With so many companies and teams quickly implementing AI, many focus on models and integrations, but completely overlook Evaluation frameworks (Evals), which can be costly to your business as a consequence of treating it as an afterthought.
Evals are how you objectively measure whether your AI systems are delivering their intended outcomes. Without them, there’s no clear way to:
- Track model performance and improvements over time
- Identify failure modes and systemic weaknesses
- Quantify ROI and communicate it to the business
- Detect model drift and degradation in real-world environments
Picture this: You launch a GenAI-powered customer support chatbot trained on your internal knowledge base. Six months pass, and your team has iterated, added features, and pushed updates. But because your teams never set up baseline Evals, you’re left guessing whether the system is performing better or worse than when it first launched six months prior. That’s a risky position for any tech leader.
Evals are foundational to AI maturity
As AI adoption grows across the business, tech leaders shift from experimentation to strategic implementation and risk assessment, making Evals essential. Evals provide the structured framework to ensure your models aren’t just functional, but also aligned with business goals, safe by design, and compliant with emerging AI regulations.
Regulatory bodies are increasingly requiring organisations to demonstrate that their AI systems are:
- Transparent in how they make decisions
- Free from discriminatory bias
- Auditable and explainable to non-technical stakeholders
Evals make these claims defensible because they generate the documentation needed to satisfy compliance requirements and reduce legal lapses, before auditors or regulators make inquiries and seek clarification.
Evals are a strategic lever
Many technical teams treat Evals as a nice-to-have, as something you build once performance issues surface or compliance flags are raised. This approach demonstrates a reactive mindset, which is a liability to your business.
On the contrary, Evals should be embedded from day one rather than as an afterthought.
They should integrate into your CI/CD pipelines, align with your observability stack, and be used to guide both model development and deployment decisions.
If your team is serious about AI driving business value, they must be equally serious about measuring that value continuously.
Demos can mislead, while Evals don’t
GenAI tools tend to shine in demos, but those demos don’t always reflect the ambiguity, noise, and edge cases of production environments.
In the real world, models will be tested with rare inputs, non-standard phrasing, and conflicting prompts, which is where models tend to break. Without rigorous Evals that stress-test models under these conditions, you’re gambling with user experience, brand trust, and operational efficiency. Robust Evals uncover those blind spots before they become problematic incidents. They allow you to strengthen your models and reduce the likelihood of failure in deployment.
The bottom line
If you’re scaling AI across your organisation, you need a solid Evaluation strategy just as much as you need great models, and clean data.
Evals aren’t just for quality assurance purposes. They are the backbone of responsible AI engineering and a vital enabler of long-term value, trust, and performance. Don’t let your AI stack grow without a way to measure what matters. If you need support in approaching Evals, AI engineering, or machine learning, contact us.
