# Workflow-first AI demand validation

> Start with one narrow AI workflow that solves a repeated job, then expand into a broader agent only after users pull for adjacent use cases.

- Canonical HTML: https://growth.iangoh.com/growth-ideas/workflow-first-ai-demand-validation/
- Source: [newsletter.posthog.com](https://newsletter.posthog.com/p/what-we-wish-we-knew-before-building)
- GrowthDex source hub: [PostHog Newsletter](/sources/posthog-newsletter-newsletter-posthog-com-2/)
- Last checked: 2026-05-26
- Rarity: rare
- Budget: low
- Channels: Product, Activation, Retention
- Stages: ai products, activation, validation, product scope


## Why this can grow

Early AI products often fail because they try to look general before they are useful. A narrow workflow gives users one clear reason to return and gives the team a cleaner way to study prompts, errors, and follow-up requests. Once users begin asking for nearby jobs inside that workflow, the team has evidence about where a larger agent would actually reduce work instead of just widening the demo.

## Ian's take

From scaling consumer platforms across MENA and Southeast Asia, my default is to distrust growth work that only looks good in a slide. My bias is to treat this as a small market test first. Make the audience narrow, make the promise concrete, and let the first real response decide whether it deserves more work. For activation, the useful question is not whether users liked the page. It is whether they got to the first meaningful win faster. For this tactic, I would watch one clear growth signal before putting more time or budget behind it.

## Action plan

1. Define one narrow startup segment where workflow-first ai demand validation can create a measurable lift.
2. Turn the tactic into one offer, page, campaign, or workflow for the Product and Activation channel.
3. Use the evidence from newsletter.posthog.com to set the first version of the message, format, and audience.
4. Launch a small test for 7 to 14 days with one success metric: one measurable growth signal.
5. Review the result, keep the winning message, remove weak variants, and turn the learning into a repeatable growth playbook.

## Source-backed example

Before making PostHog AI a broader agent, the team first shipped a workflow for data questions such as how many people signed up last week, then expanded only after users wanted adjacent actions like docs answers and feature-flag creation.

## Adjacent tactics in the same lane

- [Task-based model routing for AI speed](/growth-ideas/task-based-model-routing-for-ai-speed/) - same source, 3 shared channels, 1 shared stage
- [Suggested prompts in the empty AI state](/growth-ideas/suggested-prompts-in-empty-ai-state/) - same source, 2 shared channels, 2 shared stages
- [Weekly traces hour for agent quality](/growth-ideas/weekly-traces-hour-for-agent-quality/) - same source, 2 shared channels, 1 shared stage
- [Uncertainty, source, and progress cues in AI UI](/growth-ideas/uncertainty-source-and-progress-cues-in-ai-ui/) - same source, 2 shared channels, 1 shared stage

## Read GrowthDex essays

Browse the plain-English essay index at [GrowthDex Blog](/blog/).

## Related GrowthDex essays

- [AI products stop feeling smart when they hide their context](/blog/ai-products-stop-feeling-smart-when-they-hide-their-context/) - AI products, product-led growth, brand trust

## Advisory

If you want help turning this into a working growth system, Ian Goh offers advisory at https://iangoh.com/advisory.