A Vision for Machine-Amplified Learning

How software will help product developers learn at scale.

Image for post
Image for post

Extracting every ounce of learning from your actions is critical to solving hard problems. Every time you poke at the world is an opportunity to discover something new about the dynamics of a problem space.

Even for a small team, maximizing learning is hard. It requires discipline to routinely loop back to your previous endeavors to analyze what worked and what didn’t. It’s much easier to leave the past behind and blast forward to the next enticing plan.

For small teams, at least, it’s easier for contributors to remember the outcomes of their previous attempts and share insights. A startup’s ability to learn enables nimble pivots en route to the promised land.

Multi-team companies face larger obstacles in the learning process:

  1. It takes energy for contributors to communicate their learnings widely.

Today, maximizing learning at scale is almost impossible which takes away some advantages that bigger companies should have:

  1. Companies with longer histories should have accumulated more learning to make solving future problems easier.

The Double-Loop master plan is to remove the obstacles that prevent learning at scale. Here’s how we’ll do it.

Record keeping

The foundation of learning at scale is recording launches and results. Much of the data already exists in project management, deployment, code versioning, and analytics tools. Humans must add context such as strategies, goals, hypotheses, pictures, and results summaries.

Everyone in the company should be able to create, access, search the history of launches and results.

Communication

In realtime, every contributor should be able to follow the actions of other contributors that relate to their own work.

At the bare minimum, this can be accomplished by autogenerating high-level summaries, distributed by Slack or email, based on the record of launches and results.

But true learning at scales requires granular notifications. Teams should be able to subscribe to targeted facets of the launch record. For example, for a particular product change, customer support might need to know the details the UI while the sales team is more interested in the impact on the overall value proposition. Similarly, an engineer working on SEO should be able to see what other teams have done in the domain, what’s worked, not worked, etc..

Machine-amplified learning

While record keeping and communication provide the building blocks of learning at scale, there is potential for software to play a new role to amplify memory and learning. Here are a few ideas.

  1. Software can automatically generate a timeline of product launches based on deployments and project management software. Given the trend towards high-frequency, small deployments. Tools are needed to separate the signal from the noise.

I believe we’ve only scratched the surface of systematically cultivating learning in the innovation process.

Written by

Founder of of http://doubleloop.app, a tool for tracking product launches. Maze designer at http://mazestructure.com. Author of http://productlogic.org.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store