The Productivity Problem Nobody Talks About: Decision Drift

The issue of productivity isn't always about being productive. While task managers, calendars, notes, reminders, and project management tools are useful, they don't always answer the question of whether decisions made throughout the day reflect one's stated priorities. This phenomenon is known as decision drift - the gradual gap between what one intends to focus on and what decisions actually cause them to focus on.

Decision drift occurs when a series of small decisions move someone away from their intended goals. For example, someone might have planned to build a product feature, talk to potential users, and protect personal time, but end up answering Slack messages, attending meetings, fixing minor issues, checking analytics, responding to emails, and jumping between unrelated tasks. Despite completing many tasks, the important work didn't progress as expected.

Task lists don't reveal this issue because a completed task doesn't explain why that task was chosen. A completed task list might show "Answered email," "Fixed a small UI bug," "Joined a meeting," "Updated documentation," and "Researched a new tool," but without understanding how those decisions relate to a broader intention, such as validating customer demand, it's unclear if those choices were productive.

The consequence of decision drift is that small decisions accumulate over time, leading to a drift in focus away from intended goals. These seemingly insignificant decisions can create a different direction than the one originally intended. To address this, productivity needs another layer - not just tracking activity, but noticing direction.

Intent and tasks are different. An intent is what matters right now, while tasks are decisions made in service of that intent. For example, the intention "Build a company customers actually want" can translate into tasks like "Interview users," "Analyze feedback," "Build a prototype," and so on. While these tasks are important, they are not the intent themselves.

Decision drift can be understood through four layers: intent, decisions, outcomes, and patterns. Patterns are especially important, as they reveal whether certain behaviors consistently lead away from intended goals. Task history alone doesn't provide this insight, but a decision memory could show patterns in decision-making.

To address decision drift, productivity software should remember decisions, not just tasks. A decision memory would reveal patterns in decision-making, such as repeatedly choosing Y over X even when planning to do X. This information is valuable because it helps make the patterns visible, rather than focusing on whether individual choices were good or bad.

Edorena, a web application being developed, is an experiment around this problem. Instead of starting with a giant task list, it begins with what matters in a particular context and tries to help people notice the relationship between their intentions and the decisions they make.