The Practical Guide to Evaluating Model Performance in the Organization
Implementing an AI model in an organization is a bit like hiring a new employee for a sensitive role: if you don’t objectively test their skills before they start, you’re taking an unnecessary management risk.
In recent years, we’ve all grown accustomed to “playing” with chatbots. It’s nice, it’s impressive, and sometimes it even helps draft an email in English. However, when moving from the personal space to the organizational space—where AI becomes an integral part of decision-making, strategic data analysis, or customer service—”nice” is no longer enough. We need to know, in hard numbers and metrics, the level of accuracy and reliability of the system.
Many managers turn to us at the Sarid Institute with the same question: “How can I trust this model before I give it access to my data or my customers?” The answer isn’t a gut feeling, but a structured Evaluation methodology.
Step One: What are we actually measuring?
Before rushing into the technology, you must define what the model is supposed to perform. In the worlds of market research and surveys, we typically see two main types of tasks:
1. Classification Tasks
-
Example: Automatically categorizing thousands of open-ended answers in market research into categories like “Price,” “Service,” or “Quality.”
-
The Metric: We don’t just look at a general “accuracy percentage.” We build a confusion matrix and check two critical metrics:
-
Precision (True Positive Rate): When the model said an answer belongs to the “Service” category, how often was it actually right?
-
Recall (Sensitivity): Out of all the answers that truly dealt with “Service,” how many did the model manage to catch?
-
-
Why it matters: Precision helps us understand how much “noise” enters our results, while Recall helps us understand how many classifications we missed.
2. Generative Tasks
-
Example: A model that summarizes in-depth interviews or extracts insights from presentations.
-
The Metric: Here, the challenge is greater because the answer is free text. The professional way to evaluate this is through a “Gold Standard” (Reference Set).
-
The Method: We create a test group where a human expert writes the ideal analysis, and then we compare the AI’s output against this reference using linguistic metrics or a “judge” (a person or a stronger model) who examines factual accuracy and relevance.
The Iron Rule: Separation of Learning and Testing (Train vs. Test)
This is where many organizations fail. There is a tendency to test the model on the same examples used to “train” it or explain what is wanted.
This is exactly like giving a student the exam with the answers in advance—it doesn’t indicate intelligence, but rather memory.
True evaluation must be performed on data the model has never seen. We divide the data in advance:
-
Train Set: Used to calibrate the model and explain the business logic.
-
Test Set: “Clean” data kept aside. We only run the final test on this set.
-
The Result: The score obtained here is the true grade the model will provide in the real world.
Who Determines the Truth?
To evaluate a model, you need something to compare it to. At the Sarid Institute, creating this reference is the heart of our professional work. We use two central methods:
-
Human Evaluation: Our team of experts manually reviews a representative sample and determines the correct answer. This requires resources, but it is the most accurate measure that exists.
-
LLM-as-a-judge: Using very strong and expensive models (such as GPT-5.2, Gemini 3, or Claude 4.6 Opus) to audit smaller, faster models implemented in the organization for cost savings and response time.
Bottom Line: AI is a Tool, Not Magic
When you implement AI, you shouldn’t just buy a “black box” and hope for the best. A responsible manager should demand a performance report.
-
How many times does the model hallucinate?
-
What is its miss rate on critical data?
Understanding these metrics is the difference between a “toy” innovation project and a real work tool that saves the organization time, money, and strategic errors.
At the Sarid Institute, we combine our years of experience in market research with advanced data science capabilities. Our goal is not just to help you implement technology, but to ensure it actually delivers the value you expect.
Planning to implement AI systems or smart agents in your organization? Contact us to ensure your move is based on solid data.