05 · Reinvent Work
AI Experiment
Evidence before investment.
Validate AI opportunities through hands-on experimentation in real workflows, with real teams, measured against real outcomes.
The challenge
The organization has pilots that produce impressive demos and little evidence. Nobody can say whether the new way is better, for whom, or by how much — so decisions to scale stay stuck.
The outcome
Validated solutions and evidence of measurable improvements.
What we do
- 01 Pick a real workflow with a real bottleneck and define a hypothesis and a metric
- 02 Redesign the workflow with the team, with AI as a participant — not a plug-in
- 03 Build what the experiment needs: agentic workflows, automations, prototypes
- 04 Run it for real, measure, and decide: stop, adjust or scale
What you receive
- A working, redesigned workflow in production or close to it
- Evidence: what changed, for whom, measured against the baseline
- A team that can run the next experiment itself
Typical use cases
- Product discovery and development pilots
- Automation of administrative or support workflows
- Agentic workflows in software delivery
- Rapid prototyping of a customer-facing AI capability
Formats
- AI workflow experiments
- Product development pilots
- Automation pilots
- Agentic workflows
- Rapid prototyping
Especially for
- Product and engineering teams
- Product managers and owners
- Operations and support leaders
Related dimensions
Challenges this addresses
- Our development teams are faster, but delivery isn’t.
- We want to redesign how our teams work.
- We want to explore AI agents.
- We need to improve software quality.
Often combined with
- AI InspireSee what is possible — in your own work.
- AI LearnFrom knowing about AI to working with it.
- AI DiscoverFind the opportunities worth your attention.
Scope, duration and pricing depend on your context — we agree them together before we start.
Start an experiment
Tell us about one workflow that frustrates your team. That is usually the right place to start.
