Engineering Leadership in the AI Era

We are witnessing a shift from "code production" to "system orchestration." In a Nasdaq-listed environment like Docebo, or when bootstrapping a new SaaS startup, the bottleneck is rarely the ability to write syntax anymore; it's the ability to define intent. AI tools have turned the developer into a high-level reviewer. We are no longer just building features; we are managing an augmented workforce where the speed of delivery has increased by an order of magnitude, but the cost of a logical error remains just as high.

"System orchestration" — that reframes the entire role.

The Developer role is transitioning into that of a "Critical Architect." If you rely on AI to write your code, your primary value becomes your ability to validate that code against the system's constraints. You need to understand the side effects of every generated line on the broader infrastructure.

For the Engineering Manager, the focus shifts from tracking story points and velocity to governing architectural integrity and security. In an AI-augmented team, velocity is a given. The challenge for an EM now is preventing "AI-driven technical debt." We have to ensure that the ease of generating code doesn't lead to a fragmented codebase that no one actually understands. The EM becomes the guardian of the "Definition of Done," ensuring that AI-generated outputs meet the rigorous standards required for enterprise-grade SaaS.

It is the single most important discipline right now. AI tools are essentially "specification executors." If your specs are ambiguous, the output will be hallucinated or inconsistent. Spec-driven development — using OpenAPI, Protobuf, or detailed design docs — acts as the source of truth. By defining the "what" and the "how" through strict schemas before a single line of code is generated, we provide the AI with the constraints it needs to be effective. In a SaaS context, this ensures that different microservices remain compatible even when their implementation is heavily assisted by AI.

So the spec becomes the actual product, and the code becomes the artifact.

Seniority is more critical today than it was five years ago. While AI can help a Junior developer write code faster, it cannot provide the "systemic intuition" that comes with years of experience.

Seniority is required for three specific pillars. First, Security and Hardening — AI often suggests the most "functional" path, which isn't always the most secure. A Senior dev must audit for prompt-injection vulnerabilities, insecure dependencies, or data leakage. Second, Performance at Scale — in a SaaS environment handling millions of requests, a slightly inefficient query suggested by an AI can bring down a database. Senior engineers have the scars necessary to spot these bottlenecks. Third, Contextual Awareness — AI solves problems in isolation. Seniors ensure the solution fits the long-term roadmap and doesn't break existing patterns.

In short, AI is a force multiplier, but if you multiply by zero experience, you still get zero — or worse, a fast-moving disaster.

Multiply by zero. That's going to stick.

Focus on the "Trust but Verify" model. Automate your testing suites and CI/CD pipelines to be even more aggressive. If you are going to increase the volume of code entering the repository via AI, your automated gatekeepers must be world-class. Finally, foster a culture where the "Review" phase is treated with more prestige than the "Coding" phase. That is how you scale a SaaS product reliably in this new era.