AI Adoption · Written and maintained by Haink’s AI adoption team · Updated July 2026 · 6 min read

Functional vs Technical Design for AI Solutions

Functional design says what an AI solution does — for whom, through which process, with what behavior. Technical design says how it’s built — architecture, integrations, data flow and security. Both belong in the solution blueprint, and both must be written down and agreed before implementation. Having one without the other is a reliable way to guarantee rework.

The two layers, side by side

What functional design covers

Functional design describes the solution as the business experiences it. Beyond the obvious spec and user stories, the parts most often left out are the ones that matter most in production:

• Process flows — where the AI sits in the existing workflow, and what changes around it.
• Human-in-the-loop points — where a person reviews, approves or overrides the AI, and what they see when they do.
• Exceptions and edge cases — what happens when the AI is unsure, wrong, or the input is unusual. This is where most AI functional specs are thin, and where production is unforgiving.
• What “good” means in business terms — a plain-language definition of an acceptable output, which later becomes the acceptance criteria.

What technical design covers

Technical design describes the solution as the people building and running it experience it: solution architecture, integrations with the systems it must touch, data flow (where data comes from, where it goes, what is retained), security and compliance controls, and how it will be deployed and operated. It is also where the architecture direction — cloud, hybrid, private or sovereign — becomes concrete for this specific solution.

The AI design layer in between

Classic software has two layers. AI solutions have a third that sits between them, and skipping it is a common mistake: the AI design — model selection, agent and workflow design, prompt strategy and guardrails. It is partly functional (it determines how the system behaves) and partly technical (it determines how that behavior is implemented), which is exactly why it falls between the chairs when a team only writes the two traditional documents. Name it explicitly and give it an owner, or the most AI-specific decisions in the project get made ad hoc during the build. Haink’s AI dubbing pipeline is this layer made concrete: a chain of specialized models — analysis, translation, synthesis — whose design decisions sat squarely between the functional goal and the technical build, and drove a 10× gain in localization speed.

Order: functional first, but they iterate

Logically, functional comes first — you can’t design how until you agree what. In practice the two loop: a technical constraint (an integration that can’t deliver data in real time, a compliance rule that forbids a data flow) feeds back and reshapes functional scope. That loop is healthy. What matters is that it happens during design, not during the build — the same discovery costs a conversation in the first case and a rebuild in the second. Treat it as two passes that converge, not a waterfall with a single handoff.

What AI changes about design

One property makes AI design genuinely different from classic software design: the output is non-deterministic. The same input can produce different responses, and the system is right most of the time rather than always. That has two design consequences:

• Functional design must define acceptable behavior ranges, not just the happy path — what the system does when confidence is low, what it must never do, and who catches the misses.
• Acceptance becomes statistical. “Correct” turns into a measured quality threshold over a test set, which is why the quality layer — non-functional requirements and acceptance criteria — carries more weight in an AI blueprint than in a typical software spec.

The honest verdict: the agreement matters more than the artifacts

It is possible to produce two beautiful documents nobody reads and still build the wrong thing. The purpose of separating functional from technical design is not documentation for its own sake — it is to force two different groups of people to state, in their own language, what they think is being built, and to discover the disagreement on paper. If a short, jointly-signed specification achieves that for a simple solution, that is a complete design. Right-size the artifacts to the complexity; never skip the agreement.

Frequently asked questions

What’s the difference between functional and technical design?
Functional says what the solution does and for whom; technical says how it’s built. Business approves the first, the architect the second — both before implementation.

What does functional design include?
Functional spec, user stories, process flows, human-review points, exception handling, and what a good output means in business terms — including acceptable behavior ranges, not just the happy path.

What does technical design include?
Solution architecture, integrations, data flow, security and compliance, and the deployment model — alongside the AI design layer (model selection, agent and workflow design, prompts, guardrails).

What happens if you skip one?
Skip functional and you build the wrong thing correctly; skip technical and you build the right thing unbuildably, discovering integration and security problems mid-build.

Does functional always come first?
First in logic, but the two iterate. Technical constraints reshape functional scope — which is fine, as long as it happens during design rather than during the build.