MHR · People First / Sprint facilitation · AI prototyping
An AI-accelerated one-day design sprint.
I designed and facilitated a cross-functional sprint to explore Job Profiles in People First. Prepared research, rapid AI prototyping and synthetic user feedback helped us agree a direction, review a concept and refine it within the day.
- My role
- Product Design & sprint facilitation
- Context
- MHR · People First
- Timeframe
- 11 August 2026 · Customer follow-up: 18 August 2026
- Collaboration
- Product, UX research, Architecture, Engineering and HR perspectives
The outcome in brief
One day. A shared direction, a reviewable prototype and clear next steps.
The brief and my role.
People First needed a reusable definition of a role to reduce duplicated job information. Job Profiles was the subject of the sprint; the design challenge was to bring several product and operational perspectives together and agree a practical starting point.
I used the one-day format to compress alignment, ideation, decisions and prototyping into a shared working session. The aim was a direction and a concept the team could challenge, with enough clarity to continue detailed design afterwards.
My contribution
- Designed the sprint approach around prepared research, collaborative ideation, rapid AI prototyping and review.
- Worked with Product on the brief and stakeholder coordination, and with UX research on evidence and rapid prototyping.
- Facilitated discussion across design, product, technical and HR perspectives, helping the group move from individual ideas to a shared direction.
- Managed the scope of the day and helped separate agreed choices from questions that needed further design, technical assessment or customer validation.
Inside the one-day sprint.
Before the day / Prepare
Arrive ready to make decisions
The brief drew on research with 20 external HR practitioners, using 13 open-text questions, alongside an interview with our internal HR team. Their experience of copying role information and maintaining it across systems gave the workshop a concrete problem to work from.
Participants received the research and brief in advance and were asked to add impacts, assumptions and questions to the Miro board. We also checked AI tooling, access and contingencies. That groundwork gave the group a shared starting point and reserved workshop time for decisions and making.
Morning / Align and explore
Open up ideas, then focus
I used Crazy 8s to open up ideas, with gallery-style review, voting and discussion built into the sprint structure. Bringing technical and non-technical perspectives into this stage helped product dependencies and operational constraints shape the direction early.
The discussion expanded into Job Families and wider inheritance questions. I brought the group back to a smaller target: the reusable Job Profile and its first-release value. We kept future grouping possible, while focusing the day on a direction we could make tangible.
That scope decision mattered to the pace of the sprint. It gave us a clear prototype target while keeping wider dependencies visible as follow-up work.
Afternoon / Prototype
Give the discussion something tangible
Rapid AI prototyping helped turn the agreed direction into an administrative journey showing profile creation and job assignment. A reviewable concept gave the team a common reference for questions about access, data relationships and feasibility.
Afternoon / Review and refine
Challenge assumptions while the team is together
The emerging prototype went through synthetic critique and stakeholder review, with live changes during the afternoon. Product, technical and operational perspectives could challenge what the journey assumed while it was still easy to revise.
For example, reviewing creation access exposed the assumption that every HR administrator should be able to create a shared profile. The team challenged that and explored a more specific management role. The exact permission rules remained open, but the prototype made the decision concrete.
AI in the design process
Make. Test. Refine.
AI helped shorten the gap between an agreed idea and useful feedback. I structured the afternoon so making, critique and stakeholder review could feed directly into refinement.
01 / Make
Rapid AI prototyping
AI-assisted prototyping made the chosen direction reviewable quickly. The team could inspect a journey, question its assumptions and explore changes while the discussion was still fresh.
02 / Test
AI synthetic user testing
Simulated user feedback supplied a rapid exploratory critique of the prototype. We used it to generate questions for stakeholder review. External customer validation followed separately, testing the direction against real operational needs.
03 / Refine
Live iteration
We brought the early critique into group discussion and updated the prototype during the same afternoon. Keeping the concept and review close together helped us turn questions into visible changes and clearer next steps.
The practical gain was a shorter feedback loop within the workshop. AI accelerated making and early critique; the team retained responsibility for scope, feasibility and decisions.
What the sprint achieved.
By the end of the day, we had a shared direction, a concept prototype and explicit questions for further design. The prototype was developed enough to break into feature-level journeys, giving the next stage a concrete starting point.
We had also narrowed the first-release focus and surfaced decisions around ownership, permissions and how shared information should behave. Recording these open questions made the follow-up work visible alongside the choices we had agreed.
Later customer validation reinforced priorities around governance, bulk operations and integration. This added an external perspective to the direction established in the room and helped shape the next refinement work.
Next steps
- Break the concept into focused journeys and continue customer testing.
- Agree the open governance, permissions and data rules with Product and Engineering.
- Investigate migration and integration needs, then refine requirements and delivery stories.
What I learned.
The one-day format depended on preparation and active facilitation. Bringing evidence into the room early and keeping the scope focused gave the team room to explore ideas, make a concept and challenge it together.
AI was most useful between decisions: helping us make an idea tangible, gather an early critique and revise it quickly. Research and cross-functional judgement gave that speed a useful direction, with real customer validation continuing afterwards.