EXPERIMENT AS A SERVICE
Automation experimentation and feasibility assessment
Most automation programs stall in the gap between "that sounds promising" and "we approved a build." Experiment as a service inverts that. Instead of debating an idea, we run it: a short, funded sprint that turns a hypothesis into a working artifact and a clear go, no-go, or pivot decision. You spend a small, capped amount to remove a large, expensive uncertainty.
- A fixed price and a fixed clock, so a test never becomes a project
- A working artifact on real data, not a slide deck
- A real kill option, so a cheap no-go protects a large budget
Enterprises do not lack ideas. They lack a cheap way to test them.
Treating experimentation as a service turns a backlog of guesses into a pipeline of validated ideas.
A fixed price and a fixed clock, so a test never becomes an open-ended project. A working artifact, not a slide deck, so the decision rests on evidence rather than opinion. A real kill option: a well-run experiment that says do not build this is a win. A reusable pattern, so each experiment feeds a backlog of validated ideas ready to scale.
Validated ideas earn their way into Robotic Process Automation and the broader Intelligent Automation portfolio.
Spend a small, capped amount to remove a large, expensive uncertainty.
A compressed, time-boxed cadence from question to decision.
Sharpen the hypothesis
Define the success metric. Exit on a testable question and a measurable bar. (verify)
Assemble the thinnest thing
Build only what proves or disproves it. Exit on a working artifact running on real data. (verify)
Run against real scenarios
Capture results. Exit on evidence measured against the success bar. (verify)
Score and recommend
Exit on a go, no-go, or pivot with a scaling estimate. (verify)
Durations are typical and confirmed at kickoff. Every phase has a defined exit, so the sprint always ends with an answer. Timeline is industry-typical and scoped per experiment.
Evidence you can act on, whichever way it points.
- A working artifact that demonstrates the idea on representative data, not a mockup.
- A measured result against the success metric you agreed up front.
- A clear recommendation: scale it, kill it, or reshape it, with the reasoning shown.
- A scaling sketch if the answer is go: what a production build would take, so the next decision is informed.
A backlog of ideas, sorted into build and stop.
Challenge: A [global enterprise client] had a backlog of automation ideas and no fast way to separate the promising from the plausible.
Result: A rolling series of two-week experiments tested [X] ideas in a quarter; the strongest went to build, the rest stopped before they consumed a budget. (Softobiz to verify.)
Where a validated idea goes next.
Low-Code No-Code App Development
Where a promising experiment becomes a production app fast.
Implementation and Managed Services
Building the validated idea to standard, then running it under SLA.
Support and Maintenance
Keeping the scaled automation healthy against change.
App Migration and Modernization
Reshaping legacy applications a validated idea depends on.
Microsoft Dynamics 365 and Power Platform
Business apps and low-code automation on the Microsoft stack.
Low-Code Development
The parent practice this service belongs to.
What innovation leaders ask us first.
No, and deliberately so. The goal is evidence at the lowest cost. Production hardening is a separate, informed decision you make after the experiment answers the question.
Then it succeeded at its job. A cheap do-not-build-this protects a large budget. You keep the artifact and the learning either way.
We help you shape a portfolio of hypotheses and sequence them by potential value and testability, so each sprint attacks the most valuable open question next.
Bring your riskiest automation assumption and let’s turn it into evidence in a single sprint.
A fixed price, a working artifact, and a scored decision, so the next commitment is informed.