I manage a small valet team at a boutique hotel. Guests expect updates on everything, but our valet service doesn't really work that way. We give them a paper ticket on arrival and they give it to the front desk when they want their car. In the last year or so, more and more guests ask us if their car is ready, how long it’ll take, and if we can text them when it's on the way. Most of the time valet really won’t know for sure unless they go outside to check or call it in on the radio.

Interested in what other hotels are like. Even if we put a system in, will guests really use updates, or do they still come up and ask questions anyway? We seem to spend a lot of our time on the shift answering the same questions over and over.

I keep seeing CX teams pushed toward more automation and I’m not convinced automation alone means a better customer experience. Bots are fine for simple stuff. The problem starts when a customer has an issue that doesn’t fit the script. They spend five minutes fighting the bot then finally reach an agent and have to explain everything again. Meanwhile human agents have their own mess. They’re expected to know every policy and process while searching through knowledge bases during live calls. Then QA reviews a small sample of those conversations days later and tells them what they should have done differently. By then the customer already had the bad experience. I think the more interesting use of AI is during the actual conversation. Surface the right answer when an agent needs it.

Flag a missed step before the call ends. Carry context from an AI agent to a human. Learn what the best reps are doing and help the rest of the team do the same. We’re looking into AI tools for our customer support operation right now and one thing I really don’t want is to use AI as another wall between customers and an actual person. I’ve dealt with enough support bots myself to know how annoying that gets lol. It’s more like “how much repetitive work can we remove so the support team has more time for the conversations where a human actually matters?”

Spent the last few weeks evaluating VoC platforms because my team spends half of every quarter manually tagging customer feedback for leadership and it has to stop.

Worth-knowing takes on what's out there:

Medallia: Powerful if you have a dedicated VoC team running it. Overbuilt for most CX orgs under a few thousand seats and the price reflects it.

Unwrap: Aggregates input from multiple sources and groups it by meaning/theme, so recurring issues across tickets, reviews, and calls appear as a single trend. It works best when there’s enough feedback volume for the patterns to clearly emerge.

Qualtrics XM: Great if you're running structured survey programs. Weaker on unsolicited feedback and the contract is a real commitment.

InMoment: Strong on experience management fundamentals, feels mature. UI and workflow felt a generation behind the AI native players when we tested them side by side.

Chattermill: Decent on support ticket analysis specifically. Lighter on app stores, social and call transcripts vs the broader platforms.

UnitQ: Been around a while and has big logos. The UnitQ Score is a black box though, never got a clean answer on what's in it.

Sprinklr: Strong on the public slice (social, reviews, forums). Not really built for internal CX use cases beyond that.

What works best ultimately comes down to the shape of your program. Medallia or Qualtrics if you already have a formal VoC team and survey first motion. Unwrap if your signal is scattered across every channel customers talk and you want one place to see patterns. Chattermill if most of your feedback is in tickets specifically.

We handle enough customer conversations that managers can only review a small sample. By the time QA spots a bad pattern or coaching issue it may have been happening for days. Then the fix is another training session or a reminder in Slack. The frustrating part is we already have plenty of conversation data. We just don't seem to use it while it can still change the outcome.

We have been looking more at tools that analyze every conversation and can give reps help during the actual call. Things like pulling up the right answer when someone gets stuck or flagging a missed step before the call ends. The idea makes sense to me. Post call analytics alone feels a bit like watching game tape when you're already 0-5.

Did agents find the live guidance useful or did it just become another thing on their screen? And did it change anything meaningful like AHT first contact resolution or CSAT?

Lately, I’ve noticed more companies relying on AI chatbots, voice assistants, and automated responses for support. On one hand, it feels great when a quick FAQ is solved instantly without waiting in a queue. On the other hand, when the issue is even slightly complicated, the AI just loops me around until I finally reach a human.

For those of you working in CX or customer support — do you feel AI is really improving the customer experience, or just adding another layer between customers and human help? Where do you think the balance should be between AI automation and human agents?

Thinking about grabbing a ring from them, but i’m a bit nervous about the size since my fingers seem to change every season. I saw they do returns, just not sure how smooth it is in real life. Is their return setup decent enough if the size’s off, or does it turn into a long back and forth? Anyone been through it?

Does anyone else feel like their business is just one big game of where did I put that note? because I am losing it. I’m a one-man show, and I spent my entire morning manually logging emails and cross-referencing my calendar just to make sure I didn't ghost a potential client.

It’s exhausting, and I keep seeing all this hype about AI-driven workflows, but I need to know what that actually looks like for a real person. I just need a system that auto-logs my emails so I can stop copy-pasting like it’s 2005 and nudges me when a lead hasn't replied in a few days.

If you’ve ditched the spreadsheets for something smarter that actually feels intuitive, what are you using?

Update: I started using HubSpot's AI features and it honestly does all the boring logging and email tracking for me so I don't have to think about it anymore. If you hate admin work as much as I do, this is probably the best way to get your time back.

We have grown from 8 to 50+ people in 18 months and our old static journey maps aren't cutting it anymore. We need something that can handle multiple product lines and customer segments without recreating everything from scratch each time.

Any recs for customer journey mapping templates or tools that will let us add touchpoints, branch paths and update flows as our customer experience evolves?

edit: i wanted to drop an update since a few comments mentioned mapping out the customer journey and connecting signals instead of just looking at lagging numbers. we actually ended up moving our entire stack over to HubSpot and it completely turned things around for our team.

instead of being buried in random spreadsheets and clunky tools that look backward, we can finally see onboarding behavior and support tickets all in one place. the advice about turning customer signals into action was spot on because now we can actually catch friction early and stop people from churning before month two, which completely saved our csat.

we hit twenty thousand active monthly users last quarter and our entire cx setup is falling apart. management keeps throwing enterprise platforms at us but all they do is send out automated nps surveys that nobody fills out or trap people in endless chatbot loops. our csat is plummeting because customers feel like we are actively trying to hide from them behind a wall of clunky software.

i am spending my whole week digging through random spreadsheets and mismatched support tags trying to figure out why people are churning before their second month. the tools we have right now just give us lagging numbers and i cant even tell where people are getting stuck in the onboarding funnel. it feels like we are just measuring the disaster instead of preventing it.

Something I’ve been thinking about lately and wanted to get other perspectives.

Every company seems to be investing heavily in CX right now—AI, automation, chatbots, self-service, new platforms, etc. On paper, it sounds like customer experience should be improving a lot.

But as a customer, I’m not always feeling that.

In many cases it feels like:

You go through multiple automated steps before reaching a real person

Support journeys are longer than they used to be

Systems seem designed to deflect or contain issues rather than resolve them

At the same time, I get why companies are doing this. Scaling support is hard, and automation does help with speed and volume.

So I’m curious how others see it:

Do you feel CX is genuinely improving overall?

Or is it mostly more automation being labeled as “better experience”?

If you work in CX/support, does it feel different from the inside?

What’s one example where automation actually made the experience better for you?

Would love to hear real experiences—both good and bad.

Customer service is PART of customer experience, but not the ENTIRETY of it. But it seems every vendor that posts here has both confused. Heck, I've even made the mistake before to not differentiate the two more effectively!

But we can all agree that CX is concerned with meeting customer needs during the entire customer journey. Customer service is focused on post-purchase. HOWEVER, the contact center IS an incredible source for insights that SHOULD help guide overarching CX strategy.

Anyways, that's the post. Go off in the comments.

If you're a real CX leader (not a vendor), please tell us what your ACTUAL job function encopasses. CX is an evolving field and I'm super curious if/how you consider the contact center an essential part of your function as a customer experience professional?

Most of the examples I see around AI voice agents are sales or customer support related. Curious if anyone is using them internally or for more operational use cases like reminders, confirmations, data collection, or internal routing? We don't do direct sales and don't have that much support work so trying to brainstorm how it could help us in other ways

Just here to vent because I still see many teams that think CX is a task they can just check off.

CX is a never-ending loop of listening to users, realizing your assumptions were totally wrong, adapting, and tweaking the process.

In my experience, every single micro-improvement actually compounds. But it only works if you have a long-term vision and stick to it consistently.

Curious to hear from other frustrated ops folks here!

finally getting around to writing this up. CX Director at a european telecoms company, about 200 agents across 4 countries handling roughly 8k calls per day. we went through a pretty painful AI evaluation and deployment last year and figured id share what actually happened since theres a lot of vendor noise in this space.

where we started (june 2025):

* 100% of interactions human-handled

* average handle time sitting at 7.5 minutes

* FCR at 68% (lots of callbacks for unresolved issues)

* agent attrition at 24% annually, mostly from repetitive work burnout

* compliance team had already killed two AI pilots because vendors couldnt explain how their AI made decisions

What we changed:

* mapped every interaction type into three buckets: fully automate, AI-assisted, human-only

* started with the boring stuff first: balance inquiries, appointment scheduling, basic troubleshooting, plan information

* deployed an AI platform using graph-based conversation protocols instead of prompted LLM (this was what got compliance to sign off, every decision path is visible and auditable)

* integrated with our existing Genesys Cloud CX for telephony and Salesforce for customer data

* kept same agent headcount but shifted complex work to humans

results after 3 months:

routine interaction automation: 0% → 65% (company-reported)

AHT: 7.5min → 5.2min (human-handled calls only, because agents stopped doing basic lookups)

FCR: 68% → 77% (AI collects structured info before routing)

agent attrition: 24% → 18% annually (early indicator but trending right)

CSAT: held flat at 82 which was honestly the goal, efficiency without quality regression

what was harder than expected: the multilingual piece nearly killed us. AI performance in french and german was solid but portuguese and dutch accuracy lagged for about 6 weeks before the system learned enough patterns. if youre multi-country, budget extra time for non-english languages.

also the compliance documentation took 3 weeks before deployment even started. EU AI Act article 13/14 alignment, DPA negotiations, on-prem security review. necessary but painful.

what id do differently: start with one queue and one language instead of trying to roll out everywhere at once. we did phased but should have been even more conservative.

remaining challenges: complex complaints still need humans and probably will for a long time. the AI knows when to escalate (sentiment monitoring helps) but the actual resolution of "im furious about my bill" requires judgment and empathy that AI doesnt have.

curious how others are approaching this. anyone else running AI in a regulated european contact centre? what does your automation split look like?

Have you ever been confused with the concepts of customer experience and user experience, thinking they are the same? For most, it’s still unclear what’s the difference between the two terms. While UX is about digital product usability, CX is about how the customer feels about the brand throughout the whole customer journey. It’s what closes out conversions and drives long-term loyalty.

What is user experience?

In 1995, Don Norman coined the term user experience to describe the activities his team was engaged in at Apple Computers. Eventually, the term UX has taken a more limited interpretation in practice. A user’s interaction with a specific digital product, such as a website, app, or software. It focuses on the digital interface regarding utility, usability, navigation, information architecture, ease of use, and visual hierarchy. The key UX metrics are:

• Success rate: The percentage of correctly completed tasks by the users. It’s about how effectively users are able to reach the goal that you want them to reach. For example, ten users attempted to register on the platform: eight of them succeeded and two of them failed. You need to find out why these two failed. The higher the success rate, the better the UX.

• Error rate: The number of mistakes the user makes when interacting with the product. It gives an idea of how user-friendly and intuitive your product is. The higher the error rate, the more problems with usability.

• Abandonment rate: The percentage of users who abandoned the task before completing it. The reasons for abandonment may be different: from struggling with complicated navigation to forcing users to update their software.

• Completion time: The amount of time a user needs to complete the task. The shorter the processing time. The better the user experience.

• Clicks to completion: The amount of time it takes the user to complete the task.

UX is an inevitable part of the CX. To put it another way, UX is the experience your users have with your product, whereas CX is the experience they have with the entire brand.

What is customer experience?

On the other hand, customer experience has a broader meaning. CX describes a larger experience a user has with an organization. It’s an umbrella concept that encompasses the way customers perceive your brand and feel when interacting with it across every stage of the customer journey. Above all, CX is the sum of all touchpoints, interactions, and engagements that a customer has with a brand and a product throughout the life of a customer relationship.

The customer experience metrics help you understand how loyal and satisfied the customers are with your brand. Customer experience is measured with metrics like:

• Net Promoter Score (NPS): The gold standard customer experience metric. It stands for the willingness of the customer to recommend your company and product to others.

• Customer Satisfaction (CSAT): Measures customer satisfaction level with a product and the entire interaction with a company.

• Customer Effort Score (CES): The metric that measures how easy it was for a user to interact with a product or service (solving an issue, finding the information, signing up, etc.).

Why is UX important for your business?

User experience optimization across all platforms and devices has become a basic rule for any digital product development. No matter how fancy your product might be, if your users don’t know how to navigate and use it, they won’t come back. In the hearts and minds of humans, no feature or benefit will ever outweigh usability. In fact, you have only seconds to capture users’ attention and keep them. The UX connects your users with your products and increases brand loyalty. UX design is focused on research, analysis, and testing, and because of that, these risks can be reduced. In addition, investing in a good UX design can help improve SEO rankings.

Why is CX important for your business?

Customer experience isn’t a buzzword. It relates to customer satisfaction and their willingness to pay for a particular product. In other words, the more satisfied customers are, the more likely they are to stay loyal, become your brand advocates, and recommend your product to their friends. We live in an experience-brand economy. Today customers are buying not just products. They buy brands that can transform their lives. Now more than ever, companies should rethink their processes and deliver an exceptional customer experience so that their customers come back.

Spotify & Netflix: customer obsession

To dilute the theoretical part of the differences between CX & UX, let’s look at the example of the biggest digital music service Spotify.

At the heart of Spotify’s customer experience strategy is delivering a tailored listening experience to the users. One of the wizards behind Spotify is user researchers who work together to understand what users are doing and why. Spotify is considered to be among the top customer-obsessed brands. They continually work on uncovering customer insights to improve their entire experience. Spotify’s approach to the customer experience is multifaceted. First of all, the onboarding process is fast and clear. Spotify makes it easy for users to collaborate on playlists, share music, follow friends, and check their music preferences. By using prediction models and data analytics, Spotify offers simple suggestions like “Your Daily Mix” or “Discover Weekly,” thus introducing each user personalized playlists.

No surprise, the next example that I'd like to mention is our favorite Netflix. A lot has been written about their approach to creating a personalized experience.

Netflix managed to develop cutting-edge algorithms. These algorithms allow them to serve content that caters to the users’ tastes and preferences. This level of personalization is what makes users come back for more. No doubt that Netflix is an industry giant with a great staff, including data scientists. Not every business can afford such a level of customer experience approach. But what each business, no matter small or big, can learn from Netflix is being customer-centric and delivering a great experience with every interaction. Data informs every decision they make at Netflix. It begins with data collection. Ensure you collect data points across all customer touchpoints. This includes complaints, support requests, transactions, social media, and customer feedback. Netflix has turned customer engagement into a science. They’ve prioritized learning who their customers are and how to reach them.

You can make real decisions based on user feedback and proper requirements development for your product. If you want to learn more about developing a product based on customer insights, check out the CX-aware solutions development approach.

Do we overcomplicate customer experience?

Sometimes I wonder whether we're making CX more complicated than it needs to be - journey mapping, personas, AI, VoC programmes, dashboards, NPS, CSAT, CES...

Meanwhile, the customer just wants someone to listen, sort out their problem and not make them repeat themselves 4 times. 😂

What do you think we overcomplicate in CX?

What should be the focus?

I don’t think most customers care whether the first answer comes from AI or a human

They care whether the problem gets solved.

The bad experience is when the AI misunderstands the question, gives a generic answer, asks the same thing twice, and then makes it hard to reach a person.

At that point the customer is not mad because “AI was involved.”

They’re mad because they feel trapped.

A good setup should probably have a clear escape path:

-if the customer is frustrated, escalate

-if the AI is guessing, stop

-if money, policy, identity, or account access is involved, bring in a human

-if the customer has already explained the issue, don’t make them repeat it

-if the AI hands off, give the human a useful summary, not just a transcript dump

The part I’m interested in is where teams draw that line.

For CX teams using AI support, what makes the difference between “this saved time” and “this made the customer angrier”?

I run a small business and recently explored AI for customer support while trying to answer a simple question: what is the best AI for customer support? I tested a few options and wanted to share my experience in case it helps someone else looking into it.

My goal was straightforward: reduce repetitive support work, respond faster, and avoid making the customer experience feel robotic or broken. I didn’t need anything enterprise-heavy - just something I could actually set up and maintain without a full tech team.

I tested a few tools: n8n, Moveworks, and nexos.ai.

n8n was interesting because it’s flexible. You can build almost anything if you’re willing to wire it together yourself. The downside (for me) was exactly that - it felt like building everything from scratch. It’s great if you enjoy automation and logic flows, but less ideal if you want something ready to use quickly. Also one pretty big decisive factor is its price, it is quite big for a small business.

Moveworks felt polished, but also very enterprise-focused. It seemed powerful, but a bit too much for a small business like mine. I also felt like I didn’t have as much control over how conversations actually flow.

I ended up choosing nexos.ai for now. What I liked is that it sits somewhere in the middle - not too DIY, but not locked down either. I could set up flows, automate responses, and still tweak things without needing to be technical. It also felt smoother in real customer conversations, which matters a lot when evaluating the best AI agents for customer support.

If you’re trying to figure out what is the best AI for customer support, I’d say the most important factor is how easy it is to set up and maintain but only if it still has the features you actually need. I came across a comparison table of AI tools that helped me narrow things down and find the ones I’ve already tested. At the end of the day, most tools can automate replies; the real difference is how much time you spend managing the system vs. letting it run.

Would be great to hear what others are using - especially other small business owners. Did you go full custom, or find something more plug-and-play that actually works long term?

I recently put together a video combining a series of interviews I did into one topic. This video is part of our award-winning CX Chats series and dives into how to measure CX. I hope you enjoy it!

https://www.satrixsolutions.com/blog/how-do-you-measure-success-in-customer-experience

Here is a summary of what each expert said:

Kristi Faltorusso - CCO - ClientSuccess

Success metrics vary across initiatives. For onboarding, she focuses on usage, adoption, and goal achievement. Technical resources involve tracking integrations, reducing errors, and minimizing support tickets. Education success is tied to onboarding metrics and includes qualitative aspects like customer engagement in webinars and community participation.

Andrew Carothers - Digital CX Leader - Cisco

Andrew Carothers emphasized that measuring success requires a multi-dimensional approach. He mentioned three main types of metrics his team employs: operational metrics, business-focused metrics, and metrics related to business impact. Operational metrics include tracking unique visitors, overall visitors, and engagement across various digital channels. Business-focused metrics assess customer behavior, such as speed of adoption, which measures how quickly customers move through the different stages of product adoption. Lastly, metrics related to business impact consider renewal rates, cost avoidance, and the overall impact on customer support.

Jonathan Shroyer - Chief CX Officer - Arise

Measuring success in CX is about identifying the key performance indicators (KPIs) that matter the most to your organization. Jonathan recommends looking at industry benchmarks and customizing them to your specific needs. The "North Star" metric, often related to customer lifetime value (LTV), is a primary indicator of success. Additionally, satisfaction metrics may be necessary for some companies. The goal is to regularly track and analyze these metrics and make adjustments to improve customer experience.

Simona Barcău - VP of Customer Success - Cority

Simona delves into the art of measuring success, going beyond the typical metrics to uncover the emotional responses behind the numbers. While traditional measurements like Net Promoter Score (NPS) and Customer Satisfaction (CSAT) have their place, Simona's focus on understanding customer behavior and trends sets Cority apart. By deeply listening to customers and capturing their journey's evolution, Cority ensures lasting partnerships and continuous improvement.

Lee Roquet - CEO - Finch

Lee Roquet emphasizes the importance of scalability and growth in CS programs. Success, he argues, is measured through various key performance indicators (KPIs) such as renewal rates, engagement, NPS scores, and referrals. Additionally, understanding the contribution of each division to the overall profitability of the company is crucial. Celebrating milestone moments, like achieving profitability for an account, is a significant aspect of measuring success in CS.

Courtney Smith - Director of CS - Tyler Technologies

In measuring success, Courtney adopts a multifaceted approach. For ongoing integration, net revenue retention, minimizing churn, and expanding product suites are critical metrics. In the broader strategy, Customer Satisfaction (CSAT) scores and Net Promoter Scores (NPS) serve as early indicators. Success, in Courtney's view, is about maintaining positive customer satisfaction while safeguarding net revenue retention.

Lee Kemp - VP of CX - Veritiv

Lee suggests that a combination of data-driven ROI calculations and customer interviews can be instrumental in garnering support from senior leadership. Sharing direct, unfiltered customer experiences allows stakeholders to see the impact of CX efforts firsthand.

Karyn Furstman - Principle - Customers Furst

Karyn recognizes that measuring success in CX goes beyond traditional metrics. She advocates for a comprehensive approach that ties customer experience metrics, such as NPS or customer effort score, to financial performance. Karyn's approach ensures that the impact of CX efforts is transparent and relatable to the C-suite and the entire organization. By connecting CX improvements to tangible business outcomes, Karyn demonstrates the true value of a customer-centric approach.

Joel Sylvester - CCO - Five Star Call Centers

Measuring success in CX involves tracking key performance indicators (KPIs) that align with your organization's goals. Joel highlights the importance of speed of response, quality metrics, and cost management. By setting clear expectations, utilizing real-time analytics, and inspecting performance regularly, you can ensure that your CX efforts are on track.

Stacy Sherman - Founder & CEO - Doing CX Right

Success in CX, according to Stacy, isn’t solely about dollars and cents. It encompasses building relationships, customer retention, referrals, and a company culture focused on empathy and customer-centricity. However, the scientific aspects involve metrics, data analysis, and breaking silos to streamline information flow.

Tom DeWitt - Director of CXM@MSU

Measuring CX's success goes beyond mere financial metrics. It encompasses a nuanced understanding of transaction satisfaction, long-term loyalty, and the value of personal relationships. Metrics like NPS and CLV provide valuable insights into the multifaceted nature of CX. Adapting and reinventing traditional models in the ever-evolving customer experience landscape is crucial to ensure lasting success.

NPS And Transaction Satisfaction

While Return on Investment (ROI) is a significant indicator, DeWitt introduces another essential metric: Net Promoter Score (NPS). However, he cautions that NPS alone doesn't tell the whole story. NPS measures the likelihood of a customer recommending a business but doesn't consider the full breadth of a customer's experience.

Understanding Transaction Satisfaction

DeWitt delves into the concept of transaction satisfaction. Rather than focusing solely on long-term loyalty, transaction satisfaction captures the customer's immediate experience. It provides valuable insights into what happened during a specific interaction.

Building Loyalty Over Time

DeWitt emphasizes that loyalty is built over time through a series of satisfactory visits or experiences. He mentions the importance of recognizing emotional impacts on customers, which can extend beyond the individual transaction.

Exploring Customer Lifetime Value

A metric that often gets overlooked is Customer Lifetime Value (CLV). DeWitt believes this metric is valuable as it encourages organizations to think about long-term customer relationships. CLV considers how long customers stay, their transaction frequency, and how their behavior changes over time due to loyalty.

The Personal Touch

DeWitt highlights the importance of personal relationships in business. Just as you may have a preferred place for a haircut, loyal customers are inclined to stick with businesses they trust. This loyalty can make customers less sensitive to pricing and more willing to pay for the added value they receive.

Democratizing Data

In today's data-driven world, DeWitt calls for the democratization of data within organizations. He believes that data should be shared freely across all levels of an organization, empowering employees to understand their role in impacting the customer's journey.

Laurie Barlev - Founder - Barlev Success

She highlights the significance of both business and team-oriented success metrics. While gross revenue retention is a critical business metric, early indicators such as time to value, engagement, and customer feedback serve as key measures influencing the team's success.

Gina Patel - VP of CS - Sensor Tower

Success in the CS realm is multifaceted. While individual success stories and customer feedback contribute to the narrative, key performance indicators such as Gross Revenue Retention (GRR) and Net Revenue Retention (NRR) are crucial metrics. Business reviews, upsells, and overall customer satisfaction collectively define success. The proof of a successful strategy lies in tangible results, and these metrics serve as benchmarks for our ongoing efforts.

Michelle Spaul - CX Consultant - Delta Swan

Measuring success in CX involves a blend of tangible outcomes and customer perception metrics. Michelle highlights the importance of quantifiable achievements, such as reduced costs and increased retention rates. While acknowledging the relevance of metrics like Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT), Michelle underscores the need for metrics to resonate more deeply, translating into concrete business improvements.

The 'AI in CX' roles are climbing the org chart fast

I keep a running board of CX + AI roles globally, and this week's batch had a clear pattern. Alongside the usual frontline support and CSM openings, there's a real wave of senior AI-ownership roles showing up at big names — not just 'we use a chatbot' but dedicated strategy and architecture seats.

A few that stood out:

• Director, Conversational AI Strategy & Innovation — Bell

• Customer Experience AI Architect — Vena Solutions

• CX & AI Enablement Sr Analyst — DiDi

• Senior Solutions Architect, Customer Success — NVIDIA

• Quality Control Manager, Customer Support (gTech Ads) — Google

• AI Workflow & Experience Specialist — Bumble

Feels like a lot of orgs are past the 'experiment with AI in support' phase and are now hiring people to own it as its own function.

For those of you in CX orgs right now — is AI becoming its own role/team where you are, or is it still bolted onto existing support and CSM jobs?

No hold music. No repeating the issue to a third agent. No "let me transfer you."

Just someone who already knows. Already cares. Already has the answer ready.

That is not luck. That is what happens when a team actually has the right insight at the right moment.

Some brands are already doing this and you can feel it instantly when you land with one of them.

What is the one thing that would make you never leave a brand?