AI products are here. AI product operations are not

The breakthrough of large language models marks a fundamental shift in product innovation. AI products are no longer experimental curiosities or niche decision aids. They are rapidly becoming core components of digital products across industries, shaping how organizations create value, interact with customers and run their operations.

Yet despite this explosion of AI-powered proofs of concept, very few organizations succeed in turning them into reliable, production-grade products.

This is the AI MVP paradox: demos impress, pilots show promise but production readiness remains elusive.

The AI MVP paradox

In traditional digital product development, teams move through a familiar arc. Ideas become MVPs, MVPs harden into production systems, and operations focus on reliability, performance and continuous improvement. Service-level objectives, observability, incident response and user feedback loops provide the scaffolding that allows products to scale with confidence.

AI products follow the same lifecycle on paper, but behave very differently in practice.

At their core, AI products are built on non-deterministic systems. They embed pre-trained models, prompts, retrieval pipelines, external tools and orchestration logic into a single experience. Each layer can fail in subtle ways, and failures can compound.

As a result, success at the MVP stage is often misleading. An AI product may appear to work under controlled conditions, only to break down when exposed to real users, dynamic data, adversarial inputs or regulatory scrutiny. What looked viable in a demo suddenly feels like a gamble. This is where many AI initiatives stall.

The trust gap

The gap between a working AI prototype and a production-ready AI product isn’t primarily a technical gap. It’s a trust gap.

Product leaders, compliance teams and executives hesitate to scale AI not because the models are incapable but because they lack confidence that the system will behave reliably enough in the real world. Non-determinism — the same input can produce different outputs depending on context, data and probabilistic behavior — introduces uncertainty. Hallucinations, bias and unpredictable behavior create risks to brand trust and in some cases, compliance.

Traditional product operations are poorly equipped to address this. Monitoring uptime and latency is no longer sufficient when the core risk lies in what the system says, decides or does. Without new forms of guardrails and evidence, organizations are forced to choose between moving fast and staying safe.

The missing discipline

What’s missing is not another model, framework or agent architecture. It’s a disciplined way to operate AI products across their entire lifecycle.

AI product operations is emerging as that discipline. It treats AI systems not as static components but as evolving products that must be continuously observed, evaluated, guide and corrected. At the heart of this approach is evaluation — not as a one-time benchmark, but as a systematic practice embedded from ideation through live operations.

AI product evaluations go beyond classic model testing. They assess whether the entire product behaves as intended: whether it delivers value to users, respects safety and compliance boundaries, withstands real-world variability, and continues to improve over time. Evaluations provide the evidence needed to move from experimentation to confident production deployment.

In this sense, evaluations become the connective tissue between innovation and operations. They close the trust gap by turning uncertainty into measurable signals and subjective concerns into actionable insights.

What comes next

To move beyond AI MVPs, organizations need more than better models or faster experimentation. They need a way to operate AI products with confidence in environments defined by uncertainty, change and real-world risk.