How Long Does It Take?
Big enterprise and startup founders often commit the same mistake: they have vision and a budget and now want to know how long and how much will it take to build it. They shop around. They compare teams. They want the team they select to behave like a giant function backed by decades of experience.
Don’t sacrifice outcomes for your comfort
The requirements go in. Out comes a timeline, a cost estimate, and a series of milestones. This is comforting. The buyer knows what they will receive. The team knows what it must build. Everyone can track whether the project is on schedule.
The requirements may be incredibly well stipulated. The estimate may be honest. The team may execute perfectly. None of that proves that X solves Y.
Consider a clinic with too many missed appointments. That is problem Y. The clinic owner decides that the solution is a mobile app. That is X.
The requirements include accounts, patient profiles, appointment calendars, push notifications, rescheduling, payments, analytics, and integrations. Three teams provide estimates. The best proposal promises the app in six months for $100K, with milestones for design, backend development, beta testing, and release.
6 months later, the app may work perfectly. Patients may still not install it. X was delivered. Y was not solved.
Use your head, be less dumb
Now put Musk’s five steps inside the function.
1. Challenge every requirement
Why does the clinic need an app?
It does not. It needs fewer missed appointments. The app is only a hypothesis about how to achieve that.
The first step is to challenge the assumption that X is required to solve Y.
2. Delete aggressively
Delete everything that is not required to test the hypothesis.
There is no need for an app, an account, a profile, a custom calendar, a payment integration, or an app store release. None of these things is necessary to learn whether reminders and easier rescheduling reduce missed appointments.
3. Simplify what survives
Export tomorrow’s appointments. Have the receptionist send each patient a WhatsApp message:
You have an appointment tomorrow at 2 PM. Reply 1 to confirm or 2 to reschedule.
That is enough to test the important part of the idea. Patients receive a reminder. They can confirm. They can reschedule.
4. Shorten the cycle
Run the test tomorrow. Try it with the next 100 appointments. Measure confirmations, rescheduling, and no-shows.
Within a week, the clinic has evidence. If the message works, test a better version. If it does not, test another hypothesis.
Perhaps patients lack transportation. Perhaps they forget why the appointment matters. Perhaps appointments are booked too far in advance. Perhaps a small deposit would work better.
The clinic is now learning about Y instead of blindly constructing X.
5. Automate last
Only automate after finding something that works.
If the messages reduce missed appointments, connect the scheduling system to WhatsApp. Send reminders automatically. Process confirmations. Offer available times when someone reschedules. Escalate non-responses to a human.
The clinic may never need an app. If it eventually does, it will build one based on evidence rather than imagination.
Focus on the problem
This is the difference between building X and solving Y.
“We have $100K and 6 months to build this app” is a commitment to X.
“We have $100K and 6 months to reduce missed appointments” is a commitment to Y.
As a decision leader, your job is not to guarantee that X gets built. Your job is to solve Y within the available budget and time.
The milestones should therefore describe evidence, not construction. We verified why appointments are missed. We tested reminders manually. We measured the effect. We rejected one hypothesis. We found an intervention that works. We made it repeatable. We automated it.
X should remain disposable for as long as possible. Y is the commitment.
The old function promises certainty about X. The new function creates evidence about Y.
So please, stop asking, “How long will it take?” Start asking, “What is the fastest, cheapest thing we can do next to discover what actually solves Y?”.
This is how you keep your agency in a world where AI distorts every timeline:
- stay focused on the problem,
- don’t get attached to your solution
- seek evidence to decide what gets built