Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I watched a brilliant AI startup with $2M in funding completely implode. Their ML model was technically perfect – 95% accuracy, lightning-fast inference, beautifully architected. The problem? They built a solution looking for a problem.
This isn't uncommon. While everyone talks about product-market fit, the real challenge with AI and ML projects is something deeper: customer-solution fit. It's not just about whether people want your product – it's about whether your ML model actually solves the right problem in the right way for the right people.
Through my work with B2B SaaS clients implementing AI features and helping startups validate their ML-driven products, I've seen this pattern repeat: teams focus on model performance while completely missing what customers actually need.
Here's what you'll learn from my experience:
Why traditional product-market fit frameworks break down with ML models
The 3-layer validation system I use to test customer-solution fit before building
Real examples of how I've seen AI features succeed (and fail) in SaaS products
A practical framework for testing ML solutions with minimal technical investment
Why customer interviews for AI products require a completely different approach
If you're building AI-powered features or considering ML for your startup, this isn't about the technical side – it's about making sure you're solving a problem people will actually pay for. Let me show you how to get this right from my AI automation playbooks and client work.
Industry Reality
What every AI startup founder believes
The AI industry is obsessed with the wrong metrics. Walk into any accelerator demo day, and you'll hear the same pitch format: "Our model achieves X% accuracy with Y millisecond latency on Z benchmark dataset." VCs nod approvingly. Founders think they're onto something big.
Here's what the conventional wisdom tells you about validating AI products:
Focus on model performance first - Get your accuracy metrics perfect before thinking about customers
Build impressive demos - Show off your AI capabilities with flashy proof-of-concepts
Target "AI-ready" customers - Find companies that are already sold on AI adoption
Lead with the technology - Position your ML model as the hero of your story
Iterate on features - Add more AI capabilities to increase value
This approach exists because the AI space is still relatively new, and most advice comes from researchers and technologists rather than people who've actually had to sell AI products to skeptical customers. The startup ecosystem rewards impressive technology over market validation.
But here's where this conventional wisdom falls short: customers don't buy AI models – they buy solutions to specific problems. A 95% accurate model that solves the wrong problem is worthless. A 70% accurate model that saves someone 10 hours per week is pure gold.
I've seen too many startups with incredible AI technology struggle to find customers who actually want to pay for it. The issue isn't the quality of their ML models – it's that they never validated whether the problem they're solving is worth solving in the first place.
The traditional product-market fit framework assumes you know what problem you're solving. With AI, the technology often comes first, and the problem discovery happens backward. That's why customer-solution fit is the real challenge.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My perspective on this comes from working with AI-powered B2B SaaS clients and watching both successes and spectacular failures. The most eye-opening experience was working with a client who had built what they called an "AI-powered content optimization platform."
Their technology was genuinely impressive – natural language processing that could analyze content performance, predict engagement rates, and suggest optimizations. The founder had a PhD in machine learning, and the model's predictions were consistently accurate. They'd raised seed funding and had built a beautiful product.
The problem? After six months of sales efforts, they had exactly three paying customers.
When I started working with them to understand why their user acquisition was failing, I discovered something fascinating: their target customers (content marketing teams) weren't actually struggling with content optimization. They were struggling with content production volume.
The team was so focused on building the most sophisticated AI model possible that they never validated whether content optimization was a priority pain point. Their customer interviews were all about "Do you want better content performance?" (of course everyone said yes) instead of "What's your biggest bottleneck in content marketing?"
This taught me that AI product validation requires a fundamentally different approach. You're not just validating whether people want your product – you're validating whether they want to change their workflow, whether they trust AI with that specific task, and whether the problem you're solving is actually their number one priority.
Through various client projects implementing AI features and helping startups pivot their AI products, I developed what I now call the Customer-Solution Fit framework specifically for ML models. It's different from traditional product-market fit because AI introduces unique variables: trust, workflow disruption, and the gap between what AI can do and what customers think it can do.
Here's my playbook
What I ended up doing and the results.
Based on my experience with AI implementations, here's the framework I use to test customer-solution fit before building any ML model:
Layer 1: Problem Priority Validation
This is where most AI startups fail. Instead of asking "Would you use AI for X?" I ask "What are the three biggest time-wasters in your current workflow?" If the problem your AI solves doesn't come up unprompted in the top three, you're solving the wrong problem.
I use a specific interview technique: the "Day in the Life" walkthrough. I have potential customers walk me through their actual daily workflow, noting every friction point and time sink. Only after mapping their entire process do I mention the AI solution. This approach revealed that the content optimization client's customers spent 80% of their time on content creation, not optimization.
Layer 2: Workflow Integration Testing
AI doesn't exist in a vacuum – it has to fit into existing workflows. Before building any ML model, I create a "Wizard of Oz" prototype where humans manually perform the AI tasks while customers use the interface.
For example, with a client building AI-powered sales email personalization, we manually researched prospects and wrote personalized emails while the sales team used our interface. This revealed that the real bottleneck wasn't email personalization – it was lead qualification. The sales team was already good at writing emails; they were bad at identifying which prospects were worth emailing.
Layer 3: Value Measurement Framework
With AI products, you need to establish clear value metrics before you build. I use a three-part measurement system:
Time saved: Quantify exactly how many hours per week your solution saves
Quality improvement: Define specific quality metrics that matter to customers
Cost reduction: Calculate direct cost savings or revenue increases
The key insight from my client work: customers need to see at least 10x improvement in one of these areas to justify switching to an AI-powered solution. The friction of adopting new AI tools is much higher than traditional software.
The Manual-First Approach
Here's my most counterintuitive recommendation: never build the AI model first. Instead, I help clients deliver the end result manually while collecting data on what customers actually value.
One client wanted to build AI-powered competitive analysis for SaaS companies. Instead of training a model, we spent three months manually creating competitive analysis reports for 20 target customers. This taught us which insights customers acted on, which formats they preferred, and what data sources they trusted.
Only after proving manual demand did we build automation. This approach has a 90% higher success rate in my experience because you're building AI to scale a proven solution, not hoping to find product-market fit with an unproven solution.
Manual Validation
Test demand with human-delivered results before building any AI model to prove value first
Workflow Integration
Map existing customer processes to identify where AI actually fits vs. where you think it should fit
Value Metrics
Establish 10x improvement benchmarks in time/quality/cost - anything less won't justify AI adoption friction
Trust Building
Customers need proof that AI won't break their existing workflows before they'll consider switching
The results from this approach have been consistently better than traditional AI product development. The content optimization client I mentioned earlier pivoted to AI-powered content creation after discovering that was the real bottleneck. They went from 3 paying customers to 47 customers in four months.
The key metrics that improved:
Customer interview quality: 70% of customers now mentioned the core problem unprompted vs. 15% before
Sales cycle length: Reduced from 6 months to 2 months average
Feature usage: 80% of customers used the core AI feature daily vs. 20% for their original optimization tool
Retention rates: 6-month retention improved from 35% to 78%
The most surprising outcome was that customers were willing to pay 3x more for the content creation solution than the optimization solution, even though the optimization AI was technically more sophisticated.
This reinforced my belief that customer-solution fit isn't about building the most impressive AI – it's about solving the highest-priority problem in the customer's workflow. The technology should be invisible; the value should be obvious.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven key lessons from implementing customer-solution fit validation for ML models:
Customers lie about wanting AI - They'll say yes to AI features in interviews but won't pay for them in reality
Workflow disruption is your biggest enemy - Even superior AI solutions fail if they require too much behavior change
Manual validation is faster than model training - Spend 3 months proving demand manually instead of 6 months building
Trust comes from consistency, not accuracy - Customers prefer 80% accuracy that's predictable over 95% accuracy that's inconsistent
The problem behind the problem matters most - Surface-level pain points often hide deeper workflow issues
Value metrics must be customer-defined - Your technical metrics rarely align with what customers actually value
AI adoption friction is 10x higher than regular software - Plan accordingly in your validation process
What I'd do differently: I would spend even more time in the problem validation phase. The biggest mistake I see AI startups make is rushing to build because the technology is exciting. The technology should be the last thing you build, not the first.
When this approach works best: B2B SaaS companies adding AI features, AI-first startups in established markets, and any ML project where customers need to change existing workflows. It's less critical for consumer AI where adoption friction is lower.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this playbook:
Interview 50+ customers before building any AI features
Use "Day in the Life" workflow mapping to identify real bottlenecks
Test AI solutions manually for 3+ months before automation
Establish 10x value improvement benchmarks early
For your Ecommerce store
For Ecommerce stores considering this approach:
Focus on operational bottlenecks like inventory or customer service
Test AI personalization with manual curation first
Measure customer behavior changes, not just technical metrics
Start with internal workflows before customer-facing AI