Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Three months ago, I watched a SaaS startup burn through $200K building an AI recommendation engine that literally zero customers ever used. The founder was convinced it would be their "ChatGPT moment." Instead, it became their most expensive lesson in product-market misalignment.
Here's the uncomfortable truth: most AI roadmaps are built around what's technically possible, not what customers actually need. I've seen this pattern repeatedly across client projects - teams get seduced by AI capabilities and forget to validate demand first.
After working with dozens of SaaS companies on their AI strategies, I've developed a framework that flips the traditional approach. Instead of "AI-first" thinking, we start with customer problems and work backward to determine if AI is even the right solution.
In this playbook, you'll discover:
Why traditional AI roadmapping fails 90% of the time
My customer-backward methodology that prevents expensive AI mistakes
The three validation checkpoints that save months of development time
Real frameworks for measuring AI feature success before you build anything
When to say no to AI (even when investors are pushing for it)
This isn't about avoiding AI - it's about building AI features that customers actually want and will pay for. Let's dive into why most teams get this backwards and what actually works in practice.
Industry Reality
What every AI-obsessed startup believes
Walk into any SaaS company today and you'll hear the same AI mantras repeated like gospel. "AI is the future, so we need to be AI-first." Leadership teams are convinced that without AI features, they'll become irrelevant overnight.
The typical AI roadmap process looks like this:
Technology Survey: Engineers research the latest AI capabilities - GPT models, computer vision, machine learning algorithms
Feature Brainstorming: Teams imagine how AI could enhance every part of their product
Competitive Analysis: They look at what other companies are building and try to match or exceed it
Technical Feasibility: The focus becomes "can we build this?" rather than "should we build this?"
Launch and Hope: Features get shipped with the assumption that customers will recognize their value
This approach exists because AI hype creates FOMO at the executive level. Investors ask about AI strategies. Competitors announce AI features. The pressure to "do something with AI" overrides basic product development principles.
The problem? This technology-first approach completely ignores customer demand validation. Teams spend months building sophisticated AI features that solve problems customers don't actually have. Or worse, they solve problems customers have but in ways that don't fit into existing workflows.
I've watched companies burn through entire funding rounds this way, building technically impressive AI that generates zero customer value. It's time for a different approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, I started working with a B2B SaaS company that had fallen into this exact trap. They were a project management platform with about 500 enterprise customers, and their leadership team was convinced they needed AI to stay competitive.
Their original plan was ambitious: an AI assistant that could automatically prioritize tasks, predict project delays, and generate status reports. The engineering team had already spent two months researching implementation approaches when they brought me in as a consultant.
The first red flag? They'd never actually asked customers what they wanted.
When I dug into their customer feedback and support tickets, I discovered something interesting. The #1 complaint wasn't about task prioritization or predictive analytics. It was about data silos - customers couldn't easily share project updates with stakeholders outside the platform.
But the leadership team was already committed to their AI vision. "Everyone's building AI," the CEO told me. "If we don't have these features, we'll lose deals." This is classic technology-first thinking - assuming customers want what's technically possible rather than validating actual demand.
I convinced them to pause development for one month to run customer validation interviews. The results were eye-opening: out of 50 customers interviewed, only 12% expressed interest in AI-powered task prioritization. Meanwhile, 78% wanted better integration and sharing capabilities.
This disconnect between internal assumptions and customer reality is everywhere in the AI space. Teams get excited about cutting-edge technology and lose sight of fundamental product-market fit principles. The solution isn't to avoid AI completely - it's to approach it with the same customer-centric validation you'd use for any other feature.
Here's my playbook
What I ended up doing and the results.
After seeing this pattern repeatedly, I developed what I call the Customer-Backward AI Framework. Instead of starting with AI capabilities and finding applications, we start with customer problems and evaluate if AI is the right solution.
Here's the step-by-step process I now use with every client:
Phase 1: Problem Validation (Week 1-2)
First, we identify the actual problems customers are experiencing. I run structured interviews with 20-30 existing customers, focusing on three key questions:
What tasks take the most time in your daily workflow?
Where do you feel like you're fighting against the current system?
If you had a magic wand, what would you automate first?
The goal isn't to mention AI at all. We're looking for genuine pain points that automation might solve.
Phase 2: Solution Mapping (Week 3)
Once we have validated problems, we evaluate potential solutions. AI is just one option alongside simpler alternatives like better UX, integrations, or basic automation. The question becomes: "What's the minimum viable solution to this problem?"
I use a simple framework:
Simple Automation: Can we solve this with basic rules and workflows?
Integration: Would connecting existing tools solve the problem?
AI Enhancement: Does the problem require pattern recognition or prediction that AI excels at?
Phase 3: Demand Testing (Week 4-5)
Before building anything, we test demand through what I call "Wizard of Oz" prototypes. We mock up the AI feature and manually deliver the results to a small group of beta customers. This validates both the solution approach and customer willingness to adopt it.
For the project management client, we manually generated the "AI" reports for 10 customers over two weeks. The feedback was immediate and actionable - customers loved getting the insights but wanted them integrated into their existing dashboards, not delivered as separate reports.
Phase 4: Build and Measure (Month 2-3)
Only after validation do we start building. But even then, we focus on the minimum viable AI implementation that delivers the validated value. No fancy machine learning if simple pattern matching works. No complex models if rule-based logic solves the problem.
The key insight: customers don't care about your AI technology - they care about outcomes. Sometimes the best "AI" solution is actually just better data analysis and presentation.
Problem Discovery
Run customer interviews focused on workflow pain points, not technology solutions. Ask about time-consuming tasks and daily frustrations.
Solution Validation
Test demand with manual "Wizard of Oz" prototypes before building any AI features. Validate both the approach and adoption willingness.
Minimum Viable AI
Start with the simplest solution that delivers validated value. Rule-based logic often beats complex machine learning for most business problems.
Success Metrics
Define clear, measurable outcomes that matter to customers. Track adoption, retention, and business impact - not just technical performance.
The results from this customer-backward approach have been consistently better than traditional AI roadmapping:
For the project management client: Instead of building complex AI prioritization, we created a simple integration that automatically shared project updates with stakeholder email lists. Development took 3 weeks instead of 3 months, and 89% of customers adopted it within the first month.
Across other client projects: This framework has prevented an estimated $2M in wasted AI development. Teams consistently discover that customers want simpler solutions than the AI features they originally planned.
The most surprising outcome? When we do build AI features using this approach, adoption rates are 3-4x higher than industry averages. Customers actually use features that solve validated problems, even if the underlying technology is less sophisticated.
One e-commerce client wanted to build AI-powered product recommendations. Through customer interviews, we discovered that their main issue was showing out-of-stock items in search results. We built a simple inventory-aware search filter instead of complex recommendation algorithms. Customer satisfaction scores improved by 23% with a fraction of the development effort.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I've learned from applying this framework across dozens of AI roadmap projects:
Customers don't want AI, they want solutions. Stop selling the technology and start selling the outcomes.
Simple solutions beat complex ones 90% of the time. Before building machine learning models, try rule-based logic and basic automation.
Wizard of Oz testing is your best friend. Manual delivery of "AI" results reveals adoption challenges before you build anything.
Customer interviews must avoid leading questions. Don't ask "Would you use an AI feature?" Ask "What takes too much time in your workflow?"
Integration often beats innovation. Connecting existing tools frequently solves problems better than new AI features.
Measure business outcomes, not technical metrics. AI model accuracy doesn't matter if customers don't adopt the feature.
Executive pressure doesn't change customer demand. Build what customers will use, not what investors want to hear about.
The biggest pitfall I see teams make? Skipping the manual validation phase because it feels "unscalable." But spending 2 weeks manually delivering results to 10 customers beats spending 2 months building features that no one wants.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS implementation:
Run customer interviews before any AI development
Test solutions manually with beta users first
Start with simple automation before complex AI
Focus on workflow integration over standalone features
For your Ecommerce store
For E-commerce stores:
Analyze customer support tickets for real pain points
Test AI features with small customer segments
Prioritize purchase funnel improvements over recommendations
Measure conversion impact, not just engagement metrics