A development process is a repeatable way to move from uncertainty to a useful result. For AI systems, it must include the human context, connected tools, operating rules, real-world testing, and a feedback loop.
Do not begin by building.
The easiest way to waste AI’s speed is to automate an unproven idea. Before Dani built the ecommerce system for Pause, she researched the customer problem, studied more than 35,000 posts and comments, and compared the ads and funnels already working in the market. The research shaped the product, offer, images, scripts, and first test.
The same principle applies to internal tools and operations. First understand what is creating the mental load. The obvious request—“I need a dashboard”—may hide a more valuable need: “I need to know which three things require my attention without checking six places.”
A practical seven-step process
1. Name the pain in ordinary language.
What feels slow, heavy, unclear, repetitive, expensive, or easy to forget? Describe the lived problem before translating it into product language.
2. Find the evidence.
Use interviews, messages, support requests, search behavior, competitor systems, analytics, and your own repeated frustration. Look for frequency, cost, urgency, and what people already try.
3. Map the work.
Identify the inputs, decisions, people, tools, outputs, exceptions, and risks. Notice where a person adds judgment and where they are only moving information.
4. Choose the smallest valuable outcome.
Do not build the whole operating system at once. Choose one complete path: one weekly content run, one morning business brief, one candidate profile, or one demand-validation research package.
5. Build with real context.
Use the actual files, language, examples, and edge cases. A prototype that only works on invented examples has not reached the business yet.
6. Test the handoffs.
Check what happens before and after the AI step. Can the system reach the tool? Is the right person notified? Is approval clear? Does the output arrive where the work continues?
7. Turn feedback into the next version.
When something is wrong, diagnose the layer: missing context, weak instruction, bad source, wrong rule, broken connection, or unclear interface. Fix that layer so the system learns.
A social system should not stop after generating captions. A complete first lane starts with research and guided questions, turns the creator’s natural speech into source material, produces the approved formats, publishes what is safe, and brings performance back into next week’s run.
The process ends when the work moves.
A prototype proves possibility. A functional system changes what happens on Monday morning. It runs with real inputs, respects the business rules, survives ordinary mess, and makes the next action clear.
That is also why Switchboards workshops are built around live work. You do not need more abstract information. You need someone in the room while the real connection fails, the output sounds wrong, or the business reveals that the original request was not the highest-return problem after all.