Your CEO just asked why the AI feature isn’t driving the growth you promised. That familiar sinking feeling washes over you as you realize you have no systematic way to answer. You’re flying blind, relying on “vibes” and customer complaints to understand if your AI is actually working.
I’ve lived these challenges. After building machine learning systems at companies like LinkedIn and Google, I’ve seen both spectacular successes and painful failures in AI product development. The difference between success and failure rarely comes down to the AI itself - it’s about building systematic evaluation and observable systems from day one.
Here’s the framework I use with B2B SaaS teams to transform their AI from a liability into a predictable strategic advantage.
Start with the Problem, Not the Solution
The most common mistake I see? Teams getting excited about AI capabilities and looking for problems to solve with them. This is backwards. AI is an expensive hammer - make sure you actually have a nail.
Before writing a single line of code, answer these questions:
- Who are your users and what specific pain points are they experiencing?
- How do they solve this problem today?
- What information do they use?
- How do they format and present results?
- Most importantly: how will you measure whether you’ve actually solved their problem?
Design for Observability from Day One
AI systems are fundamentally different from traditional software - they’re non-deterministic and data-dependent. This means you need to design for observability from the start.
When I led machine learning teams at LinkedIn, we learned (sometimes painfully) that you need to instrument everything:
- User inputs
- Intermediate processing steps
- Model outputs
- User interactions with results
- Explicit and implicit feedback
This instrumentation isn’t just nice to have - it’s essential for understanding where and why your system fails.
The Power of Starting Simple
Here’s a counterintuitive truth: your first implementation should be so simple it’s almost embarrassing. Why?
Because complex systems fail in complex ways. When you start with a complex solution, debugging becomes a nightmare. You have no baseline for comparison and no way to isolate problems.
Instead:
- Create a minimal viable implementation
- Instrument it thoroughly
- Establish baseline metrics
- Only then start adding complexity
Bootstrap Your Data Flywheel
The eternal chicken-and-egg problem: you need data to build a good system, but you need a working system to get data. Here’s how to break this cycle:
- Start with a small but diverse dataset (aim for ~50 examples)
- Use synthetic data generated by LLMs to supplement real data
- Create clear metrics aligned with desired outcomes
- Implement feedback loops to capture user interactions
The Science of Iteration
Once you have your foundation, improvement becomes a scientific process:
- Analyze performance across different user segments
- Form hypotheses about underperforming areas
- Make targeted changes
- Measure impact
- Repeat
The key is making one change at a time. Multiple simultaneous changes make it impossible to understand what’s actually working.
Build for Failure
Even the best AI systems fail sometimes. Plan for it:
- Implement graceful fallbacks
- Create clear paths for user feedback
- Monitor and investigate failures
- Maintain rapid iteration cycles
From Guesswork to Systematic Knowing
The framework above transforms your AI development from reactive firefighting to proactive building. Instead of hunting phantom bugs and explaining failures to your CEO, you’ll have the data-driven answers that demonstrate real business impact.
This isn’t just about better AI - it’s about building your team’s internal capability to systematically evaluate, debug, and improve AI systems. When your next board meeting asks about AI performance, you’ll confidently answer: “That is a known failure mode. And here’s the dashboard showing exactly how, why, and what we’re improving next.”
Ready to Stop Guessing and Start Knowing?
If you’re tired of flying blind with your AI and ready to build systematic evaluation into your product development process, I can help your team implement this framework in just one week.
My intensive workshop walks your engineering team through building a robust evaluation system using your own data, in your own codebase. You won’t just learn theory - you’ll walk away with a functioning system that gives you the clarity and control you’ve been craving.
Schedule a free consultation to discuss your team’s specific challenges →