Quick summary: As AI erodes the traditional product moat, your new competitive advantage is a human-led customer experience built on trust, expertise, and empathy.
Building a defensible SaaS business used to follow a script. You identify a large total addressable market, get there first, then build the most feature-rich product in your category.
Do all three, and you create a product moat that defends you from competitors.
Alas (for the incumbents) and hooray (for the upstarts), that script is dead, all thanks to AI.
The implications for customer success and customer experience leaders are enormous. If you’re ready and able to act on it, in fact, it could be the greatest career opportunity you’ll see in a decade.
In a recent ChurnZero webinar, Jennifer Courchaine, VP of Customer Experience at Vehlo and author of The Moat: Human-Centric Customer Experience in the Age of AI, joined ChurnZero CEO You Mon Tsang to explore how the traditional product moat is evaporating, how most SaaS companies are underestimating the speed of change, and what might come next.
Watch the webinar here, then scroll down for the top takeaways and how to address them.
Why the traditional product moat is failing.
The pre-AI SaaS playbook featured three reliable levers that you could combine into a durable product moat.
- First-mover advantage. The strongest of the three, this lever used to provide a runway of up to two years before your competitors could feasibly catch up. You built your customer base, refined your product, and kept running, making your head start nearly impossible to close.
- Feature depth. If your product was comprehensive and deeply integrated into a customer’s workflow, it was genuinely hard to switch away from.
- Niche domination. Go deep enough into a specific vertical, and you could operate with almost no competition, indefinitely.
While these levers still exist, AI is taking a sledgehammer to them. Features can be cloned overnight, and depth of product no longer differentiates.
“It’s not just that AI is smart. It’s not just that AI is fast,” Jennifer says. “It’s that the barrier to replicating software has kind of gone away. Your competitors don’t need 50 engineers. They don’t need 2 years. They might need a prompt engineer and a week.”
Niche expertise, meanwhile, offers far less protection than it did, with the exception of highly regulated industries like aerospace or life sciences
All of this translates to incoming risk in every direction, from lumbering incumbents to nimble, two-person startups. A clear idea and a small amount of runway is enough to mount a challenge.
Why 2026’s SaaS landscape makes things even harder.
But wait… there’s more! Your product moat isn’t vanishing in a vacuum—there’s a broader set of SaaS industry dynamics at work to make it even more of an emergency.
- Customer acquisition costs are climbing. “Across industries, we’re seeing CAC actually rise, “ says Jennifer. “It costs more to get customers, and their lifetime value is shrinking because the barrier to switching is easier than it used to be.”
- Brand loyalty is declining. “A customer may leave because the competitor has a slight feature edge,” says Jennifer, “and it’s just not that hard to leave anymore.” Meanwhile, AI makes migrations dramatically easier by reducing the timeline and practical costs of shifting to a competitor.
- Buying behavior and budgets are shifting. “With all of the new tech flooding the market, most businesses don’t have specific budgets like they used to,” Jennifer explains. “A lot of what they’re doing is doing proof of values, proof of concepts, and pilot programs side by side, testing out multiple competing technologies. You might feel like you won the business when you’re actually still in the sales process.”
Retention matters more than ever.
“(Investors) are really going to reward those who have stronger customer satisfaction and stronger gross and net retention, even if there’s a slightly lower growth rate than a similar company with much worse retention,” Jennifer says. “It’s because that is indicative of a much healthier business.”
If product differentiation is fading, what’s left?
The new moat, says Jennifer, is human-led customer experience.
When large vendors automate everything and make it genuinely hard to reach a human, customers notice. And, as we note above, it’s increasingly easy for them to vote with their wallets.
The companies that hold the line on human-led experience, meanwhile, can win business they might never have won on product alone.
“Decent software is going to become table stakes,” Jennifer says. “Where you can differentiate is the service you provide.”
“VC or your PE backers often drive to short-term revenue and short-term results,” she says. “Those are sometimes are at odds with a delightful customer experience—and that attitude is a gift to those of us who still want to prioritize doing right by the customer.”
As software parity becomes the norm, in other words, relationships and customer experience become the last truly defensible differentiator—and thanks to AI, says Jennifer, you can really turn them up.
What makes an effective customer experience moat?
This is your big opportunity amid the gloom. AI can free your CS team to deliver an exceptional customer experience that stands head and shoulders above your competitors’ overly automated noise. Chiefly, it comes in three forms that AI can’t deliver, but your CSMs absolutely can.
Trust: the liability shield.
When things go wrong, and they will, says Jennifer, “the value is all about giving customers the confidence that they’re not going to get stuck in this AI loop. If there are mistakes, or outages, there’s accountability, with humans who are working to solve the problem and communicate.”
Expertise: the consultative layer.
AI is excellent at “how”, but less so at “why”, and weaker still at solving business problems rooted in nuances it doesn’t have access to. The CSM who asks the right question at the right moment and steers a customer towards a better outcome, meanwhile, feels irreplaceable.
Empathy: the connection that makes it land.
“If you chat with AI about what it’s good at and what it isn’t, the first thing it’ll tell you it can’t do is empathy,” Jennifer says. AI can fake it, but it doesn’t really care—and customers can tell the difference between a cosmetic interaction and one designed to solve their problem with care.”
The great thing about AI in this context is that it can handle the administrative burden, the routine tickets, the data cleanup, even the repetitive check-ins—leaving your human team to spend most of their time on exactly the three pillars above.
What should I do now to get started?
What we’ve described here is already happening, which means that standing still shouldn’t be an option. The relationship moat opportunity won’t happen organically for you; instead, it’ll take some heavy lifting at the leadership level.
In our next article, we’ll explore this from a tactical perspective: how to map the gaps in your current customer experience, design your human and AI foundations, and build your new moat from the ground up.
What attendees wanted to know: Webinar Q&A.
We concluded our webinar with audience questions moderated by You Mon.
You Mon: Janet has a question in the Q&A. She’s transforming her team, moving customer support people into customer success, or expanding that role. What should she do with her people? Those are the folks where there’s a lot of emotion and a lot of investment behind them, but they are going to have to change.
Jennifer: Change is scary for everybody. It’s a human trait that change makes us nervous, even when it’s good change.
We have to reframe change as continuous improvement and evolution to wrap our brains around it. It’s not: “oh, another change”; it’s: “this is the next step of our business.”
Culturally, say the same things over and over. Don’t talk about change; talk about this new process as part of our evolution. We have the privilege to operationalize this component. We have the privilege to expand your role because we’ve been able to automate the dumb stuff. Part of it is the words we use. A lot of people say it’s just semantics, but I believe words are all we have.
Specifically, when you’re transitioning from support to customer success, there are things you’re really good at in support. You’re really good at solving problems. You’re less good at the proactive stuff, and you’re less good at the problems you can’t solve. Focusing on those areas as part of a transition path—understanding that those aren’t weaknesses, they’re just areas you aren’t as strong in because they haven’t been required yet—can really help. Use peer mentors and team leads to work on those areas. Double down on the great technical knowledge and product expertise they already have, so they can add real value to their customers as they transition.
You Mon: Let’s talk about your specific situation. You sell into the automotive vertical. Who’s your actual customer?
Jennifer: Our customer (at Vehlo) is the service department at automotive dealerships and repair shops.
You Mon: Let’s say you had zero CSMs and were hiring a new team from scratch, with this vision of what’s changing and the moat you have to create. Are you hiring people who’ve been in automotive service, or are you looking for generalists: liberal arts majors who can learn and be flexible? If you were starting from scratch, what types of people would you look for?
Jennifer: For Vehlo, I would hire from the industry. We are constantly in a state of transformation: highly acquisitive, made up of 20 different businesses acquired over the past seven years, with products at various stages of integration.
Service writers are also very specific in the way they work. It is easier to train someone on how our software works if they already know who our buyer and user is, than to train them on our users.
That’s not true for support. For support, I hire more technical people who can dig in, because when somebody needs their problem solved, they don’t care if you’ve worked as a technician. They care if you can fix their problem. I want technical people for support, and people who understand the business need for my CSM role.
You Mon: If you’re going to make change in your organization, you need board support. How would you connect what CX does to a board-level priority? What board-level metric resonates most with CX: growth, NRR?
Jennifer: I would tie it to top-line growth. If you invest in customer experience, we are going to improve the cost to acquire a customer, drive down the sales cycle time, improve cross-sell and upsell, and shore up gross retention as well. I put them in that order because gross retention takes the longest to move.
Those other metrics are very sales-driven and we can tend to move on them quickly. Sales hates filling a leaky bucket. If we’re growing at 30% from new business, the last thing we want is to be losing 20% off the bottom and end up as only a 10% grower. Tying it to top-line revenue is the best way to get the board to listen.
Be correct on your numbers. Don’t be too conservative, but don’t be so conservative that it’s a non-starter. Be aggressive in the goals you think you can hit if you’re asking for meaningful investment.
You Mon: Michael in the audience says: multipliers and compounding customers: that’s what matters.
I love the word compound. If you’re talking to CFOs as a CX person, use that word. Retaining a customer has compounding effects. CFOs love that, it really moves a spreadsheet. The CFO understands the impact of retention more than anyone.
Among all the C-suite executives, who’s your best ally? The CPO, CTO, or CFO?
Jennifer: CX leaders need strong relationships across the board, but I’d say the CFO. You need to not negotiate against yourself, you need to present options, and if you can get in tight with your CFO, they can make real change with you. Second, the CRO. You need to be part of the go-to-market engine. I hear a lot of teams talk about go-to-market as though it’s only marketing and sales. You’ve got to be in those conversations.
You Mon: There’s one more thing worth addressing. The uneven distribution of AI creates what one attendee calls “bunching.” The implementation and use of AI is going to be uneven, and you get high productivity from one department that then backs up at another that can’t keep pace. I think that’s unanswerable.
It’s a big game of whack-a-mole until all the moles are gone, and the moles never go away. You’re going to smooth it out, and it’s going to bunch somewhere else.
