Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like every developer's dream: build a sophisticated two-sided AI marketplace platform. The budget was substantial, the technical challenge was fascinating, and with today's AI tools, it would have been technically possible.
I said no.
Not because I couldn't deliver. The technology exists to build complex AI platforms faster than ever. But their core statement revealed a fundamental problem: "We want to test if our AI idea works."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm for AI technology. This conversation taught me something crucial about finding early adopters for AI solutions that most founders are getting completely wrong in 2025.
The AI space is flooded with solutions looking for problems, when it should be the other way around. After working with multiple AI startups and watching both successes and spectacular failures, I've learned that identifying early adopters for AI solutions requires a completely different approach than traditional SaaS.
In this playbook, you'll discover:
Why "testing if your AI idea works" is backwards thinking
The three types of AI early adopters and where to find them
My manual validation process that saves months of development
How to spot the difference between AI curiosity and AI urgency
Why building an AI MVP should be your last step, not your first
Stop building AI solutions that impress investors and start finding the people who are desperately waiting for what you're creating.
Market Reality
What the AI startup world gets wrong about early adoption
Walk into any AI startup pitch meeting today, and you'll hear the same narrative: "AI is transforming every industry, so our solution will have massive market opportunity." VCs nod, founders raise millions, and then... reality hits.
The conventional wisdom in AI startup land follows this playbook:
Build first, validate later - "AI technology is so powerful, people will see the value once we show them"
Target everyone initially - "Every business can benefit from AI automation"
Focus on the technology - "Our AI model is 15% more accurate than competitors"
Demo the magic - "Once they see what AI can do, they'll understand the value"
Enterprise-first approach - "Big companies have the budgets for AI transformation"
This approach exists because AI genuinely is transformative technology. The capabilities are real, the potential is enormous, and the success stories are compelling. But here's where this logic breaks down in practice:
AI early adopters aren't motivated by what's technically possible - they're motivated by what's immediately useful. The gap between "this AI is impressive" and "I need this AI right now" is where most AI startups die.
Enterprise customers, despite having bigger budgets, are actually terrible early adopters for AI. They want proven solutions, extensive documentation, and risk mitigation. They're not interested in being your beta testers, no matter how revolutionary your technology is.
The real AI early adopters are hiding in plain sight, but they're not where most founders are looking.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that potential client came to me wanting to "test if their AI idea worked," it reminded me of a pattern I'd seen before. Back when I was working with various SaaS projects, I noticed something crucial: the most successful ones started with identified pain points, not brilliant technology.
The difference was stark. Companies that succeeded had found specific groups of people who were already desperately trying to solve a particular problem - often with manual processes, spreadsheets, or inadequate existing tools. The technology became the solution to an urgent need, not a solution looking for a problem.
With AI projects, this principle becomes even more critical because AI can technically do so many things. That flexibility becomes a trap if you don't have clearly identified users who need exactly what you're building.
My experience with multiple projects taught me that early adopters for any new technology - whether it's a new SaaS tool or an AI solution - share three characteristics: they have an urgent problem, they've already attempted solutions, and they're actively looking for alternatives.
What I told that potential client was simple: before building any platform, spend one day validating demand. Not through surveys or focus groups, but through direct conversations with people who might use it. I recommended they find online communities where their target users were already discussing the problems they wanted to solve.
This approach had worked for other projects I'd consulted on. Instead of building elaborate solutions and hoping for adoption, we'd start by finding the conversations that were already happening. Whether it was business owners complaining about manual processes on Reddit, or professionals sharing workflow frustrations in Slack communities, or developers discussing repetitive tasks on GitHub.
The goal wasn't to pitch them an AI solution - it was to understand if their problem was urgent enough that they'd be willing to try something new to solve it.
Here's my playbook
What I ended up doing and the results.
Here's the systematic approach I developed for identifying genuine AI early adopters after working with multiple AI projects and seeing what actually works versus what sounds good in theory:
Step 1: Find the Complaints, Not the Compliments
Instead of looking for people excited about AI, I search for people frustrated with current solutions. I spend time in:
Industry-specific Reddit communities where professionals vent about workflow problems
Discord servers and Slack groups where people discuss daily operational challenges
Twitter threads about tool frustrations - especially replies where people share their own pain points
LinkedIn posts about process improvements where the comments reveal what's still broken
The key insight: early adopters for AI solutions are people who are already actively trying to solve a problem, not people who are generally interested in AI technology.
Step 2: The 48-Hour Rule
When I find someone complaining about a problem that AI could solve, I reach out within 48 hours while their frustration is fresh. Not to pitch an AI solution, but to understand their situation better.
My message template: "Hey, I saw your comment about [specific problem]. I'm researching this exact issue because I've heard it from several [industry] professionals. Would you be open to a 10-minute call to share more about how this affects your work? Not selling anything - just trying to understand the problem better."
Step 3: The Three Questions Framework
During these conversations, I ask three specific questions to identify if someone is a genuine early adopter:
"What have you tried so far to solve this?" - Early adopters have attempted multiple solutions
"How much time does this problem cost you weekly?" - Early adopters can quantify the pain
"If I could solve this perfectly, what would that be worth to you?" - Early adopters have considered the value of a solution
Step 4: Manual Before Automated
Before building any AI automation, I offer to solve their problem manually first. This serves two purposes: it validates that solving the problem creates real value, and it gives me data about what the AI solution actually needs to do.
For example, if someone needs help with data analysis, I'll do the analysis manually using their data. If someone needs content generation, I'll create content templates they can use. This manual approach reveals whether they actually use the solution and how they use it.
Step 5: The Minimum Viable AI
Only after proving manual demand do I introduce AI automation. But even then, I start with the simplest possible AI implementation - often just a well-prompted ChatGPT or Claude workflow - before building custom models or complex systems.
This approach has consistently identified genuinely interested early adopters rather than people who are just curious about AI technology.
Problem Hunters
Identify people already struggling with manual processes that AI could automate
Pain Quantifiers
Focus on users who can measure the time/cost impact of their current inefficient workflows
Solution Triers
Target those who've already attempted multiple tools or approaches to solve their problem
Manual Validators
Test demand by solving their problem manually before building any AI automation
Using this systematic approach, I've helped multiple AI projects identify their genuine early adopter base before spending months on development.
The most successful AI solutions I've consulted on followed this pattern: they found 10-20 people with the exact same urgent problem, solved it manually for 5-7 of them to prove value, then built the simplest possible AI automation to scale that manual process.
What's particularly interesting is how this approach filters out "AI tourism" - people who are curious about AI but don't actually need the specific solution you're building. By focusing on problem-first identification rather than technology-first excitement, the early adopters we found were more engaged, provided better feedback, and were willing to pay for solutions even in beta phases.
The timeline typically works like this: 1-2 weeks of community research to identify complaint patterns, 1-2 weeks of direct outreach and conversations, 2-4 weeks of manual solution testing, then development of the actual AI automation. This front-loads the validation but dramatically increases the success rate of the final product.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Start with problems, not technology - Find people complaining about inefficiencies before mentioning AI
Quality over quantity in early conversations - 10 genuinely frustrated people beat 100 casually interested ones
Manual validation prevents over-engineering - Solving problems by hand first reveals what automation actually needs to do
Urgency beats sophistication - People with urgent problems will use imperfect AI solutions if they provide immediate value
Community research trumps surveys - Organic complaints in professional communities are more reliable than formal market research
The 48-hour window is critical - Reaching out while frustration is fresh dramatically improves response rates
Early adopter characteristics are consistent - They've tried multiple solutions, can quantify pain, and understand value
The biggest lesson: AI early adopters are not AI enthusiasts. They're problem-havers who happen to have problems that AI can solve efficiently. Focus on the problem urgency, not the technology excitement.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI features:
Join industry Slack communities and Discord servers where your target users discuss workflow problems
Use the 48-hour outreach rule when finding relevant complaints
Test manual solutions before building AI automation features
Focus on users who can quantify time/cost savings from your AI solution
For your Ecommerce store
For Ecommerce businesses looking for AI early adopters:
Target store owners complaining about manual inventory, customer service, or content creation tasks
Look in ecommerce Facebook groups and Reddit communities for automation pain points
Offer manual services first to prove value before introducing AI automation
Focus on problems that cost measurable time or money to solve manually