Launching your AI-driven features should accelerate growth, not lead to user frustration and damaged trust. But many executives find their carefully planned AI deployments stumble right out of the gate. Why? Because evaluating AI products is more than a technical checklist: it’s fundamentally a company-wide challenge. To ensure your AI aligns with strategic goals and delivers consistent value, evaluation must involve your entire organization: product, design, engineering, data teams, and critically, continuous user feedback.

As covered previously, evaluations ("evals") are systematic tests run on AI-driven features to measure their performance against specific criteria. Good evaluations clearly highlight when your AI works as intended and pinpoint where it falls short, revealing inconsistencies or unexpected behaviors that generic benchmarks often miss. In other words, evals are your primary tool to trust that your AI will perform reliably for users.

Evals are especially necessary when AI or machine learning features as integrated from external providers, as is common with generative AI (gen AI). Broad foundation model capabilities are great building blocks, but nowhere near a fully constructed feature.

Many companies still treat AI evaluation as an isolated engineering or data science task. But here's the issue: when evaluation happens in a silo, your teams speak different languages. Engineers optimize for accuracy metrics, product teams focus on user engagement, and design teams worry about brand alignment and usability. Without a unified, company-wide definition of success, your AI initiatives can appear successful in lab tests yet still fail in the market.

Cross-functional evals

To avoid these challenges, we need to make evals cross-functional. Cross-functional evaluation means involving product managers, designers, data scientists, engineers, and users together in defining and executing evaluations.

Without cross-functional evaluation, you'll face recurring problems that drain resources and erode trust.

Your data science team may celebrate model accuracy, while customer support scrambles to handle confused or frustrated users. Designers might complain the AI responses don't match brand voice, and product teams worry the features aren't driving business goals. Worst of all, without structured feedback loops involving real users, you risk discovering major flaws only after the feature has already harmed customer satisfaction and company reputation.

Another major pitfall is insufficient or poor-quality data practices. Without representative test cases and clearly defined success metrics, evaluations become subjective guesswork. Teams waste hours debating what's "good enough," and disagreements stall product launches. Additionally, without standardized labeling and robust documentation, valuable insights from evaluation cycles become lost, limiting your ability to improve the AI over time.

Finally, many companies fail to establish effective user feedback loops. Launching AI features without ongoing user input means you're essentially guessing what users actually want. Basic feedback mechanisms like thumbs-up or thumbs-down go a long way, but the end-game is collecting enough user context to drive meaningful improvements. Without richer, continuous user insights, you'll struggle to accurately evaluate your AI's real-world effectiveness.

In short, companies face significant evaluation challenges:

• Organizational silos create conflicting goals and misaligned metrics.

• Poor-quality or insufficient data practices lead to subjective evaluations and stalled decision-making.

• Lack of effective user feedback loops leaves companies guessing about real-world performance.

Addressing these issues is crucial for successful and trustworthy AI deployments. But how? Read on.

Integrating Evaluation into Your AI Strategy and Roadmap

Addressing these evaluation challenges isn't just about better engineering practices or improved technical tooling. For executives, it's fundamentally about ensuring your company's broader AI strategy explicitly accounts for evaluation as a critical success factor. Your strategic AI roadmap should clearly outline how you'll foster cross-team collaboration, define shared success metrics, and integrate continuous user feedback into your product lifecycle.

This means your AI roadmap must also incorporate technical AI architecture and solutions that support cross-functional evaluations. Investments might include platforms for standardized evaluation processes, improved data flows, a capable AI engine and structured user feedback mechanisms embedded directly into your products. By proactively embedding these solutions into your AI development lifecycle, you equip your organization to deliver reliable, trustworthy AI experiences that align with both user expectations and strategic goals.

Evaluations Are Strategic, Not Optional

Reliable, trustworthy AI doesn't happen by accident. It requires deliberate attention to how your company evaluates its AI-driven features. By embedding cross-functional evaluation into your broader AI roadmap, you'll position your organization not only to avoid costly surprises but also to consistently deliver the kind of AI experiences your users and your business truly value.