyalty fits into the broader CX ecosystem. Visit our Ultimate CX Guide for a practical deep dive into the people, platforms, and processes driving customer-led growth.

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

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

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

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

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

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

“With these innovations, AI moves from the periphery to the core of the contact center – linking data, people, and processes into a system more closely embedded with contact center operations in ways intended to support continuous learning, adaptation, and more efficient and meaningful customer experiences.”

So, let’s take a closer look at Five9’s newest features.

Four New Innovations in the Genius AI Suite

Agentic Quality Management (AQM)

Five9’s new quality management approach is designed to evaluate up to 100% of customer interactions.

The model focuses on generating insights that can inform routing decisions, coaching programs, and ongoing performance improvement.

Rather than operating as a separate analysis tool, AQM feeds data directly into other parts of the Genius AI ecosystem.

Genius Routing

This updated routing engine uses defined customer attributes, agent skills, and proficiency levels to determine the best match for each interaction

Because it can draw on real-time inputs from AI performance systems and self-service applications, it becomes easier for organizations to deliver faster resolutions and more personalized experiences.

OneVUE Analytics

OneVUE builds on Aceyus VUE technology but packages it in a more approachable, self-service analytics experience. Users can assemble custom dashboards with flexible metrics that cover both traditional CCaaS operations and multi-vendor environments.

It is built with AI-ready reporting in mind, giving teams a clearer view of the KPIs shaping customer interactions.

Adaptive Digital Engagement

The digital engagement update introduces a no-code Dynamic Web Messenger Configurator, allowing teams to launch or adjust webchat experiences instantly.

Five9 also revealed a new partnership with Meta, enabling native WhatsApp connectivity with template support, broadcasts, and AI Agents. The broader goal is to give organizations modular digital channels that plug easily into Five9’s AI layer.

Early Customer Feedback

Northwestern Mutual is among the customers already experimenting with the new capabilities.

Eric Schanno, the company’s Principal Solutions Engineer, explained how leveraging Five9 had allowed Northwestern Mutual to “move faster from insight to action.”

“We are very excited about the potential of Five9 AQM to help us elevate coaching across 100% of interactions. And OneVUE gives us a single source of truth for the metrics that matter.

“The combination is raising the bar on performance and helps deliver more consistent, high-quality experiences for our customers.”

A Step Toward Fully Agentic CX

Five9’s message seems to be that the next stage of CX transformation isn’t just about adopting AI tools, but weaving AI into the operational fabric of the contact center.

These Genius AI enhancements move the platform further in that direction, giving organizations a more unified foundation for delivering adaptive, insight-driven customer experiences.

More News from Five9

Afiniti has teamed up with Five9 to bring its AI Pairing technology into the Five9 Intelligent Cloud Contact Center, signalling a step forward for more sophisticated, behind-the-scenes AI in CX.

Rather than placing AI directly in front of customers, Afiniti analyses behavioural and contextual signals in the background to match each caller with the agent most likely to deliver a successful outcome. In effect, it acts as a matchmaking engine for the contact center.

IDC’s new MarketScape points to rapid momentum in Europe’s CCaaS sector, forecasting growth from $1.5BN in 2024 to $3.7BN by 2029 – a 20 percent CAGR.

Five9 is highlighted as a market leader, backed by its AI-driven platform, solid compliance credentials, and strategy tailored to European requirements.

Below, we evaluate four leading CX AI providers, comparing their data architecture, model performance and integration maturity – key traits for decision-makers comparing customer experience tools.  

What Matters When Choosing Predictive CX AI 

Before looking into specific vendors, it’s useful first to step back and remember what you need to consider. Below are three factors it’s important to keep in mind: 

Data architecture & consolidation – The ability to ingest, unify and analyse multiple touchpoints (web, mobile, contact centre, social) is critical. Without a real-time data layer your predictions will lag. Building a “vendor-neutral data layer” is increasingly becoming a central piece of the CX tech stack.  In fact, whilst 94% of businesses are investing more in AI, only 21% have fully embedded AI into their operations. 

Model performance & actionability – It’s not sufficient to just surface insights; models must forecast behaviour (churn risk, next-best-action) and feed downstream workflows. AI can anticipate customer needs before they arise by analysing vast amounts of customer interaction data.  

Integration maturity & scalability – Even the most sophisticated models mean little without seamless integration. The best customer experience tools plug into existing systems (CRM, contact centre, ERP) and scale across both geographies and channels.  

Building on these criteria, here are four vendors worth serious consideration in 2026.

NICE Ltd. 

NICE has evolved its flagship customer experience platform into an AI-native environment. It now markets its “Enlighten” engine and the “CXone Mpower Orchestrator” which combine real-time analytics with automation across the customer journey. The company claims to deliver predictive routing and next-best-action engines that trigger human or bot responses depending on customer sentiment and context.

From a data architecture perspective, NICE supports global scalability and voice/omnichannel integration, addressing both model performance and real-time action. While the vendor may come with a legacy contact-centre heritage, its recent focus shows strong alignment with modern CX technology trends. 

Strengths 

• Mature platform with global scale and broad CX footprint 

• Predictive routing and sentiment-based decision making built into live workflows 

• Strong partner ecosystem and integration credentials 

Considerations 

• Because of its breadth, implementation may require more time than niche tools 

• Buyers should validate the “predictive” claims carefully  

Google LLC (Customer Engagement Suite) 

Google has pushed hard into the CX space, driven by its growing range of conversational and engagement tools. It stands out for its ability to detect intent and entity across channels. While most known for conversational AI, Google’s platform also has predictive capabilities – for instance, forecasting contact-volume spikes and routing accordingly. 

Google offers strong cloud-data architecture and granular real-time analytics, giving teams the infrastructure to scale predictive CX models. Organizations with significant investments in cloud-native data stacks will find Google LLC a particularly compelling choice. 

Strengths 

• Cloud-native, scalable architecture with strong analytics underpinnings 

• Strong brand and ecosystem support for advanced data/machine-learning pipelines 

• Good fit where CX AI is part of a broader digital transformation 

Considerations 

• May require additional implementation effort (data-ops, custom models) for full predictive use-case 

• For contact-centre-specific workflows, you may need partner overlays 

Kore.AI  

A rapidly emerging leader in predictive CX, Kore.ai has become synonymous with intelligent virtual assistants that combine conversational and predictive automation. The company’s XO Platform delivers intent prediction, emotion analysis, and autonomous task completion across digital and voice channels.  

Kore.ai’s strength lies in merging dialogue analytics with predictive insights – enabling enterprises to move from scripted responses to proactive engagement. Its open integration framework connects with CRMs, ITSM, and workforce platforms, ensuring predictions become immediate actions. 

Strengths 

• Predictive intent and emotion detection within conversations 

• Low-code interface accelerates deployment and training 

• Flexible integration with existing customer experience tools 

Considerations 

• Still maturing in large, multi-lingual deployments 

• Requires clear governance to manage automated decisioning 

Genesys 

Genesys has re-established itself as one of the top predictive analytics vendors in CX, driven by its AI-powered orchestration layer and “Predictive Engagement” suite. It analyses customer behaviour in real time, from web interactions to voice analytics, then determines the next best action, agent or channel to optimise outcomes. 

Strengths 

• Innovative approach geared toward proactive, autonomous workflows 

• Good fit for organisations willing to experiment and scale up quickly 

• Lighter implementation burden possible 

Considerations 

• Fewer large-scale reference deployments than incumbents 

• Buyer should validate predictive-model maturity, underlying data pipeline and integration readiness 

How to Use This Guide 

At the evaluation stage, your task is to match vendor capability against your requirements. Use this article to: 

• Short-list 2–3 vendors based on architectural fit & strategic alignment 

• Map each vendor to your priority use-cases (e.g., churn prediction, real-time routing, next-best-action) 

• Ask vendors detailed questions about their model performance, integration time, and proof-points 

Key Questions to ask Vendors 

• How is the data architecture structured to support real-time modelling and activation? 

• What actual measurable outcomes (reduced churn, increased NPS, cost avoidance) can you share? 

• How quickly can you integrate into our CRM/contact-centre stack and deliver pilot value? 

• How do you avoid “AI washing” (i.e., vendors re-labelling basic analytics as predictive)?  

Choosing your Predictive CX Customer Service Tools

Ultimately, in 2026, the leading customer experience vendors will be those that pair advanced CX AI with strong predictive capabilities and mature integration frameworks. The four vendors we’ve discussed bring different strengths: whether it’s global scale, cloud-native agility, analytics depth or innovation speed.  

Use your evaluation criteria to match vendor capabilities with your organisation’s needs. With the right decision you’ll move from reactive service to proactive experience – meaning more value, happier customers and measurable business impact. 

Choosing a vendor is just step one. Choosing the right CX strategy is everything. Find out more in our Ultimate Guide to AI & Automation in CX

When “Just Faster” Becomes “Just Worse” 

Historically, lowering average handle time was the gold standard of call centre productivity: shorter calls meant more handled interactions, and more handled interactions (so the logic went) meant lower labor costs. For years, contact centre operations focused on driving down talk time, hold time and after-call work (ACW) in pursuit of efficiency. But the mantra of “faster = better” carries a risk: push agents to cut corners, and you risk eroding first contact resolution, increasing repeat calls and ultimately weakening customer satisfaction. 

Rebekah Carter, CX Today:

“Every contact centre leader wants to reduce average handling time. Yet business leaders are searching for smarter ways to reduce AHT without compromising on customer, employee and business outcomes.”

The Real Cost of Inflated AHT 

So, what are the hidden costs linked to excessive handle time? 

Labor inefficiency: Every extra minute an agent spends on a call means fewer handled contacts per shift. With staffing as your largest cost component, higher AHT directly inflates your cost per contact.  

Customer churn and loyalty risk: Customers don’t just want faster; they want right the first time. If an interaction drags, they’re more likely to abandon the call, repeat the issue later or switch providers. 

Agent burnout and attrition: When agents are under pressure to hit aggressive AHT targets, the quality of work suffers, stress rises and turnover increases. That adds hiring and training costs, and often higher AHT as new hires get to grips with systems. 

Opportunity cost: A contact centre defined purely by service becomes trapped in a cycle of cost-control, missing opportunities to evolve into a revenue-oriented organisation. 

Why Agentic AI Matters 

AI is no longer a peripheral add-on to call centre operations – it’s fast becoming the backbone of smarter call centre productivity and customer experience (CX) strategy. Modern contact centre AI solutions help organisations move beyond tactical efficiency gains to achieve measurable improvements in service quality, speed, and scalability.  

Here’s how AI can optimise your CX operation: 

Real-time guidance for agents: AI listens to live interactions and offers contextual prompts, next-best actions, and dynamic scripting to help agents resolve queries faster and more accurately. 

Automated summarisation and after-call work reduction: Intelligent summarisation tools automatically generate notes and CRM updates, cutting minutes from post-call wrap-up time. 

Knowledge surfacing and retrieval: AI can instantly fetch relevant policies, product details, or past interaction data, reducing search time and cognitive load during customer calls. 

Intent detection and smart routing: Calls and chats are triaged by AI before they reach an agent, ensuring that each issue lands with the right person or bot on the first try – driving down average handle time (AHT) and repeat contacts. 

Quality assurance at scale: Instead of manual auditing, AI analyses 100% of interactions for compliance, tone, and satisfaction cues, helping leaders identify friction points that extend handle time. 

Proactive coaching and training: AI-driven analytics flag skill gaps, coach agents in real time, and accelerate onboarding for new hires, which is key to long-term call centre cost reduction. 

Predictive workload management: Forecasting algorithms predict call spikes and suggest optimal staffing or automation levels to sustain consistent call centre productivity. 

Minimising Average Handle Time Costs  

Minimising hidden AHT costs without sacrificing service quality requires a smarter, more holistic approach to performance management. Rather than fixating solely on average handle time, contact centres should reframe success metrics to include first-contact resolution, time-to-resolution, and customer effort – giving a fuller picture of efficiency and satisfaction.  

Empowering agents with context is key – when they have access to unified cross-channel customer data and AI-driven recommendations, they spend less time searching for information and more time solving problems. AI-powered agent assist tools can further streamline operations by handling after-call work, drafting responses, summarising interactions, and routing issues intelligently.  

Optimising training and onboarding through AI-driven coaching can also reduce the lengthy “high AHT” period that typically accompanies new hires. Finally, maintaining a balance between speed and quality is essential; incentives and goals should encourage thorough, customer-centric resolutions, ensuring that average handle time remains a useful measure, but not the only one used to drive great customer experiences. 

The Smarter Path to Sustainable Efficiency

The conversation around average handle time (AHT) needs a refresh. It’s not enough to simply shave seconds off a call without improving the service your agents offer. The hidden costs of high AHT – efficiency loss, poorer CX, agent attrition – are real. But by embracing contact centre AI and the next gen of agentic AI, you can reduce call centre costs, increase call centre customer satisfaction and raise call centre agent productivity without compromise.  

Efficiency is evolving — is your contact centre keeping up?

Find Out in Our Ultimate Guide to AI & Automation in CX 

Spindle AI’s technology “combines advanced AI agents and machine learning with powerful data modeling to help businesses make faster and more effective data-driven decisions,” according to Salesforce’s announcement. It complements analytics platforms like Tableau by using AI to model agentic scenarios autonomously and forecast business outcomes.

This is designed to tackle a familiar problem for enterprises producing massive amounts of data. By simplifying analysis and interpretation, they can unlock that data’s value and generate insights to guide critical decisions.

Spindle AI was co-founded by Ryan Atallah, who previously sold his startup ClearGraph to Tableau, and Carson Kahn, who built the AI company Volley ML during JPMorgan’s In-Residence program.

The team brings experience in multi-agent systems and high-performance analytics, which are areas Salesforce clearly sees as key to helping its AI agents move beyond assisting humans to autonomously analyzing data and optimizing decisions.

Multi-agent orchestration is the next phase of AI development, where multiple specialized agents work together by communicating, reasoning and coordinating tasks. This can enable enterprises to advance their customer experience platforms from simple chatbots toward adaptive, end-to-end autonomous operations.

Adam Evans, EVP & GM, Salesforce AI Platform, framed the deal as part of the company’s larger vision:

“Bringing Spindle AI—which includes veteran leaders and incredibly talented engineers with deep expertise in advanced agentic analytics and machine learning—onboard will continue our investment in Agent Observability and Self-Improvement to help us deliver custom agentic analytics, ROI forecasting, and continuous optimization for every Agentforce user.”

Spindle’s team will join the Agentforce division to strengthen capabilities like agent observability, which tracks how agents reason and perform, and self-improvement, helping them to learn from outcomes.

How Salesforce Is Building Its Agentic AI Ecosystem

The Spindle acquisition fits neatly into Salesforce’s move towards making AI agents core to its platform.

Earlier this year, the company announced its acquisition of Informatica, a data-management company that provides tools to support the clean, governed data pipelines needed to power AI agents.

Salesforce also acquired Convergence.ai, a startup focused on developing adaptive AI agents that can navigate complex workflows and dynamic interfaces, adapting in real time to challenges like pop-ups, errors and UI updates.

These buys highlight the company’s ambition to own every layer of the agentic stack, from data quality and observability to reasoning and optimization.

Spindle fits into Spindle fits into this strategy by adding advanced agentic analytics and scenario modeling capabilities. Teams can test out pricing changes, campaign shifts, or service workflows before they happen. That kind of forecasting could make AI agents more proactive and strategic, not just reactive.

“We built Spindle AI to intelligently close the gap between what questions enterprises want to ask of their data and what their data systems can understand,” Ryan Atallah, co-founder and CEO, Spindle AI, explained.

The aim is to integrate Spindle’s capabilities into Agentforce so that agents become more reliable and better at justifying their recommendations. Carson Kahn, co-founder and Chief AI & Product Officer at Spindle AI, added:

“We look forward to accelerating Agentforce with sophisticated agentic analytics and forecasting that make enterprise LLMs more reliable and valuable.”

Salesforce’s pitch for Agentforce is that enterprises need AI agents that don’t just automate tasks but understand and improve how they work. As Jayesh Govindarajan, EVP Salesforce AI, Agentforce, put it in the announcement:

“Ryan, Carson, and Spindle AI have proven expertise in the complex AI observability and multi-agent analytics functions that are critical for measuring and forecasting AI-driven value.”

That should translate to systems capable of self-analysis—monitoring performance, adjusting strategies, and providing transparent reasoning behind every recommendation.

Still, questions remain about how quickly customers will adopt these more autonomous tools, and how companies will manage governance, explainability, and trust at scale.

The Spindle AI acquisition is expected to close in Salesforce’s fourth fiscal quarter of 2026, subject to conditions. Once complete, it will mark another stage in Salesforce’s effort to turn its customer platform into an ecosystem of intelligent, continuously improving AI agents.

By building workforce management into the 8×8 Platform for CX, the business communications platform provider aims to help small and midsize contact centers to forecast, schedule, and staff across voice and digital channels quickly without requiring professional services or complex configuration.

As customer interactions now span chat, email, and social media alongside traditional voice calls, managing staffing and service levels has become more complicated. Many organizations still rely on spreadsheets or standalone WFM tools that operate separately from the contact center, requiring business to manage multiple vendor contracts and teams to use extra logins, manual processes and fragmented data.

8×8’s platform brings together contact center, unified communications and communication APIs in one environment, helping businesses manage employee and customer interactions in real time.

By using AI throughout the system, the integration gives CX and IT leaders real-time visibility and control to adjust contact center staffing and respond faster to customer needs. They can manage demand across channels more efficiently while keeping service quality and operations on track.

Bringing in WFM functionality increases agent engagement and transparency, as they can view their schedules, track shift changes, and update their availability in one place, cutting down on admin tasks. Built-in forecasting, reusable shift templates, and easy-to-read dashboards take the place of spreadsheets, streamlining operations and making scheduling more strategic.

And as organizations grow, they can expand into more advanced capabilities or connect with partner solutions without disrupting their workflows.

“Contact centers today are being asked to do more with less – across more channels, with higher customer expectations,” said Hunter Middleton, Chief Product Officer at 8×8.

“It’s about replacing those spreadsheets with purpose-built tools that help teams stay ahead of demand, empower agents, and deliver consistently exceptional experiences – without added cost or complexity.”

8×8 Broadens Voice Reach with Mitel Support

In addition to expanding its contact centre platform, 8×8 is extending its reach with new native Session Initiation Protocol (SIP) support and direct sales for the most widely used Mitel SIP desk phones, including the 6900 Series. That allows businesses to keep their Mitel devices while gaining access to the company’s AI-powered communications and contact center capabilities.

The new integration broadens 8×8’s enterprise voice options, allowing organizations to modernize at their own pace. By pairing Mitel’s hardware with 8×8’s global cloud platform, customers can use its contact center tools without the disruption or cost of replacing devices.

“Physical handsets continue to play an essential role for many enterprises,” said Victor Belfor, Global Vice President of Business Development and Strategic Partnerships at 8×8.

Maintaining SIP handsets is particularly suited for hybrid workplaces and industries that depend on secure communications for shared spaces and compliance purposes. The 8×8 Platform for CX integrates phone, contact center, chat, video, APIs, and AI under a single service, which it backs with a 99.999 percent uptime guarantee and global PSTN coverage in more than 55 countries. Maintaining SIP handsets gives companies added flexibility for shared spaces and compliance-heavy sectors.

Native Mitel support is available globally for select models, while U.S. customers can upgrade or purchase new devices for enhanced performance. Mitel also recently moved its IP phone production to Germany through a partnership with Gigaset, a shift aimed at boosting supply chain resilience by reducing lead times in Europe and ensuring consistent quality for regulated industries.

The cloud-based software company announced its third-quarter earnings report on Monday, revealing strong results across the board. 

This has led to ongoing investments into current and future AI products and initiatives. 

“We remain on track to exceed the $100MN in ARR from new products by the end of 2025,” said RingCentral CEO, Vladimir Shmunis, adding:

“Adoption of our new AI-led products is broad-based across various customer cohorts, from small businesses to large enterprises.

“Our GSP partners are also beginning to sell these new offerings, expanding our reach and accelerating adoption.” 

This expansion has led the company to roll out multiple AI product releases this week, increasing its customer base by branching out into emerging AI trends. The launches are aimed at enhancing communication experiences for both customers and agents.

This has resulted from an R&D spend of $125MN into its new AI portfolio. The company is already seeing consistent profitable growth, in the hopes of exceeding the year-end ARR target. 

RingWEM Adds AI Workforce Tools to Cloud Contact Center

On Monday, RingCentral released its latest AI product, RingWEM, an AI-powered workforce engagement management suite designed to enhance its native cloud contact center, RingCX. 

The suite offers four capabilities to strengthen customer experience across agent performance, customer satisfaction, and operational efficiencies by using AI-powered insights: 

AI Quality Management: Designed to give human agents the skills to improve their overall performance, the quality management tool will use personalized customer quality criteria to evaluate and provide extensive insight and feedback on customer calls. 

The tool is used to analyze and observe full customer interactions and agent workflows, delivering focused guidance for reskilling. 

Furthermore, the tool offers AI-based coaching recommendations to improve agent expertise, allowing enterprises to improve their workforce by viewing their agents’ performance analytics, common communication themes and advise them on next steps using the provided data-driven advice. 

AI Workforce Management: Used to improve quality in customer service, planning for potential challenges, and overall efficiency, this tool combines precise data forecasting and resourceful scheduling to align staff with probable tasks to tackle targeted demand. 

By using precise data forecasting, the AI workforce management tool can use algorithms to analyze past and current data trends and business drivers to predict the likelihood of contact quantities, allowing time for agents to tackle abnormal spikes before they occur. 

With intelligent scheduling, the workforce tool can generate actionable schedules for agents to follow that include agent preferences, adjustable service level actions, and business requirements to keep agents on track with tasks. 

These can be modified to fit irregular changes in working conditions to ensure that service levels stay the same to avoid customer friction. 

Additionally, customer agent supervisors can run probable scenarios to analyze the effectiveness of staff models and company changes before they are implemented. 

AI Interaction Analytics: This tool provides enterprises with high-level insights into customer satisfaction with interactions, compiling data taken from surveys and summaries from the interactions themselves to address negative customer experiences. 

AI Interaction Analytics can dissect customer conversations through voice tone, language preferences, and patterns in speech to assess satisfaction. 

The tool can use this and other conversations to further analyze key customer trends and issues as a whole, allowing businesses to proactively address these concerns before escalation. 

Screen Recording: Similar to the AI Quality Management capability, this tool allows supervisors to evaluate customer-agent interactions by collectively linking calls and screen recordings for a wider range of information into quality of conversation and workflow efficiency. 

These tools can be utilized to address underlying issues with agent performance and customer satisfaction and elevate operations in contact centers to deliver smarter service. 

RingCentral Debuts Agentic Voice AI Suite

RingCentral has also released its agentic voice AI communications suite, encompassing three tools that enhance communication experiences across the lifespan of each customer interaction. 

AI Receptionist (AIR): Before a conversation begins, this tool ensures that calls are not left unanswered. 

Using the voice AI ability to interact with customers, this AI agent can comprehend a customer’s reason for calling, answer questions, hand off real-time interactions to agents with summarized caller context, and identify and log potential opportunities that may require a human agent to follow up. These can help to avoid customer friction and repeated information. 

For scheduling interactions, AIR provides multi-calendar support across a company to integrate employee schedules and harmonize teams. 

Sales opportunities are collected and stored for future use in Salesforce, HubSpot, or with AIR’s own database. 

AIR can also be used on any SIP-based phone, allowing AI customer handling to be dealt with across the cloud, any premises, or hybrid setups. 

Brian Tucker, Chief Digital Officer at Televero Health, is a customer of RingCentral’s AIR tool. 

He said, “Using RingCentral’s AI Receptionist, the results are undeniable. We saw our monthly appointments increase 14 percent in the first four months, an increase in monthly revenue of over $200,000. 

“That kind of growth and return on investment is exactly what we need.”

AI Virtual Assistant (AVA): During a conversation, AVA can provide an agent with real-time assistance across customer interactions by implementing four key capabilities: 

• Real-Time Calls and Meeting Summaries: Identify the relevant information, questions, and actionable tasks during the span of a call or meeting, generating summaries and highlight reels to allow agents to keep track of the interaction’s objective during the call. 
• AI Writer to Create and Translate Communications: This capability can draft, edit, and translate conversations in multiple languages, allowing for seamless and customer-focused messaging. 
• Multi-Use Assistance Across Workflows: By adapting to a user’s communication method, via phone, text, or chat, this tool can provide intelligent prompts and relevant actions for each task. 
• Product Adoption and Feature Discovery: AVA can advise discovery and management methods to improve RingCentral’s overall customer enterprise experience. 

Kira Makagon, President & COO, RingCentral, explained the value of AVA in an enterprise workflow: “By putting trusted voice intelligence at employees’ fingertips, AVA makes work more productive and empowering,

“AVA is your personal virtual assistant that enables you to work smarter, faster, and more efficiently.”

AI Conversation Expert (ACE): After a conversation, ACE steps in to offer evaluated business insights from these interactions and adds it into one analytics and insights layer for a simplified outlook. 

It provides real-time insight into current customer satisfaction, trends in revenue, and overall agent team performance, giving context to performance data to allow leaders to act quickly. 

When requested, ACE can turn compound data into written summaries, recommend actions and examples of improvement, and be used an interactive interface to allow leaders to inquire related queries with instant results. 

Zach Jecklin, Chief Information Officer at Echo Global Logistics, and customer of ACE, uses the tool for improving company knowledge on customer calls and data trends. 

“AI Conversation Expert provides us with the detailed coaching for individual calls, and the dashboard connects the dots by rolling up all that data into a clear, concise view of the major trends impacting the entire business,” Jecklin said.  

“We used to have call data. Now, we have business intelligence. It’s that simple.”

RingCentral Pairs New AI Tools with Solid Growth

RingCentral has launched the new AI tools in conjunction with the announcement of its strong third earnings quarter. 

During its earnings call, the company reported a total revenue result of $639MN, seeing a growth of 5 percent from the previous year. 

Subscription revenue also increased, rising by 6 percent to $616MN, with a 23 percent rise of $130MN in free cash flow, which it intends to increase during the rest of the year.

Scheduling, forecasting, and ensuring compliance were integral to ensuring organizations ran efficiently.  

But in today’s fast-changing environment – with fluctuating demand, hybrid working models, and rising customer expectations – the old approaches are starting to creak.  

“The business problem is still the same — organizations need enough people to respond to customers when they reach out,” said Magnus Geverts, VP of Product Marketing at Calabrio.  

“But the types and volume of interactions are now too complex for traditional tools to manage.”

From Management to Intelligence  

Calabrio believes the best way to address these new challenges is by moving beyond workforce management toward Workforce Intelligence.  

I can guess what you might be thinking. Is this just another example of a brand repackaging the same solution with a fancy new name and nothing else?  

But this isn’t the case with Calabrio.  

Calabrio Workforce Intelligence moves beyond simply allocating shifts and monitoring adherence by using AI-driven insights to anticipate challenges and recommend, or even automate, responses.  

“We have embedded AI at the core of workforce management,” explained Geverts.  

“The result is an application that is more of a living, breathing system that makes autonomous decisions and learns as you go along.”

This shift represents a move from reactive management to proactive optimization.  

By monitoring signals like interaction volumes, absenteeism, and forecast changes, Calabrio Workforce Intelligence can help organizations intervene before service levels are at risk.  

The Enterprise Opportunity  

For enterprises, the opportunity is considerable. Smarter workforce insights mean lower attrition, optimized costs, and better employee engagement.  

They also allow leaders to focus on higher-value activities like coaching and strategy, rather than firefighting daily scheduling issues.  

Another advantage that Geverts outlined is the automation tools and packaging of workforce management knowledge within the application itself, which can support junior resource planners and help transform them into experts.  

Interestingly, Calabrio’s VP of Product Marketing reveals that industries with tight margins are showing the strongest interest in the new solution, including the outsourcing space.  

In detailing why he believes this to be the case, Geverts explains that these companies “really need to optimize all the time, and workforce intelligence is compelling to them; it allows them to do things they simply cannot do today.”  

Spotlight: Calabrio Workforce Intelligence  

But how exactly will Calabrio’s newest tool deliver on all of this promise?  

In a nutshell, Calabrio Workforce Intelligence offers the following key capabilities:  

• Predictive forecasting to anticipate demand.  

• Trend analysis to understand long-term patterns.  

• Intraday optimization to adjust schedules in real time.  

Rather than bolting AI onto legacy systems, the company has rebuilt its approach from the ground up.  

“With this new tooling, we can rethink everything: forecasting, scheduling, intraday management, and agent empowerment,” said Geverts.  

He described the leap as moving “from a calculator to a brain transplant.  

“It can collect so many different signals, crunch them together, and come up with ways of doing it much, much faster than a human can.”  

Looking Ahead  

As CX operations grow more complex, enterprises cannot afford to remain reactive.  

Workforce Intelligence enables leaders to outpace change, rather than constantly chasing it.  

“The main goal here is to help our customers stay ahead of an ever-evolving market,” said Geverts.  

“The speed of change is so quick in contact centers now. We want to help them move ahead and be proactive.”

Calabrio views the shift from WFM to Workforce Intelligence as more than a technology upgrade; the company views it as a sea change in how enterprises think about operations – turning the contact center from a cost center into a strategic value driver.  

For CX leaders, embracing intelligence will be critical to staying ahead in the AI era.  

You can find out more about Calabrio’s wider customer service approach by checking out articles here and here. 

You can also learn all about Calabrio’s full suite of services and solutions by visiting the website. 

In fact, Gartner predicts that by 2027, half of all businesses will abandon plans to shrink their customer service teams.  

Cavell goes even further, forecasting that global human contact center roles will grow from 15.3 million in 2025 to 16.8 million by 2029. 

Such statistics underscore how live agents remain a contact center’s most valuable asset.  

Yet, they’re also a contact center’s most costly asset. Indeed, people typically account for up to 80 percent of their expenses.  

These costs rise whenever an agent leaves, as the business pays for recruiting and training replacements. Meanwhile, the contact center also loses efficiency. That impacts customer satisfaction (CSAT).  

Additionally, understaffing creates stress for remaining agents, which often leads to more turnover. It snowballs quickly, and that cycle is costly.  

As such, agent retention is critical to improving not just business outcomes but also cornerstone customer and employee engagement outcomes.  

While improving retention is tricky, there is a tonic: an effective agent empowerment strategy. Such a strategy starts by acknowledging a hard truth… 

The Hard Truth 

Here’s the hard truth: very few people aspire to be a contact center agent. It’s not typically on anyone’s vision board. Most people land in customer support roles by circumstance, not because it was a lifelong goal. 

That reality can create resentment over time. Agents start questioning whether their work matters, whether they’re making an impact, or if they’re meant for something “better” – and, honestly, they might be. 

Contact center leaders can’t fight this. But they can reframe the agent role, so instead of seeing the contact center as a dead end, agents see it as a catalyst for the future.  

Think of it this way: this is a role where people learn communication, problem-solving, empathy, and resilience. These skills apply everywhere.  

So, leaders must ask themselves: where does this person want to be, and how can we help them get there? By thinking this way, the job becomes more about development and growth, and less about performance metrics.  

As such, the job isn’t just tolerable; it’s meaningful. 

Focus on how this experience shapes them for what’s next, and accept that “next” might be outside the contact center. 

That empowers the agent; they become the boss of their own future and are determined to stay with the business, as long as they perceive that they’re moving forward.  

As Paul Hughes, Head of Customer Experience for the UK/I/SA at Mitel, explained: “Agents should feel they’re learning and adding value. 

“Involve them in designing training and coaching programs. It’s not just a matter of adding tools, it’s about building new habits, roles, and expectations.” 

Yet, a comprehensive agent empowerment strategy has three more essential elements.  

These are all about giving agents more control over: when they work, where they work, and what they do. Here’s a closer look at each. 

1. When They Work

Agents will stay longer if they can fit their work around their outside lives.  

As such, learning their schedule preferences is becoming a much more normalized activity. But, it’s what the contact center does with that data that makes the difference.  

Many have turned to automated scheduling software to wrap agent shifts around these preferences. But don’t just sit back and rely on the tech. Consider: can we add extra shift patterns that align with these preferences?  

For instance, many contact centers have implemented slant schedules, where instead of agents working eight hours, five days a week, they work ten hours on Monday with an hour less every day. So, by Friday, they work just six hours. These schedules align with inbound demand, which typically drops throughout the week, and many people’s preference to build toward the weekend. Meanwhile, the agent’s core hours stay the same.  

Alongside new shift options, think about the extra flex mechanisms that are possible to build into the schedule. Shift-swaps are the classic example. However, offering overtimes when the contact center is overrun, with the option of taking it back at a time that suits, is another possible lever.

2. Where They Work

It is key to understand preferences for in-office, remote, and hybrid work and consider how the contact center can support them.  

However, there’s another often-overlooked part of this conversation: which channels are agents most often working on, and do they align with their preferences?  

Also, agents must be empowered to move conversations across channels. For example, if an issue isn’t clear after a chat exchange, the agent should be able to switch to a call and resolve it quickly without losing any of the case context on their screen. 

Meanwhile, an AI assistant should work alongside them, every step along the way, considering all that omnichannel context.  

As Hughes summarized: “AI workflows can trigger actions during interactions, no matter where they happen. Yet, teams must trust the tools, and leaders must integrate AI as part of how work gets done, not as an afterthought.” 

3. What They Do

AI is simplifying processes, cutting through the noise and distractions. But, before diving in, contact centers must define what meaningful work looks like for agents. 

 “If we remove tasks they enjoy, that can feel threatening,” Hughes noted.  

“So, involve agents from the start, including in the design phase. We do a lot of discovery with agents, not just IT leaders.”

In bringing interested agents into the fold here, contact centers can also offer an opportunity to boost their AI knowledge and personal development.  

Beyond processes, contact centers can also take a step back and ask: what contacts do you prefer to handle, and would you like to take on more?  

From there, they can adjust the routing mechanism so that agents take on more of the contacts that most interest them.  

Work With a Contact Center Provider That Gets It 

“Without empowered agents, even the best technology won’t deliver great experiences,” summarized Hughes.  

“Job satisfaction rises when agents feel trusted, have clarity, and maintain control. AI can support that, but only if the culture allows teams to own the change.”  

As Hughes suggests, the human experience comes first for Mitel. Its AI assistance tools are designed to give agents fast, accurate insights. Yet, it’s not only delivering artificial intelligence, it’s supporting the human intelligence and culture behind the implementation.  

That’s crucial. After all, it’s where transformations often fall short.

Now watch the new guide on best practices for workforce management in the AI-era.

Discover more about how Mitel can help empower agents and transform contact centers, here: www.mitel.com