A major automotive retailer · 2025 Co-facilitator & lead researcher · Four Dimensions framework author

From "we should do AI" to a working vocabulary for AI in services

The case in 6 moves

Pressure without language
AI in the corner
The Four Dimensions
Paper, not laptops
Value first, then AI
Travels back to product teams

designers + execs

dimensions framework

Re-engaged

client re-engaged

Client: A major automotive retailer
Role: Co-facilitator and lead researcher; author of the Four Dimensions of AI in Services framework
Team: Co-led with Patrick Quattlebaum (Harmonic Design principal); delivered to ~50 designers, researchers, and executives
Duration: August – September 2025 (6 weeks)
Deliverables: Four Dimensions of AI in Services framework · 6 AI design principles · value-centered AI canvas · AI-actor-inclusive service blueprint adaptation · Harmonic AI POV document
Outcome: Client re-engagement commissioned; frameworks now part of Harmonic's AI practice materials

A design team had grown from ten people in 2018 to more than fifty by 2025. AI was the new ambient pressure on every product team: the most enthusiastic designers were leaving meetings to vibe-code prototypes in Figma; the most skeptical wanted to stop hearing the word entirely. Both reactions are symptoms of the same problem — AI treated as either destination or contaminant, never as a design material with properties, constraints, and appropriate use. The workshop's job was to give the team a vocabulary for the distinction — and a practice they could carry back into their own product teams.

Chapter 01

A 50-person team under AI pressure, with no shared language for it

The brief: help the team get comfortable with AI as a design material. Build solutions that aren't AI-for-AI's-sake. Make it something they can carry into their own product teams — not a lecture, a practice.

Automotive retail is high-stakes: high-ticket purchases, low-frequency transactions, trust-sensitive at every step. An AI that misfires in a car-buying journey doesn't just fail to convert — it damages the relationship that makes the next visit possible.

I led the research and knowledge-base development ahead of the two days: primary conversations tapping my network, secondary sources pulled into a Harmonic AI POV document covering human-to-AI interaction patterns, front-stage and back-stage use case typologies, and emerging service design methods being adapted for AI contexts.

During the two days, I introduced the AI design principles and the Four Dimensions of AI in Services framework — original work I authored because no existing taxonomy did what the team needed.

A workshop table viewed from above: hands sketching on a printed concept canvas in pen, several other canvases nearby with handwritten notes, and sticky notes clustered at the edge of the table. — Mid-activity at one of the workshop tables. Concept canvases were filled in by hand and small group, then walked back through together.

Chapter 02

The organizing move: AI in the corner of the canvas, not at the center

The instinct in most rooms right now is to start from "we should do AI" and look for somewhere to put it. That order makes the technology the design problem. The brand promise, the value exchange, the moments where it meets a customer, the feedback loops — all get rearranged around the AI rather than the other way around.

The workshop's organizing move was to flip that order. Borrow from Henry Dreyfuss writing about industrial design in the 1950s: fit the new materials to people, not people to the materials. Treat AI the way an industrial designer treats wood or steel — a material with properties, constraints, and an appropriate use. Not a destination.

Patrick introduced a value-centered canvas that made this concrete: value in the center, moments that matter at the top, brand tensions on one side, feedback loops below — and AI deliberately in the corner. That placement is the pedagogical move. AI is on the canvas because it belongs there, but it's in service of value, not the other way around.

The value-centered canvas. Value in the middle. AI in the corner.

Chapter 03

The Four Dimensions framework: a vocabulary for AI design choices

Most AI design vocabulary is interface-level — chatbot heuristics, prompt patterns. The team needed something a service designer could use to reason about an AI's role across an omnichannel journey. I built the Four Dimensions of AI in Services for this workshop.

Actors: Humans — customer, front-stage associate, back-stage associate, supplier. AIs — the customer's personal agent, the company's customer-facing AI, the company's employee-facing AI. Naming them matters because the team had been collapsing all of these into "the AI."
Interface: Explicit (chatbot — the user knows), ambient (the lot reads a license plate), or mixed. Most teams default to explicit; ambient is where service design has the most to contribute.
Collaboration model: Sequential, parallel, independent, or orchestrated. Naming the other three opens design space the team was systematically missing.
Control model: Manual, assisted, monitored, automated, or autonomous. Control is a knob, not a binary. Every AI choice in a service is a position on this scale, with consequences for trust, liability, and brand.

The framework also underpinned an adapted service blueprint. Standard blueprints divide front stage from back stage. We added a further division: within each stage, separate human actors from AI actors. Once AI is named as an actor, the team has to say what it does, where its authority ends, and what the handoff to a human looks like.

Adapted service blueprint for a car-buying journey, with Frontstage AI and Backstage AI/ML rows added alongside their human counterparts — showing where AI acts, where its authority ends, and where human handoffs occur. — The adapted service blueprint — AI named as an actor in its own right, not a feature embedded in someone else's row. The Four Dimensions diagram (stand-alone) is being extracted from workshop materials.

Chapter 04

Why we worked on paper, not laptops

The workshop was deliberately low-fidelity: 50 people in a room, working on paper. Some participants left and came back having built something in Figma. We let it happen, but the two days were about practicing the thinking, not generating outputs. Laptops accelerate solutioning past the point where the framework can do its work.

The deeper bet: a 50-person team that has practiced putting AI in the corner of a canvas, naming it as an actor in a blueprint, and locating it on a control spectrum will recognize those moves later, in their own product teams, when someone walks in with a solution-shaped brief. The workshop isn't the deliverable. The recognition that happens six months later is.

Don't just start vibe coding when you don't have the foundational thinking done. Go all the way back to what you needed to do. Go all the way back to sketching.

— Whitney Masulis, during the workshop

The reframe

Replacing "where should AI go?" with "what value are we creating?"

The brief was to help the team get comfortable with AI as a design material. The harder job was interrupting the instinct to start from "where should we put AI?" and replacing it with "what value are we creating, and is AI the right material for this moment?"

That second question is genuinely difficult to hold in a room where the loudest voices are already in solution mode. The framework is the tool for holding it. The two days of practice are what builds the muscle to reach for that tool under pressure.

What stays behind

Frameworks and practice that travel back into product teams

Three artifacts live beyond the two days: the Four Dimensions framework, the value-centered canvas, and the adapted service blueprint with human and AI actor rows. The Harmonic AI POV document, built for this workshop, continues to be updated as the field moves. These aren't deliverables in the usual sense; they're tools for a practice that travels back into 50 product teams working on 50 different briefs.

The outcome signal: Harmonic was asked back for more work. Whether the team is using the canvas, the dimensions, and the adapted blueprint inside their actual product work is something we don't yet have ground truth on. What the workshop produced, materially, is a team that has practiced asking "what value are we creating?" before "what AI should we use?"

Frameworks live on

Four Dimensions, the value-centered canvas, and the adapted blueprint travel back into 50 product teams.