FutureCraft GTM Special Episode: Why Customer Success Can’t Be Automated (And What AI Can Actually Do)
In this special year-end episode of the FutureCraft GTM Podcast, hosts Ken Roden and Erin Mills sit down with Amanda Berger, Chief Customer Officer at Employ, to tackle the biggest question facing CS leaders in December 2026: What can AI actually do in customer success, and where do humans remain irreplaceable?
Amanda brings 20+ years at the intersection of data and human decision-making—from AI-powered e-commerce personalization at Rich Relevance, to human-led security at HackerOne, to now implementing AI companions for recruiters. Her journey is a masterclass in understanding where the machine ends and the human begins.
This conversation delivers hard truths about metrics, change management, and the future of CS roles—plus Amanda's controversial take that "if you don't use AI, AI will take your job."
Unpacking the Human vs. Machine Balance in Customer SuccessAmanda returns with a reality check: AI doesn't understand business outcomes or motivation—humans do. She reveals how her career evolved from philosophy major studying "man versus machine" to implementing AI across radically different contexts (e-commerce, security, recruiting), giving her unique pattern recognition about what AI can genuinely do versus where it consistently fails.
The Lagging Indicator Problem: Why NRR, churn, and NPS tell you what already happened (6 months ago) instead of what you can influence. Amanda makes the case for verified outcomes, leading indicators, and real-time CSAT at decision points.
The 70% Rule for CS in Sales: Why most churn starts during implementation, not at renewal—and exactly when to bring CS into the deal to prevent it (technical win stage/vendor of choice).
Segmentation ≠ Personalization: The jumpsuit story that proves AI is still just sophisticated bucketing, even with all the advances in 2026. True personalization requires understanding context, motivation, and individual goals.
The Delegation Framework: Don't ask "what can AI do?" Ask "what parts of my job do I hate?" Delegate the tedious (formatting reports, repetitive emails, data analysis) so humans can focus on what makes them irreplaceable.
Timestamps00:00 - Introduction and AI Updates from Ken & Erin
01:28 - Welcoming Amanda Berger: From Philosophy to Customer Success
03:58 - The Man vs. Machine Question: Where AI Ends and Humans Begin
06:30 - The Jumpsuit Story: Why AI Personalization Is Still Segmentation
09:06 - Why NRR Is a Lagging Indicator (And What to Measure Instead)
12:20 - CSAT as the Most Underrated CS Metric
17:34 - The $4M Vulnerability: House Security Analogy for Attribution
21:15 - Bringing CS Into Sales at 70% Probability (The Non-Negotiable)
25:31 - Getting Customers to Actually Tell You Their Goals
28:21 - AI Companions at Employ: The Recruiting Reality Check
32:50 - The Delegation Mindset: What Parts of Your Job Do You Hate?
36:40 - Making the Case for Humans in an AI-First World
40:15 - The Framework: When to Use Digital vs. Human Touch
43:10 - The 8-Hour Workflow Reduced to 30 Minutes (Real ROI Examples)
45:30 - By 2027: The Hardest CX Role to Hire
47:49 - Lightning Round: Summarization, Implementation, Data Themes
51:09 - Wrap-Up and Key Takeaways
Erin Mills: Your career reads like a roadmap of enterprise AI evolution—from AI-powered e-commerce personalization at Rich Relevance, to human-powered collective intelligence at HackerOne, and now augmented recruiting at Employ. This doesn't feel random—it feels intentional. How has this journey shaped your philosophy on where AI belongs in customer experience?
Amanda Berger: It goes back even further than that. I started my career in the late '90s in what was first called decision support, then business intelligence. All of this is really just data and how data helps humans make decisions.
What's evolved through my career is how quickly we can access data and how spoon-fed those decisions are. Back then, you had to drill around looking for a needle in a haystack. Now, does that needle just pop out at you so you can make decisions based on it?
I got bit by the data bug early on, realizing that information is abundant—and it becomes more abundant as the years go on. The way we access that information is the difference between making good business decisions and poor business decisions.
In customer success, you realize it's really just about humans helping humans be successful. That convergence of "where's the data, where's the human" has been central to my career.
The Jumpsuit Story: Why AI Personalization Is Still Just SegmentationKen Roden: Back in 2019, you talked about being excited for AI to become truly personal—not segment-based. Flash forward to December 2026. How close are we to actual personalization?
Amanda Berger: I don't think we're that close. I'll give you an example.
A friend suggested I ask ChatGPT whether I should buy a jumpsuit. So I sent ChatGPT a picture and my measurements. I'm 5'2".
ChatGPT's answer? "If you buy it, you should have it tailored."
That's segmentation, not personalization. "You're short, so here's an answer for short people."
Back in 2019, I was working on e-commerce personalization. If you searched for "black sweater" and I searched for "black sweater," we'd get different results—men's vs. women's. We called it personalization, but it was really segmentation.
Fast forward to now. We have exponentially more data and better models, but we're still segmenting and calling it personalization. AI makes segmentation faster and more accessible, but it's still segmentation.
Erin Mills: But did you get the jumpsuit?
Amanda Berger: (laughs) No, I did not get the jumpsuit. But maybe I will.
The Philosophy Degree That Predicted the FutureErin Mills: You started as a philosophy major taking "man versus machine" courses. What would your college self say? And did philosophy prepare you in ways a business degree wouldn't have?
Amanda Berger: I actually love my philosophy degree because it really taught me to critically think about issues like this.
I don't think I would have known back then that I was thinking about "where does the machine end and where does the human begin"—and that this was going to have so many applicable decision points throughout my career.
What you're really learning in philosophy is logical thought process. If this happens, then this. And that's fundamentally the foundation for AI. "If you're short, you should get your outfit tailored." "If you have a customer with predictive churn indicators, you should contact that customer."
It's enabling that logical thinking at scale.
The Metrics That Actually Matter: Leading vs. Lagging IndicatorsErin Mills: You've called NRR, churn rate, and NPS "lagging indicators." That's going to ruffle boardroom feathers. Make the case—what's broken, and what should we replace it with?
Amanda Berger: By the time a customer churns or tells you they're gonna churn, it's too late. The best thing you can do is offer them a crazy discount. And when you're doing that, you've already kind of lost.
What CS teams really need to be focused on is delivering value. If you deliver value—we all have so many competing things to do—if a SaaS tool is delivering value, you're probably not going to question it.
If there's a question about value, then you start introducing lower price or competitors. And especially in enterprise, customers decide way, way before they tell you whether they're gonna pull the technology out. You usually miss the signs.
So you've gotta look at leading indicators. What are the signs?
And they're different everywhere I've gone.
I've worked for companies where if there's a lot of engagement with support, that's a sign customers really care and are trying to make the technology work—it's a good sign, churn risk is low.
Other companies I've worked at, when customers are heavily engaged with support, they're frustrated and it's not working—churn risk is high.
You've got to do the work to figure out what those churn indicators are and how they factor into leading indicators: Are they achieving verified outcomes? Are they healthy? Are there early risk warnings?
CSAT: The Most Underrated MetricKen Roden: You're passionate about customer satisfaction as a score because it's granular and actionable. Can you share a time where CSAT drove a change and produced a measurable business result?
Amanda Berger: I spent a lot of my career in security. And that's tough for attribution.
In e-commerce, attribution is clear: Person saw recommendations, put them in cart, bought them. In hiring, their time-to-fill is faster—pretty clear.
But in security, it's less clear.
I love this example: We all live in houses, right? None of our houses got broken into last night.
You don't go to work saying, "I had such a good night because my house didn't get broken into." You just expect that.
And when your house didn't get broken into, you don't know what to attribute that to. Was it the locked doors? Alarm system? Dog? Safe neighborhood?
That's true with security in general. You have to really think through attribution. Getting that feedback is really important.
In surveys we've done, we've gotten actionable feedback. Somebody was able to detect a vulnerability, and we later realized it could have been tied to something that would have cost $4 million to settle.
That's the kind of feedback you don't get without really digging around for it. And once you get that once, you're able to tie attribution to other things.
Bringing CS Into the Sales Cycle: The 70% RuleErin Mills: You're a religious believer in bringing CS into the sales cycle. When exactly do you insert CS, and how do you build trust without killing velocity?
Amanda Berger: With bigger customers, I like to bring in somebody from CX when the deal is at the technical win stage or 70% probability—vendor of choice stage.
Usually it's for one of two reasons:
One: If CX is gonna have to scope and deliver, I really like CX to be involved. You should always be part of deciding what you're gonna be accountable to deliver.
And I think so much churn actually starts to happen when an implementation goes south before anyone even gets off the ground.
Two: In this world of technology, what really differentiates an experience is humans.
A lot of our technology is kind of the same. Competitive differentiation is narrower and narrower. But the approach to the humans and the partnership—that really matters. And that can make the difference during a sales cycle.
Sometimes I have to convince the sales team this is true. But typically, once I'm able to do that, they want it. Because it does make a big difference.
Technology makes us successful, but humans do too. That's part of that balance between what's the machine and what is the human.
The Art of Getting Customers to Articulate Their GoalsKen Roden: One challenge CS teams face is getting customers to articulate their goals. Do customers naturally say what they're looking to achieve, or do you have a process to pull it out?
Amanda Berger: One challenge is that what a recruiter's goal is might be really different than what the CFO's goal is. Whose outcome is it?
One reason you want to get involved during the sales cycle is because customers tell you what they're looking for then. It's very clear.
And nothing frustrates a company more than "I told you that, and now you're asking me again? Why don't you just ask the person selling?" That's infuriating.
Now, you always have legacy customers where a new CSM comes in and has to figure it out. Sometimes the person you're asking just wants to do their job more efficiently and can't necessarily tie it back to the bigger picture.
That's where the art of triangulation and relationships comes in—asking leading discovery questions to understand: What is the business impact really?
But if you can't do that as a CS leader, you probably won't be successful and won't retain customers for the long term.
AI as Companion, Not Replacement: The Employ PhilosophyErin Mills: At Employ, you're implementing AI companions for recruiters. How do you think about when humans are irreplaceable versus when AI should step in?
Amanda Berger: This is controversial because we're talking about hiring, and hiring is so close to people's hearts.
That's why we really think about companions.
I earnestly hope there's never a world where AI takes over hiring—that's scary.
But AI can help companies and recruiters be more efficient.
Job seekers are using AI. Recruiters tell me they're getting 200-500% more applicants than before because people are using AI to apply to multiple jobs quickly or modify their resumes.
The only way recruiters can keep up is by using AI to sort through that and figure out best fits.
So AI is a tool and a friend to that recruiter. But it can't take over the recruiter.
The Delegation Framework: What Do You Hate Doing?Ken Roden: How do you position AI as companion rather than threat?
Amanda Berger: There's definitely fear. Some is compliance-based—totally justifiable.
There's also people worried about AI taking their jobs.
I think if you don't use AI, AI is gonna take your job. If you use AI, it's probably not.
I've always been a big fan of delegation. In every aspect of my life: If there's something I don't want to do, how can I delegate it?
Professionally, I'm not very good at putting together beautiful PowerPoint presentations. I don't want to do it. But AI can do that for me now. Amazingly well.
What I'm really bad at is figuring out bullets and formatting. AI does that.
So I think about: What are the things I don't want to do? Usually we don't want to do the things we're not very good at or that are tedious.
Use AI to do those things so you can focus on the things you're really good at.
Maybe what I'm really good at is thinking strategically about engaging customers or articulating a message. I can think about that, but AI can build that PowerPoint. I don't have to think about "does my font match here?"
Take the parts of your job that you don't like—sending the same email over and over, formatting things, thinking about icebreaker ideas—leverage AI for that so you can do those things that make you special and make you stand out.
The people who can figure that out and leverage it the right way will be incredibly successful.
Making the Case to Keep Humans in CSKen Roden: Leaders face pressure from boards and investors to adopt AI more—potentially leading to roles being cut. How do you make the case for keeping humans as part of customer success?
Amanda Berger: AI doesn't understand business outcomes and motivation. It just doesn't. Humans understand that. The key to relationships and outcomes is that understanding. The humanity is really important.
At HackerOne, it was basically a human security company. There are millions of hackers who want to identify vulnerabilities before bad actors get to them.
There are tons of layers of technology—AI-driven, huge stacks of security technology. And yet no matter what, there's always vulnerabilities that only a human can detect.
You want full-stack security solutions—but you have to have that human solution on top of it, or you miss things.
That's true with customer success too.
There's great tooling that makes it easier to find that needle in the haystack. But once you find it, what do you do? That's where the magic comes in. That's where a human being needs to get involved.
Customer success—it is called customer success because it's about success. It's not called customer retention.
We do retain through driving success.
AI can point out when a customer might not be successful or when there might be an indication of that. But it can't solve that and guide that customer to what they need to be doing to get outcomes that improve their business.
What actually makes success is that human element. Without that, we would just be called customer retention.
The Framework: When to Use Digital vs. Human TouchErin Mills: We'd love to get your framework for AI-powered customer experience. How do you make those numbers real for a skeptical CFO?
Amanda Berger: It's hard to talk about customer approach without thinking about customer segmentation.
It's very different in enterprise versus a scaled model. I've dealt with a lot of scale in my last couple companies.
I believe that the things we do to support that long tail—those digital customers—we need to do for all customers. Because while everybody wants human interaction, they don't always want it.
Think about: As a person, where do I want to interact digitally with a machine?
If it's a bot, I only want to interact with it until it stops giving me good answers. Then I want to say, "Stop, let me talk to an operator."
If I can find a document or video that shows me how to do something quickly rather than talking to a human, it's human nature to want to do that.
There are obvious limits. If I can change my flight on my phone app, I'm gonna do that rather than stand at a counter.
Come back to thinking: As a human, what's the framework for where I need a human to get involved?
Second, it's figuring out: How do I predict what's gonna happen with my customers? What are the right ways of looking and saying "this is a risk area"? Creating that framework.
Once you've got that down, it's an evolution of combining: Where does the digital interaction start? Where does it stop? What am I looking for that's going to trigger a human interaction?
Being able to figure that out and scale that—that's the thing everybody is trying to unlock.
The 8-Hour Workflow Reduced to 30 MinutesErin Mills: You've mentioned turning some workflows from an 8-hour task to 30 minutes. What roles absorbed the time dividend? What were rescoped?
Amanda Berger: The roles with a lot of repetition and repetitive writing.
AI is incredible when it comes to repetitive writing and templatization. A lot of times that's more in support or managed services functions.
And coding—any role where you're coding, compiling code, or checking code. There's so much efficiency AI has already provided.
I think less so on the traditional customer success management role. There's definitely efficiencies, but not that dramatic.
Where I've seen it be really dramatic is in managed service examples where people are doing repetitive tasks—they have to churn out reports.
It's made their jobs so much better. When they provide those services now, they can add so much more value. Rather than thinking about churning out reports, they're able to think about: What's the content in my reports?
That's very beneficial for everyone.
By 2027: The Hardest CX Role to HireErin Mills: Mad Libs time. By 2027, the hardest CX job to hire will be _______ because of _______.
Amanda Berger: I think it's like these forward-deployed engineer types of roles. These subject matter experts.
One challenge in CS for a while has been: What's the value of my customer success manager? Are they an expert? Or are they revenue-driven? Are they the retention person?
There's been an evolution of maybe they need to be the expert. And what does that mean?
There'll continue to be evolution on that. And that'll be the hardest role. That standard will be very, very hard.
Lightning RoundKen Roden: What's one AI workflow go-to-market teams should try this week?
Amanda Berger: Summarization. Put your notes in, get a summary, get the bullets. AI is incredible for that.
Ken Roden: What's one role in go-to-market that's underusing AI right now?
Amanda Berger: Implementation.
Ken Roden: What's a non-obvious AI use case that's already working?
Amanda Berger: Data-related. People are still scared to put data in and ask for themes. Putting in data and asking for input on what are the anomalies.
Ken Roden: For the go-to-market leader who's not seeing value in AI—what should they start doing differently tomorrow?
Amanda Berger: They should start having real conversations about why they're not seeing value. Take a more human-led, empathetic approach to: Why aren't they seeing it? Are they not seeing adoption, or not seeing results? I would guess it's adoption, and then it's drilling into the why.
Ken Roden: If you could DM one thing to all go-to-market leaders, what would it be?
Amanda Berger: Look at your leading indicators. Don't wait. Understand your customer, be empathetic, try to get results that matter to them.
Key TakeawaysThe Human-AI Balance in Customer Success: AI doesn't understand business outcomes or motivation—humans do. The winning teams use AI to find patterns and predict risk, then deploy humans to understand why it matters and what strategic action to take.
The Lagging Indicator Trap: By the time NRR, churn rate, or NPS move, customers decided 6 months ago. Focus on leading indicators you can actually influence: verified outcomes, engagement signals specific to your business, early risk warnings, and real-time CSAT at decision points.
The 70% Rule: Bring CS into the sales cycle at the technical win stage (70% probability) for two reasons: (1) CS should scope what they'll be accountable to deliver, and (2) capturing customer goals early prevents the frustrating "I already told your sales rep" moment later.
Segmentation ≠ Personalization: AI makes segmentation faster and cheaper, but true personalization requires understanding context, motivation, and individual circumstances. The jumpsuit story proves we're still just sophisticated bucketing, even with 2026's advanced models.
The Delegation Framework: Don't ask "what can AI do?" Ask "what parts of my job do I hate?" Delegate the tedious (formatting, repetitive emails, data analysis) so humans can focus on strategy, relationships, and outcomes that only humans can drive.
"If You Don't Use AI, AI Will Take Your Job": The people resisting AI out of fear are most at risk. The people using AI to handle drudgery and focusing on what makes them irreplaceable—strategic thinking, relationship-building, understanding nuanced goals—are the future leaders.
Customer Success ≠ Customer Retention: The name matters. Your job isn't preventing churn through discounts and extensions. Your job is driving verified business outcomes that make customers want to stay because you're improving their business.
Stay ConnectedTo listen to the full episode and stay updated on future episodes, visit the FutureCraft GTM website.
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Disclaimer: This podcast is for informational and entertainment purposes only and should not be considered advice. The views and opinions expressed in this podcast are our own and do not represent those of any company or business we currently work for/with or have worked for/with in the past.