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.

In its third-quarter earnings call, the software company announced that its total number of customers had increased to almost 300,000.

These tactics reveal how enterprises are willing to adopt AI with the correct tools given.

HubSpot CEO Yamini Rangan, remains confident in HubSpot’s continuing strategy to improve its customer tool adoption and outreach. 

She said: “We are uncovering new ways to drive efficiency and finding signals to show our customers what’s possible with AI.  

“I’m more confident than ever in our strategy and our ability to deliver value for customers in this new era.”

Rangan recognised that the result of this was down to three different factors: 

1. Multi-hub adoption
2. Answer Engine Optimization strategy
3. Platform consolidation

Multi-Hub Adoption  

HubSpot has adopted the multi-hub strategy to encourage consumers to involve their enterprises in more than one hub. 

This solution addresses the current trending issue of tool fatigue by supporting its enterprise customers to meet these AI innovations head-on. 

In fact, this has become the model standard for a large number of customers, with 43% of customers who subscribe to HubSpot’s Pro Plus also subscribing to all three primary hubs. 

Along with this strategy, the company has continued to enhance its agents across the board, as well as launching a new data agent towards the end of the quarter. 

This approach has seen higher results and activity across all hubs and agents. 

In one example, customers who used the Marketing Hub saw improvements in results and click-through rates thanks to its embedded AI features, such as the AI-powered email. 

Its Prospecting agent saw a total of 6,400 customers during the quarter, with an increase rate of 94% and high rates of engagement with over 1,000,000 recipients. 

Data agent, which launched recently at INBOUND in September, has already collected 700 customers. 

HubSpot’s digital assistant ‘Brief’ has more than doubled in weekly usage for record summarizing and finding performance engagement insights. 

Its Data Hub can be beneficial to this process, by helping customers to unify data from across an enterprise into one location. 

Along with its standardized bots, HubSpot has also introduced its Breeze studio, allowing customers to create and design their own agents to fit their enterprise’s needs. 

AEO

HubSpot’s Content Hub has also launched its Answer Engine Optimization (AEO) strategy to improve its customers’ visibility online and in AI-generated answers, allowing them to measure and refine their tactics. 

This has included the launch of its AEO-focused tool, such as the Loop, a clear guide for how companies can drive traffic growth from both human and machine intelligence. 

The tool has received strong responses from customers, with a total of 270 million viewers and 100,000 on the Playbook experience. 

HubSpot’s Data and Marketing hubs have also been beneficial to the launch of Loop, helping to create personalized customer profiles and content targeted towards buyer objectives. 

The second AEO product launch was the AEO Grader, which allows companies to grasp their popularity levels and image when they’re searched on AI engines. 

Platform Consolidation

From strong results in the third quarter, HubSpot has seen substantial benefits for its customers from its unified, platform-first customer solution. 

A primary justification for this upwind in the platform’s customer growth is its cost-efficiency method, with more companies choosing to go for unified operations to avoid integration expenses, as well as to view their marketing, sales, and customer services all in one place to simplify their AI innovation process. 

This result has also benefited from HubSpot’s LLM connector approach, allowing its platform to connect with large-language-model (LLM) providers, such as ChatGPT and Gemini. 

This approach has seen an upsurge in consumer reach across its LLM providers, with ChatGPT reporting more than 47,000 customers activating its connector, with over half of them being Pro Plus users. 

Its cloud connector has also seen high levels of traffic, having been accessed by over 6,000 users. 

HubSpot refers to this approach as a “key part of [its] AI strategy”, with the LLMs using public available data to create the insights, HubSpot can offer context to the insights and make them ready for market teams to use. 

The company have also shown great success in its CRM, becoming the first to connect with all three – ChatGPT, Quad, and Gemini – for successful customer outcomes. 

HubSpot also outlined the success of its shift towards a universal usage-based pricing model during the quarter, set to extend across the entire platform. 

The system focuses on its AI agents’ actions, data hub syncs, and automation all under one operational framework. 

To track and monitor customer usage, HubSpot uses credits to measure customer value growth and their use of AI and data inside the system. 

By using more HubSpot tools, AI features, and data capabilities, customers can scale their value effectively without having to change their system environment. 

Rangan said:

“Our vision is to make the core seat essential with AI and data value for every go-to-market employee. Credits are another powerful emerging lever.” 

Key Q3 Financial Results

• HubSpot has revealed a global customer base increase of nearly 297,000, its total number of customers had increased by 10,900 
• Its total revenue saw a strong increase at $810MN, up by 18.4% year-over-year 
• The percentage of customer revenue retained above 80%, with customer net revenue retention at 103%
• Subscription revenue went up at almost $792MN, up 21% on an as-reported basis compared to Q3 2024 
• Other professional services and revenue had risen to almost $18MN, up by 19% on an as-reported basis compared to Q3 2024 

Yet, the needle is starting to move. Soon, talking to AI will feel just like having a normal conversation, and success rates will continue to grow.  

Gartner even predicts that by year-end 2027, conversational AI applications will automate approximately 70 percent of customer support interactions within enterprises.  

“Voice AI is quickly approaching human parity, as it does adoption is going to accelerate quickly” said Jake Tyler, AI Market Lead at Glia, a leading Voice AI provider for banks and credit unions. “This pattern is playing out in other markets already, from translation services to driverless cars, and contact centers and customer service will be next.” 

The contact center economic model will shift with that change. Leaders will need to rethink who works there, how many people are required, and what they do. At Glia, Jake and his team are helping bank and credit union leaders reconfigure how their contact center and frontline teams are positioned to best support the communities they serve in the AI-era.  

It’s not an easy conversation, it’s downright uncomfortable sometimes, but it’s one many companies need to have. 

The Three Options Businesses Face 

Don’t skip ahead to 70 percent of customer support conversations. Consider: what if AI did half the work? If AI takes on this much labor, what happens next? 

In this scenario, a business would have three options (which they may blend). 

1. Reinvest

This is the crowd pleaser. In this scenario, the contact center would keep its headcount flat and reinvest the savings in its people.  

The classic example is to give agents more time to interact with high-value customers and graft away at meaty, complex issues.  

Yet, there are other ways in which a fully-staffed contact center can add value.  

For instance, leaders could rearchitect customer journeys. Here, a contact center may pass an issue that is possible to automate over to a human representative, if it’s particularly emotive. The rep could then offer warm, firm reassurance to boost loyalty. Similarly, they can have humans answer contacts that have high upsell or cross-sell potential.  

In addition, the contact center could expand its customer and community outreach. 

Consider a financial institution. It could contact customers to explore new opportunities to grow loans and deposits. Meanwhile, it may reach out to the community to promote financial literacy programs and other initiatives. 

Case Study 

Busey Bank has expanded its customer base by 25 percent. Yet, because of voice AI, it managed to keep its headcount steady.  

In fact, it even managed to reskill two frontline employees, placing them into more strategic research roles.  

As its AI exploits continue it even plans to offer career advancement for other reps into areas like career advancement in areas like treasury and commercial. 

As Caitlin Drake, SVP and Director of CX & Support, said: 

“By investing in technology, we can put our people on a career path that opens more doors for them and allows them to serve the company at a higher level.”

2. Right-Size

From the crowd pleaser to the town villain. Right-sizing is not an easy conversation to have, but it’s a realistic one.  

After all, a business may sense the opportunity to significantly reduce or even eliminate costly overflow and after-hours contact centers.  

Perhaps a more sensitive approach, however, is to stop backfilling agents who churn, aligning the strategy with forecasts and AI performance insights. 

Alternatively, for brands experiencing high growth, there’s the option to simply stop adding headcount. Whatever the case, the workforce management team will be busy!  

Case Study 

Service 1st Federal Credit Union implemented its virtual agent, “Scout”, to interact with customers across voice. But, it didn’t stop there. It also provided real-time conversation transcription, automated agents’ post-call processing, and streamlined managers’ quality assurance (QA) tasks to eliminate busy work.   

Since it has decreased human-handled monthly contact volumes by 29 percent, it has also cut average wait times by 71 percent, slashed average speed of answer times from three minutes to 18 seconds, and cut call abandonment from 25 to 1 percent. 

That’s all while its service headcount has dropped. However, Service 1st didn’t dramatically cut employees; it reduced its headcount with natural attrition and continues to work with Glia to unlock new efficiencies as staff trickle out of the business, as they inevitably do in contact centers. 

3. Reallocate 

Finally, the contact center could reallocate reps, starting with those wanting to try something new or those looking for a long-term career path.  

In doing so, the business could grow its other customer-facing functions. At a bank or credit union, this could include fraud prevention, financial planning, or proactive outreach. 

Alternatively, the firm may assign personnel to lead business development projects within the community or invest in branch modernization. There are options aplenty! 

Case Study 

Sticking to finance, Granite Credit Union is a successful credit union out of Utah. After implementing a virtual agent, it achieved a 60 percent containment rate while saving 1,400 hours of manual work in only four months.  

Like the other examples, Granite also implemented agent assist tools to boost that reduction in manual work, again lowering its labor requirement.  

So, it invested in reskilling its excess staff and started training employees to work in branches, collections, and fraud prevention. 

As Cindy Clark, CIO of Granite Credit Union, summarized:  

“With Responsible AI, we can keep pace with the industry, while still doing it right.”

Don’t Skip Too Far Ahead…  

The contact centers furthest ahead are thinking about how to reinvest AI’s efficiency gains into innovation and human potential. 

For many, that’s going to be a struggle. As Justin Robbins, Founder & Principal Analyst at Metric Sherpa, said:  

“AI is becoming table stakes, but too many leaders are still running old playbooks. Until contact centers both measure their strategic impact and have a stronger hand in AI decisions, they’ll leave enormous value on the table.” 

Capturing that “enormous value” requires a fundamental shift in how businesses align people, processes, and measurement. 

For more on how brands can do that, check out Robbins’ latest whitepaper: The New Equation: Redefining Value, Effort, and Impact in the AI-Era Contact Center 

Yet in many enterprises, the tools remain underutilized.  

With many WEM capabilities bundled into larger CCaaS deployments, they often sit idle, reduced to basic scheduling and adherence reporting.  

This ‘switch-on-and-forget’ approach means that despite investing in powerful platforms, leaders rarely unlock their full potential.  

The result is a workforce managed by numbers, not supported by people.  

As Jim Fleming, WFM Solutions Consultant at Sabio, warns:  

“Although many organizations have feature-rich WEM platforms, they’re only scratching the surface of their capabilities, often driven by outdated or redundant processes.” 

The ripple effects are clear: agents get locked into rigid shifts, coaching sessions slip through the cracks, feedback loops stall, and KPIs from another era still define performance.  

In too many operations, contact centers are stuck firefighting, not optimizing.  

WEM Needs a Rethink  

WEM can often be viewed as a cost center, instead of a value driver. 

However, when done right, WEM creates a dynamic, agent-centric environment where scheduling, coaching, quality, and analytics all work together, driving improved CX. 

The tool’s real value lies in creating a dynamic, agent-centered environment where scheduling, coaching, and quality all feed into one another.  

Unfortunately, many enterprises never manage to make that leap.  

“Organizations go through rigorous processes to ensure they invest in the right tools, yet they don’t invest in appropriate support to fully leverage the solutions,” Fleming explains.  

“They essentially turn the lights on with basic functionality rather than innovating through technology and processes to differentiate from competitors.”  

In a nutshell, WEM is not just about tools; it’s about design.  

Features like AI-driven scheduling or automated quality management can have a significant impact – but only when paired with processes that value agents as much as SLAs.  

Tackling Everyday Frustrations  

When organizations are able to shift their mindsets around the role of WEM, results often come quickly.  

Take one of the most common pain points in any contact center: booking time off.  

At Benenden Health, staff used to wait for manual approval of holiday requests. Now, thanks to a ‘Time Management’ app developed with Sabio inside Genesys WEM, they get instant decisions.  

“The automated decision-making means advisors receive instant decisions on their time-off requests, completely eliminating the need for human intervention,” Fleming explains. 

However, the effect stretched beyond efficiency. Transparency boosted morale, giving advisors confidence and control over their allowances.  

It’s a small change with a big impact, and proof that WEM isn’t just about operational gains.  

As Fleming puts it:  

“When properly implemented with agent-centric design, WEM tools don’t just improve operational metrics; they create happier, more empowered employees.”

Where WEM Trips Up in the Real World  

The insights from Benenden’s WEM implementation paint a picture of what it is like on the frontline of customer service and experience.   

This is an area that Sabio is particularly passionate about, as evidenced by its Community Days, where planners and CX leaders share stories of their individual experiences.  

One common frustration that seems to come up time and time again is the gap between expectation and execution.  

“There’s often a disconnect between what organizations think their WEM technology will do versus the processes needed to actually achieve those outcomes,” Fleming says.  

Leaders may think automated leave is already ‘live’ – only to realize the processes behind it were never built.  

Another blocker is trust. AI-driven forecasting and scheduling promise huge efficiency gains, but many planners remain hesitant, as Fleming explains:  

“There’s leadership pressure to embrace AI, but planners need to understand how systems arrive at their recommendations because they’ll inevitably be asked to explain the rationale.”  

Still, the sessions also reveal plenty of wins.  

For instance, one large retailer cut overstaffing by 12 percent after ditching static Excel models for dynamic scenario planning. SLA stability also improved.  

The message from these Community Days seems to be that when enterprises break out of firefighting mode, WEM starts delivering real results.  

Designing WEM Around People  

So, what does success for your WEM program actually look like?  

According to Fleming, it’s about making WEM a continuous process rather than a set of disconnected tools:  

“We integrate real-time adherence, agent self-service, learning nudges, AI-driven quality management, and analytics-driven insights into single, continuous processes.” 

That kind of design pays off across the board: businesses cut inefficiencies and protect SLAs; employees get fairer schedules, faster feedback, and better coaching; and customers enjoy quicker responses and higher first-call resolution.  

It also helps to shift the emphasis from compliance to empowerment.  

WEM stops being an admin system and becomes the connective tissue of the modern contact center.  

The Untapped Value of WEM  

For enterprise leaders, the lesson is clear: WEM isn’t about ticking boxes or measuring adherence; it’s about shaping the agent experience. And in shaping that experience, customer outcomes naturally improve.  

As Fleming concludes:  

“The goal is always to make agents’ working lives easier while delivering measurable business outcomes, because when you get that balance right, everyone benefits: agents, customers, and the business.”  

For those unsure if their WEM is pulling its weight, a fresh look at design and processes is the best starting point.  

You can learn more about Sabio’s WEM philosophy by visiting the website today.   

You can also gain insights into the company’s wider expertise and experience by checking out this exclusive interview with Sabio’s Chief Revenue Officer, Ioan MacRae. 

First, he predicted human customer support will be “totally, totally gone”. Next, Altman suggested that he is “confident” contact center jobs will be the first to be replaced by AI.  

With leading AI voices making such predictions, AI experiments are ramping up, CIOs are paying more attention to the contact center, and leaders are honing in on deflection and containment rates.  

Yet, while these are helpful metrics, they’re not a strategy. A hyperfocus on these measures can lead to poor customer experiences. As Goodhart’s Law suggests, when a measure becomes a target, it ceases to become a good measure. 

In this case, by valuing automation above all else, contact centers risk high-value calls getting diverted away from agents. These may be make-or-break to customer loyalty or offer a significant upsell opportunity.  

As such, contact centers should be clear on why customers are contacting the business and the value each of those contacts has.  

Breaking Down Customer Demand 

Take the contact center’s most common contact drivers and consider which have the most value to the company and which have the most value to the customer.  

Those with low value to both are prime candidates to eliminate completely. Contact automation is a powerful solution here. However, by tracing back the root cause of the issue, through an analytics initiative, the service team can often remove the need to reach out to the business altogether. That’s pre-emptive customer support at its finest! 

For those with high value to the company or customer, look to simplify the process. Again, a virtual agent flow may be an effective solution. However, also consider opportunities for proactive customer service, detecting signals in back-end systems to automate the entire process without the customer lifting a finger.  

Finally, there are those calls of high value to the company and customer, where loyalty is won and lost. That’s where the human touch shines, and human reps will ideally be equipped and ready to deal with these common contacts as they reach them.  

Supporting Agents to Deliver in the Moments That Matter 

Handling high-value customer contacts well starts long before an agent picks up the phone. The experience should be carefully orchestrated.  

When an front-end generative virtual agent deciphers the customer intent, leveraging a LLM trained on business and customer specific data, it will ideally engage with the customer to take information from them that’s pertinent to the intent and helpful to the agent.  

That agent should also understand which channel is best to handle the incoming query and route it accordingly, bolstering the possibility of a successful outcome.  

From there, the contact reaches the agent, who should have all the necessary information at hand (and the coaching) to guide the customer through the contact.  

Along the way, generative agent assist tooling uses the LLM to synthesize the trained data set with the ongoing conversation and available customer metadata, which serves up helpful information at the moment of need, and support the agent in handling objections. As such, they can focus on listening to the customer, showing empathy, and building real connections.  

That vision may seem like a distant prospect for those still leveraging on-premise technology and first-generation cloud solutions. Yet, it’s one that some vendors are making a possibility every day.  

Work with a Partner That Gets It 

In 2015, UJET broke into the contact center technology market with a solution designed around the modern smartphone, enabling a multimodal experience and simplifying channel shift. Still, that differentiates its CCaaS platform.  

Since then, it has built out its solution further, leveraging a tight relationship with Google to apply the latest generative AI advancements to the contact center via virtual agents, agent-assistance, conversational intelligence, and much more.  

Its cloud contact center has resonated especially well in the midmarket, where companies are more agile and ready to embrace a strategy to best balance the strengths of human and AI agents.  

For more on its transformative contact center technologies, visit: ujet.cx  

The reasons for high time to resolution (TTR) rates aren’t mysterious. Workflows in service teams are often fragmented. Agents toggle between systems, re-enter the same details, and chase context across email, chat, and phone. Handoffs take longer than they should. Meanwhile, customers wait.

Fortunately, the right approach to workflow automation could be the solution. By removing unnecessary steps and connecting the dots between systems, automation clears the path to faster answers.

Service teams that automate see an average 37% drop in first response times, according to 2023 research by Gartner. Some companies see even bigger results, like the Formula 1 team, using Agentforce to speed up service response rates by 80%.

For enterprise leaders, TTR reduction isn’t a side benefit of automation anymore, it’s one of the clearest, earliest examples of intelligent systems paying off.

TTR Reduction: Why Time-to-Resolution is Rising

TTR measures how long it takes to fully resolve a customer issue, from the moment they reach out until the case is closed. It’s a snapshot view of process efficiency and customer experience strategies working in tandem.

Unfortunately, lately, the average time it takes to resolve customer issues has been increasing. Companies have plenty of intelligent tools to help, but they’re still dealing with:

• Workflow Friction: Agents often juggle multiple systems: CRM, ticketing, knowledge bases, while switching screens and re-entering information. This wastes minutes (or more) with every handoff.
• Fragmented Knowledge & Repeat Triage: Without a single source of truth, customers end up repeating their stories, even to the same company. Up to 56% report having to restate their issues to multiple agents. That duplication drags resolution out.
• Channel Disconnects: Customers want one conversation across phone, chat, email, and self-service. When those touchpoints don’t connect, context disappears, progress stalls, and resolution times stretch out.
• Growing Complexity of Interactions: Modern enterprise issues often span compliance checks, cross-functional handoffs, or legacy systems, making a simple fix go through multiple layers and moving the TTR needle upward.
• Agent Strain & Morale: When agents face ticket backlogs and long, drawn-out resolutions, morale drops. Burnout increases turnover, and with it, onboarding delays and knowledge loss, pushing TTR even higher.

Rising TTR is a flashing warning of rising customer dissatisfaction, growing operational inefficiency and cost creep. It highlights where processes are stuck, where systems don’t talk to each other, and where support design isn’t scaling.

TTR Reduction with Automation: The Opportunity

Automation can’t fix every problem in customer service. Gartner has already warned that “limitless automation” is a myth, with most leaders expecting only modest workforce savings even as demand for agents grows globally.

But what automation can do, when it is applied with focus, is eliminate wasted steps, speed up handoffs, and give agents the context they need. This is where workflow automation consistently drives measurable TTR reduction, improving resolution times without adding headcount.

Intelligent Triage & Routing

Every extra handoff adds minutes to a case. Traditional queues often assign tickets on a “first in, first served” basis, which means specialists waste time on basic queries while complex issues bounce from desk to desk. Automated triage uses intent detection and skills-based routing to match tickets to the right agent immediately.

Even a simple AI-driven solution like NiCE’s SmartSpeak language translation tool could detect a customer’s language and make sure they’re routed to an appropriate agent instantly, reducing the need for handover and transfers.

Agent Assist: Context in the Moment

Agents often lose time toggling between systems to piece together customer history and policies. Agent assist tools change this by surfacing the right information mid-interaction – previous tickets, knowledge articles, or even suggested responses.

This reduces friction and improves first-contact resolution. In practice, teams using AI assist are more efficient, and more engaged. For example, Yopa, a real estate company, improved contact center productivity by 4 times, just by implementing intelligent automation and agent assist tools.

Self-Service & Virtual Agents

Routine requests related to password resets, delivery updates, and account changes clog service queues. When customers can handle these through well-designed self-service or AI-guided bots, resolution happens instantly, without waiting for an agent.

Deutsche Telekom used Rasa CALM to handle common service requests. The system resolved half of all incoming inquiries on its own, easing agent workloads by 30%. That freed staff to focus on complex cases, cut down queues, and moved high-value issues to the front more quickly.

Agentic Automation for End-to-End Action

Most automation still supports the agent. Agentic AI takes the next step by completing tasks autonomously, like processing refunds, provisioning accounts, or updating orders. This eliminates entire handoffs and accelerates resolution.

Vonage, a cloud-based communications provider, is a prime example: provisioning time dropped from four days to just minutes after adopting Salesforce Agentforce. That isn’t just faster, it transforms customer expectations, turning a process once measured in days into one resolved before the call even ends. The impact on TTR reduction is immediate and dramatic.

Proactive Prevention & Outreach

The quickest fix is sometimes the one customers never have to ask for. Proactive automation can spot failed payments, outages, or late deliveries and send alerts before support is contacted. This prevents inbound volume and shortens resolution when customers do call, because the fix is already in motion.

NICE has shown that proactive AI agents can re-engage “silent” customers, reducing drop-off and unnecessary contact volume. Prevention doesn’t just contain—it accelerates resolution for those who still need service.

Post-Interaction Automation

Closing a ticket still involves wrap-up: writing summaries, logging notes, quality checks, and sometimes triggering refunds. Each task adds minutes. Automating them frees agents to move on and ensures consistency. Pharma company Elanco claims its homegrown AI summarization tool reduced routine task time by 70%, saving $2.3M in its first year.

FedPoint saw similar gains with NiCE; average speed to answer fell from 35 to 15 seconds, while QA scores jumped nearly 10 points. These post-work automations may be invisible to the customer, but their impact on TTR is unmistakable.

Deploying Automation to Reduce TTR: Quick Tips

Automation only works for TTR reduction if it’s planned carefully. It’s not enough to just automate everything. Every business needs a focused approach:

• Start Narrow, Then Expand: Don’t automate too fast. Start slow with one or two high-volume irritants like password resets, order lookups, claim checks, and automate those first. The payoff is immediate. At Frontier Airlines, everyday questions moved to a virtual agent. It didn’t just lighten the load; it supported 15–30% annual growth without ballooning service costs.
• Measure What Matters: Average Handle Time has been the yardstick for decades, but it misses what customers actually care about: getting an answer and moving on. The smarter approach is tracking TTR, first-contact resolution, and effort scores. Those numbers reveal whether automation is working or just creating new loops.
• Orchestrate, Don’t Isolate: A chatbot that can’t share context with an agent doesn’t solve much. The same goes for RPA scripts running outside the CRM. When automations don’t talk to each other, journeys break apart and time to resolution rises, not falls.
• Build Guardrails for Agentic AI: Automation that makes decisions on behalf of customers has to be handled carefully. Without controls, even a small error can damage trust. That’s why governance matters: permissions, escalation paths, and monitoring.
• Get Data Ready and Connected: Long time to resolution is often a data problem. Agents hop between tools, knowledge bases, and CRMs to find the basics. Integration clears that clutter. Without connected data, automation is half-blind. With it, TTR drops quickly.

Plus, invest in people as much as technology. The reality is that the demand for human services is still growing. Agents aren’t out of the picture; they’re just more valuable when workflows remove the repetitive, time-wasting parts of the job.

Handling TTR Reduction the Smart Way

Slow TTR drains money and patience. Each extra handoff adds cost; each long wait increases the chance of churn. The effect is visible in higher operating expense and lower customer loyalty. Fortunately, the solutions for TTR reduction are evolving.

CX leaders are building contact center solutions with integrated journey orchestration, agentic AI, and support tools for human professionals. Business leaders are beginning to see examples of how intelligent automation can deliver not just speed, but better customer satisfaction rates and reduced agent attrition.

Now’s the time to move forward strategically. “Limitless automation” may be hype, but targeted workflow automation delivers. It reduces waste, lowers costs, and keeps customers from walking away. The cost of waiting is too high. Find the bottlenecks, automate the basics, and expand from there.

The prediction comes as companies rush to invest heavily in AI, with the analyst firm forecasting that AI application software spending will climb to $269.7BN in 2026, after more than doubling to $172BN this year.

As AI spending accelerates, Sam Altman, CEO of OpenAI, has been among the AI executives vocal in warning that customer service jobs could be among the first to disappear as AI agents advance.

But Kathy Ross, Senior Director Analyst in the Gartner Customer Service & Support Practice, warned companies against attempting to replace human customer support entirely. She said:

AI and automation are transforming how customer service organizations serve customers, but human agents are irreplaceable when it comes to handling nuanced situations and building lasting relationships.

“We expect fewer human agents, but not completely agentless organizations.”

What’s more, in another report, Gartner predicted that by 2027, 50 percent of organizations that planned to reduce their service workforce significantly by implementing AI will drop these plans.

Per the research firm, instead of aggressively pursuing staffing cuts, organizations should focus on using AI to scale support and give agents the tools they need to help them deliver more effective customer experiences.

The analyst also said that replacing humans with AI agents entirely in the future “is not only unlikely, it is also undesirable.”

Start with the Basics…

Rather than rushing to pursue complete automation, Gartner advises that service and support leaders take a more measured approach that uses AI for basic, repetitive tasks. That would then free up employees to focus on higher-level tasks and human interactions that can enhance the customer service experience and, in turn, lead to revenue growth.

“Service and support leaders should leverage AI for efficiency, but not at the expense of human talent,” Ross advised. “While AI excels at handling routine and well-defined problems, it often struggles with exceptions and high-risk scenarios.

Leaders who strategically focus their workforce to support complex, high-value customer interactions will set their organizations apart and drive long-term growth and satisfaction.

While that’s Gartner’s advice, some companies have made moves to slash their customer support headcount, with Atlassian and Salesforce recently announcing contact center layoffs.

Klarna made a similar move last year, yet that seemingly backfired, with the business recently drafting in employees from marketing, engineering, and even legal to pick up the phones, per Business Insider.

Such assumptions that AI agents will replace customer service staff also often discount consumer preferences, with customers still typically showing a strong preference for talking to human agents.

Indeed, 41.5 percent of respondents said they would pay extra for access to human representatives, per a survey by cloud hosting provider Kinsta earlier this year

The survey also suggests that 41.4 percent of respondents said that human customer service has gotten worse because of AI, and 49.6 percent would cancel a subscription over AI-driven customer service.

Some of these concerns may wane over the long term as AI agent deployments become more sophisticated. Already, around half of the respondents can’t tell if they’re talking to a human or AI.

But in the meantime, “[T]he most successful organizations are those that balance technology with the human touch, redeploying their teams to focus on growth and customer satisfaction,” Ross said.

Speaking on ‘The Tucker Carlson Show’ last Wednesday, Altman stated: 

I’m confident that a lot of current customer support that happens over a phone or computer, those people will lose their jobs, and that’ll be better done by an AI.

The CEO didn’t only pick on customer service jobs; he suggested programmers could be next, as AI makes its presence felt.  

“Someone told me recently that the historical average is about 50 percent of jobs significantly change… every 75 years, on average,” he said. “My controversial take would be that this is going to be like a punctuated equilibria moment where a lot of that will happen in a short period of time.” 

Given this, Altman seems to think the staffing make-up of the contact center will change quickly. That comes after he previously predicted the end of human customer service altogether.  

Despite that, he now suggests that jobs, like a nurse, are unlikely to meet their end due to the demand for human connection. “No matter how good the advice of the AI is or the robot, you’ll really want that,” he noted. However, surely, support agents can offer similar reassurance.  Just think about vulnerable customers…. 

Nevertheless, Altman’s prediction is not unique, with Oracle sharing its aim last year to automate “all” of customer support.  Meanwhile, Salesforce CEO Marc Benioff recently boasted about slashing 4,000 live agents from the company’s support team.  

The OpenAI CEO’s recent predictions have, however, been met by an eyeroll from many industry professionals.  

As Steve Blood, VP of Market Intelligence & Evangelism at Five9, cheekily said, “There have been plenty of books written on how to run companies. Does that mean we can get rid of CEOs, too?” 

Meanwhile, Tod Famous, Chief Product Officer at Crescendo, predicted: “There will be more people working CX 3 years from now than… today.”

Interestingly, recent Cavell research backs this up. The analyst suggests that demand for contact center agents will grow from 15.3 million in 2025 to 16.8 million by 2029. 

Stephen Vandevenne, co-founder of QontactAI, added more of a perspective. “Customer service isn’t just about answering questions. It’s about empathy, nuance, and building trust.  

“While AI can absolutely assist and enhance support operations, removing the human altogether overlooks the complexity and emotional intelligence required in many interactions.” 

Interestingly, Five9, Crescendo, and QontactAI all offer customer support automation solutions. As does Glia, and Jake Tyler, GMT Strategy Leader at Glia, offered a similar view to Vandevenne in a LinkedIn post. “Totally gone?? Maybe…for some use cases, for some businesses, it’s possible. Not for most of us, though,” he wrote. 

I think the hard lesson with AI is that while AI models are getting really great, there is still a lot of steps in a lot of processes and systems that still require humans.

With Gartner recently predicting that 50 percent of companies will abandon their plans to reduce headcount in their customer support team by 2027, that appears to be a critical piece of advice.    

Yellow.ai has become the latest firm flagged for a security flaw in its online chatbot, a vulnerability hackers could have used to hijack accounts.

While that may not seem headline-worthy, with the recent spate of conversational AI attacks, this has a twist… Yellow.AI is a chatbot provider.

Last month, it even featured as a Challenger in Gartner’s conversational AI Magic Quadrant.

Nevertheless, researchers at Cybernews found a flaw that allowed them to trick the company’s chatbot into generating HTML or JavaScript (JS) malicious code that could enable cross-site scripting (XSS) attacks. They reported:

The reflected XSS vulnerability allows the attacker to steal session cookies for the support agent’s account, in turn hijacking their account, which can lead to further data exfiltration from the customer support platform.

Indeed, the flaw left cookies created by Yellow.ai’s own customer service chatbot open to theft, although it’s unclear if clients using the bot in their CX implementations were exposed to the same vulnerability, the researchers said.

The likes of Sony, Hyundai, Domino’s, and Logitech use the Yellow.ai platform in their customer support operations.

While Yellow.ai did not acknowledge Cybernews’ disclosure of the security flaw, the company did fix it, sanitizing the generated code so that it would not be executed.

Cybernews’ researchers recently found a similar flaw in Lenovo’s customer service assistant, Lena, which was built on Microsoft Copilot Studio. It allowed them to hijack live session cookies from customer support agents to gain access to sensitive company information.

AI Chatbots Are Exposing Enterprises to Security Vulnerabilities

The incidents highlight the growing security risk to enterprises incorporating AI chatbots and agents into their customer service operations.

In recent days, the US Federal Bureau of Investigations (FBI) warned that cybercriminal groups have been targeting organizations’ Salesforce platforms by exploiting API software integrations with a third-party AI chatbot and conducting phishing attacks on CRM users, like customer support reps.

And the “sycophantic helpfulness” that Cybernews’ researchers note is ingrained in many of the large language models (LLMs) on which such AI tools run can make them vulnerable to misuse. Indeed, attackers can use simple prompts to get unprotected chatbots to inadvertently teach them how to produce malicious HTML and JavaScript code.

Executing JavaScript code, in particular, can have serious implications for security. Attackers can use JS to manipulate the behavior of web applications or even gain access to backend systems through further exploits.

The risk of account hijacking demonstrates why enterprises must be wary of the hype-driven push to implement LLM-based tools quickly without ensuring they have adequate security systems in place. According to Cybernews’ researchers:

The flaw highlights multiple security issues, such as improper user input sanitization, improper chatbot output sanitization, the web server not verifying content produced by the chatbot, running unverified code, and loading content from arbitrary web resources… For example, attackers could bypass sanitization to inject unauthorized code into the system.

AI chatbot scams are one of 2025’s most significant digital threats, according to cybersecurity firm Quick Heal Technologies. Security labs are detecting thousands of new AI-built fraud tools every month.

In addition to generating malicious code, criminals exploit pre-trained LLMs to deploy automated fraudulent attacks that can target thousands of victims simultaneously. As AI becomes more sophisticated, hackers are imitating trusted brands such as banks, delivery services, and government agencies with increasing accuracy.

The $60MN deal follows its $80MN acquisition of Amelia last year, as SoundHound grows its footprint across the contact center space.

Like Amelia, Interactions has a long history in the market, developing two decades’ worth of innovation and IP. It also offers a deep portfolio of enterprise integrations and large amounts of production data.

Additionally, given Interactions’ heritage, the acquisition offers an opportunity to expand SoundHound AI’s channel strategy, geographic presence, and support services.

Celebrating the roll up, Keyvan Mohajer, CEO & Co-Founder of SoundHound AI, said: “We’re rapidly moving towards a future of AI agents, where voice and conversational AI are absolutely integral to high-quality customer service. This has always been SoundHound’s vision, and we’re committed to working with the very best to get there.

Interactions is unquestionably a pioneer in the field, with a client roster with incredible breadth and depth. Bringing them on board makes SoundHound even stronger as we establish the company as a true leader for the new AI era.

As the AI era looms, Gartner has predicted that by year-end 2027, conversational AI applications will automate approximately 70 percent of customer support interactions.

That statistic underlines conversational AI as a growth market. Yet, it’s crowded. The likes of Microsoft, Google, AWS, Salesforce, and ServiceNow are all active in the space. Meanwhile, NiCE wrapped up a big acquisition of AI high-flyer Cognigy earlier this week.

SoundHound AI can compete, however, as evidenced by its Visionary placement in the latest Magic Quadrant report dissecting the conversational AI market.

The report underscores how it has leveraged its Amelia acquisition to develop a robust presence and expertise in the financial service contact center. Its broader portfolio of edge solutions can also enable differentiated AI experiences, like those that start on smart devices. That’s another key differentiator.

Its multimodal capabilities are also notable, with its recently launched Vision AI, enabling the development of AI that both sees and talks simultaneously. With this technology, SoundHound could help to set a new direction for the space.

However, SoundHound needed to expand its support capabilities and customer-service-specific knowledge to move from a visionary to a market leader. The Interactions roll-up will help the business do precisely that.

Excited to join the SoundHound adventure, Mike Iacobucci, CEO of Interactions, stated: “SoundHound has consistently set itself apart from the pack, with exceptional technology, a history of unparalleled innovation, and a deep understanding of what businesses need to uplevel their customer experience.

The union of our two companies will give the businesses we serve unrivalled choice, flexibility, and scale. And as the market shifts towards more agentic interactions, SoundHound can rise to meet that need.

As that market shifts, SoundHound’s moves to consolidate it are welcome, given the hundreds, if not thousands, of vendors selling AI to contact centers.

Those that stand out offer flexibility in how businesses build virtual agents, ensuring advanced and less-advanced functionality for various user types.

Additionally, they’ll go deep on trust and safety, sharing tools for simulation testing, inbound protection features (i.e., prompt injection detection), outbound monitoring, and more.

Leaders may also close the gap with broader contact center conversational intelligence tools and confidently communicate why their pricing model aligns with the individual customer’s needs.

Of course, a streak for innovation is important, and SoundHound will be thinking about how it can advance its AI agents to become self-reflective and change resolution flows based on brand goals. After all, as an Opus Research study recently isolated, that’s the future.

Nevertheless, the fundamentals are important, and, with this acquisition, SoundHound scoops up a vendor that does the fundamentals well.