If you've worked in finance or healthcare, you already know this: risk management isn't optional. It's the price of doing business. Compliance frameworks exist because regulators learned, the hard way, that companies will cut corners. So they force you not to. And honestly? It works.
AI evals are the same thing. They're not a nice-to-have. They're risk insurance. For regulated companies (banking, insurance, healthcare, anything with regulators), they're mandatory. For everyone else, they're still the cheapest insurance policy you can buy.
Regulated Industries: Evals Are Non-Negotiable
Let's say you're a fintech deploying an LLM for loan underwriting. Or an insurance company using it for claims assessment. Or a healthcare provider using it for triage. RBI, SEBI, SEC, FCA. They don't care how good your model is on average. They want three things:
- You can explain what it's doing: When the model makes a decision, can you actually explain it? Is there an audit trail?
- It's not biased: Are approval rates the same across gender, income level, geography? Or is it systematically unfair?
- It's consistent: Does it work the same way in a recession as a boom? For different demographic groups? Or does it fall apart under stress?
Evals prove all three. They're your evidence to regulators that you're monitoring the system, catching issues, and operating fairly. Without them, you're flying blind. The first time you hear something's broken is from regulators or angry customers. By then you've already caused damage.
The Risk Matrix: Evaluating the Cost-Benefit of Evals
This framework helps quantify why evals are risk reduction investments, not engineering costs:
Here's what matters: if you're regulated, evals aren't optional. They're the baseline.
What Regulators Actually Want to See
In India: RBI & SEBI
RBI put out guidelines on AI/ML in banking (2023). They want three things: transparency (your decisions are explainable), fairness (you're not discriminating), and oversight (you're monitoring continuously). Evals tick all three boxes. Same with SEBI on algorithmic trading. They want audit trails and testability. Evals are how you prove it.
In Europe & UK: AI Act
The EU AI Act basically says "appropriate testing and validation." That's eval language. The FCA says the same thing for financial firms. Evals are your evidence of "appropriate."
In the US: FDA, SEC, CFPB
FDA wants algorithm validation. SEC wants testing and monitoring. CFPB wants fairness testing. Different regulators, same requirement: demonstrate you're monitoring performance and catching problems.
In Healthcare: FDA & Clinical Validation
If you're using AI for clinical decisions, FDA clearance is required. Clearance requires clinical validation. Evals against clinical benchmarks are literally how you get cleared.
The Expected Value Math: Why Evals Make Economic Sense
Even if you're not regulated, the numbers work out.
Even spending $300K/year on evals, the math works. You're buying down your expected loss from $375K to $16K. That's $359K of expected loss reduction for a $300K cost. That's a 120% return right there, and we haven't even counted the other stuff.
Plus there's:
- Regulatory fines: Some jurisdictions fine 5-10% of annual revenue if you're not monitoring. That's not $375K, that's millions.
- Lawsuits: Build a biased model, get sued, legal costs alone can be $1M+.
- Downtime costs: Major failure, you roll back, your SaaS is down for 2 hours. That's $100K+ in lost revenue gone.
A Real Story: Caught Before Disaster
A fintech built an LLM for loan underwriting. They ran evals and caught something weird: approval rates weren't uniform across regions. One geography was getting 63% approval rates, another was 58%. Subtle, but consistent.
They catch this pre-launch. They fix the training data. They re-deploy. Nobody ever knows there was a problem. Evals cost them $80K.
Now imagine they'd skipped evals. Regulators find this during an audit. RBI fines them. Press covers it. Model gets retrained on their dime. Final cost: $3M+ and a hit to their reputation.
One eval investment. $80K. Saved them from a $3M+ disaster. The ROI on that one catch alone is absurd.
For Non-Regulated Companies: Different Argument, Same Conclusion
Maybe you're not regulated. You're still taking on risk if you don't have evals.
Think of Evals Like Insurance
You buy business insurance not because you think you'll need it. You buy it because if something bad happens and you don't have it, you're ruined. Same logic with evals.
You pay for evals because without them, the probability of a catastrophic failure times the cost is big enough that you can justify it on pure risk math. Nothing else matters.
The fact that evals also help you ship better models faster? That's a bonus. The real reason is risk. Just like insurance.
How to Sell This to Your CFO
Don't talk about engineering. Talk about risk and money.
Finance speaks ROI. Risk speaks probability and cost. Talk in their language and you get approval fast.
Where to Start: Prioritize by Risk
You don't eval everything. You start with highest risk first:
- What decisions get you fined if they're wrong?
- What decisions affect customer money or safety?
- What's publicly visible and could become a PR crisis?
- What could generate liability?
Build evals for those decisions first. Everything else is lower priority.
Bottom Line: You Can't Afford Not To
The pattern's becoming clear across the industry: shipping AI without evals is indefensible. Regulators expect them. Customers expect them. If your board's risk committee has any sense, they expect them too.
Regulated companies? This isn't a question. Evals are mandatory. For everyone else, they're the cheapest insurance policy you can buy. The expected value math is obvious. The competitive moat is obvious. The only question is why you'd wait.