The Eval Gap in Enterprise AI
You bought the model.
You deployed it.
How do you know it works?
Most enterprises cannot answer that question. They have outputs, dashboards, and weekly demos. None of that proves anything. The system runs, the people watching it run feel reasonably good about it, and that is the entire quality process.
We call this the eval gap. It is the most underdiscussed problem in Canadian enterprise AI right now.
Why Evals Aren’t Optional
In normal software, a test suite tells you what works. In AI systems, the equivalent is an eval suite. That is a set of inputs paired with the behaviours you expect, run against the system on a schedule.
Without evals, you get three failure modes.
- Silent drift. The model degrades over time and nobody notices.
- Vendor lock-in. You can’t compare providers when you can’t measure either one. Buying the next platform becomes a leap of faith.
- Theatre. The system runs. Someone watches it run. That is the entire quality process.
Theatre is the most common of the three.
What Most Teams Do Instead
A typical enterprise quality process for an AI system looks like this.
- A product manager scrolls through outputs once a week, usually on the train home.
- An engineer fixes the few obvious failures they spot.
- Nobody writes anything down.
- The next sprint starts.
Call it quality assurance if you want. It does not catch silent failure, vendor drift, or anything else that matters.
If you can’t say what “working” means in numbers, you don’t have a system. You have a vibe.
What an Eval Suite Actually Contains
Evals are not exotic. The structure is simple.
| Component | What it is |
|---|---|
| Test inputs | Real examples your system will see in production |
| Expected output | What a correct answer looks like for each input |
| A grader | A rule, a script, or a model that scores the result |
| A schedule | The cadence you run the whole set against |
Start with twenty cases. Twenty real inputs, scored honestly, will tell you more than any dashboard.
What Procurement Should Ask
If you are buying an AI system, the eval gap is your problem the moment you sign.
- Can the vendor show you their eval suite?
- Do the test cases match your data, not a generic benchmark?
- How often do the evals run after deployment?
- Who sees the results when a score drops?
A vendor who cannot answer these is selling you theatre at enterprise prices.
Make It Measurable
You cannot manage what you refuse to measure. AI is no exception, and the absence of measurement is not neutral. It is a slow, expensive failure waiting for an audit.
The eval suite is the cheapest insurance in the building. Write the first twenty cases this week.
Software is only the surface. Infrastructure is the rest.
Build there.
ORKA Briefings are short, strategic readings on the systems shaping AI in Canada. For inquiries, visit orkaai.ca.
