Tasks / Strategy

Develop product strategy

Can the model make a coherent choice grounded in the evidence, rather than list aspirations?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 16 graded outputs by 8 models. 38% were usable with at most a quick edit.

Reliably right

Produces the required deliverable100% pass
Complete strategy document in the requested form, length, and for the named reader; the board could act on it.
GPT-6 Astra · ChatGPT · Supply or demand for a stalled marketplace
Diagnosis before prescription100% pass
Diagnosis: students can't find the right tutor; actions (intake, recommendations, rematch, manual matching) directly address it.
GPT-6 Astra · ChatGPT · Supply or demand for a stalled marketplace
Tells the CEO plainly100% pass
It clearly states the enterprise-first plan is wrong for next year, backs it with numbers, and presents the alternative.
GPT-6 Astra · ChatGPT · Enterprise, says the CEO

Where it slips

Names an advantage that's hard to copy11% pass
Does not name a benefit and a barrier that a well-funded competitor couldn't copy; no moat is identified.
GPT-6 Astra · ChatGPT · Supply or demand for a stalled marketplace
Proposes tests that could fail42% pass
The SMB pilot lacks a numeric threshold for success; while the payback guardrail has a threshold and action, not every proposed test/gate meets the full requirement.
GPT-6 Astra · ChatGPT · Enterprise, says the CEO
Avoids unsupported claims44% pass
Presents 'root cause: we've let the loudest requests set the roadmap' and 'SMB remains our top-of-funnel' as facts without labelling them as hypotheses or citing evidence.
Haiku 5.5 · API · Enterprise, says the CEO

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

Diagnosis before prescription
Does the strategy name the crux, the specific obstacle or opportunity the evidence points to, and do the proposed actions follow from that diagnosis?
Passes when States one crux grounded in the supplied evidence, and each major action visibly addresses it.
Bangaly KabaLenny’s Podcast
Names an advantage that's hard to copy
Does the strategy say what will make its position hard for an informed, well-funded competitor to copy (a benefit customers value plus a barrier to copying it), rather than relying on being first, on features, or on funding?
Passes when Names the benefit and the barrier, and why a capable competitor couldn't simply replicate it.
Hamilton HelmerLenny’s Podcast
Says what it gives up
Does the strategy name what the company will stop doing, or deliberately not do, in order to pursue it?
Passes when Names at least one real thing being dropped or declined, with the reason it loses out.
Gokul RajaramLenny’s Podcast

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Proposes tests that could fail · and 2 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the VP Product at Cadence. Our CEO wants a strategy for next year the exec team can rally behind, and believes it's enterprise (the note is below). Write the strategy memo, in no more than 1,200 words: the diagnosis, the guiding policy (where we play and what we stop), the coherent actions for next year, and how we'll know it's working. Tell the CEO if you think they're wrong. The files are attached.

What the model was given6 items: About Cadence, ceo_note.md, unit_economics_by_segment.csv, enterprise_detail.md, mid_market_detail.md, smb_detail.md

About CadenceStaff scheduling and time tracking, sold to businesses with hourly workers.

ceo_note.md1 lines · Download

From the CEO: "I want a strategy for next year that the exec team can rally behind. I believe it's enterprise. Tell me if I'm wrong."

unit_economics_by_segment.csv3 rows · Download

segment,customers,avg_arr_usd,gross_margin_pct,support_cost_per_customer_year_usd,cac_usd,payback_months,net_revenue_retention_pct,logo_churn_year_pct
SMB self-serve,6200,1100,78,610,740,31,88,34
Mid-market,890,14500,81,1900,9800,9,118,7
Enterprise,34,96000,64,21000,118000,26,104,9

enterprise_detail.md4 lines · Download

# Enterprise detail

- Average sales cycle: 9 months. 5 of the last 8 deals needed custom features (built by the core team, about 14 engineer-weeks each).
- The CEO's plan: "Win enterprise. Hire 10 enterprise AEs and a solutions team, and build SSO, audit logs and custom workflows this year."

mid_market_detail.md5 lines · Download

# Mid-market detail

- Best-retaining segment. Expansion comes from adding locations and seats.
- Top requests: multi-location reporting, payroll integrations, shift templates.
- Win rate against our main competitor: 61%.

smb_detail.md4 lines · Download

# SMB self-serve detail

- 62% of support tickets come from SMB accounts. Free trial converts at 4%.
- Most SMB customers are on the $79/month plan with unlimited phone support.

What a strong answer doesThe answer key the graders mark against

Diagnoses from the unit economics: mid-market is the engine (payback 9 months, NRR 118%, churn 7%, wins 61% against the main competitor); SMB self-serve loses money once support and churn are counted (about $1,100 a year at 78% gross margin is about $860, minus $610 of support, leaves about $250 a year against $740 to acquire, with 34% leaving each year; payback 31 months, NRR 88%) and generates 62% of tickets; enterprise is slow and expensive (payback 26 months, 64% gross margin, 9-month cycles, 5 of 8 deals needing about 14 engineer-weeks of custom work). Tells the CEO the enterprise bet is wrong for next year: it would pull the core team into custom work and stretch payback. Policy: win mid-market, expanding on locations and seats; stop subsidising SMB support (change the plan or pricing, self-serve support, raise the entry price) rather than abandon it; serve enterprise only where it buys the product as is. Coherent actions: mid-market roadmap (multi-location reporting, payroll integrations, shift templates), SMB support and pricing changes, no custom enterprise work. Names what it gives up and how it'll know (mid-market NRR and payback, SMB support cost per customer).

Critical failures (cap the score at 40)

Recommends hiring the enterprise team and building custom workflows as next year's main strategy

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6 AstraChatGPT87Usable with a quick edit Sonnet 5.5API87Usable with a quick edit GPT-6.1 SolAPI85Usable with a quick edit Haiku 5.5API81Usable with a quick edit GPT-6 LunaAPI75Needs more work Opus 5.5Claude71Needs more work Gemini 3.8 FlashAPI67Needs more work Gemini 3.5 Flash-LiteGemini56Needs more work

Haiku 5.5API

Usable with a quick edit9 of 12 checks passed

Minor unsupported claims can be removed or rephrased in an edit; the core evidence and logic are sound and the memo is actionable.

Output

To: CEO From: VP Product Re: Next-year strategy

Bottom line: I think you're wrong. We are a mid-market company, and the enterprise plan would spend our scarcest resources, engineering time and hiring budget, on the segment with the weakest economics. Enterprise can be a real option later. It shouldn't be the strategy for next year.

Diagnosis

Annualized from the unit economics file (my calculations, not reported figures):

	SMB self-serve	Mid-market	Enterprise
Customers	6,200	890	34
ARR (share)	~$6.8M (30%)	~$12.9M (56%)	~$3.3M (14%)
Gross margin	78%	81%	64%
Support cost / customer / yr	$610	$1,900	$21,000
Gross profit less support / customer	~$250	~$9,850	~$40,400
CAC	$740	$9,800	$118,000
Payback (months)	31	9	26
NRR	88%	118%	104%
Logo churn	34%	7%	9%

Mid-market is our engine. It produces most of our ARR, expands at 118% NRR, pays back in 9 months, and churns at 7%. We win 61% of head-to-head deals against our main competitor. Customers are asking for multi-location reporting, payroll integrations, and shift templates, which are the features that drive expansion.

SMB is a support sink. SMB accounts generate 62% of support tickets. Support costs about $3.8M a year against roughly $6.8M of SMB ARR, so over half of SMB revenue goes to support. Most SMB customers are on a $79/month plan with unlimited phone support. Churn is 34%, trial conversion is 4%, and lifetime contribution barely covers CAC.

Enterprise is a real market but a poor fit for us today. We have 34 logos. Sales cycles run nine months. Gross margin is 64%, and payback is 26 months before custom work is counted. Five of our last eight enterprise deals needed custom features built by the core team at about 14 engineer-weeks each. That is roughly 70 engineer-weeks, or about 1.5 engineer-years, in eight deals. The plan doesn't account for that cost.

Root cause: We've let the loudest requests set the roadmap and support model. Enterprise asks for custom builds, SMB asks for phone support, and neither request is what drives our best economics.

Guiding policy

Where we play: Mid-market multi-location employers with hourly workforces. This is the customer we win, keep, and expand, and where our product is strongest.

What we stop: 1. Custom builds for individual deals. No bespoke engineering without a paid statement of work that covers full cost and passes a "does this generalize?" test. 2. Unlimited phone support for SMB. SMB moves to self-serve, chat, and knowledge-base support, with paid priority support as an option. 3. Dedicated enterprise hiring and outbound. Enterprise becomes inbound-only, served by the mid-market product. We don't build a headcount plan around it. 4. Single-segment edge-case features that don't help mid-market or the platform.

What we don't stop: The 34 enterprise customers keep full service. We don't reprice or abandon them. SMB remains our top-of-funnel, but it stops being a support-heavy product.

Coherent actions for next year

Build the mid-market expansion product. Ship multi-location reporting first, then shift templates, then the two payroll integrations that come up most in mid-market accounts. This gets roughly 70% of core product capacity.

Move the enterprise hiring budget to mid-market. Use it for mid-market AEs (where a 61% win rate and 9-month payback make each dollar work faster) and for expansion coverage on multi-location accounts.

Restructure SMB support. Remove unlimited phone support. Create a clear upgrade path to mid-market plans when an SMB account adds a second location. Use product changes to deflect the most common tickets. The goal is to halve support cost per SMB customer.

Productize enterprise table stakes, nothing more. Build SSO and audit logs once as standard features. Some mid-market buyers need them too, and building them once is cheaper than building them repeatedly for deals. Don't build custom workflows. Keep the 34 enterprise accounts well served through CS.

Govern the roadmap. A product council with exec sign-off reviews any request that isn't on the mid-market or platform roadmap. Every custom request must pass the segment-fit test.

Capacity split (proposed): ~70% mid-market roadmap, ~15% platform (SSO, audit logs, reliability), ~15% SMB self-serve and ticket deflection.

How we'll know it's working

Mid-market (primary): - NRR ≥ 120% (from 118%) - Logo churn ≤ 7% - Win rate vs. main competitor ≥ 61% - Payback ≤ 9 months on new mid-market CAC - Share of expansion ARR from added locations and seats, tracked quarterly

SMB: - Support cost per SMB customer down from $610 to ≤ $300 - SMB tickets per customer down year over year - Gross profit after support positive per customer on the new support model - SMB churn trending down; we accept some SMB logo loss as the price of the change

Enterprise: - Zero unplanned custom engineering weeks outside approved SOWs - Enterprise gross margin trending up toward 70%+ - No loss in renewals among the 34 existing accounts

Company: - Blended gross margin up, driven by the mix shift - Total ARR growth on the mid-market base

Revisit enterprise in 12 months if mid-market hits its targets and we see a steady flow of inbound enterprise deals (for example, three per quarter) that fit the standard product without custom work.

Risks

We'll lose some SMB and enterprise revenue. The bet is that mid-market growth and margin improvement outweigh it. We should show the exec team the expected net effect before we commit.
Mid-market buyers may demand SSO and audit logs sooner. Building them as standard features in the platform bucket covers this.
The enterprise story matters to investors and some of the team. We should explain it as focus with a defined path back, not as retreat.

I'm glad to walk through the model and the capacity assumptions with you before the exec session. My recommendation is to take this to the team as the choice between being a strong mid-market company now and a weak enterprise company later.

Claims the judge couldn’t find in the brief

Root cause: 'We've let the loudest requests set the roadmap and support model. Enterprise asks for custom builds, SMB asks for phone support, and neither request is what drives our best economics.'
SMB remains our top-of-funnel.

Grades and run details

Decision model 83 · LLM judge 77

Decision model checks

passUses the supplied evidence correctly35%
passAddresses the actual decision100%
passRespects explicit constraints48%
passIdentifies material uncertainty51%
partialAvoids unsupported claims52%
passProduces the required deliverable89%
passReads the segment economics98%
passTells the CEO plainly100%
passDiagnosis before prescription100%
partialProposes tests that could fail79%
failNames an advantage that's hard to copy46%
passSays what it gives up100%

Run

Run: #1
API response time: 36 s
Submitted: 8 Oct 2026

GPT-6 LunaAPI

Needs more than a quick edit10 of 12 checks passed

The memo lacks a competitive moat and specific test thresholds with trigger actions; adding these would require more than a quick edit without reworking the reasoning.

Output

To: CEO and Executive Team From: VP Product Subject: Next year’s strategy: win the mid-market, fix the cost to serve

Diagnosis

I think you’re wrong that enterprise should be our primary strategy next year. Enterprise can be a selective opportunity, but the evidence does not support building the company around it.

Mid-market is our strongest growth engine: it has the best retention (118% net revenue retention, 7% logo churn), a 61% win rate against our main competitor, and clear expansion paths through more locations and seats. Its requested improvements—multi-location reporting, payroll integrations and shift templates—reinforce the core scheduling product rather than pull us into one-off work. At $14,500 average ARR, it represents roughly $12.9 million of ARR across 890 customers.

Enterprise has only 34 customers and about $3.3 million of ARR. Its economics and delivery model are less attractive: 64% gross margin, $21,000 annual support cost per customer, 26-month reported CAC payback, and a nine-month sales cycle. Five of the last eight deals required custom features, consuming about 70 engineer-weeks in total. Hiring 10 AEs and building custom workflows would scale a motion we have not shown is repeatable. SSO and audit logs may be worthwhile if they serve a broader, standard product—not as the opening move in a bespoke enterprise program.

SMB is also not where we should prioritize growth investment. It has 6,200 customers, but 34% annual logo churn, 88% NRR, and 62% of support tickets. Unlimited phone support on the $79 plan appears poorly matched to the segment’s economics.

One caution: the reported payback figures do not reconcile consistently with ARR, gross margin and support cost. We should standardize their definition before using them to allocate capital.

Guiding policy

Play to win multi-location mid-market employers with hourly workforces. Make Cadence the easiest dependable way to schedule and track time across locations, with strong payroll connections and repeatable workflows.

Stop treating enterprise as the company’s growth thesis. Do not hire 10 enterprise AEs or build customer-specific workflows with the core team. Pursue enterprise deals only when they fit the standard product, support attractive economics and do not displace mid-market priorities.

Stop subsidizing SMB support through unlimited phone service on the entry plan. Keep serving SMB customers, but shift the offer toward scalable self-service and appropriately priced support—not a broad retreat from the segment.

Coherent actions for next year

Make the mid-market roadmap the top product priority. Deliver multi-location reporting, payroll integrations and shift templates in that order, validating scope with customers and tying releases to adoption and expansion. Build configurable, reusable capabilities; decline requests that create customer-specific branches.

Focus go-to-market on repeatable mid-market wins. Equip sales and onboarding around multi-location use cases, implementation playbooks and expansion from existing locations and seats. Invest in customer success where it can turn adoption into expansion and reduce avoidable churn.

Set an enterprise investment gate. Pause the proposed 10-AE hiring plan. Evaluate SSO and audit logs as reusable platform capabilities, but fund them only with evidence of demand beyond individual deals. Require enterprise opportunities to fit the standard product; price implementation and customization explicitly, and do not commit core engineering to bespoke workflows. Revisit enterprise hiring only when deals are repeatable and payback is demonstrably attractive.

Repair SMB service economics without breaking the customer experience. Move entry-plan support toward self-service, improve onboarding and help content, and test paid higher-touch support. Measure the effect on conversion and retention before making a broad pricing or support change.

Create one trusted segment-economics view. Reconcile CAC payback and consistently include gross margin, support, implementation and custom engineering costs. Review it quarterly before adding segment-specific capacity.

How we’ll know it’s working

Mid-market: NRR rises from 118% to at least 120%; logo churn falls from 7% to 6% or less; win rate stays at or above 60%. Track ARR expansion from added locations and seats, plus adoption of the three roadmap investments.
Enterprise: No broad AE expansion; custom core-engineering work falls sharply, with a target of no more than two engineer-weeks per won deal. Track fully loaded payback and sales-cycle length before increasing investment.
SMB: Reduce support tickets per customer and support cost per customer; improve trial conversion from 4% without worsening churn. Make any support-tier changes conditional on those customer outcomes.
Company-wide: Review segment ARR, gross profit after support and implementation, and fully loaded CAC payback together. Growth that depends on unrepeatable engineering or uneconomic service is not success.

This strategy does not abandon enterprise or SMB. It puts our next year’s scarce product and hiring capacity behind the segment with the clearest evidence of repeatable growth, while setting firm conditions for investing more elsewhere.

Grades and run details

Decision model 79 · LLM judge 77

Decision model checks

passUses the supplied evidence correctly52%
passAddresses the actual decision98%
passRespects explicit constraints64%
passIdentifies material uncertainty70%
partialAvoids unsupported claims30%
passProduces the required deliverable87%
partialReads the segment economics56%
passTells the CEO plainly100%
passDiagnosis before prescription100%
partialProposes tests that could fail77%
failNames an advantage that's hard to copy47%
passSays what it gives up100%

Run

Run: #1
API response time: 29 s
Submitted: 6 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 3

Uses the supplied evidence correctlyMixedRight

Haiku 5.5 · API

Two statements about current situation (root cause and SMB as top-of-funnel) are not from the brief nor arithmetic.

GPT-6 Luna · API

All factual claims about the current situation are taken directly from the supplied context or follow by arithmetic.

Avoids unsupported claimsWrongRight

Haiku 5.5 · API

Presents 'root cause: we've let the loudest requests set the roadmap' and 'SMB remains our top-of-funnel' as facts without labelling them as hypotheses or citing evidence.

GPT-6 Luna · API

Uses cautious language ("I think", "appears") for interpretations and does not present forecasts as established fact.

Proposes tests that could failRightWrong

Haiku 5.5 · API

Proposes numeric thresholds (NRR ≥ 120, churn ≤ 7%, support cost ≤ $300, zero custom weeks) and a 12-month revisit gate with specific conditions.

GPT-6 Luna · API

The 'How we'll know' section lacks specific numeric thresholds for SMB and enterprise tests, does not define measurement windows, and does not state what actions would follow if targets are missed.

All got wrong 1

Names an advantage that's hard to copyWrongWrong

Haiku 5.5 · API

Does not name a benefit plus a barrier that would make the position hard for a well-funded competitor to copy; relies on features and current win rate.

GPT-6 Luna · API

The memo describes benefits (easiest way to schedule, payroll connections) but does not name a barrier that would prevent a capable competitor from copying the position.

All got right 8

Addresses the actual decisionRightRight

Haiku 5.5 · API

Commits early to disagreeing with the CEO, says enterprise is the wrong bet for next year, and specifies conditions to revisit.

GPT-6 Luna · API

Commits early to rejecting enterprise as the primary strategy, proposes mid-market focus, and states conditions for revisiting enterprise.

Respects explicit constraintsRightRight

Haiku 5.5 · API

The memo contains diagnosis, guiding policy, actions, and measures, is under 1,200 words, and addresses the CEO.

GPT-6 Luna · API

The memo addresses all requested sections, stays under 1,200 words, and directly tells the CEO they are wrong.

Identifies material uncertaintyRightRight

Haiku 5.5 · API

Names the risk of SMB/enterprise revenue loss and the condition (mid-market targets + inbound enterprise flow) to revisit the enterprise decision in 12 months.

GPT-6 Luna · API

Identifies the inconsistent payback figures as an uncertainty that could affect capital allocation, and notes the need to standardize definitions; sets repeatability and payback as conditions to revisit enterprise.

Produces the required deliverableRightRight

Haiku 5.5 · API

Complete strategy memo in the requested form, addressed to the CEO, within word limit, and directly usable by the exec team.

GPT-6 Luna · API

Complete strategy memo with diagnosis, policy, actions, and metrics, well under the word limit and usable by the CEO and exec team.

Reads the segment economicsRightRight

Haiku 5.5 · API

Works through payback, retention, and support cost for all three segments and concludes mid-market is the strongest, SMB loses after support, enterprise is slow and costly.

GPT-6 Luna · API

Works through retention, support cost, and payback for all three segments, concludes mid-market is the strongest, enterprise is slow and costly, and implies SMB is uneconomic by calling for an end to subsidizing its support.

Tells the CEO plainlyRightRight

Haiku 5.5 · API

States plainly and respectfully that the enterprise-first plan is wrong, backed by the segment numbers.

GPT-6 Luna · API

States directly that the CEO is wrong about enterprise as the primary strategy, backed by segment numbers.

Diagnosis before prescriptionRightRight

Haiku 5.5 · API

Names a crux (resource allocation driven by loudest requests) and all major actions address it.

GPT-6 Luna · API

Diagnosis identifies mid-market as the engine and enterprise/SMB issues; each major action visibly addresses those findings.

Says what it gives upRightRight

Haiku 5.5 · API

Names clear stops: custom builds for deals, unlimited SMB phone support, dedicated enterprise hiring and outbound, single-segment edge-case features.

GPT-6 Luna · API

Explicitly stops enterprise AE hiring, custom engineering work, and unlimited phone support for SMB, with reasons.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 84% of 76 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	83.3	88.5	2	None
2	Haiku 5.5withAPI	77.1	80.8	2	None
3	Sonnet 5.5withAPI	77.1	80.8	2	None
4	GPT-6.1 SolwithAPI	79.2	76.9	2	None
5	GPT-6 LunawithAPI	77.1	76.9	2	None
6	Opus 5.5withClaude	83.3	65.4	2	None
7	Gemini 3.8 FlashwithAPI	68.8	57.7	2	None
8	Gemini 3.5 Flash-LitewithGemini	60.4	42.3	2	None

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Nobody beats these on both score and costCost partly estimated (app runs with no usage meter)

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	74	1.0×
Haiku 5.5 · API	80	1.7×
Gemini 3.5 Flash-Lite · Gemini	49	3.1× (partly estimated)
Gemini 3.8 Flash · API	61	8.1×
GPT-6.1 Sol · API	77	12×
Sonnet 5.5 · API	78	27×
Opus 5.5 · Claude	71	68× (partly estimated)
GPT-6 Astra · ChatGPT	87	84× (partly estimated)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

The PM job

Writing a strategy memo that leadership can act on.

Why it matters

Strategy that doesn't choose isn't strategy. Models write fluent strategic prose easily; making a choice the evidence supports and naming what it gives up is harder.

What good looks like

Diagnoses the one obstacle that matters before prescribing
Makes one clear choice and names what is sacrificed
Grounds the choice in the supplied evidence
Surfaces the tension or contradiction in the data
Defines how we would know it is working

Deliberately not measured

Market sizing accuracy beyond the supplied data
Financial modelling

Capability tested

Choosing where to play and what not to do, from supplied evidence

The failure we’re looking for

Strategic-sounding aspirations without a diagnosis or a choice

Grading

Decision model and LLM judge, calibrated against a blind PM review

What AI gets right here, and what you’ll still have to catch

Reliably right

Where it slips

How it’s graded

Diagnosis before prescription

Names an advantage that's hard to copy

Says what it gives up

Plus our standard checks

The tasks

The outputs

Output

Diagnosis

Guiding policy

Coherent actions for next year

How we'll know it's working

Risks

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Output

Diagnosis

Guiding policy

Coherent actions for next year

How we’ll know it’s working

Grades and run details

Decision model checks

Run

Check by check

Where they differ 3

All got wrong 1

All got right 8

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured