Evals 101 for executives: Your all-in-one AI PRD, roadmap, and ROI model

Enterprise AI pilots rarely fail because the models are weak. They fail because success isn’t defined, progress isn’t measurable, and impact isn’t tied to dollars.

In fact, it's the same reason why any pilot fails, but the key insight here is that we are in the middle of a platform shift with AI. This requires us to have new tools and methods to create a "definition of done", to measure progress and most importantly measure ROI.

A practical fix is evals. Already on the rise in product and technical circles as a testing and benchmarking tool, evals can – and should – play a much bigger role. Done right, they serve as your AI product’s PRD, roadmap, and ROI model rolled into one. They force clarity on what you want AI to do, provide a repeatable way to compare options, and tie AI performance directly to business outcomes.

Think of evals as the connective tissue between your business objectives and your AI system’s behavior – the artifact that keeps strategy, delivery, and measurement linked together.

This post explains what evals are, why they matter now, what goes into a good eval set, and how to use them to estimate ROI before you scale.

At their simplest, evals are designed to measure how well an AI system performs a given task.

An eval is a repeatable test that measures an AI system against clear criteria, using realistic tasks from your domain.

Thought simply, they act like unit tests or benchmarks for traditional software – but with key differences. Unlike traditional software, AI systems are not deterministic and the input space itself is variable / open-ended. That makes defining success criteria more complex than just checking whether the answer is “correct.”

Academic work over the last few years has converged on a multi-metric view: it’s not just about accuracy, but also reliability, safety, and efficiency.

Some of the key questions a well-formed eval should answer:

• Did the model return the right answer?
• Did it follow instructions?
• Did it avoid hallucinations?
• Did it respond within the expected latency?
• Did it respect safety and compliance constraints?
• Did it stay within the acceptable unit cost per task?

Here's and example eval question for a retail analytics agent:

• “Which customers are most likely to be bargain hunters based on their last 12 months of purchases?”

Acceptance Criteria:

• >90% agreement with human-annotated labels
• Query logic aligned with business definitions
• Respects access control rules
• Response latency under 3 seconds, and cost ≤ $0.01 per query.

This illustrates how an eval goes beyond “did it work?” to capture accuracy, reliability, speed, and cost – the dimensions that matter for both technical and business stakeholders.

Yes, evals are a valuable and necessary testing method. They give you a structured way to measure AI performance and are often compared to unit tests for the AI era. But stopping there is a misstep – that’s the narrow, engineering definition.

In practice, evals can be a strategic tool that goes far beyond testing.

For executives, the power of evals lies in making AI business-aligned: turning vague objectives like “faster real-time customer insights” into precise, measurable criteria tied to real outcomes.

Well-designed evals force clarity of vision, create alignment across business and technical teams, and articulate impact in business terms.

This is why the best AI teams treat evals as a strategic starting point – a guide that shapes product direction and investment decisions, not just a QA checklist at the end.

A PRD (Product Requirements Document) traditionally consists of user stories, feature descriptions, and acceptance criteria.

An eval is a forcing function: it compels teams to articulate the exact problems they want AI to solve, in the form of questions to be answered or tasks to be completed, with measurable success criteria. Nothing crystallizes business intent more clearly than putting pen to paper on the questions you want the AI to answer and how you’ll know it did so correctly.

Take this example eval question from a sales copilot example:

• “List top 5 customers for [Rep X] likely to churn in the next 90 days based on their purchase and browsing history?”

That single eval communicates far more about the product’s intent and expected value than a dozen bullet points in a PRD.

For traditional software, a roadmap is typically a list of features and delivery milestones. But with AI, features aren’t buttons or screens – they’re the tasks the system can reliably tackle.

Since evals are the articulation of those tasks, they naturally become the roadmap. In effect, your roadmap is the sequence of questions your AI must be able to answer to deliver business value. Each group of eval questions represents a milestone.

The best AI projects we’ve seen start with domain experts categorizing their eval questions, stacking them by importance and timeline. The roadmap emerges organically.

Here's how that might play out for a retail analytics agent.

Phase 1: Foundation – Data Integrity & Basic KPIs

Establish trust by ensuring the AI can return correct, consistent answers on core metrics and handle missing data.

Sample Evals:

• “What were sales in Region Z last month?”
• “Can you calculate Average Order Value and Customer Lifetime Value?”
• …

Phase 2: Descriptive Insights – Segmentation & Trends

Unlock insights by segmenting customers, surfacing growth categories, and understanding performance.

Sample Evals:

• “Which customers are most likely to be bargain hunters?”
• “Which category drove the biggest YoY revenue growth last quarter?”
• …

Phase 3: Predictive & ROI-Linked Analytics – Forecasts, Campaigns & Anomalies

Move from describing the past to guiding future actions and measuring impact.

Sample Evals:

• “How much incremental revenue did the buy-one-get-one promotion generate for shampoo last month?”
• “What are the expected weekly sales for SKU 12345 over the next 4 weeks?”
• …

In short: your roadmap is just a stack of evals – ordered by importance and weighted by business payoff.

ROI on AI is the hottest, and most fraught, topic in the industry. According to a widely-cited MIT study, 95% of enterprise AI projects fail before they deliver measurable value.

A well-designed eval process is one of the most practical ways to avoid this trap.

Every eval should implicitly answer three questions:

1. Why does this question matter to the business?
2. How is it answered today – by whom, how often, and at what cost?
3. What’s the cost of getting it wrong, or answering too late?

This framing turns evals into a practical ROI calculator.

Evals also gives you a powerful tool to design your roadmap and rollout to maximize adoption and minimize risk.

• Start with simple, high-value tasks the AI can perform reliably.
• Deprioritize “shiny” tasks that carry high risk but low impact – they can sink adoption before you build trust.

Retail analytics example:

• Eval Question: “How much incremental revenue did the buy-one-get-one promotion generate?”
• Today: 3 analysts spend ~10 hours/week producing this manually.
• Cost of delay or error: Misallocated marketing budget → ~$2M annual risk.
• ROI with AI: Automating this eval saves analyst hours and directly protects revenue.

In our work, we often find that teams either aren’t thinking big enough (their eval set is too narrow to create transformative value), or the ROI of their current evals is too low to justify AI investment. That clarity is invaluable: it prevents wasted spend and redirects resources to the use cases that actually move the needle.

Evals are the foundation for building AI that actually delivers business value. But designing the right evals and matching them with the right AI approach is hard to do alone.

That’s why we created the GenAI Assessment Test (GAT) Design Service. The GAT is like an SAT for your AI.

You’ll work with an AI engineer who has deployed large-scale AI systems into production to build a custom GAT for your project. It’s a lightweight, delivery-focused engagement that is completed in under 5 business days.

Your GAT becomes a clear set of evals that define success in your business terms. You will also receive a 3x3 recommendation on the best approach × capability mode (off-the-shelf, framework, or custom × Search, Act, or Solve) for your use case.

The result is a vendor-agnostic playbook that makes vendor claims testable, aligns stakeholders, and accelerates your path from PoC to production.

Reach out to us to book your GAT Design Service.