I'm running commercial operations at magicplan, an AI-powered vertical SaaS company serving the restoration and trades industry. We had the classic setup: Marketing generating MQLs, SDRs qualifying, AEs closing, CS retaining. Pipeline reviews every Monday. Quota attainment dashboards. The whole machine.
And it was working: steady growth, a solid team, a predictable motion. But "working" wasn't enough. Not when the product was shifting toward AI-driven execution that required customers to fundamentally rethink how they operate.
You can't funnel someone into a behavioral transformation. You can't BANT-qualify someone's willingness to change.
Our product required customers to fundamentally change how they work, trusting AI with documentation, scoping, and project planning that they'd done by hand for decades. A contractor who's documented jobs by hand for 15 years isn't worried about ROI; he's worried about looking incompetent in front of his crew. And you definitely can't hand him off four times and expect him to feel safe adopting something that threatens his professional identity.
So I redesigned the entire go-to-market from scratch.
What I Built
I replaced the funnel with a Readiness Path. Instead of tracking leads through linear stages (MQL → SQL → Closed), I built a qualification system based on behavioral signals of change-readiness. Two new frameworks replaced the old pipeline logic:
Framework 01
OPEN — identifies who's ready to change
- O — Outcome focus: Are they driven by a specific outcome, not just a problem?
- P — Problem awareness: Do they understand the root cause, not just the symptom?
- E — Engagement: Are they showing active curiosity rather than passive interest?
- N — Novelty receptiveness: Do they have a track record of adopting change?
Contacts who pass become Change-Qualified Contacts (CQCs), people showing real openness to transformation, not just casual interest.
Framework 02
FIRE — determines who's ready to act
- F — Fit: Traditional ICP alignment: industry, size, use case.
- I — Intent: Active signals of evaluation, not just awareness.
- R — Readiness: Team dynamics, internal champion, tooling context.
- E — Environment: Timing: is there a forcing function or window of change?
Contacts who pass FIRE become Portfolio Accepted Contacts (PACs), the accounts an advisor actively invests time in.
Portfolio managers, not account executives
I turned account managers into portfolio managers. The company benefited from a strong product-led growth motion that supplied a steady stream of leads, but PLG alone doesn't create adoption, especially in field services, a traditionally conservative industry when it comes to technology.
I restructured the commercial team around entirely new roles:
- BDRs → Growth Analysts: focused on qualifying readiness rather than booking meetings.
- AEs → Solution Architects: who sell by advising, designing activation plans around the customer's real workflow rather than pushing demos toward a close.
- Account managers → Growth Advisors: who manage portfolios of users the way fund managers manage assets, allocating time based on a Value Realization Index that weighs each account's activation stage and effort required, not close dates.
They still run proper qualification for portfolio acceptance and do outbound, but increasingly warm outbound fueled by referrals from realized customers rather than cold lists. They don't chase revenue; they curate usage, because usage is revenue in a consumption model.
The emotional side of value
Most GTM strategies only address rational value: ROI, efficiency, cost savings. That's only half the picture. I built a seven-dimension value framework spanning both emotional drivers (Identity, Confidence, Empathy, Risk) and rational ones (Operational, Economic, Evidence) that shaped everything from messaging to qualification to advisor playbooks.
AI copilots across the entire commercial process
GPT-powered tools that screen leads against OPEN criteria, assess portfolio acceptance via FIRE scoring, run weekly portfolio reviews, and provide real-time playbook guidance to advisors. The result: advisors spend their time on high-judgment conversations instead of data gathering and prioritization, making a lean team operate like one twice its size.
Underpinning all of this is a strong RevOps function capable of orchestrating agentic workflows in coordination with HubSpot, leveraging tools like Freckle and Orbital to enrich data, automate sequences, and keep the system running without manual overhead.
What Happened
Growth came through usage and adoption rather than paid acquisition. Sales cycles shortened significantly; by the time someone entered an advisor's portfolio, they weren't being convinced, they were being enabled.
The deeper shift was cultural. The team stopped selling and started coaching. Customers responded differently when the first conversation wasn't a demo pitch but a genuine exploration of whether they were ready to change how they work. Churn dropped as a direct consequence: when you qualify for change-readiness rather than budget authority, the customers who come through actually stick.
The approach drew on patterns from consumption-based and product-led companies, but pushed further by eliminating handoffs entirely. One advisor as the customer's counterpart from first conversation to renewal, from activation to expansion. No warm transfers, no re-introductions, no lost context.
The Bets That Paid Off
Behavioral change is the product. When your software requires users to work differently, adoption isn't a post-sale problem; it's the entire business model. GTM has to be designed around it from day one.
Emotional readiness gates everything. You can have perfect ICP fit and strong ROI, but if a contractor who's been doing things his way for 20 years doesn't feel safe changing, nothing moves. Qualifying for emotional readiness is the single highest-leverage improvement most B2B companies can make, and it's especially true in conservative, hands-on industries like field services.
PLG is necessary but not sufficient. Product-led growth generates leads; it doesn't generate trust. The companies that will win the next decade are the ones who layer human-led change guidance on top of self-serve motion, turning activated users into referral engines that make outbound feel like inbound.
I built this as COO at magicplan, an AI-powered vertical SaaS company, managing the full commercial stack from demand generation through retention.