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Longhand AI competency course

RICE for AI PrioritiesTurn a list of AI ideas into a challengeable ranking.

Use one equation and consistent evidence to compare recurring workflows—then test whether uncertain assumptions change the decision.

Version 2.075 minutesPractice evidence required

The finish line

An evidence-backed ranked decision brief

Your table shows the equation, units, sources, assumptions, sensitivity, constraints, and selected next step. It is practice evidence, not a certified priority decision.

Progress is saved in this browser.

Longhand AI competency course

RICE for AI PrioritiesTurn a list of AI ideas into a challengeable ranking.

Use one equation and consistent evidence to compare recurring workflows—then test whether uncertain assumptions change the decision.

Version 2.075 minutesPractice evidence required

The finish line

An evidence-backed ranked decision brief

Your table shows the equation, units, sources, assumptions, sensitivity, constraints, and selected next step. It is practice evidence, not a certified priority decision.

Progress is saved in this browser.

Measurable learning objectives

By the end, you can produce evidence—not just repeat the framework.

Calculate a RICE score with consistent reach, impact, confidence, and effort units.

Support each estimate with visible evidence and lower confidence when evidence is weak.

Compare candidates and test whether plausible changes reverse the ranking.

Use RICE as decision support while documenting strategic, safety, and capacity constraints it does not capture.

You will see a worked example, diagnose a weak example, complete guided practice, and leave with an independent practice artifact. The artifact prepares you for a later human-scored performance assessment.

Course requirements

Know what you need—and what counts as evidence.

Prerequisites

At least three recurring workflow candidates or the three practice candidates in this course.
A shared result and comparison period for the candidates.
Permission to use the underlying operational evidence or a synthetic practice case.

Assessment connection

Milestone 1 — Opportunity Diagnosis · planned

A RICE table for at least three candidate workflows
Sources and assumptions for every factor
Sensitivity analysis and a defended priority decision

Lesson practice is formative. This course does not issue proficiency or certification; the listed performance assessment requires separate evidence and qualified human evaluation.

Authoritative sources and course version

Version 2.0 · estimated 75 minutes · sources reviewed 2026-08-10

RICE: Simple prioritization for product managersIntercom · Origin, equation, example scales, and limitations of the RICE prioritization model.Higher Education Rubric, Seventh EditionQuality Matters · Constructive alignment among objectives, assessment, materials, activities, and technology.OSCQR SUNY Online Course Quality Review RubricSUNY Online · Online-course review, authentic assessment, interaction, feedback, support, and accessibility.

The guide

Explain it. See it. Challenge it. Apply it. Save the evidence.

Step 1 of 5 · 12 min

Define each factor and keep units comparable across candidates.

Use the equation consistently

RICE disciplines comparison by combining reach, impact, confidence, and effort. The arithmetic is simple; rigor depends on comparable units and evidence that another reviewer can inspect.

Do this

1Use the equation: RICE score = (Reach × Impact × Confidence) ÷ Effort.
2Reach: count affected people, events, or workflow runs in one shared time period. Do not compare monthly reach with annual reach.
3Impact: estimate effect on the shared result. Intercom's example scale is 3 massive, 2 high, 1 medium, 0.5 low, and 0.25 minimal.
4Confidence: discount the estimate based on evidence. Intercom's example scale uses 100% high, 80% medium, and 50% low; enter the equation as 1, 0.8, or 0.5.
5Effort: estimate total person-time across implementation, data preparation, review, testing, training, governance, and maintenance using one shared unit.

Remember: Longhand applies RICE to recurring AI workflows. The named framework and example scales come from Intercom; the score is not an external certification standard.

What you leave with

RICE scoring guide

Shared units and period

RICE equation and example scales

Factor	Example measure	Common error
Reach	Workflow runs per quarter	Mixing periods or double-counting the same people/events
Impact	3, 2, 1, 0.5, 0.25 against one result	Calling excitement or strategic language impact
Confidence	1.0, 0.8, 0.5 based on evidence	Using 80 instead of 0.8 in the equation
Effort	Total person-weeks	Counting build time but omitting review, data, and maintenance

Step 1 of 5 · Run it

Apply it before you move on

Do this

1Use the equation: RICE score = (Reach × Impact × Confidence) ÷ Effort.
2Reach: count affected people, events, or workflow runs in one shared time period. Do not compare monthly reach with annual reach.
3Impact: estimate effect on the shared result. Intercom's example scale is 3 massive, 2 high, 1 medium, 0.5 low, and 0.25 minimal.
4Confidence: discount the estimate based on evidence. Intercom's example scale uses 100% high, 80% medium, and 50% low; enter the equation as 1, 0.8, or 0.5.
5Effort: estimate total person-time across implementation, data preparation, review, testing, training, governance, and maintenance using one shared unit.

Remember: Longhand applies RICE to recurring AI workflows. The named framework and example scales come from Intercom; the score is not an external certification standard.

Set the comparison rules

Create a RICE scoring guide for these AI workflow candidates. First choose one reach period, one impact scale tied to the same result, a confidence scale based on evidence quality, and one effort unit that includes implementation and human review. Do not score the candidates until the units are explicit.

Candidates: [paste]

What you leave with

RICE scoring guide

Shared units and period

Step 2 of 5 · 14 min

Treat every factor as a claim with a source, assumption, and uncertainty.

Make the evidence visible

A precise score can create false confidence when its inputs are guesses. Visible sources and assumptions let reviewers challenge the factor that matters instead of arguing about the final ranking.

Do this

1Attach a source and observation date to reach: workflow logs, transaction counts, support volume, or another traceable record.
2Tie impact to the TDAR result and state whether the estimate comes from experiments, comparable cases, user evidence, or judgment.
3Set confidence after reviewing the evidence, not after seeing the desired score. Lower confidence when reach, impact, or effort is weakly supported.
4Estimate effort across all contributors and recurring operating cost, including human review and exception handling.
5Label unknowns and record the cheapest evidence that could narrow them before a build.

Remember: Confidence is not the average of confidence in every input unless you define and validate that method. Use a consistent rule and explain it.

What you leave with

Evidence ledger

Revised confidence values

Unknown-reduction plan

Evidence strength guide

Strength	Example	Treatment
Observed	Timestamped workflow runs and measured handling time	Use with stated period, sample, and exclusions
Comparable	Prior pilot on materially similar work	State differences and reduce confidence if transfer is uncertain
Elicited	Structured interviews or expert estimate	Preserve method and sample; do not present as measured behavior
Assumed	Unverified belief about adoption or effort	Label it, lower confidence, and define a test

Step 2 of 5 · Run it

Apply it before you move on

Do this

1Attach a source and observation date to reach: workflow logs, transaction counts, support volume, or another traceable record.
2Tie impact to the TDAR result and state whether the estimate comes from experiments, comparable cases, user evidence, or judgment.
3Set confidence after reviewing the evidence, not after seeing the desired score. Lower confidence when reach, impact, or effort is weakly supported.
4Estimate effort across all contributors and recurring operating cost, including human review and exception handling.
5Label unknowns and record the cheapest evidence that could narrow them before a build.

Remember: Confidence is not the average of confidence in every input unless you define and validate that method. Use a consistent rule and explain it.

Audit the input evidence

Audit this RICE table before calculating. For each factor, identify the source, date, unit, assumption, and uncertainty. Recommend a confidence level only after reviewing the evidence. Include full implementation and operating effort. Do not invent missing data.

RICE table: [paste]

What you leave with

Evidence ledger

Revised confidence values

Unknown-reduction plan

Step 3 of 5 · 14 min

Follow the arithmetic and inspect why the lower-impact candidate can still rank first.

Calculate and compare two workflows

A worked comparison reveals the effect of each factor and makes calculation mistakes easier to catch before a ranking influences real investment.

Do this

1Candidate A, incident-report drafting: (120 quarterly runs × 2 impact × 0.8 confidence) ÷ 3 person-weeks = 64.
2Candidate B, support-ticket classification: (300 quarterly runs × 0.5 impact × 1.0 confidence) ÷ 2 person-weeks = 75.
3Rank B above A under the stated assumptions because its greater reach, confidence, and lower effort outweigh lower per-run impact.
4Check the math, units, evidence, and strategic constraints before interpreting 75 as meaning 'automatically build B.'

Remember: The example scores support comparison under defined assumptions. They do not establish real-world impact for either workflow.

What you leave with

Two calculations

Ranking explanation

Constraint list

Worked comparison

Candidate	Reach	Impact	Confidence	Effort	Score
A — Incident report drafting	120/quarter	2	0.8	3 person-weeks	64
B — Ticket classification	300/quarter	0.5	1.0	2 person-weeks	75

Step 3 of 5 · Run it

Apply it before you move on

Do this

1Candidate A, incident-report drafting: (120 quarterly runs × 2 impact × 0.8 confidence) ÷ 3 person-weeks = 64.
2Candidate B, support-ticket classification: (300 quarterly runs × 0.5 impact × 1.0 confidence) ÷ 2 person-weeks = 75.
3Rank B above A under the stated assumptions because its greater reach, confidence, and lower effort outweigh lower per-run impact.
4Check the math, units, evidence, and strategic constraints before interpreting 75 as meaning 'automatically build B.'

Remember: The example scores support comparison under defined assumptions. They do not establish real-world impact for either workflow.

Reproduce and explain

Calculate both RICE scores and show each substitution into the equation. Then explain in plain language why the ranking differs from comparing impact alone. Finally list three non-score constraints that could still change the decision.

A: reach 120, impact 2, confidence 0.8, effort 3
B: reach 300, impact 0.5, confidence 1.0, effort 2

What you leave with

Two calculations

Ranking explanation

Constraint list

Step 4 of 5 · 15 min

Test uncertain inputs and document limits RICE does not resolve.

Challenge the ranking

A ranking is fragile when a plausible change to one uncertain factor reverses it. Sensitivity analysis tells you whether to decide, gather evidence, or avoid false precision.

Do this

1Identify the least certain or most decision-sensitive factor for the top candidates.
2Recalculate a plausible low and high case. If Candidate B effort rises from 2 to 4 person-weeks, its score falls from 75 to 37.5 and Candidate A becomes first.
3State whether the ranking is stable, fragile, or tied and what evidence would reduce the important uncertainty.
4Apply constraints outside RICE: strategic fit, safety, legal or policy restrictions, dependencies, capacity, equity, and the cost of failure.
5Do not use RICE to launder a prohibited or unsafe option into consideration; remove ineligible candidates before ranking or explicitly gate them.

Remember: RICE structures comparison; it does not replace judgment, validate causality, or price every harm and dependency into one number.

What you leave with

Low/base/high cases

Stability judgment

Constraint gate

Next evidence action

Interpretation rules

Finding	Action
Stable ranking	Proceed to the DMP review if constraints and permissions also pass.
Fragile ranking	Collect the evidence most likely to reverse the order before committing.
Near tie	Use strategic fit, risk, sequencing, or a bounded comparative test.
Ineligible candidate	Do not rank past a safety, policy, authority, or dependency gate.

Step 4 of 5 · Run it

Apply it before you move on

Do this

1Identify the least certain or most decision-sensitive factor for the top candidates.
2Recalculate a plausible low and high case. If Candidate B effort rises from 2 to 4 person-weeks, its score falls from 75 to 37.5 and Candidate A becomes first.
3State whether the ranking is stable, fragile, or tied and what evidence would reduce the important uncertainty.
4Apply constraints outside RICE: strategic fit, safety, legal or policy restrictions, dependencies, capacity, equity, and the cost of failure.
5Do not use RICE to launder a prohibited or unsafe option into consideration; remove ineligible candidates before ranking or explicitly gate them.

Remember: RICE structures comparison; it does not replace judgment, validate causality, or price every harm and dependency into one number.

Run sensitivity analysis

For the top two RICE candidates, identify the most uncertain decision-relevant inputs. Calculate plausible low and high cases, state whether the ranking is stable or fragile, and recommend either decide now or gather specific evidence first. Apply safety, policy, dependency, capacity, and strategic constraints separately from the score.

Table and evidence: [paste]

What you leave with

Low/base/high cases

Stability judgment

Constraint gate

Next evidence action

Step 5 of 5 · 20 min

Score at least three new candidates and defend what should happen next.

Produce your independent ranked decision brief

Transfer requires more than reproducing the worked calculation. The independent brief combines comparable units, source quality, sensitivity, constraints, and a decision another reviewer can challenge.

Do this

1Choose at least three recurring workflow candidates tied to one result and reach period.
2Define the scales and units before scoring. Attach a source or assumption to every input.
3Calculate the scores and show the equation for each candidate. Check that confidence is entered as a decimal.
4Run a plausible sensitivity case for the top two candidates and apply eligibility constraints.
5Select the next candidate to test, defer, reject, or investigate. Explain what evidence would reverse the decision and save the complete brief.

Remember: Use synthetic or de-identified data when operational evidence is not authorized for the AI system used during practice.

What you leave with

Three-candidate RICE table

Sensitivity analysis

Ranked decision brief

Step 5 of 5 · Run it

Apply it before you move on

Do this

1Choose at least three recurring workflow candidates tied to one result and reach period.
2Define the scales and units before scoring. Attach a source or assumption to every input.
3Calculate the scores and show the equation for each candidate. Check that confidence is entered as a decimal.
4Run a plausible sensitivity case for the top two candidates and apply eligibility constraints.
5Select the next candidate to test, defer, reject, or investigate. Explain what evidence would reverse the decision and save the complete brief.

Remember: Use synthetic or de-identified data when operational evidence is not authorized for the AI system used during practice.

Independent RICE brief template

# RICE AI Workflow Decision Brief

## Shared result and scoring rules
[Result, reach period, impact scale, confidence rule, effort unit]

## Candidate table
| Candidate | Reach + source | Impact + evidence | Confidence + rationale | Full effort + basis | RICE score |
|---|---:|---:|---:|---:|---:|

## Calculations
[Show each substitution into (R × I × C) ÷ E]

## Sensitivity analysis
[Low/base/high case for the top two; stable, fragile, or tied]

## Eligibility and constraints
[Safety, policy, authority, dependencies, capacity, strategic fit]

## Decision
[Test, defer, reject, investigate, or choose an alternative; explain why]

## Evidence that would reverse the decision
[Specific evidence and how it would change an input or constraint]

What you leave with

Three-candidate RICE table

Sensitivity analysis

Ranked decision brief

Formative practice complete

You have a ranking that can survive questions.

Keep the calculations, sources, sensitivity analysis, and constraints with your TDAR and DMP records. The planned Opportunity Diagnosis assessment will evaluate the combined evidence.

Next course

Responsible AI Practice

Apply the data, risk, human-accountability, and professional controls that determine whether a candidate is eligible to proceed.

Open training

Review your gaps

Return to the AI Tune-Up

Update your formative self-rating and open the next direct lesson. Green remains self-reported, not certified.

Open training