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 

The AI giant also took the wraps off its Realtime API, now generally available with new capabilities as it moves out of beta.

OpenAI hopes enterprises and developers will leverage both the model and the API to build “production-ready voice agents”.

Some of the API’s latest features will help here. For instance, the new ability of the Realtime API to support image inputs and remote MCP servers will make these agents more capable.

Yet, there’s also an exciting capability to develop better customer support voice AI agents.

As Peter Bakkum, Member of Technical Staff at OpenAI, said in the announcement video:

We’ve added support for SIP telephony, which makes it much easier to build applications for voice-over-phone situations like customer support.

With this, a developer could easily grab a phone number from Twilio, feed that into the SIP interface provided by OpenAI, add prompts, feed it data, and let it go.

As Andreas Granig, CEO at Sipfront, observed in a LinkedIn post, that is quite the threat to many conversational AI startups.

“There are quite some startups, who only provide an interface to the public phone network for existing speech-to-speech AI services, often without much telco moat, but relying mostly on Twilio and others… They are in hot water now,” noted Granig.

The CEO acknowledged that startups specializing in tool calling for advanced integrations remain safe, since that remains a specialist field. However, he added:

The voice interface for AI assistants just became [a] commodity.

As a result, it will be more difficult to differentiate use cases for AI assistants, signalling to many conversational AI startups that now is the time to step up.

What About the New gpt-realtime Model?

OpenAI hopes many customer support teams will leverage gpt-realtime, alongside the Realtime API, as they advance their customer support automation strategies.

Indeed, as Peter Bakkum, Member of Technical Staff at OpenAI, said in the announcement video:

We carefully aligned the model… to real scenarios like customer support and academic tutoring.

There are many reasons why support leaders would consider the gpt-realtime model. For starters, it enables AI agents that can understand and produce audio without relying on separate transcription, language, and voice models.

Additionally, there are performance benefits. For instance, these agents will respond faster, as it’s just one model, and capture subtleties like laughter or sighs while expressing various emotions.

OpenAI also claims the model can deliver more natural, high-quality audio while following instructions across complex, multi-turn conversations.

Developers can also adjust pace, tone, style, and even roleplay characters.

Meanwhile, OpenAI claims the model can better handle unclear audio and long alphanumeric strings, like phone and license numbers. One study recently highlighted these strings as a big problem for rep-facing AI assistants leveraged in contact centers.

However, despite all the model’s advantages, there are cautions.

For instance, its cost is relatively high at $32 / 1M audio input tokens ($0.40 for cached input tokens) and $64 / 1M audio output tokens.

As such, Alex Levin, CEO at Regal, estimated that the cost of the speech-to-speech model is still approximately four times higher than chaining a speech-to-text (STT), large language model (LLM), text-to-speech (TTS) pipeline for Voice AI Agents.

In a social post, the CEO also cautioned toward limited control over the model. He wrote:

The Realtime model is missing the control/observability that Voice AI Agent companies have in the “chained” model.

“And it’s missing the ability to vary the model, voice, guardrails, etc, in each step of the conversation, which is currently easily achieved with a multi-state agent builder and a “chained” model today.”

Despite these concerns, some enterprises are working with OpenAI to start testing the model, including T-Mobile…

T-Mobile Uses gpt-realtime for Customer Conversations

T-Mobile has tested OpenAI’s models for six months and recently unlocked access to gpt-realtime. Together with the Realtime API, it claims to have already seen “huge improvements”.

In the announcement video, Julianne Roberson, Director of AI at T-Mobile, highlighted how T-Mobile is already experimenting with the model to reimagine the device upgrade process, one of its most common demand drivers.

During the demo, Roberson showed how the AI assistant guided a customer through selecting a phone under $300, checked compatibility with satellite services, and confirmed plan eligibility.

In doing so, she emphasized that the model feels far more human, able to follow customers through unpredictable conversations while recognizing emotions and handling multimodal inputs.

These multimodal capabilities will boost T-Mobile’s objective to provide “expert-level service everywhere” with AI.

Given its close ties to OpenAI, it will be fascinating to see how this partnership develops, and whether T-Mobile shares CEO Sam Altman’s prediction of the end of human customer service.

They miss the point, literally. They misinterpret customer intent, create more work for agents, and erode brand trust with every failed interaction. And while expectations have skyrocketed, many brands are still clinging to outdated tools that can’t keep up. 

Research from Globalization Partners showed 91 percent of global executives are actively scaling up their AI initiatives. But most of the tech on the market today, especially for enterprises, simply won’t get you where you need to go. 

And yes, Generative AI is part of the equation, but it’s not the whole story. Everyone has GenAI baked into their platform. What separates standout AI agents isn’t purely smart language models; it’s everything that supports them: how you build, train, test, deploy, and orchestrate at scale. 

To build AI agents that deliver across channels, use cases, and languages, businesses need a modern toolkit. These six tools are a game-changer for many organizations as they get serious about scaling AI-powered customer service. 

1. Hybrid AI

Freeform creativity isn’t always necessary. In customer service, AI needs to stay task-focused. 

That’s where Hybrid AI shines. The best AI platforms today offer the flexibility to blend generative responses with deterministic, rule-based logic – all within the same conversation. That means AI Agents can smoothly switch between handling complex, unstructured customer queries and executing strict, compliance-bound processes without missing a beat. 

As such, the business stays in control. With the ability to hard-code logic gates and permissions, they can dictate exactly which tools the AI can access, when, and under what conditions. This keeps workflows compliant, business logic intact, and AI accountable. 

Without this level of precision, customer service teams can minimize the risk of off-brand interactions that do more harm than good. 

2. Collaborative Workspaces

Disconnected teams build disconnected experiences. 

Top-performing AI agents today are built in real-time collaborative environments where developers, designers, and CX stakeholders work together, live. Co-editing, commenting, and updates all happen in one place. No clunky handoffs. No rework. Only aligned, rapid iteration. 

If AI platform still forces users into a ticket-and-wait cycle to update a single flow, the business burns time and budget.  

Here is an example of what a collaborate workspace looks like in action: 

3. Simulation & Scalable Testing

Modern AI teams test like product teams. The best platforms now offer conversation simulation tools that let builders preview user experiences, live debug real-world interactions, and auto-run test scripts at scale to catch issues before customers notice them. 

Granular insights also help flag broken paths, outdated APIs, and any possible logic conflicts before they hit production. 

Businesses that aren’t simulating and testing at scale risk not delivering quality. 

4. Unified Knowledge Integration

AI agents are only as good as the knowledge they can access. 

Leading platforms now offer built-in RAG pipelines for knowledge ingestion and turnkey integrations that enable pulling data from CRMs, help centers, SharePoint, product databases, and more. Both AI and human agents receive grounded, real-time answers that reflect the latest policies, pricing, and procedures. 

Ultimately, this minimizes hallucinations, kills duplicate agent applications, and ensures consistency across every channel. 

5. Model Context Protocol (MCP)

Fluency is table stakes. Execution is the differentiator. 

Modern AI Agents need more than language skills; they need context and capabilities. Beyond native tools, the Model Context Protocol (MCP) delivers an architecture that lets AI Agents tap into any external tools, APIs, or data sources they need to get the job done. 

As such, AI Agents not only answer questions but also take action. They can book appointments, trigger workflows, fetch real-time inventory data, or resolve billing issues autonomously. And because MCP abstracts the complexity away, businesses can scale these capabilities without rebuilding each agent from scratch. 

This isn’t just about smarter agents. It’s about making them truly useful, fast. 

6. Integrated Analytics and Optimization

Here’s the new rule: every bot update is a hypothesis. Every conversation is a data point. And that’s only possible with platforms that offer built-in analytics and visibility into what’s working, what’s not, and where to optimize. Service leaders can track outcomes, resolution rates, escalation patterns, and user behavior across every conversation. 

The smartest teams are using this data to constantly optimize their AI agents: adjusting prompts, refining flows, and doubling down on where it matters most. 

Additionally, they’re integrating with analytics from broader enterprise conversations, unpacking the flow of conversations that escalate between AI and live agents, and leveraging this insight to make targeted improvements.  

The Bottom Line: It’s Not Just About Generative AI. It’s About What’s Next.

AI agents are no longer futuristic experiments. They’re operational infrastructure: visible, measurable, and fast-becoming mission-critical. And in a landscape where generative AI (GenAI) is now a baseline, real differentiation comes from the systems, tooling, and strategy wrapped around it. 

The tools outlined here aren’t trends. They constitute the core infrastructure for building AI agents that deliver, evolve, and scale with your business. 

Fall short here, and service teams risk ending up with fragile bots, frustrated agents, and customers who never come back. Get it right, and they’ll build something far more powerful: AI agents that actually work, because the foundation is built to last. 

If a brand’s AI agent platform isn’t offering this level of control, collaboration, and intelligence, it’s holding them back. 

Thanks to Nhu Ho, Senior Product Marketer at Cognigy, for co-authoring this article.  

First came Agentforce for Public Sector, swiftly followed by Agentforce for Manufacturing.

The offerings target the respective sectors with a library of prebuilt skills and actions to more quickly configure AI agents.

Both announcements are an extension of Salesforce’s industry-specific strategy, with the CRM leader previously releasing similar solutions for consumer goods, education, financial services, healthcare, and retail.

Salesforce has also developed department-specific Agentforce offerings, covering commerce, field service, sales, service, and – more recently – HR.

The strategies seemingly have the same objective: to get businesses experimenting with Agentforce, securing quick wins, and instilling confidence in AI agents.

In doing so, Salesforce hopes to inspire broader adoption and build an appetite for more mature AI agent approaches.

Such approaches may include chaining AI agents together to mechanize multi-step processes.

Kishan Chetan, EVP and GM for Service Cloud, shared more on the strategy during a press briefing before the launch of Agentforce for HR.

“The reason we created these specialized solutions is to ensure the topics and actions are relevant to each specific workflow,” he said.

“For example… in health and life sciences, it’s about tasks like logging health information, interacting with payers, or requesting provider appointments.

Essentially, the goal is to provide the right actions for each function, while keeping everything on one unified platform.

“If customers want to combine these capabilities and create agents with multiple skills, they absolutely can,” concluded Chetan. “That flexibility is a major advantage.”

Lastly, as vendors make AI agent announcements much faster than most enterprises can execute, these Agentforce offerings can help them catch up.

In this sense, Salesforce is challenging innovation fatigue, which is particularly prevalent amongst its customer base, having released Agentforce 3 little more than nine months after the original platform’s debut.

What Can I Build with Agentforce for Manufacturing?

With Agentforce for Manufacturing, businesses can build AI agents that assist with tasks such as monitoring demand fluctuations, streamlining inventory, and optimizing incentives.

As Achyut Jajoo, SVP and GM of Manufacturing for Automotive and Consumer Goods at Salesforce, explained:

We’re delivering that with out-of-the-box agents that understand their (manufacturers) specific challenges, from preventing asset downtime to aligning sales plans and operations, and immediately work alongside their teams to drive results.

Here are four examples of what manufacturing companies can now more easily build on Agentforce.

1. A Production and Sales Alignment Agent

With a new “Sales Agreement Management” skill, manufacturers can implement an AI agent that monitors build plans and flags significant deviations between planned and actual product sales.

When such deviations occur, the agent can schedule a meeting with key stakeholders, sharing suggested talking points with the account team.

In doing so, the AI agent helps optimize production alignment and resource allocation.

2. An Inventory Checker

Agentforce for Manufacturing enables the development of an AI agent that sales and service teams can use to instantly check inventory during customer conversations.

If necessary, the sales and service team can also request stock replenishment via the agent.

Lastly, the agent may also suggest upsell and cross-sell opportunities in real time, helping boost sales and manage inventory more efficiently.

3. An Incentive Program Analyzer

Manufacturers can also build an AI agent that assists product teams and rebate program managers in tracking incentive programs.

In doing so, the agent surfaces insight into insights into performance, payouts, and profitability.

Additionally, it can aid channel sales teams in spotting underperforming programs, so they can instead focus on those that drive real profits.

4. An Asset Health Monitor

With the “Prevent Asset Downtime with AI” skill, businesses can build an agent that does exactly what it says on the tin: prevent asset downtime.

Indeed, the agent helps service reps and fleet managers by detecting potential asset issues early and automating tasks like generating repair quotes, creating work orders, and sending maintenance summaries.

Alongside that, the agent may also proactively warn customers of problems, reducing downtime and protecting revenue.

What Can I Build with Agentforce for the Public Sector?

With Agentforce for Public Sector, businesses can build AI agents that engage with constituents and employees, accelerate recruitment, simplify complaint management, and more.

Celebrating its launch, Nasi Jazayeri, EVP & GM of Public Sector at Salesforce, said:

With AI agents working alongside dedicated government workers and providing 24/7 support for constituents, helping with everything from routine inquiries to complex, time-consuming tasks, Agentforce will power a more responsive, agile, and effective government.

Here are some examples of the AI agents governments may now develop.

1. An Inspection Support Agent

The new Agentforce offering allows brands to build an AI agent that provides detailed summaries of prior violations and license compliance, helping inspectors conduct more efficient inspections.

Moreover, it could automate documentation and other follow-up tasks, reducing administrative load and supporting overall regulatory compliance.

2. A Complaint Analyzer

One possible agent could help agencies by summarizing and analyzing constituent complaints. It might then identify related past issues and suggest next steps based on policies and regulations.

The agent may also categorize the complaints, flagging frequent and emerging issues so that the agency can improve its services and policies.

3. A Recruitment Specialist

Consider an AI agent that matches resumes, engages candidates, and automates communications.

Agentforce for Public Sector could enable such an agent. It may also assess qualifications, filter candidates for hiring managers, and accelerate the recruitment cycle.

4. A Candidate Assistant

Government agencies may now more quickly build an AI agent that helps improve the candidate experience by matching their CV with best-fit postings.

Indeed, it can gauge their job history, skills, and goals to pair them with relevant postings, so they save time scouring jobs and increase application success rates.

5. A Benefit Advisor

Another possible AI agent could take questions from constituents regards their benefits and eligibility across numerous languages.

From there, it may provide assistance while accelerating their application processing, minimizing process errors, and enhancing policy compliance.

6. A Compliance Supervisor

One final example is an AI agent that works with employees and complainants, helping them spot compliance information and navigate through filing complaints.

In doing so, the AI supervisor ensures the user adheres to regulations, accelerating reviews and chopping down resolution times.

More Changes to Agentforce

Alongside its industry-specific Agentforce offerings, Salesforce is making other moves to drive adoption of its AI agent platform.

For instance, last week, it announced two big changes to the Agentforce pricing model. These introduced pay-as-you-go and pre-commit pricing options.

The former is ideal for those wishing to experiment and ramp. Meanwhile, for those ready to go all-in, the latter is perhaps the better option.

Either way, Salesforce is covering all the bases.

Meanwhile, the CRM leader expanded its Agentforce platform in June to include a Command Center. The solution offers a platform to monitor enterprise-wide AI agent deployments, tracking their adoption, relevant success indicators, and costs.

Salesforce is far from the first vendor to launch such a solution. However, Tableau’s business intelligence heritage and agentic analytics vision give it a differentiator here.

Expect more announcements on Tableau, Agentforce, and Salesforce’s broader ecosystem at September’s Dreamforce conference.

At a time of so much disruption, it promises to be a don’t-miss event.

Indeed, its Q2 revenues rose 4.7 percent year-over-year (YoY) to $1.217BN.

A key driver behind that achievement is its contact center business, which continues to expand at a “high double-digit rate”.

Zoom execs shared several additional statistics during the earnings call, including “triple-digit growth” in its Elite tier contact center package geared toward enterprises.

Additionally, they highlighted how four in every five CCaaS wins now come through the channel.

However, Eric Yuan, CEO of Zoom, revealed perhaps the most surprising statistic:

Our top ten contact center deals were all displacements of leading competitors, and all but one were cloud displacements.

Why is that surprising? Because CCaaS transformations involve a lot of legwork. As such, hopping between vendors is not an easy choice to make.

Analysts on the call picked up on this. In response to their questions, Yuan highlighted that it wasn’t necessarily Zoom’s role as a market disruptor that drove the migrations; it was the shortcomings of rival vendors.

“Many customers have been unhappy with their existing contact center providers,” he said.

If they were completely satisfied, no matter what we did, they wouldn’t want to switch. The reality is, they’re not happy.

“Sometimes it’s due to quality issues, frequent outages, high costs, resistance to innovation, poor architecture, or very slow AI adoption. The reasons vary.”

Like Zoom, CX Today has picked up on this trend and several more reasons for CCaaS dissatisfaction.

For starters, numerous CCaaS players offered “sweetheart deals” at the beginning of the pandemic when many contact centers rushed to the cloud to simplify remote working. Yet, as they come up for renewal and vendors charge their normal prices, businesses are reconsidering.

In addition to the ‘high costs’ Yuan mentioned, many providers require businesses to purchase a fixed number of seats annually under their CCaaS contracts. Companies with high seasonal demand now see an opportunity to reduce costs by switching to a more flexible pricing model.

On pricing, some vendors also locked contact centers into annual increases in seat numbers. As such, when service teams didn’t scale as quickly as anticipated, they paid a hefty price. That may have caused some resentment.

Another big reason is that many contact center leaders perceive they didn’t receive sufficient support post-deployment, which, as Yuan suggested, may have impacted AI adoption.

However, even those that did adopt AI have faced issues that reflect not so well on their vendors. For instance, many providers offer AI features at extra cost to their core offering. So, when contact centers add more, costs stack and the CFO takes offence.

Zoom can differentiate here, with its AI Companion included at no additional fee.

Nevertheless, Yuan believes that Zoom is winning customers not only because of its pricing model, reputation for being easy to work with, and high brand loyalty.

Discussing the CCaaS migrator wins, Yuan noted: “These customers are actively looking for modern contact center solutions. When they try Zoom, they often say, “Wow, I can’t believe this.”

“We offer nearly every feature they need, and more importantly, they trust us,” he concluded. “They trust the core Zoom platform and what it represents. Our company culture is focused on delivering happiness; we strive to delight our customers.”

Finally, Yuan noted Zoom’s product as a differentiator, highlighting how it built its platform entirely in-house to ensure a consistent user experience.

Other Big Takeaways from Zoom’s Earnings

Sticking with the Zoom Contact Center, the number of customers paying more than $100,000 annually for the platform grew by 94 percent (YoY) last quarter to 229.

As previously noted, new partnerships are helping drive that growth. One newly established collaboration is with PWC, which focuses on CCaaS and AI for the enterprise, per Yuan. He said:

Together, we have already co-sold several large deals, including a Fortune 50 technology firm for which PWC will provide advisory and implementation services.

Beyond the contact center, Zoom AI Companion monthly active users (MAUs) have grown over four times YoY. While the video communications pioneer didn’t share exact numbers, it noted that “millions” of employees now use the AI assistant.

They include 60,000 staff members at a Fortune 200 US tech company, which implemented the AI Companion last quarter.

Zoom Phone also sustained its “mid-teens” growth in recurring revenue, while WorkVivo has reached 168 customers paying more than $100,000 annually, up 142 percent YoY.

Much of that business has stemmed from the shuttering of Meta Workplace. For instance, Zoom won a 10,000-seat deal with Marubeni Corporation to migrate from Meta to WorkVivo.

Expect more news like this and innovation from Zoom’s September Zoomtopia event.

While recent innovations, such as the Zoom Virtual Agent 2.0, may take center stage, Yuan suggested many more significant announcements will come.

But what’s coming next is bigger than just faster answers or smarter chatbots.  

Speaking to CX Today, John Burghart, Managing Director at Deepdesk, explained: “We’re entering the era of agentic AI, where AI isn’t just assisting agents, but beginning to act more independently, with humans still in the loop.” 

Deepdesk sees this as an evolution happening in three phases: 

1. The Current State: AI Tools Assisting Humans

Most contact centers today are using AI to support their agents, not replace them. Tools like real-time knowledge assist, summarization, and sentiment detection are helping human agents work faster and more efficiently. 

For example, if a customer asks whether their bike insurance applies abroad, AI can instantly search through policy documents and bring the answer to the agent.  

“It’s fast, accurate, and removes the need for agents to dig through multiple systems during a call.” 

In many cases, AI is even completing small tasks in the background, like updating addresses in Salesforce or retrieving package tracking info, while the agent stays focused on the conversation. The agent is still in control of what the customer sees and hears. 

2. The Next Phase: AI and Humans Working Side by Side

Some contact centers have grown more confident in the quality of AI outputs, therefore “we’ll continue seeing a shift with more and more collaborative models,” said Burghart.  

In this current stage, AI handles more complex tasks, while agents focus on nuance, empathy, and higher-stakes interactions. 

Think of it as a flexible partnership. Depending on the industry or specific task, companies are now deciding how much work the AI handles vs. how much stays with the human.  

A financial services company may lean more cautiously, while a retail brand might move faster into automation. 

 “This stage also introduces AI orchestration, where different AI tools, platforms, and systems work together to complete end-to-end workflows,” Burghart explained. “Deepdesk is already playing a key role here, acting as the connective layer that coordinates actions across your tech stack.” 

3. The Horizon: Fully Agentic AI (with oversight)

The general consensus from the sector is, ‘We’re getting close to a future where AI can manage entire conversations autonomously, resolving high-volume, routine inquiries. But we’re not quite there yet.’ 

Deepdesk adheres to this principle. Burghart told CX Today: “Even as AI becomes more capable, the human agent will still have a role, as a supervisor.”  

“Just like a contact center manager might monitor calls and step in when needed, future human agents will oversee multiple AI agents.”  

In this scenario, they’ll receive alerts when sentiment drops, when the conversation goes off track, or when escalation is needed. 

This model ensures that AI can scale support without sacrificing quality or oversight. For certain high-value customers or emotionally complex situations, human agents will always be preferred. 

What This Means for Your Contact Center 

Agentic AI is not about replacing humans. It’s about building a smarter, more scalable system in which AI handles the heavy lifting and humans bring the context, empathy, and judgment. 

In addition, it’s about AI and humans working collaboratively, side by side, and strengthening each other’s capabilities.  

“Deepdesk is designed to support this transition, from basic assistive tools today to fully orchestrated, agentic systems tomorrow,” Burghart said.   

“We meet you where you are and help you move at the right pace for your business.”  

To find out more about how Deepdesk can help on your journey, visit their page.

Often, queries flow through to the contact center, where an agent either transfers the customer or plays the go-between.

However, thanks to this new capability, Zoom customers can unlock a new “24/7 AI receptionist”. That receptionist routes the customer to the best-placed department on the agent’s behalf.

Critically, it also interacts with customers, gauges their intents, processes inputs, and uses that intelligence to offramp the customer.

In “processing inputs”, the Zoom Virtual Agent (ZVA) takes helpful information from the customer and passes it to the employee, boosting their troubleshooting process.

Additionally, departments using Zoom Phone can utilize the Virtual Agent to automate tasks, such as booking appointments and providing customer updates. No longer are these capabilities limited to the contact center; they’re democratized.

Celebrating the announcement, Smita Hashim, Chief Product Officer at Zoom, said: “When someone calls your business, it should feel easy and personal from the first hello. By combining AI that can listen, understand, and take action with the reach of Zoom Phone, our concierge virtual agent provides seamless and personalized support to all callers.

Whether a customer is calling to schedule an appointment, check an order status, or check product availability, Zoom’s concierge is available 24×7 and can deliver answers instantly, escalating to live employees only when needed.

In releasing this innovation, Zoom exploits the benefits of serving customers on one unified platform that includes CCaaS, UCaaS, and conversational AI solutions.

Few can offer such capabilities, which will particularly benefit businesses that don’t have big centralized contact centers, but smaller, scattered, and informal service operations.

Think of healthcare, for instance. The ZVA can take calls from a single number and route contacts, with context, to their closest GP’s office. Also, medical practitioners can leverage the AI receptionist to schedule appointments on the voice channel.

Also, consider a sector such as education. Many universities have dispersed helpdesks covering medical, hospitality, and other functions. The ZVA can route interactions between locations, collecting context and enabling a centralized support function.

That said, the ZVA can also act as a concierge in large contact centers, not only in routing contacts between departments but in gauging intent and collecting relevant information before a contact. As a result, the employee can move the contact forward without having to search for relevant knowledge; the ZVA has collated it for them.

Yet, while few can offer such an AI concierge that breaks the boundaries of the contact center, Zoom’s fellow CCaaS-UCaaS provider, RingCentral, released a similar solution in February 2025.

Within three months, 1,000+ businesses deployed the RingCentral AI Receptionist (AIR), highlighting the market’s desire for such solutions.

Tim Banting, Head of Research & Business Intelligence at Techtelligence, picked up on this and told CX Today:

Both Zoom and RingCentral are directing their AI efforts toward practical business solutions rather than the more mundane, table-stakes AI features like meeting summaries, rewriting messages, and transcripts. This demonstrates both companies’ commitment to meaningful R&D.

However, where Zoom may differentiate from RingCentral is in the broader capabilities of ZVA, a comprehensive conversational AI solution that many contact centers have already deployed. These businesses may leverage the new integration to extend their implementation and bolster their swarming strategies.

Additionally, Zoom promises a deployment “within minutes”, with a no-code configuration. Meanwhile, the ZVA currently supports conversations in English, French, German, Portuguese, Japanese, and Spanish, although Zoom pledges that support for more languages is “on the way”.

More Additions to the Zoom Enterprise Communications Platform

Across its enterprise communications platform, Zoom has made several other announcements.

Firstly, it has become “among the first” tech providers to integrate OpenAI’s GPT-5 into its AI stack. Yet, most other news flashes concern new platform features. Here are the three most notable.

1. AI Companion Auto-Scheduling

AI Companion is the virtual assistant that assists users across Zoom’s platform. It’s currently available at no extra charge.

Zoom has bolstered the solution with a meeting scheduling skill, so it considers everyone on a meeting invite list – including internal employees and external partners – and spotlights the best-placed time.

That “best-placed time” isn’t only based on calendar availability. Indeed, the AI Companion also tracks time zones and out-of-office notices.

From there, it sends the invites and keeps tabs on responses. When someone declines, it suggests an alternative time to the organizer, who can book the meeting as quickly as possible.

That’s the aim of this solution: to remove the tedious back-and-forth of arranging meetings.

2. The All-New Zoom Hub

Within Zoom Workspace is a new asset Hub, where users can store meeting summaries, documents, whiteboards, clips, and more.

Employees can navigate these files via the AI Companion, which spotlights insights from assets based on natural language commands. That means employees don’t need to jump between tools to find what they need.

Moreover, Zoom hopes its new Hub will help teams stay organized, quickly surface assets during conversations, and quickly save collaborative content drafts created during meetings.

3. A Refurbished Zoom Team Chat Experience

Finally, Zoom has embedded AI Companion into the Zoom Workplace mobile app’s Team Chat compose bar.

As a result, it hopes users can prompt the AI Companion to surface draft messages and help them catch up on what they missed.

Moreover, when using Team Chat on a desktop, users may summarize files by clicking on a new “summarize icon” when hovering over a file stored within Zoom.

Last month, Zoom made many more platform enhancements geared toward CX personnel. Here’s the rundown: Zoom Drops an Auto Dialer, Expands Its Portfolio for Sales and Revenue Teams

Over 70 innovations have been rolled out across its Calabrio ONE suite since January this year, with many being powered by AI.  

This evolution signals not just product expansion but a deeper reimagining of how contact centers can optimize agent wellbeing and customer satisfaction in an AI world.  

The timing is pertinent, as the recent State of the Contact Center report found that 32% of contact center leaders cite agent distrust in AI as a significant issue, yet surprisingly 59% fail to provide ongoing coaching and support to help agents navigate AI-driven workflows. 

This disconnect is echoed in Calabrio’s Voice of the Agent research, where 56% of agents say AI tools don’t actually help them with their day-to-day tasks. 

The gap is clear: organizations must invest in the proper support, not just the right technology, to truly empower agents.  

An Intentional Approach to AI 

Calabrio’s embrace of AI isn’t unique within the customer service and experience sector. The technology is now a prerequisite of any serious vendor in the space.  

But implementing AI and implementing AI effectively are two very different things, and this is where Calabrio differentiates itself from its competitors.  

Dave Rhodes, CEO of Calabrio, detailed how his company has taken a considered approach to its AI solutions. He said: 

“Calabrio has made very thoughtful investments to create AI-driven features, not just for the sake of AI, but for the humans who use them.” 

From performance management to vacation planning, Calabrio’s enhancements aim to simplify workloads and give agents and managers the insights and autonomy needed to perform at their best. 

Feature Highlights: Efficiency Meets Experience 

The Calabrio ONE suite now includes a robust set of AI-enabled tools designed to help contact centers pivot from reactive cost center to proactive intelligence hub. 

Auto QM delivers AI-powered quality management by evaluating 100% of interactions and surfacing coaching opportunities through configurable generative prompts. And for contact center managers with no time to configure questions, Auto QM has a pre-tested question library built on Calabrio’s decades of QM experience to get you up and running quickly.  

Complementing this, Trending Topics automatically identifies conversation themes to accelerate issue resolution and support targeted training initiatives.  

Interaction Summary produces concise overviews of agent-customer exchanges, ensuring compliance and audit readiness.  

Meanwhile, Real-Time Desktop Analytics monitors agent behavior across desktop environments to expose bottlenecks and enhance overall performance. 

Other features include:  

• Vacation Planner Pro: Brings fairness and transparency to leave scheduling while slashing administrative overhead.  

• Activity Requests for WFM: Enables agents to self-schedule approved tasks, enhancing flexibility and accountability.  

• Periodization: Aligns actual hours worked with scheduling targets on a rolling weekly basis, aiding compliance and forecasting.  

• WFM Notifications: Delivers real-time updates to agents and supervisors for better day-to-day awareness. 

While all of these features have individual strengths, as a collective they are designed to reduce friction, ensuring agents can spend less time navigating systems and more time delivering meaningful customer experiences. 

How Does Performance Management Fit Into This?  

Plainly put, high attrition is one of the most persistent, and costly, issues today’s contact centers face.  

With average annual rates frequently topping 30%, attrition can cost a 500-seat organization $2-3 million in direct and indirect costs each year.  

This is where Calabrio ONE Performance Management becomes an indispensable tool bringing AI-powered recommendations directly to agents and supervisors.  

The new Performance Management solution eliminates the need for third-party tools with intelligence woven into every workflow, and elevates contact centers by: 

• Driving consistent coaching with curated insights and auto-surfaced opportunities 

• Giving agents ownership with on-demand visibility into goals, progress, and performance 

• Recognising top performers and closing gaps using unified data 

• Supporting agent growth with personalised feedback, recognition, and development tools 

• Reducing manager workload with streamlined workflows and automated feedback loops 

In describing what differentiates the tool, Calabrio stated: 

“Our end-to-end solution empowers you to overcome your biggest challenges and build a culture of performance that sticks.”  

From Manual Oversight to Strategic Enablement 

At the heart of this transformation is a fundamental shift in how managers approach contact center operations.  

Managers can now move away from reactive oversight and toward proactive strategy, while agents benefit from actionable insights and more control over their working lives. 

Magnus Geverts, VP of Product Marketing at Calabrio, captured the essence of this pivot: “Contact centers need to balance efficiency with employee wellbeing and customer satisfaction. These features help organizations create a more agile, efficient and engaged workforce.” 

The message is clear: AI should not replace people; it should empower them. With this bold rollout, Calabrio gives contact centers the tools to reimagine operational excellence from the inside out. 

But how do you know who to trust, and which platform is actually ready to deliver real value? 

Here’s what to look for when choosing the right AI provider for your contact center: 

1. Look Beyond the Buzzwords

Terms like “agentic AI” are now everywhere, but they’re often used loosely. If a vendor says they can make you fully agentic in a few months, it’s worth pressing for details. 

• What do they actually mean by “agentic”? 

• What tasks will the AI handle, and how? 

• Is a human still in the loop? 

• How is oversight managed?
   

2. Ask for Proof

Any serious provider should be able to point to real results.  

Jon Quayle, Product Evangelist at Deepdesk, explained: “At Deepdesk, we’ve been doing this for six years, well before ChatGPT made AI mainstream.” 

“We’ve helped partners like Rabobank and DHL save millions through AI-powered support.”  

“We can walk you through real-world examples of how we helped teams streamline operations, reduce handling time, and improve service quality,” Quayle added.   

If a provider can’t show evidence, they may still be in the proof-of-concept phase. 

3. Consider Time to Value

Some platforms promise powerful features but take a year to implement. Deepdesk takes a different approach.  

 “We can usually have a working trial live in 6–8 weeks, so you can start learning and iterating sooner,” Quayle explained.  

“Quick implementation matters. The sooner you can test and refine AI in your real workflows, the faster you’ll see results.” 

4. Think Long Term and Stay Flexible

Your contact center tech stack will evolve. The best AI platforms are flexible enough to evolve with it. 

Deepdesk sits alongside your existing systems and plugs into whatever tools you already use—Salesforce, Genesys, Zendesk, or others.  

“We make it easy to add or replace platforms without starting from scratch.” 

5. Don’t Just Buy Software—Choose a Partner

Finally, remember that implementing AI is a journey. You don’t need a vendor that just sells you software and leaves you to figure it out. You need a partner that will walk you through use case design, implementation, and scaling. 

Deepdesk takes a consultative approach, helping each customer find the right balance of automation, human oversight, and orchestration.  

Whether you’re just getting started or ready to scale, we help you move at the pace that works for you. 

AI in the contact center isn’t one-size-fits-all. Choose a provider who understands that, and is ready to grow with you. 

To find out more about how Deepdesk can help on your journey, visit their page.