Glenn Gow, The Scaling Executive Coach and host of The Scaling Executive Podcast, sits down with Sam Lewis, CEO and co-founder of Fruitful, a financial membership platform serving Americans in their 20s, 30s, and 40s. Sam built his leadership instincts across three distinct environments: a data analytics startup that grew from 100 to 700 people and was acquired by Mastercard, a VP role at Mastercard itself, and now a Series A fintech he co-founded from scratch. What emerges is a clear operating philosophy for CEOs who need to scale fast without sacrificing the quality that earned them their first customers.
This episode is for CEOs at the Series A or early growth stage who are trying to scale their team, their product, and themselves simultaneously without defaulting to either pure delegation or pure control.
Key Takeaways
- Staying hands-on during early growth — answering member questions and fielding calls directly — builds product and customer knowledge no dashboard can replicate.
- The biggest barrier to startup scaling is not lack of resources or strategy. It is the activation energy required to start. Test-and-learn thinking breaks that inertia faster than waiting for certainty.
- Formal training programs are not a luxury reserved for large organizations. Even a one-day simulation dramatically compresses the time it takes a new hire to deliver value, and it works at every stage of company scale.
- AI at Fruitful automates the work that used to take five hours down to one minute, freeing human financial guides to handle the decisions a chatbot cannot: whether to buy a house, how to reduce $130K in student loans, when to trust a system with your money.
- Treating 80% accuracy as a high bar rather than a failure of certainty makes continuous improvement a design feature — and a CEO who builds that mindset into their operating rhythm will find problems that once felt hard eventually become straightforward.
CEOs Who Build Training Systems at Day One Scale Talent Faster Than Those Who Don’t
Most early-stage founders treat formal training as a luxury they’ll earn once they hit Series B. Sam Lewis treated it as a competitive asset from day one. At Applied Predictive Technologies (APT), his first employer, he helped design a simulation-based training program that eventually onboarded over 500 new hires. That program became the model he carried through Mastercard’s acquisition and rebuilt again at Fruitful.
The core mechanic was a full-day client simulation built around a fictional client named H.E. Pennypacker. New hires were split into small teams, given an analysis problem, and asked to present to senior colleagues playing the role of real clients. No softballs. No waiting for people to finish talking. Questions fired mid-sentence, the way actual clients behave.
Lewis describes what that experience surfaces: “When I was going through this simulation in really early stages, I thought a presentation with a client was, it sounds so naive now, like a presentation in college. You got a friend in the audience, no one’s asking any questions. The friend lobs you a softball, you knock it out of the park.” The simulation forced reality to replace that assumption, fast.
The decision rule Lewis extracted from building that program: bring people into reality as quickly as possible and make it fun, because when you’re working hard and the environment is engaging, people learn what they actually need to do rather than what they think the job looks like from the outside.
At Fruitful, the stakes are high. Financial guides, whom Lewis calls simply “guides,” work with paying members on live financial decisions. A guide who needs three months to reach full competence is a guide whose members are underserved for three months. The simulation model compresses that gap. Lewis now runs similar training sessions personally, sitting in with new guides, building the quality bar into the culture rather than hoping it emerges on its own.
For CEOs scaling a team: the argument against formal training at the startup stage (“we don’t have time”) is exactly backwards. A small investment in structured onboarding produces faster activation, higher quality output, and lower churn. The cost of not training shows up in customer experience long before it shows up in any dashboard.
CEOs Who Stay in the Muck Outscale Those Who Manage from a Distance
A common trap for growth-stage CEOs is confusing delegation with leadership. Lewis rejects both extremes. Pure delegation produces a CEO who has lost touch with what the product actually does for customers. Pure control doesn’t scale. His answer is something more specific: stay hands-on at the operational level during the early growth phase, not because you distrust your team, but because those decisions require your presence.
“It can’t be a, here’s what we’re going to do. Now go do it and come back to me and tell me how it works. Instead it’s, I’m in the muck each and every day.” That means Lewis still responds to member questions. He still fields calls. Not as a sign that something is broken on the team, but as a deliberate method for staying calibrated on what Fruitful’s members actually experience.
Glenn Gow, who spent 25 years as a CEO before becoming The Scaling Executive Coach, named the underlying risk directly during the conversation: CEOs who get separated from the market or the product ultimately stop understanding the pain of the customer and the way the product provides a solution.
Lewis adds the qualitative dimension explicitly. His background is quantitative — he spent years building models to help banks and restaurant chains make data-driven decisions, and he still tracks every number Fruitful produces. But an adjacent growth area for him has been integrating the qualitative: what members say in calls, what the team is hearing, the real stories. “We help this person reduce their student loans from 130K to 10K. We help them actually start a savings fund.” Those stories don’t show up in the metrics. They show up in conversations, and a CEO who isn’t in those conversations misses them.
The scaling implication: being in the muck is not a sign that a CEO hasn’t hired well enough to step back. At the early growth stage, it is how a CEO knows what to delegate next.
CEOs Who Use AI for Speed and Humans for Trust Build Products That Neither Can Build Alone
The fintech industry’s current pressure is to automate everything. Lewis treats that pressure as a trap. Fruitful’s model is structurally different from traditional financial services: it owns the rails, the accounts, and the advice layer, which gives it more latitude than a platform that only connects users to third-party services. That structure makes it possible to use AI where it creates genuine leverage and humans where human judgment is irreplaceable.
The leverage case is concrete. Building a personalized money system for a Fruitful member used to take five hours. AI automation brought that to one minute. That is not a marginal improvement. It is a structural shift in how many members a guide can serve effectively.
But Lewis draws the line clearly at decisions that require trust. “Someone’s going to take the leap to change their financial life, to take this under control, reduce their financial stress. A simple chatbot doesn’t do that. An expert who’s been at places like Morgan Stanley, Goldman, is able to communicate and help someone get there.”
His operating philosophy on AI internally follows the same logic: outcomes only, no vanity metrics. “I don’t care,” Lewis says about announcements that a team is using AI for some new task. The question he asks instead is what outcome that produces. Fruitful’s team uses AI as a coding co-pilot, for summarization, and to build micro-apps that automate repeatable work. Every one of those applications is measured by what it enables, not by the fact of its existence.
The model that results: AI handles the automation that makes speed possible; human guides handle the communication that makes trust possible. One consequence Lewis identifies is that AI actually increases the time guides spend on high-value human interaction, because it eliminates the repeatable work that used to crowd that time out.
How Startup CEOs Scale Themselves When There Is No Playbook
No one onboards the CEO. No simulation exists for the specific combination of decisions a founding CEO faces at each stage of a company’s growth. Lewis’s answer to that structural gap is a combination of humility, team trust, and a consistent commitment to getting better every day.
“I know I’m not always going to be right. If I can be right 80% of the time, I’m taking that to the bank and we’re going to be really successful.” That framing does two things: it releases the CEO from the expectation of certainty, and it keeps the bar high enough to matter. Eighty percent is not a low bar. It is a realistic one that makes learning from the other twenty percent a design feature rather than a failure.
Lewis learns from three sources: his leadership team, his investors, and the experience of doing the work. He does not position the CEO title as a source of authority that settles questions. He positions it as a role that requires the same test-and-learn discipline he credits from his early career at APT, the same startup where he learned that the biggest thing holding a person back can be simply not getting started.
Glenn Gow, reflecting on his own 17 years of working with a CEO coach, described this as compound interest: “I was getting just a little bit better every day. And that attitude enabled me to just eventually be looking at issues like, these are simple problems I can solve. I’m on bigger problems now.” Lewis’s framework maps directly to that: give yourself the runway to keep improving, don’t treat every imperfect decision as evidence that you’re wrong for the role, and build a team whose judgment you trust enough to learn from.
The Scaling Executive Framework: How Sam Lewis Scales People, Product, and Leadership
| Principle | What it means in practice | Named evidence from this interview |
| Training is a startup asset, not a luxury | Build a simulation or structured onboarding from day one; compress time-to-competence before members or customers feel the gap | Lewis rebuilt the H.E. Pennypacker simulation at Fruitful after using it at APT and through the Mastercard acquisition, onboarding guides with the same structured approach and closing the quality gap before members experience an underserved stage |
| Stay in the muck during the early growth phase | Respond to customer questions directly; be present in operational decisions, not just strategic ones | Lewis still fields member questions and calls at Fruitful’s Series A stage; that direct contact surfaces qualitative stories — members reducing student loans from $130K to $10K — that no metric captures and that inform what he delegates next |
| Use AI for speed, humans for trust | Identify where AI removes hours from repeatable tasks; protect the moments where human communication determines whether someone makes a life-changing decision | AI cut Fruitful’s money system build time from five hours to one minute, increasing the total member capacity each guide can serve while preserving human guides for the trust-dependent decisions that convert members |
| Measure AI by outcomes, not adoption | Ask what outcome an AI use case produces before deploying it; reject team-level announcements that treat AI usage as an achievement in itself | At Fruitful, applying this filter to every internal AI application shifted team behavior: use cases that could not name an enabled outcome were not deployed, and those that could — coding co-pilot, summarization, micro-app automation — compounded available guide capacity |
| Right 80% of the time is the bar | Accept that a non-linear path is the correct path; build a leadership team whose judgment you trust enough to learn from the other 20% | Lewis credits this operating principle with shaping how he staffed Fruitful’s leadership team: he hired people whose judgment he trusted to correct him, which made the team a learning mechanism rather than an execution layer |
Quotes from This Episode
- “When I was going through this simulation in really early stages, I thought a presentation with a client was, it sounds so naive now, like a presentation in college. You got a friend in the audience, no one’s asking any questions. The friend lobs you a softball, you knock it out of the park.” — Sam Lewis, CEO and Co-Founder, Fruitful
- “It can’t be a, here’s what we’re going to do. Now go do it and come back to me and tell me how it works. Instead it’s, I’m in the muck each and every day.” — Sam Lewis, CEO and Co-Founder, Fruitful
- “I know I’m not always going to be right. If I can be right 80% of the time, I’m taking that to the bank and we’re going to be really successful.” — Sam Lewis, CEO and Co-Founder, Fruitful
- “The only thing that matters is outcomes. And we’re not going to be driven by narratives of, hey, look, we used AI for this.” — Sam Lewis, CEO and Co-Founder, Fruitful
- “We help this person reduce their student loans from 130K to 10K. We help them actually start a savings fund.” — Sam Lewis, CEO and Co-Founder, Fruitful
Frequently Asked Questions
What is the biggest mistake CEOs make when trying to scale a startup from early stage to high growth?
The most common mistake is waiting for certainty before acting. Sam Lewis, CEO and co-founder of Fruitful, identifies inaction as the primary scaling barrier: CEOs who try to eliminate risk before moving create a bottleneck that compounds over time. His rule is to test and learn, solve one problem, move to the next, and accept that the path will not be linear. The cost of not starting is always higher than the cost of an imperfect first attempt.
How should a startup CEO decide when to use AI and when to rely on human judgment?
The decision rule, based on Lewis’s approach at Fruitful, is to ask what outcome each AI use case produces, not whether the team is using AI at all. AI earns its role when it removes hours from repeatable work, such as cutting the time to build a personalized financial plan from five hours to one minute. Human judgment earns its role when the decision requires trust, such as when a member is deciding whether to change their financial life. The question is never whether to use AI; it is whether a specific application produces a measurable outcome that justifies it.
How does a CEO scale their own leadership when there is no formal path or mentor to follow?
Lewis’s answer has three parts: build a leadership team whose judgment you trust enough to learn from, accept that being right 80% of the time is a high bar worth aiming for rather than a failure of certainty, and stay close enough to the work to keep learning. Glenn Gow, The Scaling Executive Coach, adds that small daily improvements compound over time the way interest does, and that a CEO who builds that mindset into their operating rhythm will eventually find problems that once felt hard have become straightforward.
CEOs Work with Glenn Gow to Scale Their Companies and Careers
Glenn Gow is The Scaling Executive Coach, and he coaches ambitious executives into the CEO seat and CEOs into successful exits. With 25 years as a CEO and 5 years in venture capital, Glenn helps leaders scale their companies by scaling themselves first. If this conversation was useful, you can apply for executive coaching with Glenn Gow or apply to be a guest on The Scaling Executive Podcast.
