Kirsten Mann is a board director, product leader and, with this episode, a three-time guest on the podcast. Last time she was here, she was building Vizory, an AI tool that helps company directors get through board packs. This time she’s back to explain why she isn’t building it any more. Talking to Randy Silver, she walks through exiting Vizory, the friction tests she ran before writing a line of code, and why she now believes distribution is where most AI products actually die.
Key takeaways
- Before writing a line of code, Mann tested for force rather than sentiment: she asked directors for two confidential board packs and an audio recording of themselves reviewing one, and only started building once 10 people agreed to do it. Coffee-shop enthusiasm is cheap; handing over a company’s most sensitive document is not.
- Vizory’s most-loved feature in demos, cross-pack search, was barely used once shipped. Directors said they wanted it and reacted strongly when they saw it, but in practice they went straight to an automated triage view instead — a clean case of stated preference diverging from revealed preference.
- Board software runs on quarterly, not weekly, cycles, and trust compounds slower than value does. Mann modelled two review cycles before directors would rely on the tool; in practice it took three, meaning some customers on quarterly boards needed close to a year before the value became obvious, a timeline no 30-day trial can accommodate.
- Pricing got caught in a double anchoring trap. Directors first benchmarked Vizory against the $20-a-month ChatGPT and Claude price point, so Mann positioned against the cost of a governance failure instead, then created a second anchor by pricing early pilots at $150 a seat against a $300 target, which made the later increase read as a 100% price rise.
- Cutting friction is the wrong instinct early on. Mann deliberately added friction, asking for sensitive documents and charging as early as possible, to separate real demand from polite interest, arguing that most founders run tests designed to confirm they’re right rather than to find a reason to stop.
- Mann’s main lesson for other founders is that distribution is now the product problem. AI has collapsed the cost of building software but done nothing to the cost of reaching people who trust you, which is why she pushed to add distribution, adoption and willingness to pay as a third discipline in the Makers’ Manifesto, alongside building the right thing and building it right.
- Multi-model products carry a new operational risk. One of Vizory’s AI “judges” silently stopped being selected because the underlying model had been deprecated overnight without warning, a reminder that model drift means the QA and evals work never really stops, however fast the initial build was.
Chapters
- (00:00) Introduction: Kirsten’s third time on the show
- (00:47) What happened to Vizory
- (03:12) Exiting the business
- (03:58) Was Vizory a product or a feature?
- (05:51) Why viability was the real problem
- (07:34) Choosing an existing channel over building one
- (08:57) The danger of being your own customer
- (11:31) From the mum test to the me test
- (12:51) Testing with AI agent personas
- (13:51) Why pain is not the same as demand
- (16:24) Testing for force, not sentiment
- (18:57) When friction helps and when it hurts
- (20:30) Getting people to commit before building
- (21:38) The cross-pack search feature nobody used
- (24:36) Stated versus revealed preference
- (26:40) The broccoli principle
- (27:33) Deciding whether to cut underused features
- (28:45) Building for a product used only quarterly
- (29:18) Time to value versus time to trust
- (32:24) Why users wanted future packs, not past ones
- (34:36) Setting price in a brand new category
- (38:12) The anchoring trap on pilot pricing
- (40:14) Why distribution is the real product problem
- (42:41) Advice for founders starting today
- (45:11) The four-month build and the toll of moving fast
- (47:58) Treating AI like an unreliable employee
