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Actionable
Takeaways
Optimization doesn't compound fast enough in AI categories
Natalie's framing here is precise: it's not that journey maps are bad, it's that in fast-moving AI categories, by the time you've optimized a touchpoint, the underlying problem has already shifted. Her response was to stop tweaking journeys and start running AI hackathons — value experimentation sessions designed to surface what's possible in a single day. "The market and AI are just moving faster than you can build a journey map or build a solution — and therefore you're solving the wrong problem."
The change agent model: redeploy your most curious people
Rather than hiring forward-deployed engineers, Natalie identified the most curious people already on her CS and services teams and made them internal AI change agents. The leverage point: those same people deploying new AI workflows internally then turn around and bring those proven workflows directly to customers. Internal deployment becomes the product demo.
Success plans used to take pages. Now they take minutes.
Natalie describes feeding every customer email, conversation, Confluence page, and Slack thread into an AI tool to generate a success plan that the CSM, AE, and SE can iterate on together. The insight isn't the time saved — it's that it makes cross-functional iteration on account strategy actually happen, where before the friction meant it mostly didn't. "You could take all of the data that you have and insights around the customers, every email, every conversation, and then pair that with your confluence page of knowledge, everything that's happening in Slack and input that into Claude or your AI tool of choice and make a success plan in mere moments."
AI maturity is the new retention metric
Natalie's team is now measured on moving customers up an AI maturity curve: from experimentation to scaling to transformation. The urgent problem she's sitting with: even the largest enterprise customers she visits are nowhere near scaling. "A lot of customers are stuck in experimentation — and it is our job to help move them up that curve." Health score alone doesn't tell you whether a customer is actually getting more capable with your product.
No siloed forecasting
Every renewal forecast call at Backstory has shared ownership between the CSM, AE, and CRO. Natalie shows up for new growth calls for the same reason. "We don't do any of our forecasting rhythms in silos." The structural point: shared presence in revenue rhythms changes accountability in ways that async alignment never does.
Activation debt becomes structural after $30-40M
For founders: the window to fix onboarding and activation is earlier than you think. Natalie's observation from the field is that companies hit $30-40M and then try to fix it — and by then it's embedded in the product, the team motion, and customer expectations. Hard to undo. "If activation and onboarding are hard, everything else becomes harder and hard to undo."
Focus and finish is a hiring filter, not a value
Natalie tests for prioritization explicitly in the interview process: give candidates a full set of competing priorities and watch whether they try to do all of them. The tell is distraction. "There's a lot of ability in a growing company to do 10 things really poorly. I'm maniacally focused on we do three things really well."
Conversation
Highlights
Backstory: Why the CCO Threw Out Her CS Playbook and Started Measuring AI Maturity
Natalie Wolf had built the playbook over a decade. She knew how to map the customer journey end to end, how to sequence onboarding sprints, how to set health scores and run QBRs. She had done it at Deloitte, at Accenture, at Anaplan, at Salonis. It worked.
Then, roughly four to five months before this conversation, it stopped working.
“Everything I thought I knew and felt like I had a playbook for really got thrown out the window,” she said. “In fast moving categories like AI, optimization doesn’t just come fast enough — it’s compounding.”
This wasn’t a product problem or a market problem. It was a structural problem with how CS itself was designed. The assumption baked into every journey map — that you can identify a moment, optimize it, and move on — collapsed when the underlying category started moving faster than the iteration cycle. By the time you’d fixed the onboarding touchpoint, the customer’s definition of value had shifted. Natalie wasn’t behind on execution. She was behind on the unit of work.
In a recent episode of BUILDERS, Natalie shared how she rebuilt her CS motion from scratch at Backstory, where she leads customer success, services, support, and the full revenue and customer operations function.
Replace the Journey Map with the Experiment
The first thing Natalie changed was the format. Instead of onboarding sprints built around milestones, she started running AI hackathons — same-day value experimentation sessions designed to show customers what was possible immediately. The logic is direct: if the market is moving faster than you can build a journey, stop trying to build journeys and start compressing the time to insight.
“What I’m doing with my customers is hosting AI hackathons, value experimentation sessions, iteration on what could be possible in one day,” she said. “The market and AI are just moving faster than you can build a journey map or build a solution — and therefore you’re solving the wrong problem.”
The shift isn’t just tactical. It reflects a different theory of customer value. Traditional CS assumes value is delivered through structured progression. Natalie’s model assumes value has to be demonstrated before the customer fully understands what they’re trying to achieve. The hackathon isn’t an onboarding alternative — it’s a mechanism for collapsing the time between contract and conviction.
The Change Agent Model: Internal First, Customer-Facing Second
Natalie didn’t hire to solve this. She redeployed.
She identified the most curious people already on her CS and services teams and redesignated them as internal AI change agents. Their job: deploy new AI workflows inside Backstory first, prove they work, then bring those workflows directly to customers.
“I’ve basically taken our most curious people in my services team and my CSM team and said, you’re going to be now our change agents internally for deploying AI in everything we do and you’re also going to bring that expertise to customers,” she said.
The structural insight is the sequencing. Internal deployment isn’t separate from customer work — it generates the proof points that make customer conversations credible. A CSM who has actually built a working AI workflow inside their own company is not pitching a capability. They’re demonstrating one.
One concrete output of this model: success plans. Natalie’s team feeds every customer email, Slack thread, and Confluence page into an AI tool and generates a working success plan in minutes — one the CSM, AE, and SE can iterate on together. “You could take all of the data that you have and insights around the customers, every email, every conversation, and then pair that with your confluence page of knowledge, everything that’s happening in Slack and input that into Claude or your AI tool of choice and make a success plan in mere moments,” she said. The time saved matters less than what it enables: cross-functional iteration on account strategy that rarely happened before because the friction was too high.
The New Retention Metric: AI Maturity Progression
The change that signals how seriously Natalie has rethought CS fundamentals is what she now measures.
Her team is no longer measured primarily on health scores. They’re measured on how many healthy customers they can move up an AI maturity curve — from experimentation to scaling to transformation.
“My team has measured on how many customers in green health can you move from experimentation to scaling to transformation? And that’s our new measure,” she said.
The problem this solves is concrete. Health scores are a lagging indicator of relationship stability, not a leading indicator of customer growth. A customer can be green — no churn risk, regular engagement — while being completely stuck at the experimentation stage. Natalie visits large enterprise customers and finds them nowhere near scaling. The health score says they’re fine. The maturity curve says they need help.
Structural Alignment: Take the Silos Out of Forecasting
Natalie’s other structural change was to forecasting rhythms. At Backstory, no renewal forecast call happens without shared ownership between the CSM, AE, and CRO. She shows up for new growth calls for the same reason.
“We don’t do any of our forecasting rhythms in silos. So think every renewal forecast call has shared ownership between the CSM and the AE and the CRO,” she said.
The insight isn’t that collaboration matters. The insight is that shared presence in revenue rhythms changes accountability in ways that async communication never does. When the CSM and AE are looking at the same renewal number in the same room, ownership is unambiguous.
The Activation Debt Threshold
For founders building their CS motion, Natalie’s most specific warning is about timing. Activation and onboarding debt compounds quietly until it becomes structural — and the threshold is earlier than most founders expect.
“A lot of startups hit like 30 to 40 million and then decide to fix this and then by then it’s so hard to do,” she said. The downstream consequences are specific: innovation slows because teams get stuck on fundamentals, field teams spend cycles on problems the product should solve, and the market moves on.
The fix isn’t a CS hire or a new onboarding sequence. It’s product architecture — which is why it has to happen before scale makes product changes expensive.
The principle connecting all of Natalie’s changes is the same: stop optimizing the system you built for a market that no longer exists. In a category moving as fast as AI, that’s not a one-time reset. It’s the permanent operating condition.
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