Engineering Quality Digital Products with AI
Learn how to implement AI systems that enhance customer experience while maintaining quality, reliability, and measurable outcomes.
AI is not magic. It’s engineering, with feedback loops.
The Systems View of AI in Product Development
There’s a seductive optimism around artificial intelligence—partly deserved, mostly inflated. But strip away the hype, and what’s left is still transformative: not because AI "thinks" like us, but because it lets us encode learning and decision-making into our systems at scale. The real value? It's in the boring stuff—fewer support tickets, smarter defaults, fewer steps in the journey. Deming would nod in approval.
So how do you engineer quality with AI? Not by chasing the next model release, but by tuning systems that measure, respond, and improve—systems that learn responsibly, and behave predictably in production.
AI ≠ Automation
Automation is rule-based. AI is pattern-based. Confusing the two leads to brittle products: deterministic logic pretending to be adaptive. A well-engineered AI system doesn’t just trigger based on inputs; it observes behavior over time, adapts in context, and—crucially—knows when not to act.
“Rules work well in static environments. AI works well in messy ones.”
Which means your design and engineering teams need to treat AI not as a feature, but as a co-worker: fallible, but trainable. That demands new QA disciplines, new testing models, and new expectations around failure modes.
Avoid the Hype Traps
According to Gartner’s 2025 Hype Cycle for AI, much of what excites execs is years away from practical deployment. Quantum ML? Sure. Autonomous agents? Maybe later.
What’s real now:
- Language models that help users navigate complexity.
- Recommendation systems that surface better options, faster.
- Predictive analytics that quietly reduce operational risk.
What’s not:
- One-click AI “transformations.”
- AI replacing whole departments.
- Zero-maintenance AI anything.
The trick, as Rory Sutherland might say, is not to ask what AI can do, but what it might do in an illogical, emotionally resonant, or surprisingly delightful way that rules-based systems can’t.
Where to Start: CX-Driven AI Engineering
1. Start with the customer, not the model
Which part of the journey has the most friction? Where do users drop off, hesitate, or abandon? AI isn’t the solution to every problem—but when it is, it should be invisible. Like a great concierge.
2. Don’t build AI features. Build AI systems.
Think telemetry, observability, feedback loops. The system should learn not just from input, but from impact. Did the suggestion increase conversion? Did the automation reduce response time?
3. Validate like it's 1993
Before "move fast and break things" became a slogan, software was tested with the rigor of aircraft systems. Engineering AI requires a return to those principles: simulate failure, test boundaries, verify outcomes.
4. Make it legible
Legibility isn’t about transparency for its own sake—it’s about operational clarity. Can a support engineer explain why a recommendation was made? Can your system explain itself under pressure?
Finding the Right AI Engineer
Look for engineers who:
- Think in systems, not just models.
- Value instrumentation as much as inference.
- Ask how it fails, not just how it performs.
- Understand human behavior as well as technical constraints.
This isn’t about chasing unicorns—it’s about recruiting engineers who appreciate nuance, edge cases, and the strange entropy of human behavior.
Quality in AI systems doesn’t come from intelligence. It comes from design.
Design that respects uncertainty. Design that measures consequence. Design that asks not what can we automate, but what should we enhance.
When you get that right, the AI disappears.
And the product just feels... smarter.