Why Engineering Matters to AI
- Raimund Laqua
- 2 days ago
- 3 min read
As organizations rush to adopt artificial intelligence, one common mistake is treating AI systems like just another IT solution.
After all, both are software-based, require infrastructure, and are built by technical teams.
But here’s the thing: AI systems behave fundamentally differently from traditional IT systems, and trying to design and manage them the same way can lead to failure, risk, and even regulatory trouble.
To use AI responsibly and effectively, we need to engineer it—with discipline, oversight, and purpose-built practices. Here’s why.
Traditional IT Systems: Predictable by Design
Traditional IT systems are built using explicit rules and logic. Developers write code that tells the system exactly what to do in every scenario. For example, if a customer forgets their password, the system follows a defined process to reset it. There's no guesswork involved.
These systems are:
Deterministic: Given the same input, they always produce the same output.
Transparent: The logic is visible in the code and can be easily audited.
Testable: You can run tests to verify whether each function behaves correctly.
Static: Once deployed, the system doesn’t change unless someone updates the code.
This predictability makes traditional systems easier to govern. Compliance, security, and operational risk controls are well-established.
AI Systems: Learning Machines with Unpredictable Behaviour
AI systems—especially those based on machine learning (ML)—work differently. Instead of being programmed with rules, they are trained on data to find patterns and make decisions.
Key characteristics of AI systems include:
Probabilistic Behaviour: The same input can produce different outputs, depending on the model’s training.
Emergent Logic: The rules are not written by developers, but learned from data, which can make them hard to understand or explain.
Continuous Change: Models may be retrained over time, either manually or automatically, as new data becomes available.
Hidden Risks: Bias, drift, or performance degradation can emerge silently if not monitored.
In short, AI systems are dynamic, opaque, and complex—which makes them harder to test, trust, and manage using traditional IT approaches.
Why Engineering Matters for AI
Because of these differences, AI systems need a new layer of discipline—AI engineering—to ensure they are safe, reliable, and aligned with business and societal goals.
Here are some key concepts behind engineering AI systems:
1. Robustness
AI needs to perform reliably, even when it encounters data it hasn’t seen before. Engineering for robustness means testing models under various scenarios, stress conditions, and edge cases—not just relying on average accuracy.
2. Explainability
When an AI system makes a decision, stakeholders—whether users, regulators, or auditors—need to understand why. Explainability tools and techniques help uncover what’s driving the model’s decisions, which is essential for trust and accountability.
3. Adaptive Regulation and Monitoring
AI systems can degrade over time if the data they see starts to shift—a phenomenon known as model drift. Engineering for AI involves setting up real-time monitoring, alerting, and feedback loops to catch and respond to issues before they cause harm.
4. Bias and Fairness
Since AI learns from historical data, it can inherit and amplify existing biases. Engineering practices must include fairness checks, bias audits, and tools that help identify and mitigate discriminatory behavior.
5. Life-cycle Management
AI development doesn’t end at deployment. Engineering includes versioning models, tracking data changes, managing retraining pipelines, and ensuring models continue to meet performance and compliance requirements over time.
Comparing the Two Approaches
Here’s a simplified comparison:
The Bottom Line
AI systems hold enormous potential—but with that power comes greater complexity and risk. Unlike traditional IT systems, they:
Learn instead of follow
Adapt instead of stay static
Predict instead of execute
To manage this effectively, we need to engineer AI with rigor—just like we do with bridges, aircraft, or medical devices. This means combining the best of digital engineering with new practices in data and cognitive science, systems and model engineering, adaptive regulation, AI safety, and ethical design.
It’s not enough to build AI systems that work. We need to build AI systems we can trust.
This article was written by Raimund Laqua, Founder of Lean Compliance and Co-founder of ProfessionalEngineers.AI
