Designing the AI layer, not just using it.
Most teams add AI tools to an existing process. This project — an internal exploratory initiative within IKEA's digital experience design team — asked a different question: what does it look like to architect AI into a design workflow from first principles, with explicit checkpoints, structured agent roles, and a human decision layer that genuinely controls the output?
The wrong question was everywhere
The dominant question in design teams exploring AI was "which tool should we use?" The answer was almost always a product recommendation — a specific assistant, a specific plugin, a specific integration. Teams would adopt it, use it for a few weeks, and find that the outputs were inconsistent, the process was murkier than before, and nobody could explain why a particular output was good or bad.
The problem wasn't the tool. It was that AI had been added to a process that wasn't designed to accommodate it. Without explicit phases, defined agent roles, human checkpoints, and context management, AI-assisted work produces something more dangerous than failure: it produces plausible-looking output that drifts from intent without anyone noticing.
AI is a process layer, not a feature
The reframe that made this project possible: treat AI the same way you'd treat any other design systems problem. Define the inputs. Define the outputs. Define the handoffs. Define who decides what and when.
That reframe shifts the question from "what can AI do?" to "where does AI fit in this process, and how do we verify that it's working correctly?" The second question is harder — and far more useful. It's also the kind of question that systems thinkers are better positioned to answer than most, because it requires understanding the whole before optimising any part.
Three tiers. Five phases. Human gates throughout.
The framework organises AI-assisted design work into a three-tier agent hierarchy operating across five defined phases. Each tier has explicit scope, defined inputs and outputs, and clear escalation paths to human decision-makers.
Two threads from the same structure
The framework wasn't built in the abstract. It emerged from two parallel workstreams inside an enterprise design team, both of which exposed the same underlying problem: AI without process produces work nobody can own.
Shape Up facilitation
Shape Up shaping sessions are cognitively intensive — multiple stakeholders, ambiguous scope, competing constraints. I designed facilitation workflows that use AI to support the shaping process: structuring the problem before the session begins, surfacing edge cases before pitches are written, and synthesising multi-participant outputs into pitch-ready documentation. The AI handles structure; the facilitator handles judgment.
Multi-agent orchestration for complex features
For features involving product, engineering, content, and business stakeholders simultaneously, a single-agent approach produces generic outputs that satisfy nobody. The multi-agent approach distributes the shaping task: one agent decomposes the problem, another maps constraints, another evaluates solution directions. Outputs are structured to flow into existing documentation formats — so the AI layer and the process layer are the same system.
AI raises the bar for human judgment — it doesn't lower it
The most common misconception about AI-augmented workflows is that they simplify the designer's role. They don't. The role shifts from producing to directing, evaluating, and integrating. That shift requires skills that aren't traditionally part of design education:
- Prompt craft — Setting direction with enough specificity to guide useful output, enough openness to allow genuine AI contribution. Closer to brief-writing than specification.
- Output evaluation — Assessing AI output critically and quickly: what's right, what's wrong, what needs investigation. Requires domain knowledge and a clear model of what the work is trying to achieve.
- Context architecture — Deciding what information must carry forward across phases, how it should be compressed, and what can be safely left behind. A new kind of information design skill.
- Drift detection — Noticing when the AI's framing has shifted subtly from original intent. Requires keeping the original intent visible and referring back to it constantly.
- Calibrated trust — Knowing when AI output is likely reliable and when it requires deeper scrutiny. High-confidence AI output is not a signal of correctness — it is a trigger for more careful review.
These are skills that senior designers, systems thinkers, and design leads are best positioned to develop — because they already understand process at a level that makes AI augmentation legible rather than magical.
A framework that separates process from tooling
The most durable result of this work is a process architecture that is independent of any specific AI tool or platform. Because the framework defines phases, agent roles, checkpoints, and context management protocols at the process level — not the implementation level — it survives tool migrations, platform changes, and team turnover.
That separation also makes it teachable. The framework is now being shared through an active AI literacy programme for senior design professionals, with a focus on workflow design rather than tool use. The target audience is designers and design leads who already think in systems — and who are ready to apply that thinking to how AI fits into the process, not just what AI can do.
"The AI is a layer. The process is the design."
The framework is documented in full as a standalone piece — written to be adopted, adapted, and evolved by any design team working seriously with AI. It draws from the real-world constraints and failure modes encountered during this engagement and represents a position: that responsible AI augmentation in design is a systems design problem, and should be treated as one.
Read the full framework — 18 min →
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