Field research, a jobs-to-be-done framework, annual planning redesign
Team
A team of one, with two close partners
Industry
Agriculture
Too busy running a business for our tools
I was asked to get more data from the few sales agents already on the platform. I found most were too busy running their businesses to use it, and turned the roadmap toward helping them run those businesses.
$50Ma year, now planned around sales agents’ jobs
The call I made
Engagement was low, and the roadmap was aimed at the sales agents already on the platform, with features built to collect more of their data.
I moved the question upstream, to why most sales agents weren’t there. Commercial’s plan to consolidate agencies was turning them into business managers with no time for field tools, so I raised the lens to the territory and the agency they run. The business still needed farmers’ data, and farmers were wary of sharing it. My bet was that they would share it more comfortably as their relationships with their sales agents grew stronger, with more trust and loyalty.
The hard part
My first answer, journey management, didn't fit. Sales agents’ days are run by weather, disease, commodity prices and a phone that can't go unanswered, so I switched to jobs-to-be-done: design for the outcomes they want, with solutions that flex with an unpredictable day.
I was a team of one with no dedicated budget, asking an organization that had always planned from its software to start from the sales agent’s day.
What it changed
The year's investment white papers were approved and turned the roadmap toward the sales agent’s whole territory and toward running the agency as a business, giving sales agents their time back.
Without a mandate, I spread the method through allies I mentored and a first experience team, so the outside-in view showed up in rooms and studies I never touched. Engagement later closed at 44% against a 35% goal, and adoption at 52% against 50%.
I founded a Design Strategy Center of Excellence to work a year ahead on who we were building for and why, and redesigned annual planning around the jobs sales agents are trying to get done, so funding decisions started from their needs. That planning process is still running.
What I learned
People rarely move through the world in straight lines, and software tends to solve problems in one. It’s tempting to force that messy reality into a tidy artifact, but the mess is how real change unfolds inside a system, and part of the work is making it comfortable to adopt.
Patient-comment analysis, a feedback dashboard, the case for scaling it
Team
A team of two, then four
Industry
Military health care
Killing our own project
I inherited a printed booklet for patients. The bigger problem was that leaders could only act on the few patient comments someone had time to read. I set the booklet aside, and we built the tool that let eleven military treatment facilities hear all of them and see where to act first.
Adoptedacross the facilities, then escalated to the Defense Health Agency
The call I made
I arrived mid-project across eleven military treatment facilities around Washington, D.C., and inherited a printed passport booklet meant to help patients find their way through the hospital.
In an interview, a chief experience officer scrolled through the patient survey for us: over a hundred thousand open-ended comments in a raw Excel export. Leaders could only act on the few someone had time to read, and the critical problems sat in the rest.
I pushed us to set the booklet aside, because it added cognitive load for patients already in pain, and to work upstream on the problems behind their experience, where the long-term impact would be far greater: a way for the network to hear the whole story and know where to start.
The hard part
I had to bring our client with us. I built the case and a prototype, in collaboration with my design strategy partner, from their own patients' comments, and the prototype won it.
This was before large language models were everywhere. We brought on two data scientists, and I hand-tagged the sentiment of thousands of comments so the natural language processing had something to learn from.
What it changed
We built a dashboard where every new batch of comments arrived sorted by topic, sentiment and critical issue, with each facility's performance beside the others'. A top-ten comment about bathroom cleanliness was acted on right away.
It was adopted across the facilities, outlived our engagement, and was escalated to the Defense Health Agency as something to fund and scale across all of its facilities.
What I learned
Do the primary work as well as it can be done, without becoming so focused on it that you miss a bigger opportunity beside it. When new evidence points somewhere bigger, have the nerve to leave the path you were on.
Service blueprint, customer archetypes, research strategy
Team
Largely solo, with my manager
Industry
Delivery and logistics
Designing my own job
It started with a single checkout feature. It ended with a shared definition of who the organization’s customers were.
Adoptedas the organization’s shared definition of its customers
The call I made
In a meeting about two lines on the checkout page, nobody could say when a customer should ship to a pickup location instead of home. We were guiding that choice on a hunch.
Pickup was being built one feature at a time, and the picture of who our customers were and their use cases was blurry. I moved from feature work to service design for the whole access point ecosystem, giving up quick feature wins to find out who our customers were and what they were counting on.
The hard part
I did it largely solo, on a senior UX designer’s title, with no charter for service design.
The world was bigger than the one we pictured: lockers customers were afraid to visit, addresses given as landmarks, and expectations that differed by culture.
What it changed
A wall-sized service blueprint connected the Last Mile and access point teams and let people see how the whole system fit together.
Nine archetypes became the Durable Customer Outcomes, adopted as the organization’s shared definition of its customers.
My director approved a multi-city research proposal, aimed where the business was already expanding, as the department’s most important research for the next year, alongside a three-year plan for the design team I co-wrote with my manager.
What I learned
The leverage point is different in every organization. At Amazon, a proposal moved when the evidence tied a customer outcome to investments the business was already making. I was improving the return on something already committed, rather than asking for something new.
A governed workspace where AI agents do real work against my files under written rules. Every decision that governs future work gets an ID, a date and a quote, so a rule made once is still in force a week later. The agents read the register before they act.
Assumptions get expensive when they become roadmaps.
I help organizations make better decisions about complex human systems.
The long way upstream
One maker who kept dividing into new capabilities, and is still one person.
Beyond the work
Five questions
What sent me upstream?
Early in my career I shipped campaigns, commercials, brands and music videos. But the thing that stuck with me most was a failed product launch followed by failed iterations. I saw the defeat a failed launch has on an organization and its people, and I wanted to prevent that in the future.
What am I actually interested in?
I love to untangle the tangible and intangible systems that dictate how experiences unfold. I'm especially interested in how organizations make decisions, which is really their epistemic operating model. The best external outcomes for customers start with making better decisions internally, and that's a hard thing to get right.
Why does this matter more now?
In this new age where AI is rapidly accelerating the building and output cycles, it matters more than ever to identify the right problems early, because the downstream effects compound. Coherent systems of knowledge and context become the most valuable currency inside an organization, and building those is the part I want to be doing.
How do I work with AI?
Mostly as something that needs governing. What I build are systems of connected information with rules about what is true, who owns which fact, and what has to be checked before anything gets acted on. Inside that, the work is closer to coaching than prompting: guiding, critiquing, running feedback loops, and noticing the small things that separate adequate output from good. The noticing is the part it still cannot do. It fills real gaps in my capability, mostly technical, and it lets me see a dozen executions in the time one used to take. The value there is not that it produces more. It is that it produces things I can reject quickly, and getting to the right answer is mostly a matter of discarding the wrong ones sooner. But it is confidently wrong often enough that the interesting design problem is the system around it rather than the prompt itself.
Where am I less useful?
Hand me a settled roadmap and one screen to perfect, and you'll get competent work from someone quietly going flat. The further I am from the decision, the less I'm worth, which is a strange thing to advertise, but better said now than discovered together in month six. I like repairing very large systems, and I'm not the person you want polishing a small one.
Tell me where you're trying to go. I'll tell you if I can help you get there.