Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, I was having coffee with a fintech founder who just burned through $2M on an AI project that never shipped. "We hired the best AI talent, bought the most expensive tools, followed every best practice guide," he told me, staring into his coffee. "But somehow, we built something nobody wanted."
This conversation reminded me why I've become increasingly skeptical of the AI hype in finance. Everyone's rushing to add AI to everything, but most projects are failing spectacularly. The problem isn't the technology—it's that we're treating AI like a magic solution instead of what it actually is: a very powerful pattern recognition tool that needs specific conditions to succeed.
After 6 months of deep diving into AI implementations across various clients, I've seen the same mistakes repeated over and over. The finance industry is particularly vulnerable because everyone's convinced they need AI yesterday, but nobody's asking the right questions first.
Here's what you'll learn from the trenches:
The 3 critical mistakes that doom 80% of finance AI projects before they start
Why "AI-first" thinking is backwards and what to prioritize instead
The exact framework I use to evaluate if an AI project will actually work
Real failure case studies and what went wrong
How to structure AI experiments that minimize risk while maximizing learning
This isn't another "AI is the future" article. This is about why most AI projects fail and how to be in the 20% that succeed. Check out our complete AI strategy playbooks for more insights on intelligent implementation.
Industry Reality
What the finance industry believes about AI
Walk into any fintech conference today and you'll hear the same mantras repeated like gospel:
"AI will revolutionize finance" - Every speaker starts with this. Machine learning will automate trading, AI will detect fraud perfectly, chatbots will replace customer service, and predictive analytics will eliminate risk. The promise is seductive: plug in AI and watch your business transform overnight.
"You need AI to stay competitive" - The fear-based selling is everywhere. Consultants warn that companies without AI strategies will be obsolete by 2026. Investment firms are prioritizing "AI-enabled" startups. Everyone's scrambling to add AI to their pitch decks.
"Start with your data and find AI use cases" - The standard advice is to audit your data, identify patterns, and apply machine learning. Most consulting frameworks follow this approach: data → insights → AI implementation → profit.
"Hire AI talent and they'll figure it out" - Companies are paying premium salaries for data scientists and AI engineers, assuming that smart people with the right tools will naturally create valuable solutions.
"AI projects should pay for themselves quickly" - There's an expectation that AI implementations will show immediate ROI, often within 6-12 months, through cost savings or revenue increases.
This conventional wisdom exists because it sounds logical. AI is powerful technology, finance has lots of data, therefore AI + finance = success. The consulting industry has built entire practices around this narrative because it's what clients want to hear.
But here's where it falls apart: this approach treats AI as a solution looking for problems, rather than addressing real business problems that might benefit from AI. Most companies end up building technically impressive systems that solve the wrong problems, or solve the right problems in ways that don't fit their actual workflow.
The result? Expensive failures that make everyone gun-shy about AI's real potential.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My perspective on AI failures in finance comes from watching multiple attempts go sideways across different client projects. I'm not talking about academic theories or industry reports—I'm talking about real money, real deadlines, and real consequences when things don't work.
The turning point for me was working with a B2B SaaS client who wanted to "add AI" to their financial analytics platform. They'd raised a Series A partly on the promise of machine learning capabilities, and investors were asking pointed questions about when these features would ship.
The founders came to me with what seemed like a straightforward request: help them implement AI-powered predictions for their users' cash flow forecasting. They had user data, transaction histories, and a team excited to build something cutting-edge. On paper, it looked perfect.
But as I dug deeper into their actual user behavior and business model, red flags started appearing everywhere. Their users were mostly small business owners who barely trusted their existing dashboard, let alone AI predictions about their money. The data they had was messy, incomplete, and often inaccurate because users weren't consistently categorizing transactions.
More importantly, when I interviewed their most engaged customers, none of them were asking for AI features. They wanted better reporting, simpler workflows, and more reliable integrations with their existing tools. The AI project was solving a problem that existed more in the boardroom than in actual user workflows.
This experience made me realize that most AI failures in finance aren't technical failures—they're product-market fit failures. Companies build sophisticated solutions that work perfectly in demos but fall apart when real users try to integrate them into their daily operations.
That's when I started approaching AI projects differently, and it's probably saved my clients hundreds of thousands of dollars in avoided failures.
Here's my playbook
What I ended up doing and the results.
After watching multiple AI projects crash and burn, I developed what I call the "Problem-First AI Framework." Instead of starting with the technology and finding applications, I start with genuine business problems and evaluate whether AI is actually the right solution.
Step 1: The Reality Check Audit
Before any AI discussion, I force clients through an uncomfortable exercise: prove that people actually want what you're planning to build. For the financial SaaS client, this meant conducting user interviews about their current pain points. The results were eye-opening—users were struggling with basic features, not craving AI predictions.
I require three pieces of evidence before moving forward:
At least 10 customers explicitly asking for this specific capability
Current manual processes that are genuinely painful and time-consuming
Clear metrics showing the problem costs more than the solution
Step 2: The Manual-First Prototype
Here's where I break from industry convention: I insist on building the solution manually first. No AI, no automation, just humans doing the work the AI would eventually do. This reveals whether the core logic actually works and whether users find value in the output.
For the cash flow prediction project, we spent two weeks manually analyzing transaction patterns for five pilot customers and providing personalized forecasts. The results were telling: customers appreciated the insights, but they needed them presented differently than we'd planned, and they wanted explanations for every prediction.
Step 3: The Minimum Viable AI Test
Only after proving manual value do I introduce AI—but in the smallest possible increment. Instead of building a comprehensive prediction engine, we started with simple pattern recognition for categorizing transactions. This solved an immediate user pain point while generating the clean data needed for future AI features.
The key insight: AI should enhance existing workflows, not replace them entirely. Users need to understand what the AI is doing and maintain control over the outcomes.
Step 4: The Gradual Intelligence Scaling
Rather than launching with fully automated AI, I implement what I call "AI with training wheels." The system makes suggestions, but users approve or reject them. This builds trust while improving the algorithm with real user feedback.
For financial applications, this approach is crucial because users need to understand and trust AI recommendations before they'll act on them. A prediction engine that's 95% accurate but incomprehensible is worse than a 80% accurate system that users understand and trust.
This gradual approach has helped multiple clients avoid the "black box" problem that kills most finance AI projects. Users feel in control, adoption rates are higher, and the AI actually improves based on real usage patterns rather than theoretical models.
Technical Validation
Test core assumptions with minimal AI before building complex systems
User Acceptance
Build trust through transparent AI that users can understand and control
Business Logic
Ensure AI enhances existing workflows rather than replacing them entirely
Risk Management
Use gradual scaling to minimize downside while maximizing learning opportunities
The manual-first approach revealed something crucial: the financial predictions users actually wanted were different from what we'd planned to build. Instead of complex 90-day forecasts, they needed simple "cash flow alerts" for the next 7-14 days.
More importantly, by starting with manual processes, we identified data quality issues that would have undermined any AI implementation. Users were miscategorizing 40% of their transactions, which meant our beautiful machine learning models would have been trained on garbage data.
The gradual scaling approach led to much higher adoption rates. Instead of the typical 15-20% adoption rate for new finance features, we saw 60%+ engagement because users understood and trusted what the system was doing. The AI felt like a helpful assistant rather than a mysterious black box.
This methodology has been applied across multiple client projects with similar results: fewer failures, higher adoption, and AI implementations that actually solve real problems rather than impressive-sounding fake ones.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson: AI is not a product strategy, it's a tool. Most finance AI failures happen because companies treat AI as the destination rather than the vehicle. The goal should be solving genuine user problems, with AI as one possible approach among many.
Timing matters more than technology. Even perfect AI solutions fail if users aren't ready for them. In finance, trust and adoption curves are slower than in other industries. Users need to be comfortable with basic features before they'll trust advanced AI.
Data quality beats algorithm sophistication every time. The most common failure mode is building complex models on bad data. It's better to have simple analytics on clean data than machine learning on messy data.
Manual validation saves millions. Every AI project should start with humans doing the work manually. This reveals whether the core assumptions are correct and whether users actually want the output.
Transparency is a feature, not a bug. Finance users need to understand AI recommendations before they'll act on them. "Explainable AI" isn't just a nice-to-have—it's essential for adoption.
Start small, scale gradually. The biggest failures come from trying to automate entire workflows at once. Better to nail one small use case and expand from there.
User education is part of the product. Don't assume users understand what your AI is doing or why they should trust it. Education and onboarding are crucial for AI adoption in finance.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS founders considering AI features:
Start with user research, not technology research
Build manual prototypes before any AI development
Focus on explainable AI that builds user trust
Implement gradual scaling with user feedback loops
For your Ecommerce store
For ecommerce businesses evaluating AI investments:
Prioritize data quality over algorithmic complexity
Test AI recommendations manually with real customers first
Ensure AI enhances existing workflows rather than replacing them
Focus on transparent AI that customers can understand