Improvement

Prioritization

Choosing what to change and where to invest next.

The question
When we decide what to improve next, are we reading real signal or chasing the loudest voice?
The discipline

The field does not hold still: new tools, new techniques, a new thread every week claiming to change how the work gets done. Prioritization is the discipline of steering your team's attention through that churn: which sources of signal you let in, how you weigh them against the work already in front of you, and which changes you take up versus deliberately let pass.

When that steering runs on evidence, your team spends its effort on the changes that compound, and keeps pace instead of waking up a year behind.

The investment tiers

Exploratory

No plan. Changes happen reactively, driven by whoever cares most or whatever thread landed last.

Enables
Movement without a prioritization tax, and a cheap read on which signals are worth acting on. Chasing a few loud ideas by hand teaches you what an intake process later gets built to filter for.
Deficiencies
Effort scatters with nothing to rank it, no outside signal comes in, and the field moves while the team does not.
Cost to maintain
Compounding obsolescence, plus the opportunity cost of investing in the wrong things.

Techniques

None yet. Manual and ad hoc.

Expert-led

One engineer's reading and judgment are the whole team's intake and plan.

Enables
Some signal reaches the team, and the occasional well-chosen bet gets made.
Deficiencies
A single point of failure whose feed and priorities are unwritten, so the awareness leaves when they do.
Cost to maintain
Their time scanning, triaging, and choosing, unrecognized and invisible.

Techniques

  • . A written scan of the external AI landscape (new tools, techniques, threads) that ends in a recommendation rather than a pile of links.
  • . Catalog documents that encode the choices already made for a domain: which libraries to use, how to structure things, and the anti-patterns to avoid.

Documented

Signal gets collected and discussed, and priorities are written down, though intake and prioritization stay uneven.

Enables
Shared awareness and an agreed set of priorities, with debate happening in the open rather than in one head.
Deficiencies
Discussion without a decision, signal and noise mixed in one feed, and priorities that drift from what actually gets done.
Cost to maintain
Curating the intake and holding the prioritization, plus the time spent on threads that go nowhere.

Techniques

  • . A written scan of the external AI landscape (new tools, techniques, threads) that ends in a recommendation rather than a pile of links.
  • . A written rubric that ranks candidate changes against shared criteria, so what to invest in next is chosen legibly rather than by whoever pushes hardest.
  • . Catalog documents that encode the choices already made for a domain: which libraries to use, how to structure things, and the anti-patterns to avoid.

Structural

A standing cadence pulls signal in and produces prioritized investment decisions, independent of who spotted what.

Enables
The team stays current and invests deliberately rather than reactively, and priorities are legible and revisited.
Deficiencies
A cadence can chase novelty over value if the prioritization bar is loose, and its upkeep competes with shipping.
Cost to maintain
The recurring ritual, the intake pipeline, and the follow-through to hold the priorities.

Techniques

  • . A written scan of the external AI landscape (new tools, techniques, threads) that ends in a recommendation rather than a pile of links.
  • . A written rubric that ranks candidate changes against shared criteria, so what to invest in next is chosen legibly rather than by whoever pushes hardest.
  • . Catalog documents that encode the choices already made for a domain: which libraries to use, how to structure things, and the anti-patterns to avoid.
  • . Reusable prompt documents that encode a workflow, its rules, and its anti-patterns, so a task starts from a runbook instead of a fresh explanation.
Discipline overlap

Prioritization rarely stands alone. Where it overlaps a sibling discipline, an investment on one side quietly depends on the other. The Improvement domain page lays these out in full.

  • Prioritization and Evaluation

    You cannot rank what you have not measured. Evaluation turns a quarter of AI work into numbers a leader can defend, and Prioritization is how those numbers get spent.

    Measure without ever choosing from what you found and the evidence just sits there, an expensive read nobody steers by. The real failure is the other order: a team that chooses without measuring hands the invest-next call back to the loudest voice in the room, the one habit Prioritization exists to break.

  • Prioritization and Convergence

    Prioritization decides which change earns the team's energy; Convergence gets that change out of one head and into everyone's workflow. Pick the right bet and never converge on it and you have a correct decision quietly decaying unused, re-earned by hand each time someone new needs it. Converge hard with nothing ranking what spreads and you propagate whatever got tried last, noise moving as fast as signal.

    Held together, they are the difference between a good idea becoming standard practice and a good idea becoming a story about the one engineer who had it figured out.

Browse the framework
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Improvement

The team gets better over time.

  • EvaluationMeasuring whether your processes work, by the numbers and by gut-check.
  • Prioritization (current page)Choosing what to change and where to invest next.
  • ConvergenceMaking a change stick across the team.