Process Designing for and with AI
While AI substantially speeds up our ability and ease of creating and communicating high-fidelity concepts and experiences,
domain knowledge & deep understanding of people and their objectives matters, to ensure we’re tackling the right problem, in the most appropriate way possible.
Principles:
Intent vs apps
Aggressive disambiguation
Confident decision-making requires evidence
Provide and learn from ‘overrides’
There’s a place for design though
the nature and output of our ‘craft’ will evolve.
Before AI…
My activities during the design process depend on the size of my team, and my role on the project (project or people manager, product owner or creative director) and includes hands-on design. While the actors, timelines and scope may be different, projects typically go through the phases below.
And, if phases or steps are skipped, we’ll often ‘pay’ for it later..
I love the ambiguity in the earlier phases of defining what we’ll be creating, and more importantly: why, turning deep understanding of the customer into scenario-driven designs. And, I get a lot of satisfaction of getting all the ‘trains on the rails’ to ship a compelling product, system or service - preferably all three.
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Inquire
Business development
Objectives
Planning, scoping, resourcing
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Immerse
Research & interviews
Technology deep-dive
Recommendations report
Product/project strategy
High-level concepts
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Design
Use cases and scenarios
Design - concepts, experience, interaction model, visuals
Requirements and business rules
Design reviews
Concept testing
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Implement & deploy
Planning & scoping
Product and release strategy
Detailed design & specs
Exit criteria
UI copy & documentation
Sprint planning
Quality control