I grew up on a cattle farm in Alberta, where chores before school teach a specific kind of pragmatism: look at the animal, look at the numbers, then decide. I have spent 16 years turning that instinct into design and research practices built from zero, translating ambiguous technology, most recently AI, into evidence-led products with measurable outcomes.
Fintech, e-commerce, public service and agriculture. Founder of ORBIT, an AI decision-support product for livestock operators. MIT Professional Education, AI and Machine Learning.
87%digital penetration at US Foods, company-reported
Selected work
01Case studies
Five engagements, told the same way: the problem, the decisions I made, and what shipped. Metrics carry timeframes and honest attribution. Original research artifacts are available under NDA on request.
1.1ORBIT · FounderAI decision support · Agriculture
Designing narrow AI for a high-consequence domain: every suggestion cited, every decision human.
RoleFounder: product, design, research
ScopeDecision-orchestration layer over existing farm systems
PrincipleBounded five-action taxonomy, human review on every suggestion
MeasureAgreement and override rates, per suggestion type
ORBIT is a decision layer for livestock operators. It reads the systems a farm already runs (ear-sensor data, weigh heads, ration tools, vet logs, tag records), and each morning lines up the calls that need a human: which animal to pull, weigh, treat or shift. It suggests exactly one next action per animal, shows every number's source, and the operator confirms or overrides. ORBIT never makes the call, and it stores nothing: confirmed events post back to the farm's own system of record.
I grew up on the farm this product is designed for. The design problem is trust under consequence: a wrong call is a sick animal or a lost sale, so the recommendation space is deliberately bounded to five action types, every suggestion carries its why in plain words, and overrides are treated as training signal rather than failure. Agreement and override rates are measured per suggestion type, which keeps the model honest and the operator in charge.
ORBIT● 5 SOURCES ● WIFI · BARN
Morning check · Pen 14 & 9
Morning, Bob.
5 animals need a call. ORBIT checked your systems overnight and lined them up.
Pull · BRD suspectTag 124 000 7344
Pull tag 7344, BRD suspect.
Temperature is up and she's gone quiet at the bunk overnight. The signal is showing before you'd see it walking the pen.
EAR TEMP COWMNGR
+0.9°C
39.8° vs 7-day avg
RUMINATION COWMNGR
298 min
was 460 baseline
WEIGHT GALLAGHER
910 lb
+0.7 lb ADG · day 21
WITHDRAWAL MEDS
CLEAR
last tx 92 days ago
Why this call? · Show sources
PULL · SEND TO COWMANAGERPRE-FILLED · 1 TAP
NOT THIS ONELOG & LEARN
ORBIT prepares · your system of record keeps the event · you make the call
Fig. 01The decision card, recreated to spec from the current Alberta review build (AB-DEMO v1.0). One call, the why in plain words, and every number citing its source system. The only actions are confirm or override.
Fig. 02The user flow, from an operator's own evaluation: one morning at Barhaven from chore boots to record filed. ORBIT reads five sources, surfaces one call, and the confirmed event lands back in the system of record.
Fig. 03Onboarding, from the working prototype: the walkthrough opens with what ORBIT is and what it is not. Two minutes, once.
Fig. 04The only hardware in the product: one rugged button mounted chute-side. Press it and the next animal's card is on screen before she stands still.
Fig. 05The walkthrough ends on a practice animal: the call, the why in plain words, the sources behind every number, and the decision.
Result
A working review build live with Alberta operators, with agreement and override telemetry in every card from day one.
1.2US FoodsB2B commerce · Chicago
Modernizing food-service commerce around the people who cook, order and run restaurants.
RoleLead researcher and designer, via Relevance Collective
MethodIn-restaurant studies with chefs and operators, archetype workshops, card sorts
Outcome87% digital penetration; roughly +1.5 cases per order (company-reported)
US Foods is one of the largest food-service distributors in the United States. The digital platform had to serve chefs and independent operators whose real workplace is a kitchen, not a desk. I led field research inside working restaurants: watching orders happen at the pass, at the walk-in, and at 6 a.m. deliveries, then translating that context into the platform's information architecture and ordering patterns.
The research program ran in two phases: generative studies to define operator archetypes and their subconscious motivators, then evaluative rounds against the redesigned sign-up flow, product detail pages and the Inspire content area. Recommendation logic was grounded in what operators actually reorder, not what merchandising wanted to push.
Fig. 06Research model from the archetype program: the subconscious motivators of the independent restaurant operator. Perceptions, values, vulnerabilities, fears, needs.
Fig. 07From the same journey-map series: how operators build and manage shopping lists, with the pain points and opportunities that shaped list-building, substitutions and reorder logic.
Fig. 08The third artifact in the series: where the motivators model explains why operators buy, the product-discovery journey maps how they actually do it, with the pain points and opportunities that set the redesign backlog.
Fig. 09The shipped platform: the storefront serving 400,000+ products, structured around the Inspire, Attract, Optimize model that came directly out of operator research.
Fig. 10Product detail redesign: spec-first layout, chef-facing merchandising, reviews and reorder logic where operators expect them.
Result
87% digital penetration and roughly +1.5 cases per order.
Both figures are company-reported, and the redesign was contributory rather than the sole cause. Full research repository available under NDA.
1.3Neo FinancialFintech · Research leadership
Scaling a research function inside a high-velocity fintech without slowing it down.
RoleUX Research leadership
ScopeVision-led research strategy, KPIs, operating model, hiring
Timeline50% of a multi-quarter strategy delivered in the first 4 months
Outcome+50% research headcount; research embedded in product cadence
Neo ships fast. The failure mode for research in that environment is becoming a service desk: reactive studies, findings that arrive after decisions. I wrote a vision-led strategy that repositioned research as an engine for product direction, backed by KPIs the executive team could hold the function to, and an intake-to-insight process fast enough to live inside sprint cadence.
The operating model made the practice legible: a five-step process from intake to shared insight, three-day turnaround on early-stage steps, and a repository (Dovetail) so insights compound instead of evaporating. The case for growth was made with evidence, and the team grew 50% while the strategy was still being executed.
Fig. 11How the strategy ran in practice: three topics taken through twelve sessions across four disciplines, distilled into three themes and opportunities, each ending in action items.
Fig. 12Opportunity two from the strategy: baking rich customer insights into design strategy, with action items for tomorrow and the outputs named for each.
Fig. 13From the strategy itself: the opportunity framing for operationalizing research at Neo, proving its ROI, and making customer insight second nature to every team, with action items and named outputs.
Result
+50% research headcount, and half of a multi-quarter strategy shipped inside 4 months.
Strategy artifacts shown are sanitized; full versions available under NDA.
1.4Canada PostPublic service · Practice building
Establishing research inside a crown corporation, and proving it with an 18-point SUS lift.
RoleUX Research, practice founder
ScopeBuilt the research discipline: methods, cadence, lab studies, stakeholder education
MeasureSystem Usability Scale, tracked release over release
Outcome+18-point SUS lift on a national consumer service; +50% YoY generative research volume
Crown corporations are not famous for moving quickly, and there was no research function to inherit. I built one: recruiting pipelines, a usability lab cadence, generative programs ahead of roadmap decisions, and the internal education needed to make evidence part of how a very large, very established organization ships.
The proof had to be quantitative to land internally. We instrumented a national consumer service with the System Usability Scale and tracked it across releases as research findings were designed in. The score rose 18 points. Generative research volume grew 50% year over year as teams began pulling research upstream instead of auditing downstream.
Step 1Prove value on one high-traffic service
Step 2Instrument it: SUS as the shared scoreboard
Step 3Move upstream: generative ahead of roadmap
Step 4Scale the practice: cadence, lab, education
Fig. 14Concepting at the wall: a one-screen tool sketched state by state, beginner and advanced paths included, before anything reached wireframes.Fig. 15The room at work: participants clustering insights on the wall mid-session. The practice lived or died on people showing up to do this.Fig. 16The whole room, mid-session: a co-design session I facilitated, stakeholders and users at the wall together, laptops down and everyone on their feet.
Fig. 17The national service on mobile today: the four jobs people come for, track, postal code, rate and post office, first on the page.
Fig. 18Same tools, new front door: MyMail sign-up promoted at the top of the mobile experience, digital preview of physical mail.
Fig. 19The small business program on mobile: plain-language value, two clear actions, nothing else competing.
Result
+18 SUS points on a national service, and generative research volume up 50% year over year.
Session photos shown with detail at arm's length; full synthesis outputs and the SUS trend data are available under NDA.
1.5CIBC Rewards · via BondLoyalty commerce · Banking
Redesigning how a bank's loyalty points get spent: one evidence-led experience, desktop and mobile in lockstep.
RoleResearch and design lead, via Bond Brand Loyalty
ScopeEnd-to-end redemption: homepage, discovery, cart and checkout, pay with points, wish list, profile
MethodFull UX package: user flows, site map, component library, annotated wireframes, evaluative findings
OutputEvery template specified for desktop and mobile together; creative iterated through two rounds
CIBC Rewards is where the bank's loyalty program becomes real: the moment points turn into flights, gift cards and products. Working through Bond Brand Loyalty, I ran research and design for the redemption redesign, rebuilding the journey from guest landing through browsing, wish list, cart, checkout and pay with points.
The work shipped as a complete UX package: context and user behaviours, the redemption flow, a site map with an explicit MVP cut, a component library, and annotated wireframes for every template, with desktop and mobile specified together rather than mobile adapted after. Evaluative research rounds fed findings back into the wireframes, and the creative went through two rounds before build.
Fig. 20The spine of the package: the points-for-products redemption flow, mapped as touchpoints rather than pages, from landing through checkout.
Fig. 21The site map with the MVP cut made explicit: navigation tiers colour-coded so scope decisions stayed visible to every stakeholder.
Fig. 22Annotated homepage wireframes: desktop and mobile side by side with numbered behaviour notes, so build inherited decisions, not guesses.
Fig. 23Round-two creative for the logged-in desktop homepage: points balance, wish list and cart persistent, promotions and recommendations doing the selling.
Fig. 24The same homepage on mobile: points balance, wish list and search inside the first screen, primary nav one thumb away.
Fig. 25An alternate pass at the same screen: the dark header treatment, with the points balance and travel credit still holding the top of the hierarchy.
Result
A build-ready specification for the full redemption experience, every template annotated for desktop and mobile, with research findings designed in before launch.
Wireframes and creative shown are from the delivered UX package; the evaluative findings deck is available under NDA.
Leadership
02How I lead
My leadership approach is documented, not improvised: it is the discipline I practice on users, turned on teams. What follows is drawn directly from the leadership framework I present internally.
"To create an environment of innovation and experimentation while being an invested leader, so my team always feels heard and respected."
My mission as a leader
P.1
Be present and encourage collaboration
Leadership that is present and participatory creates a sense of unification and builds culture.
In practice: facilitating kick-off workshops, participating in ideation sessions, supporting research initiatives directly.
P.2
People come first
Teams are made of humans with emotional needs. Active listening and staying true to your word builds trust.
In practice: ad hoc check-ins and regular surveys on team health and happiness.
P.3
Freedom to test and learn
Creating space to test new approaches to problem solving creates a culture of growth, freedom and constructive feedback.
In practice: weekly presentation and feedback sessions, quarterly innovation workshops, regular retrospectives.
P.4
Passion and engagement
Interactions, relationships and attitudes are critical to a healthy design culture. Be inclusive with design decisions and processes.
In practice: team off-sites and monthly opportunities to share experiences and failures.
I also run human-centred design on the team itself: discover team pain points the way you would user pain points, reframe the organizational gaps behind them, craft a team vision with a roadmap of actions, and keep iterating. Team growth and maturity are continuous, and it is leadership's job to ensure both.
Bond Brand LoyaltyBuilt the research discipline and proved its ROI inside 3 months; ran research and design across CIBC loyalty programs.
VarageSaleEstablished in-house research for a consumer marketplace at startup speed.
ShopifyDesigned and ran a field research program inside a scaling product org.
Tribal WorldwideFirst in-house UX hire; grew the function from one seat to a practice.
About
03The person
Dale McRaeToronto, relocating to Amsterdam.
The farm taught me to look first and decide second, and a career in research keeps proving that this is simply how good decisions get made. That instinct carried through 16 years of building design and research practices where none existed: an agency's first in-house UX hire, a crown corporation's first research function, a fintech's research engine, a loyalty giant's evidence discipline. The pattern repeats because the skill is the same: walk into ambiguity, define the actual problem, build the practice that answers it, and prove it with numbers.
ORBIT is where the two halves of my life meet: the farm I come from and the research discipline I built a career on, in one product. It is also why I hold a certificate in AI and Machine Learning from MIT Professional Education; if I am going to put AI in front of people making high-consequence decisions, I want to understand the machinery, not just the interface.
Amsterdam is the deliberate next step: a design-mature market where evidence-led leadership is the expectation, and where I want to build the next chapter. I am relocating, and I am looking for the right full-time leadership role: Head of Design, Director of UX, or Research Lead.
ThenWhere the pragmatism comes from.NowStill the same farm. Harvest, last September.
04Contact
Let's talk.
I am relocating to Amsterdam and available for full-time design and research leadership roles. If a portfolio walkthrough would be useful, I present any of these case studies as a full narrative arc: problem, decisions, shipped impact.
