“Agile” is one of those horribly overused words in the IT industry that means different things to different people. Views range from “Great! No plans, documents or deadlines!” to “I am a Certified Practitioner and we do it this way.”
Successful “agile” means having enough process (and no more) to enable the release of meaningful, incremental product changes – then measure what difference those changes make and feed that back into deciding what to do next – a “Learning Machine”.
Most new product ideas turn out to be unsuccessful. Moreover, it is notoriously hard – indeed impossible – to predict in advance which of a range of plausible-sounding product ideas is actually going to work.
Most successful products are actually the sum of a large number of good design decisions. (Think Macbook Air.) If you can find a way to generate a lot of individual improvements, then you have a chance to make a big difference to your product. A good brainstorming session, combined with customer feedback, will generate a long list of candidates for “how to make the product better”.
So how does it work?
- For each plausible idea generate a quick and inexpensive plan for how to test it out and measure success – a hypothesis
- Run the Learning Machine in week-long cycles; sift the ideas and decide which experiments to run
- Is this idea going to pan out in a reasonable timeframe?
- Will the idea make a measurable, meaningful difference?
- Is this going to be costly to develop and test?
- Run the experiments and analyse the findings. Most hypotheses will prove unsuccessful. What matters is to test a lot of ideas in a short space of time, and minimise the amount of time and effort it takes to discover whether a hypothesis is successful
- Kill off the ideas with no legs; rework and retest the ideas with some potential until you find something that really works
- Once on to a winner, develop the idea properly – this can take longer than seven days for more complex ideas
The Learning Machine attempts to merge the best of Lean UX and agile to foster a culture of experimental iteration and continuous improvement. It directs resources and energy to the features that matter most to customers and saves considerable amounts of wasted time and effort.