You've Been Sitting on This Idea for Months and Part of You Is Wondering
Not out loud. But quietly. In the back of your mind, especially late at night when the excitement fades and the doubt creeps in.
Is this actually a good idea? Or have I just convinced myself it is because I've been thinking about it for so long?
That's a completely normal place to be. The entrepreneurs who pretend they never doubt their ideas are either lying or haven't thought deeply enough about the competition.
The good news is that in 2026, you don't have to guess. Validation is faster, cheaper, and more accessible than it has ever been. You can get meaningful answers before writing a single line of code or spending a dollar on development.
Here's how.
First, Understand What You're Actually Validating
Most founders make the mistake of treating "is my idea good?" as a single question. It's actually three separate questions, and they require different types of evidence:
Question 1: Is the problem real? Do people actually experience the pain your product solves, or are you projecting a problem onto a market that has already adapted around it?
Question 2: Is your solution the right answer? Even if the problem is real, is AI the best way to solve it? Could a simpler tool do the same job?
Question 3: Will people pay for it? This is the question most founders skip. Enthusiasm from potential users is very different from willingness to put a credit card down.
Each question requires different evidence. A great idea can fail on any one of them.
Step 1: Get Honest About the Problem
Start with the people who currently have this problem. Talk to ten of them. Not surveys, not polls: actual 30-minute conversations.
The questions that matter most:
- "Walk me through the last time this problem cost you time or money."
- "What are you doing right now to deal with it?"
- "How much is the current workaround costing you, in time, in money, or in errors?"
- "If this problem disappeared tomorrow, what would change for you?"
You're looking for specificity. People who can give you exact examples ("I spend four hours every Monday pulling this data manually") are experiencing the problem deeply. People who say "oh yeah that's definitely an issue" are not.
Artheon Medical's problem was specific enough to measure: hundreds of hours per month processing Explanation of Benefits documents by hand. That kind of quantifiable pain is a strong signal that a solution has real value. When FeatherFlow built PureClaim for them, they weren't guessing at the problem. They were solving something the client could express in exact hours and exact dollars.
If the people you interview can't quantify the problem, either the pain isn't deep enough or you're talking to the wrong people.
Step 2: Map the Existing Solutions (Be Ruthless)
Before building anything, understand what people are already using to solve this problem. There are three categories:
Direct competitors: Products that do what you're planning to do. Search deeply. Use Google, Product Hunt, G2, Capterra, and industry-specific directories. If you can't find any competitors, either you've found a genuine gap or you're solving a problem that doesn't exist at scale.
Indirect competitors: Adjacent solutions people hack together. Spreadsheets, manual processes, generic software used in ways it wasn't designed for. This is often where the best AI opportunities live: the industries where the "solution" is still a person doing repetitive work that a well-designed AI system could automate.
Internal tools: Large enterprises sometimes build this internally and don't sell it. If this is common in your target market, it confirms the problem is real and that companies are willing to invest in a solution. It also means your market exists.
Write this out honestly. If there's a well-funded competitor with 10,000 customers, you need to know that before you start. If there are indirect competitors but nothing purpose-built, that's a genuine opportunity.
Step 3: Test the Solution Before Building It
This is the step most business founders skip, and it's the most valuable one.
Before writing any code, describe your product to potential users and ask them if it solves their problem. Be specific about what it does. Show mockups if you have them. Describe the workflow.
Then ask the question that reveals everything: "If this existed today and cost $X per month, would you buy it?"
The word "would" is doing a lot of work in that sentence. "Would you buy it" requires them to imagine committing. "Do you think this is a good idea?" requires nothing from them and means nothing.
Even better: ask for a letter of intent. "If you'd be willing to be one of our first customers, would you sign a non-binding letter saying you'd pay $X per month for this?" The friction of signing anything, even a non-binding document, separates genuine interest from polite encouragement.
If you can collect five to ten letters of intent at a price that makes the business work, you have strong evidence. If everyone says the idea is great but nobody will sign anything, you have a marketing problem or a pricing problem, and you want to discover that now.
Step 4: Validate the AI Component Specifically
AI products have a validation layer that traditional software doesn't: does the AI actually work well enough to be useful?
There are two common failure modes:
The AI is too inaccurate: If a document processing system is 70% accurate, it creates more work than it saves, because someone still has to check and correct the output. Users will abandon it fast.
The AI is accurate on your test data but not on real data: Models often behave differently when exposed to the full messiness of real-world inputs. Your clean test files don't represent the badly scanned PDFs, inconsistent formatting, and edge cases that real users will throw at it.
The best way to validate this is a proof of concept. Before building the full product, test the core AI component with real data from a real potential customer. Build the minimum possible wrapper around it, share it with one user, and measure accuracy under real conditions.
FeatherFlow did exactly this for PureClaim: they built a proof of concept to validate the extraction logic before scaling it into the full SaaS product. That decision meant they knew the AI worked before they committed to the full architecture.
A two-week proof of concept can save four months of building the wrong system.
Step 5: Run a Fake Door Test (Optional but Powerful)
This is the laziest form of validation and sometimes the most informative.
Build a landing page that describes your AI product. Include a "Get Early Access" or "Join the Waitlist" button. Run a small amount of paid traffic to it (Google Ads targeting the search terms your future customers use). Measure signups.
You're not deceiving anyone: the landing page describes something you're building, and the waitlist is real. You're just testing market interest before committing to development.
A landing page with a compelling headline and a working contact form can be live in a weekend. Ad spend of $500 to $1,000 over two weeks can give you a meaningful signal on conversion rate. If 3 to 5% of visitors sign up for the waitlist at a price-point you mention explicitly, the market interest is real.
If nobody signs up, that's data too, and it's data you want to have now.
Step 6: Get a Strategy-First Partner to Pressure-Test It
One of the most underused validation tools is a strategic conversation with someone who has shipped similar products before.
A product studio that specializes in AI products has seen dozens of ideas across different industries. They know which patterns work and which look promising but don't survive contact with users. A 30-minute discovery call with the right team is not just a sales meeting: it's a perspective from someone who has seen this before.
FeatherFlow, for example, positions strategy as the first step before anything is built. Their discovery process starts with business goals, market fit, and user needs before a single design or line of code. That philosophy, clarifying before building, is how they avoid the expensive rebuild cycle that plagues teams that jump straight into development.
A conversation like this costs you nothing and can surface blind spots that weeks of solo research would miss.
What Good Validation Evidence Looks Like
After two to four weeks of validation, you should have:
- Interviews with 10+ potential users who confirmed the problem with specific examples and quantified pain
- A competitive landscape document showing where your product fits and why it's better than what exists
- Letters of intent or pre-signups at a price that makes the business viable
- A proof of concept result showing the AI component works on real data with acceptable accuracy
- A landing page conversion rate that suggests market interest
You don't need all five. Two or three of these, done well, is enough to make a confident decision.
When to Stop Validating and Start Building
Validation has a point of diminishing returns. More interviews don't make the decision easier after a certain point: they just delay it.
The signal to move forward is not certainty. It's enough evidence to make a bet you can afford to be wrong about.
If you have conversations showing real pain, two or three letters of intent at a workable price, and a proof of concept showing the AI works accurately, you have more evidence than most funded startups had at their first funding round.
The signal to pause is not doubt: doubt is always present. The signal to pause is when the evidence consistently points in the wrong direction: nobody can quantify the pain, existing solutions satisfy the need, or the AI component doesn't perform accurately enough to be useful.
The Cost of Not Validating
Skipping validation and going straight to development is the single most expensive mistake in product development. Not because development costs a lot (though it does), but because six months of work in the wrong direction is six months of your life and your competitor's opportunity.
The founders who validate first consistently launch better products faster. They know what users actually need before building. They have customers waiting before launch. They spend their development budget on the right features.
Two weeks of validation before development costs essentially nothing. It saves resources, time, and the particular kind of exhaustion that comes from building something nobody uses.
Frequently Asked Questions
What if my idea is in a niche industry where I can't find many people to interview?
Niche markets are often better opportunities, not worse ones. Ten interviews in a niche industry where you have existing relationships is more valuable than ten interviews with strangers in a crowded market. Use your professional network, LinkedIn, and industry events to find the right people. Quality of interviews matters more than quantity.
What if my idea has obvious competitors?
Competition is evidence that the market exists. The question is whether you can serve a segment better, enter with a meaningfully different approach, or build for a customer the incumbents are ignoring. Compete by being more specific, not by being more general.
How do I interview people without giving my idea away to a competitor?
Share the problem space, not the solution. "I'm researching how companies handle EOB document processing" reveals nothing proprietary. Most people are happy to discuss their workflow problems. You're not required to reveal your solution to get useful information about the problem.
What if validation shows the idea needs to change significantly?
That's the best possible outcome of validation. An idea that pivots based on evidence before development is an idea that survives. An idea that stays rigid through development and fails in market is an expensive lesson.
Do I need a technical co-founder to validate?
No. Everything in this guide can be done by a non-technical founder. A landing page, customer interviews, competitive research, and letter-of-intent collection require zero engineering. Even a proof of concept can be outsourced to a studio for a few thousand dollars without committing to a full build.
Conclusion
The doubt you feel about your AI idea is not a reason to delay. It's a reason to validate.
Two to four weeks of structured validation will either give you the confidence to move forward or save you from an expensive detour. Both are excellent outcomes.
Start with the interviews. Map the competition. Test the AI component early. Talk to someone who has shipped similar products.
By the time you're ready to hand over a build budget, you won't be wondering if the idea is good. You'll know.