Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Two years ago, I watched a promising B2B startup burn through €50,000 on an AI project that was supposed to "revolutionize their customer support." Six months later, they were back to square one with a demoralized team and a custom AI solution that worked worse than their original human-powered process.
This isn't another "AI is overhyped" rant. This is a real story about what happens when you chase the AI trend without understanding the fundamentals. As someone who's been deliberately avoiding the AI hype for two years to see what actually works, I've witnessed enough AI project failures to spot the patterns.
The problem isn't AI itself - it's how businesses approach AI implementation. Most companies treat AI like a magic solution rather than what it actually is: a pattern-recognition tool that needs massive amounts of high-quality data and careful integration to work properly.
Here's what you'll learn from this expensive lesson:
Why "AI-first" thinking leads to project failure
The real costs of custom AI development vs. existing solutions
How to evaluate if your business actually needs AI
A framework for AI projects that actually succeed
When to use existing AI tools vs. building custom solutions
Check out our AI implementation playbooks for more insights on what actually works in 2025.
Industry Reality
What the consultants won't tell you about AI failures
Walk into any startup accelerator or tech conference, and you'll hear the same AI success stories on repeat. Companies that "10x'd their efficiency" or "reduced costs by 80%" with custom AI solutions. These cherry-picked case studies create a dangerous narrative that AI is always the answer.
Here's what the AI consulting industry typically pushes:
AI-first strategy: Start with AI and work backwards to find problems it can solve
Custom solutions: Build proprietary AI models for competitive advantage
Big data assumption: More data always equals better AI performance
Automation everything: Replace human processes with AI wherever possible
Technical complexity equals value: More sophisticated models deliver better business outcomes
This advice exists because it's profitable for consultants and AI vendors. Custom AI projects are expensive, long-term engagements that justify high fees. The complexity creates dependency, ensuring ongoing revenue streams.
But here's the uncomfortable truth: most businesses don't need custom AI solutions. They need better processes, cleaner data, and often just existing tools used properly. The "AI revolution" narrative obscures the fact that 80% of business problems can be solved with simple automation or off-the-shelf software.
The real issue isn't whether AI works - it's whether your specific business problem actually requires AI to solve it. And most of the time, it doesn't.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, I was consulting with a B2B SaaS startup that was convinced they needed a custom AI solution for their customer support. They'd raised Series A funding and the board was pushing for "AI innovation" to justify their tech-forward positioning.
The company was handling about 200 support tickets daily with a team of 3 people. Response times averaged 4-6 hours, and customer satisfaction was decent at 4.2/5. Nothing was broken, but the CEO was convinced AI could automate 70% of responses and reduce their support costs dramatically.
The initial plan looked impressive on paper: build a custom natural language processing model trained on their specific product documentation and historical ticket data. The AI would categorize incoming requests, provide instant responses to common questions, and only escalate complex issues to humans.
They allocated €50,000 for development and hired an AI consulting firm that promised a 6-month timeline. The consultants were confident - they had "similar experience" and showed impressive demos with generic customer service scenarios.
Three months in, reality started hitting. The AI model needed more training data than expected. Their existing ticket history was inconsistent - different agents used different language, and many responses were incomplete or referenced verbal conversations. The "clean data" they thought they had was actually a mess.
The consultants recommended extending the timeline and budget to "properly clean and structure the data." What started as a 6-month project became 9 months. The €50,000 budget ballooned to €75,000 with additional data preprocessing work.
Meanwhile, customer support quality was declining. The team was spending time answering consultant questions instead of helping customers. Response times increased to 8-12 hours as resources were diverted to the AI project.
When the AI system finally launched, it was a disaster. The model correctly categorized only 60% of tickets and provided accurate responses to maybe 30%. Customers started complaining about robotic, irrelevant answers. The support team spent more time fixing AI mistakes than they would have spent just answering tickets manually.
Six months after launch, they quietly shut down the AI system and went back to their original human-powered process. €75,000 and a year of distraction for worse results than where they started.
Here's my playbook
What I ended up doing and the results.
This failure taught me a framework for evaluating AI projects that I now use with every client. It's not about whether AI can theoretically solve a problem - it's about whether AI is the right solution for your specific situation.
The Problem-Solution Fit Assessment
Before considering any AI project, I now run through this evaluation:
1. Define the Core Problem (Not the Solution)
In this case, the real problem wasn't "automating customer support." It was managing growing ticket volume with limited resources. The startup assumed AI was the answer without exploring alternatives.
I walked them through a proper problem analysis. Customer support wasn't actually their bottleneck - most tickets were repetitive questions that could be prevented with better onboarding and documentation. Instead of automating responses, they needed to reduce the need for support in the first place.
2. Evaluate Existing Solutions First
Before building anything custom, we researched what was already available. Tools like Intercom, Zendesk, and Help Scout had AI features that could handle their use case at a fraction of the cost.
We tested Intercom's Resolution Bot, which uses pre-trained models and required minimal setup. For €200/month, it could categorize tickets and provide automated responses to common questions. No custom development needed.
3. Calculate True Implementation Costs
Custom AI projects have hidden costs that nobody talks about:
Data cleaning and preparation (often 60-80% of the project)
Ongoing model training and maintenance
Integration with existing systems
Team training and change management
Performance monitoring and debugging
When we calculated the total cost of ownership over 3 years, the custom solution would have cost €200,000+ versus €7,200 for the off-the-shelf alternative.
4. Test with Manual Processes First
Instead of jumping into AI development, we implemented a manual version of their "AI workflow." One support agent spent a week categorizing tickets and creating template responses for common questions.
This manual test revealed that 40% of tickets were unique situations that required human judgment. No AI system would have handled these effectively. The remaining 60% could be addressed with better self-service resources and improved onboarding.
5. Implement Simple Automation Before AI
We started with basic automation using Zapier and their existing tools. Automatically routing tickets based on keywords, creating template responses for common scenarios, and setting up proactive email sequences reduced ticket volume by 30% without any AI.
This approach cost €500 to implement and saved 10 hours per week of support time.
Data Quality
Your AI is only as good as your data. Most companies overestimate data quality and underestimate cleaning costs.
Existing Solutions
Test off-the-shelf AI tools before building custom solutions. They're often 90% as effective at 10% of the cost.
Manual Testing
Always test your AI workflow manually first. If humans struggle with the process, AI will fail spectacularly.
True ROI
Calculate 3-year total cost of ownership, not just development costs. Maintenance often exceeds initial investment.
After implementing the simple automation approach, here's what actually happened:
Immediate Impact (First Month):
Ticket volume decreased 25% through better onboarding emails
Response time improved to 2-3 hours with automated routing
Customer satisfaction increased to 4.6/5
Total implementation cost: €500
6-Month Results:
Support team handled 300+ daily tickets with same 3-person team
Prevented tickets reduced overall volume by 35%
Template responses covered 45% of remaining tickets
No ongoing maintenance or training costs
The contrast with their original AI approach was stark. Instead of spending €75,000 on a system that worked 30% of the time, they spent €500 on automation that solved 70% of their problem immediately.
The startup saved the remaining AI budget and used it for actual product development. Their customer support became more efficient, not through artificial intelligence, but through intelligent process design.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This expensive lesson revealed patterns I now see in most failed AI projects:
1. Solution-First Thinking
Starting with "we need AI" instead of "we have this specific problem" leads to over-engineering. Most business problems are process problems, not technology problems.
2. Underestimating Data Requirements
AI needs clean, consistent, well-structured data. Most companies overestimate their data quality by 50-70%. Data cleaning often costs more than the AI development itself.
3. Ignoring Change Management
AI projects fail when teams resist adoption. People need training, processes need updating, and workflows need redesigning. This "soft" work is often harder than the technical implementation.
4. Misunderstanding AI Capabilities
AI is pattern recognition, not intelligence. It works well for repetitive tasks with clear patterns but fails with nuanced, contextual decisions that humans handle easily.
5. Custom vs. Off-the-Shelf Bias
Building custom AI feels innovative, but existing solutions are usually better. They're tested, maintained, and continuously improved by dedicated teams.
6. Hidden Maintenance Costs
AI models degrade over time and need retraining. What seems like a one-time development cost becomes an ongoing maintenance burden.
7. Perfectionism Over Progress
Waiting for AI to solve 100% of problems prevents implementing solutions that work 70% of the time immediately. Perfect is the enemy of good, especially with AI.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI:
Start with existing AI tools like Intercom or Zendesk before building custom
Test workflows manually first - if humans struggle, AI will fail
Calculate 3-year total cost including maintenance and training
Focus on preventing problems rather than automating solutions
For your Ecommerce store
For ecommerce stores considering AI:
Use platform-native AI features (Shopify, BigCommerce) before custom development
Test recommendation engines with existing apps first
Improve product data quality before implementing AI search or recommendations
Start with simple automation using Zapier before complex AI workflows