In this episode, we’re joined by Jakob Lilholm, Co-founder & CEO at Formalize, the Danish-based compliance SaaS that went from a single-point whistleblowing tool to a multi-product GRC platform used by 8,000+ customers across ~80 countries. Jakob shares how his team timed EU regulatory tailwinds, built whistleblowing software, and then layered products on top, shifting from high-volume transactional sales to a focused, consultative motion for regulated industries.

Fresh off announcing a €30M Series B, Jakob walks through the internal rewiring it took: carving out an innovation pod with its own OKRs, resisting flattering false positives from the existing base, and proving platform demand with new-logo sales first, going from ~€100k ARR on the platform to >50% of company revenue in a year.

Here are some of the key questions we address:

• When do you expand from a point solution to a platform? We discuss the timing model Formalize used (EU roadmap + S-curve “next wave” before the first peaks).

• What’s the right ICP for a platform? Why did they end up narrowing their ICP and say “not yet” to others?

• How do you avoid false positives when you already have thousands of customers? Jakob explains why he decided to validate platform fit with new logos first.

• What org design supports a second act like this?

• How do you shift GTM, pricing, and messaging? What is the process moving from low ACV sales to higher-ACV, consultative deals without breaking the engine?

• Which metrics matter in the first year of a platform bet? How do you prove value creation, track conversion quality, and know when to re-inject the core team?

🎧 Tune in to hear how Jakob and team rewired a fast-growing SaaS from a single product to a multi-product platform play, aligning product, GTM, and finance while keeping the culture hungry enough to chase unicorn status by 2028.

In this episode, we’re joined by Rav Dhaliwal, recovering software exec turned early-stage VC at Crane. A longtime CS leader and board-level advisor, Rav breaks down how AI is reshaping Customer Success, from onboarding and telemetry-driven predictions to agentic workflows, while pushing CS to converge with account management and get far more revenue-centric.

We spoke with Rav about what AI should (and shouldn’t) automate, how to keep relationships authentic, and how leaders actually drive adoption, treating AI not as a tool drop but a behavioral change program.

Here are some of the key questions we address:

• Will AI compress or redefine CS, and where does it create leverage vs. require human expertise?

• What does the CS–Account Management convergence look like in practice (discovery, multi-threading, commercial acumen)?

• Which AI use cases move the needle now: telemetry-based churn/upsell prediction, voice sentiment, and agentic next-best-action?

• How do you avoid the “AI for efficiency only” trap and tie it to revenue, cost, and risk outcomes that customers actually buy?

• What’s the playbook for AI adoption in GTM/CS? How do leaders run a change program (not a tool rollout) and measure progress?

• Where are the authenticity risks and how do you keep the customer relationship human?

• How far can we push AI-led onboarding and what’s the 90% automated vs. 10% bespoke split likely to be?

🎧 Tune in to hear Rav’s pragmatic take on CS in the age of AI: more signal, smarter workflows, tighter revenue alignment and leadership that treats AI as an operating change, not a shiny tool. Also, watch out for his new book coming out shortly on Founder-Led Sales!

In this episode, we’re joined by Inês Lourenco, VP of Growth at Usercentrics, the privacy-led MarTech company that is powering consent on 2M+ websites & apps across 200 countries for 600k customers.

Inês unpacks how she built a 50-person growth org of cross-functional pods (Monetization & Pricing, Acquisition & Virality, Activation, Partner Experience, CMS Integrations, and Retention) around a simple thesis: growth = product distribution. She details the early, scrappy phase with manual signal detection, nightly queries, and “product specialist” outreach - through to a scalable system that routes users to self-serve, low-touch, or high-touch paths based on predicted AOV, with a hard-earned lesson to start with activation before everything else.

We spoke with Inês about turning signals into revenue, structuring pods, and creating an operating cadence where every pod owns its metric such as ARPA growth, NRR/GRR, activation time, and virality share, while collaborating tightly with Product, Marketing, Sales, and CS.

Here are some of the key questions we address:

• How do you define growth as product distribution and design pods that actually move revenue, not vanity metrics?
• What signals predict AOV early, and how do you route users to self-serve vs. low/high-touch experiences accordingly?
• Why did “product specialist” outreach outperform SDR-style messaging, and how do you structure those “baby-step” nudges?
• If you’re starting from scratch, why begin with an Activation pod and what are the first experiments to run?
• How do you set pod-level metrics (ARPA growth, activation time, virality share, NRR/GRR) and avoid cross-metric collateral damage?
• What collaboration model keeps Growth aligned with Product, Marketing, Sales, and CS, and who should Growth report to?
• When shouldn’t you build a growth team (and what to do instead if your top of funnel is small)?
• How do you evolve from consent management to a privacy-led marketing platform, while staying profitable and fast?

🎧 Tune in to learn how Inês turned signals into a system, building a growth engine that’s rigorous, cross-functional, and activation-first, proving that modern growth isn’t a channel, it’s product distribution by design.