Engineering First: Building Trustworthy (Gen)AI at Scale

Long before generative AI became the latest hype, many organizations were already struggling to industrialize traditional AI. Almost everyone recognizes the pattern: that one churn model that technically works, lives in a notebook and is maintained by a single data scientist. Predictions are generated through manual steps, updates happen ad hoc, monitoring is informal at best and everything runs on a local laptop.

Even today, many companies are still trying to regain control over these legacy AI setups. And now, on top of that, GenAI has arrived. If the old way of working was risky, GenAI multiplies those risks. Larger models, external APIs, unstructured data and new compliance concerns all raise the stakes considerably.

If you want GenAI to deliver real and trustworthy business value, proper engineering standards are not optional. They are foundational.

Engineering is not a phase, it’s the foundation

One of the most common mistakes in (Gen)AI initiatives is postponing engineering concerns until after the proof-of-concept. That approach almost guarantees technical debt, compliance issues and operational chaos.

Strong engineering practices need to be present from the very beginning. That means involving people with platform, infrastructure, security and operational expertise early on, even when the use case is still exploratory. Thinking a few steps ahead during the PoC phase can save plenty of rework.

A real-world example: building the platform while delivering value

At one of our clients, we helped establish the foundations for a proper AI platform while simultaneously delivering a first high-value use case. Instead of treating the use case as a one-off experiment, we used it as a launch pad to design and implement reusable platform capabilities.

From day one, we focused on answering the questions that define a scalable and trustworthy AI platform:

• How do we integrate the data platform and the AI platform?
• Who needs access to what and under which conditions?
• Which data sources are allowed to be used?
• How do we handle scheduling, logging and observability?
• Which technology stack aligns with the company’s security standards?
• Which stack is scalable and manageable given current skills and maturity?
• How can we reuse existing data platform capabilities to reduce cost and complexity?

Once the answers to these questions are defined and implemented, they enable multiple use cases to be built and deployed consistently and safely.

Starting with a concrete use case helped ground these discussions. It is always easier to design when there is real business value at stake. While we productionized the first use case, we simultaneously laid down the platform foundations that all future use cases would rely on.

Of course, every (Gen)AI use case also introduces its own specific challenges:

• Which LLM endpoints meet security requirements and fit the use case?
• How do we manage model lifecycle and retraining?
• How do we monitor quality and mitigate hallucinations?
• How do we automate deployments and rollouts?

While these questions are use-case specific, the underlying principle remains the same: design for reuse. If a solution, pattern or way of working can be reused, make it modular and platform-ready. This software-engineering mindset is often underestimated or introduced too late in AI initiatives.

Rome wasn’t built in a day

Building a solid AI platform takes time. When the platform is still an empty box, the first use cases will inevitably feel slow. Managing business expectations is therefore critical. Not only to dispel the myth that AI is a magic bullet, but also to avoid the belief that production-ready (Gen)AI can be delivered in a week.

That said, pragmatism matters. Some shortcuts are unavoidable if you want to deliver value within a reasonable timeframe. The key is to choose them consciously, weigh cost versus benefit and plan their removal before they become permanent. What we for example did:

• Postponed automated rollout of IAM roles. Instead, we manually created minimal, least-privilege roles while collaborating with the security team and the CISO to define future standards.
• We immediately setup a CI/CD pipeline for infrastructure deployment. This ensured that code and infrastructure stayed aligned and that DEV, ACC and PRD environments could be deployed consistently.

The bottom line

GenAI is powerful and exciting but without strong foundations, it quickly becomes a liability. Treat your GenAI stack like any other enterprise-grade system: with governance, security, scalability and operational excellence baked in from day one.

This work should not happen in isolation. In many organizations, a solid data platform already exists, along with a data platform team that deeply understands these principles. By empowering that team to own a meaningful part of the AI stack, you significantly increase your chances of building AI solutions that go beyond impressive demos. Solutions that are trustworthy, resilient and able to scale with the business.