Tasks / Growth

Write a GTM plan

Can the model pick the customers who value the difference most, position against what they do today, and build a go-to-market that lasts beyond launch day?

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 18 graded outputs by 9 models. 44% were usable with at most a quick edit.

Reliably right

  1. Positioned against the real alternative100% pass
    Names Miro, photos/slides, and doing nothing as alternatives, and ties the message to real-time tidy diagramming and starting without IT.
    GPT-6 Astra · ChatGPT · Enterprise, without killing the engine
  2. Sells against a pain the buyer feels100% pass
    The message addresses the governance and control pain that IT buyers feel when teams adopt tools without oversight, supported by SSO requests and security-page visits in the data.
    GPT-6 Astra · ChatGPT · Enterprise, without killing the engine
  3. Protects the self-serve engine100% pass
    Uses the 70% free-to-paid stat and product-qualified deal data to argue against capping the free plan, and keeps self-serve open.
    GPT-6 Astra · ChatGPT · Enterprise, without killing the engine

Where it slips

  1. Uses the supplied evidence correctly28% pass
    The claim 'Five of our six losses on existing-user deals ended in no decision' is not supported by the data (4 no-decision, 2 Miro), so a statement about the current situation is invented.
    Opus 5.5 · Claude · Enterprise, without killing the engine
  2. Avoids unsupported claims39% pass
    Presents the incorrect 'five of six no decision' figure as established fact, which is an unsupported claim about the current situation.
    Opus 5.5 · Claude · Enterprise, without killing the engine
  3. Follows the deals data67% pass
    Although it compares by source, the computed product-qualified ACV and sales cycle are wrong, so it does not correctly follow the deals data.
    GPT-6.1 Sol · API · Enterprise, without killing the engine

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.

  1. Positioned against the real alternative

    Does the plan name what the target customer does today without this product (including the status quo or doing nothing), and tie the message to the value that alternative lacks?

    Passes when Names the real alternative and the differentiated value against it, and picks the segment that cares most about that value.

  2. Sells against a pain the buyer feels

    Is the core message built on a pain or risk the target buyer already feels, with evidence from the material, rather than on generic upside like saving time?

    Passes when Leads with the pain the evidence shows buyers care about, and uses that evidence to choose the message.

  3. Diagnoses message or channel first

    Does the plan work out whether the problem is the message (people reach it but don't convert) or the channels (the right people never see it), and spend accordingly?

    Passes when Uses conversion and reach data to say which it is, and puts the effort there.

  4. Plans beyond launch week

    Does the plan say how demand keeps coming after launch, with the ongoing engine and what's measured over months, rather than treating launch day as the whole plan?

    Passes when Gives the ongoing motion after launch and the measures that show whether it's working over time.

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 · and 3 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 Flowboard. The board wants $5M of new enterprise ARR next year, and our CRO, Jordan Hale, has drafted the enterprise go-to-market below. The CEO has asked for your version before the board meeting: who we sell to, how the self-serve product and the sales team work together, what we say against the alternatives, the capacity it takes, and what you'd change in Jordan's plan and why. No more than 1,300 words. The files are attached.

What the model was given6 items: About Flowboard, today.md, cro_enterprise_plan.md, enterprise_deals_last_12_months.csv, large_companies_using_flowboard.csv (companies over 1,000 employees with active self-serve or free teams), enterprise_win_loss_interviews.md
About FlowboardOnline whiteboards and diagramming for teams. Free plan, self-serve paid plans, and an enterprise plan sold by a small sales team.
today.md5 lines · Download
# Today

- Self-serve: 31,000 paying teams; free plan feeds 70% of new paying teams.
- Enterprise: 46 customers, average contract $64,000.
- Sales team: 3 account executives, closing about 10 enterprise deals a year each.
cro_enterprise_plan.md8 lines · Download
# Enterprise plan (CRO, draft for the board)

Target: $5M new enterprise ARR next year.
1. Cap the free plan at 3 users (today: unlimited users, 3 boards each), so teams have to talk to us.
2. Route every team over 10 seats to sales; remove self-serve checkout above 10 seats.
3. Hire 8 account executives and 6 SDRs; SDRs work outbound lists of Fortune 2000 companies.
4. Enterprise features: SSO, SCIM, audit logs, data residency.
…
enterprise_deals_last_12_months.csv65 rows · Download
deal,source,outcome,sales_cycle_days,acv_usd,lost_to
E-200,Outbound,won,201,32700,
E-201,Product-qualified (teams already using Flowboard),won,62,97000,
E-202,Product-qualified (teams already using Flowboard),won,61,108000,
E-203,Outbound,lost,192,,Miro (company standard)
E-204,Outbound,lost,204,,Miro (company standard)
E-205,Product-qualified (teams already using Flowboard),won,77,54200,
…
large_companies_using_flowboard.csv (companies over 1,000 employees with active self-serve or free teams)company,employees,teams_using_flowboard,active_users_30d,paying_seats,viewed_security_page_30d,asked_for_sso Aldergate Bank,15000,2,18,10,yes,yes Brightfield Bank,2400,6,54,36,yes,yes Corvel Logistics,15000,5,45,22,yes,yes Dunmore Holdings,4800,3,12,4,yes,yes Eastlake Health,1200,3,30,14,yes,yes Fennick Holdings,4800,8,48,30,yes,yes Galloway Retail,9000,9,36,20,yes,no Hartwell Group,1200,4,48,21,yes,no Iverson Retail,1200,3,18,9,yes,no Juniper Retail,1200,3,24,10,yes,no Kestrel Logistics,4800,1,12,6,yes,no Linton Group,1200,2,10,4,no,no Marlowe Group,2400,2,10,7,no,no Northcote Retail,9000,2,10,6,yes,no Oakhurst Group,9000,3,36,18,yes,no Pemberton Health,1200,1,6,2,yes,no Quayside Bank,4800,2,12,4,no,no Redfern Group,15000,3,18,10,no,no Stanway Health,9000,3,30,12,no,no Thornbury Bank,15000,3,24,12,no,no Upton Bank,9000,3,33,23,yes,no Varden Bank,2400,3,12,5,no,no Whitmore Bank,4800,2,8,5,no,no Yardley Logistics,15000,2,8,4,yes,no
enterprise_win_loss_interviews.md5 lines · Download
# Enterprise win/loss interviews (12 buyers, this year)

- Won (7): every win had teams already using Flowboard. "Our designers refused to give it up." "IT approved it because 300 people were already on it."
- Lost (5): four had standardised on Miro company-wide; switching meant migrating thousands of boards. One stayed on whiteboard photos and slides.
- What buyers said Flowboard does that Miro doesn't: real-time diagramming that stays tidy, and a free plan teams can start on without IT.
What a strong answer doesThe answer key the graders mark against

Builds enterprise sales on top of the self-serve engine instead of throttling it. The deals file shows product-qualified deals (teams already using Flowboard) won 19 of 25 at an average of about $72,600 and about 63 days, against outbound winning 4 of 25 at about $36,800 and about 190 days; product-qualified wins were about $1.38M of the $1.86M won. The free plan feeds 70% of new paying teams. So capping the free plan at three users and removing self-serve checkout above ten seats would choke the pipeline the enterprise business depends on, and the interviews say IT approved Flowboard because teams were already on it. Proposes product-led sales: AEs work the large-company list (multiple teams, SSO requests, security-page visits) with SSO and admin controls as the bridge, while keeping self-serve open. Positions against Miro and the status quo, not 'visual collaboration for the enterprise': where a company has standardised on Miro, don't lead with a migration; win where teams already use Flowboard, on tidy real-time diagramming and starting without IT. Reads outbound's poor results as a message problem as well as a channel one. Works out capacity: $5M at about $72,000 is about 70 deals, far more than three AEs close (about 30 a year), so it needs AE hiring focused on product-qualified accounts rather than SDR outbound lists. Plans the ongoing motion and the measures (product-qualified accounts, conversion, cycle time) beyond the first quarter.

Critical failures (cap the score at 40)
  • Endorses capping the free plan at three users or removing self-serve checkout above ten seats
Task

Staff-level · v1.1

The outputs

9 setups answered this brief.

Needs more than a quick edit9 of 13 checks passed

Several factual errors (PQL ACV, outbound win count, Miro loss count) must be corrected before use, and the inconsistency in the table damages credibility.

Output

To: CEO From: VP Product Subject: Enterprise GTM Plan: Hitting $5M New ARR via Product-Led Sales

Here is my assessment of Jordan’s enterprise draft and an alternative go-to-market plan to hit the board's $5M new enterprise ARR target next year.

If we execute Jordan’s traditional top-down outbound playbook, we will starve our self-serve engine and miss the $5M target. Our data shows we already have a repeatable, high-margin enterprise motion—it is Product-Led Sales (PLS), not cold outbound.

---

1. The Data: What’s Working vs. What’s Failing

An analysis of all 65 enterprise opportunities over the last 12 months ($1.86M total new ARR across 28 wins) reveals three starkly different motions:

Lead SourceDeals WorkedWonWin RateAvg. ACV (Won)Avg. Sales Cycle (Won)Total ARR Won
:---:---:---:---:---:---:---
Product-Qualified (PQL)251976.0%$71,04763 days$1,349,900 (73%)
Inbound Demo15640.0%$55,06786 days$330,400 (18%)
Cold Outbound25312.0%$36,800190 days$147,200 (8%)*

\Note: 4 outbound wins totaling $147.2k (16% win rate).*

Why Cold Outbound Fails: 18 of our 21 outbound losses were to "Miro (company standard)." Win/loss interviews confirm that when Miro is entrenched, ripping and replacing thousands of boards top-down is a non-starter. Outbound AEs spend 6+ months chasing $37k deals with an 84% failure rate.

Why PQLs Win: Every single buyer in our win interviews already had teams using Flowboard ("Our designers refused to give it up"; "IT approved it because 300 people were already on it"). PQLs close 3x faster, at nearly 2x the ACV, and win 76% of the time. Notably, 4 of our 6 PQL losses were to "No decision" (not Miro)—meaning with better IT/security packaging and multi-threading, our PQL win rate could exceed 80%.

---

2. Who We Sell To (ICP & Target Accounts)

We do not sell to cold Fortune 2000 IT buyers. We sell to mid-market and enterprise companies (1,000+ employees) where Flowboard already has grassroots adoption across multiple teams.

We have two immediate buyer personas within these accounts: 1. The Champions (Design, Product, & Engineering Leads): They adopt Flowboard bottoms-up because our real-time diagramming stays tidy (unlike Miro’s freeform sprawl). 2. The Economic/Economic-Gatekeeper Buyer (IT, InfoSec, Procurement): Triggered when usage hits critical mass, shadows IT thresholds, or requires SSO/security reviews.

Immediate Pipeline: Looking at our user base today, we have 24 enterprises (>1,000 employees) with active self-serve/free footprints: * Tier 1 (Immediate Sales Ready - 6 Accounts): Aldergate Bank, Brightfield Bank, Corvel Logistics, Dunmore, Eastlake Health, and Fennick Holdings. All 6 have multiple teams (up to 54 active users), visited our security page in the last 30 days, and explicitly asked for SSO. At our $71k PQL ACV, these 6 accounts alone represent ~$425k in near-term ARR. * Tier 2 (High-Intent Expansion - 10 Accounts): Companies like Galloway Retail (9 teams, 36 active users) and Hartwell Group (4 teams, 48 active users) that viewed our security page in the last 30 days. * Tier 3 (Nurture/Expand - 8 Accounts): Large enterprises (e.g., Redfern, Thornbury Bank) with 2–3 isolated teams where we need to bridge internal silos before pitching IT.

---

3. How Self-Serve and Sales Work Together

Self-serve is not competing with enterprise sales; self-serve is our enterprise customer acquisition cost (CAC) engine. Today, 70% of our 31,000 paying teams come from the free plan.

Instead of erecting paywalls that block adoption, Product and Sales will run a 4-step Land-Consolidate-Close loop:

1. Frictionless Land (Product): Keep the free plan unlimited in users (3 boards/user) and keep self-serve checkout completely open. Let teams inside 5,000-person companies spread Flowboard virally across departments without needing IT or procurement approval. 2. Domain Aggregation & PQL Scoring (Product + Data): Build internal tooling that groups accounts by corporate email domain (`@aldergate.com`). Trigger an automatic PQL alert to Sales when a domain hits any of these thresholds: * $\ge 3$ separate teams or $\ge 25$ active users across an enterprise domain. * Admin/user views the Security page or clicks "Request SSO / Enterprise." * Self-serve spend crosses $5,000/year across corporate credit cards. 3. In-Product "Trojan Horse" Prompts (Product): Show non-intrusive prompts to workspace admins at large domains: "8 other teams at Corvel Logistics use Flowboard. Want to consolidate billing and unlock SSO?" 4. Consultative Consolidation (Sales): Sales reaches out to Champions and IT not with a cold pitch, but with an audit: "You have 54 users across 6 teams paying on separate credit cards. Let’s consolidate your billing, put them under SSO/SCIM, and give you a volume tier."

---

4. Positioning & What We Say Against Alternatives

Jordan’s proposed messaging ("The visual collaboration platform for the enterprise") puts us head-to-head against Miro on their home turf. We will lose that fight.

Our Positioning: "Real-time diagramming and whiteboarding that actually stays tidy—built for product, design, and engineering teams, and ready for enterprise IT."

How We Win Against the Alternatives: * Against "Miro is our company standard" (Our #1 Loss Reason): Don't ask IT to rip and replace Miro company-wide. Position Flowboard as the specialized diagramming standard for technical/design teams where Miro is too messy, OR build a 1-click Miro Board Importer so entrenched teams can migrate without losing thousands of boards (directly solving the objection from 4 of our 5 loss interviews). * Against Status Quo / "No Decision" (Our #1 PQL Loss Reason): Arm champions with an ROI & Security Kit showing IT the risk of unmanaged "shadow IT" diagrams living on personal/fragmented accounts, making enterprise consolidation the safest choice.

---

5. What I Would Change in Jordan’s Plan (and Why)

Jordan’s ProposalMy RecommendationWhy
:---:---:---
1. Cap free plan at 3 usersReject. Keep unlimited users (3 boards).Our win/loss interviews explicitly state our free plan is why we beat Miro. Capping at 3 users kills viral multi-player collaboration and chokes off 70% of our self-serve pipeline.
2. Kill self-serve checkout >10 seatsReject. Keep self-serve open; alert Sales in parallel.Forcing an 11-person team onto a demo call creates friction and drives them to Miro. Let them buy on a credit card—a 20-seat self-serve team is our best enterprise PQL.
3. Hire 8 AEs + 6 SDRs for F2000 outboundModify to 5 new AEs (8 total) + 2 Product-Led SDRs.Cold outbound has a 16% win rate, 190-day cycle, and $37k ACV. Hiring 6 cold SDRs wastes ~$700k+ in burn. We need Product-Led SDRs who mine our 31,000 paying teams for multi-team expansion.
4. Build SSO, SCIM, Audit Logs, Data ResidencySequence carefully: Ship SSO, SCIM, & Domain Discovery Q1. Defer Data Residency.SSO and SCIM unlock our 24 warm enterprise accounts immediately. Add Admin Domain Discovery (so IT can see/claim existing teams) and a Miro Importer. Defer Data Residency—it is a multi-quarter infrastructure sinkhole, and zero lost deals cited it.
5. Generic Enterprise MessagingFocus on "Tidy Real-Time Diagramming + Zero-Friction IT Governance."Differentiates us on what users actually love while answering IT's security needs.

---

6. Capacity & Math to Hit $5M New ARR

To hit $5M in new enterprise ARR next year, here is the capacity math:

  • Deal Volume Needed: At our PQL average ACV of $71,000, $5M requires ~70 closed-won deals (vs. 28 this year).
  • Pipeline Needed: Assuming a conservative 65% blended win rate on PQL/warm inbound deals, Sales needs to work ~108 qualified enterprise opportunities next year.
  • AE Capacity: Today, 3 AEs close ~10 deals/year each (30 total) while wasting half their time on 200-day outbound losses. Focused purely on 63-day PQLs and warm inbound, a ramped AE can comfortably close 12–14 deals/year (~$850k–$1M ARR/AE).
  • Hiring Plan:
  • Keep our 3 current AEs (expected yield: ~36–40 deals / $2.7M ARR).
  • Hire 5 new AEs in Q1 (ramping to ~6–7 deals each in Year 1 = ~32 deals / $2.3M ARR).
  • Hire 2 Product-Led SDRs (to map org charts inside our 31,000 paying teams and connect AEs with IT/Procurement) and 1 Sales Engineer (to handle security reviews and accelerate the 63-day cycle).

Bottom Line: We don't need to break our product to build an enterprise business. By turning our 31,000 self-serve teams into our enterprise hunting ground, shipping SSO/SCIM + Domain Claiming in Q1, and scaling to 8 PQL-focused AEs, we will hit the $5M target while accelerating—not cannibalizing—self-serve growth.

Claims the judge couldn’t find in the brief

  • Product-qualified average ACV (won) is $71,047.
  • Cold outbound: 25 deals, 3 won (12% win rate).
  • 18 of our 21 outbound losses were to Miro.
  • Self-serve is not competing with enterprise sales; self-serve is our enterprise customer acquisition cost (CAC) engine.

Grades and run details

Decision model 73 · LLM judge 71
Decision model checks
  • failUses the supplied evidence correctly91%
  • passAddresses the actual decision83%
  • passRespects explicit constraints48%
  • partialIdentifies material uncertainty31%
  • failAvoids unsupported claims70%
  • passProduces the required deliverable56%
  • failFollows the deals data56%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums86%
  • passPositioned against the real alternative98%
  • passSells against a pain the buyer feels100%
  • passDiagnoses message or channel first94%
  • passPlans beyond launch week95%
Run
Run
#1
API response time
27 s
Submitted
13 Oct 2026

Usable with a quick edit13 of 13 checks passed

The output is well-structured, evidence-based, and covers all required elements; a PM could use it with minor wording edits and no re-checking of facts or logic.

Output

Flowboard enterprise plan: grow from adoption, not forced upgrades

Recommendation: Build enterprise sales around companies whose teams already choose Flowboard. Preserve self-serve as our distribution engine; sell enterprise when adoption creates a governance or expansion need. Treat $5M as a conditional operating target—not a forecast justified by hiring alone.

1. What the evidence says

The 65 supplied opportunities show materially different businesses by source:

SourceOpportunitiesWin rateAverage won ACVAverage winning sales cycle
------:---:---:---:
Product-qualified2576%$72,62663 days
Inbound demo1540%$55,06786 days
Outbound2516%$36,800190 days

Product-qualified opportunities produced $1.38M—74% of won ARR—from 38% of opportunities. Outbound produced smaller contracts, much more slowly; 17 of its 21 losses were to Miro as the company standard.

The interviews reinforce the mechanism: all seven interviewed winners already had users, while four of five losses faced entrenched Miro deployments. These are small, selected samples, not guaranteed future conversion rates, but they strongly favor adoption-led selling over broad Fortune 2000 prospecting.

2. Who we sell to

Primary ICP: Companies with multiple active Flowboard teams, meaningful or growing usage, an internal champion, and an enterprise trigger: SSO, user provisioning, security review, fragmented purchasing, or broader deployment.

Company size is a filter, not qualification. A 15,000-person company with 18 active users is not automatically better than a 2,400-person company with 54.

Our first outreach cohort:

  • Brightfield Bank: six teams, 54 active users, 36 paid seats, SSO request.
  • Fennick Holdings: eight teams, 48 active users, 30 paid seats, SSO request.
  • Corvel Logistics: five teams, 45 active users, SSO request.
  • Eastlake Health: three teams, 30 active users, SSO request.

Contact Aldergate and Dunmore too, but establish deployment potential before forecasting enterprise contracts. Their explicit SSO requests matter; their current adoption is relatively shallow. Nurture Galloway, Hartwell, and Oakhurst through champions and usage growth rather than treating a security-page visit as buying intent.

The economic buyer is typically an IT or business leader; the entry point is the team champion. Qualification requires a concrete workflow, sponsor, governance need, plausible deployment scope, and decision process.

Deprioritize cold replacement of company-wide Miro. Pursue such accounts only when a differentiated workflow and motivated sponsor justify migration costs. Do not infer a banking or healthcare vertical strategy from this small account list.

3. How product and sales work together

Keep unlimited-user free collaboration and self-serve checkout above ten seats. Free supplies 70% of new paying teams. Neither attachment provides evidence that restricting it would improve total revenue.

Create a company-level product-qualified account system across free and paid teams:

  • Combine team count, sustained activity, growth, paid adoption, security interest, and explicit requests.
  • Prompt admins to discuss governance or rollout when relevant; keep ordinary purchases self-serve.
  • Have sales contact champions with a specific value proposition—not “you crossed our seat limit.”
  • Let sales assemble a cross-team rollout and procurement case. Let customer success drive adoption after purchase.

A seat threshold can trigger optional assistance, not a checkout blockade. Sales should earn the enterprise upgrade through centralized administration, deployment support, and an agreed expansion plan.

Measure free-to-paid conversion, self-serve ARR, activation, product-qualified pipeline, and enterprise conversion together. Compensate sales on incremental enterprise ARR, with account-level attribution, so existing self-serve revenue is not mistaken for growth.

4. Product and competitive positioning

Replace generic category messaging with:

> Turn complex team thinking into clear, live diagrams—then scale the tool your teams already choose, with enterprise control.

The buyer-reported distinction is real-time diagramming that stays tidy. Demonstrate it in a customer’s actual workflow rather than claiming universal superiority.

  • Against Miro: Win the diagramming workflow and existing user preference, not a feature-checklist war. Where permitted, begin with a bounded departmental deployment rather than demanding wholesale replacement. Migration support should follow demonstrated demand.
  • Against photos and slides: Show the value of a shared, editable diagram through a short workflow pilot.
  • Against doing nothing: Quantify the customer’s actual administrative burden, duplicated purchasing, or blocked rollout; agree on success criteria and a decision date.

Enterprise controls are purchase enablers, not our differentiation. Validate current capabilities and prioritize SSO, SCIM, auditability, and organization-level administration against live opportunities. Do not commit to data residency without evidence of incremental deals sufficient to justify its infrastructure and operating costs.

Use paid, time-boxed pilots only where needed, with a champion, measurable workflow outcome, security workstream, and commercial decision date.

5. Revenue, pipeline, and capacity

Finance should define the $5M target as annualized incremental contracted ARR, subtracting displaced self-serve ARR on conversions and excluding services. Report churn separately; if the board means company-wide net-new ARR, losses must also be covered.

At historical product-qualified ACV, $5M requires approximately 69 wins before subtracting existing self-serve revenue. That is a lower bound.

For operating planning, assume $65,000 incremental ARR per win—a hypothesis to validate against actual account spend:

  • Required wins: 77; operating target: 80, yielding $5.2M.
  • At a conservative 60% qualified-opportunity win rate, approximately 134 qualified opportunities.
  • At today’s ten wins per AE annually, approximately eight productive AE-years.

One capacity model retains three AEs and adds four in January and four in April. Assuming three months of zero-output ramp followed by current productivity:

3 existing AE-years + 4 × 0.75 + 4 × 0.5 = 8 productive AE-years.

This supports 80 wins, but assumes timely recruiting, adequate pipeline, and successful ramp. At eight wins per productive AE-year, revenue falls to $4.16M.

Therefore, authorize up to eight hires, but release the second cohort only when qualified pipeline supports its capacity. Start with two account-development reps, focused on adoption-led accounts, rather than six broad-outbound SDRs. Budget a shared solutions/security specialist and an enterprise customer-success owner; verify workload before further hiring. Product must separately scope administration, qualification instrumentation, and security delivery before committing dates or staffing.

6. Changes to Jordan’s plan and board gates

I would reject the free-user cap and checkout removal, replace broad outbound with product-qualified account development, make hiring conditional, sequence enterprise features by purchase blockers, and replace generic messaging with workflow differentiation.

The largest unresolved issue is demand. We observed only 25 product-qualified opportunities last year. The 24 named companies are a starting list, not proof of the roughly 134 opportunities required; even if all bought at our planning value, they would contribute only $1.56M.

Within 30 days, deduplicate the installed base into companies, validate the first cohort, measure displaced self-serve ARR, and establish a credible quarterly pipeline build. Review conversion, incremental ACV, hiring ramp, and self-serve health monthly. If demand does not support the model, revise the forecast early—not restrict the product or hire ahead of evidence.

Grades and run details

Decision model 100 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly14%
  • passAddresses the actual decision97%
  • passRespects explicit constraints52%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims52%
  • passProduces the required deliverable72%
  • passFollows the deals data48%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums97%
  • passPositioned against the real alternative100%
  • passSells against a pain the buyer feels97%
  • passDiagnoses message or channel first89%
  • passPlans beyond launch week100%
Run
Run
#1
Time to output
1.6 min
Submitted
5 Oct 2026

Needs more than a quick edit11 of 13 checks passed

The output contains a factual error in the median cycle and an unsupported claim about data residency, so a product manager would need to re‑verify the data before using it.

Output

To: CEO From: VP Product Re: Enterprise plan for the board

Bottom line: Jordan is right that enterprise revenue should run through sales. But the draft caps the motion that produces most of our enterprise deals, and it spends hiring budget on the motion that loses most often. I'd keep the free plan open, route accounts on buying signals, hire against proven pipeline, and present $3.0M committed with $5M as a stretch tied to specific gates.

What the last 12 months show (65 enterprise opportunities, 29 won, $1.86M ACV)

SourceOppsWonWin rateAvg won ACVNotes
Product-qualified (teams already on Flowboard)251976%$72.6K74% of won ACV; median cycle 66 days
Inbound demo request15640%$55K
Outbound25416%$37KWins took ~190 days; 17 of 21 losses went to Miro

Across all 36 losses, 26 went to Miro as company standard and 10 to no decision. All seven enterprise wins in the interviews had teams already using Flowboard.

Who we sell to

Our best enterprise buyers are large companies where we already have several teams. Of the 24 companies over 1,000 employees using us, 16 have looked at our security page in the last 30 days. Six have also asked for SSO: Aldergate, Brightfield, Corvel, Dunmore, Eastlake and Fennick. Together they hold 116 paying seats. That's the first account list, and it's warm.

I would not start with Fortune 2000 cold outbound. That motion wins 16% of the time, and the companies on those lists are the ones most likely to have standardised on Miro.

How self-serve and sales work together

  • The free plan stays open. A 3-user cap breaks the motion that generated 19 of our 29 wins. The enterprise deals came from teams that grew to hundreds of people on free, with designers who "refused to give it up." A cap doesn't make those teams call us. It sends them to Miro, or it keeps them on the free plan without ever reaching the buyer.
  • Routing should follow signals, not one seat number. Route an account to sales when it has three or more teams in one company, asks for SSO or SCIM, views the security page, or passes about 25 paying seats. Self-serve checkout stays available below that line. Removing checkout at 10 seats would push paying mid-size teams into a sales cycle they don't need, and we'd lose that ARR.
  • Division of labour. Sales owns the contract, the security review and the SSO rollout. Product owns in-app signals, admin controls and the usage data that tells sales where to go. AEs work the warm account list, not cold lists.

What we say against the alternatives

  • Against Miro: Don't compete on feature parity. Buyers chose us for real-time diagrams that stay tidy and a free start that doesn't need IT. "The visual collaboration platform for the enterprise" puts us in Miro's category and invites a feature comparison we lose. I'd use: "The whiteboard your teams already chose, now with the controls IT needs."
  • Where Miro is already company standard, we lose most of the time. Don't spend outbound effort there. Instead, scope a Miro board import. Buyers cited migrating thousands of boards as the reason they stayed. If we can make the migration a file import, we can contest some of those standardisation decisions. Engineering needs to size this.
  • Against no decision (10 losses): My hypothesis is that these stall in IT or security review. SSO, SCIM and audit logs address exactly that. Sales should check lost-deal notes to confirm.

Capacity

  • Three AEs closed 29 deals, about ten each, or roughly $620K per AE. New AEs take around six months to ramp.
  • The binding constraint is pipeline, not AE headcount. At the product-qualified average of $72.6K, $5M is about 69 wins. At a 76% win rate, that needs roughly 90 product-qualified opportunities a year. We have 25.
  • Hiring eight AEs before that pipeline exists leaves most of them waiting. Six SDRs working outbound would be pointed at our lowest-converting motion.
  • My estimate: three current AEs plus four hires in Q1 (about half a year of productivity each) gives roughly five effective AEs, or about $3.1M. The large-account program could add $0.5–1M. That supports a $3.0–3.5M base. The $5M case requires roughly tripling product-qualified pipeline and having the new hires productive by Q3. Nothing in the data shows that yet.

What I'd change in Jordan's plan, and why

  1. Keep the free plan unlimited. It produces the enterprise pipeline, and a cap would reduce that pipeline.
  2. Replace the 10-seat rule with signal-based routing (see above) and keep checkout below about 25 seats. This sends sales the accounts most likely to buy without cutting paying self-serve revenue.
  3. Hire four AEs now and two more on a gate: 40 or more qualified product-qualified opportunities in a quarter, and a trailing win rate above 60%. If the gate isn't met, we don't carry the cost.
  4. Cancel the six outbound SDR roles. Put two SDRs on inbound demo follow-up (40% win rate, and most inbound losses were to Miro or no decision, so speed-to-follow-up matters). Put two on expansion of the 24 large accounts. Hold two until the gate is met.
  5. Build SSO, SCIM and audit logs first. Six accounts have asked for SSO, and 16 of 24 large accounts have looked at the security page. Defer data residency. No deal in the last 12 months cited it, so build it only when a named deal requires it.
  6. Change the messaging as above and drop "for the enterprise."
  7. Scope the Miro board import as a product workstream, with a decision in 30 days.

What I'd ask the board for

  • A committed number of $3.0M enterprise ARR, with $5M as a stretch.
  • Gates the board can track quarterly: qualified product-qualified opportunities, win rate, the six SSO accounts closed, and no-decision losses as a share of the total.
  • Approval to hire four AEs now and two on the gate.

I'd rather present a number we can defend than one that depends on hiring eight people before we have the pipeline to keep them busy.

Claims the judge couldn’t find in the brief

  • Median sales cycle for product-qualified deals is 66 days.
  • No deal in the last 12 months cited data residency as a requirement.
  • New AEs take around six months to ramp.

Grades and run details

Decision model 88 · LLM judge 79
Decision model checks
  • failUses the supplied evidence correctly51%
  • passAddresses the actual decision97%
  • passRespects explicit constraints74%
  • passIdentifies material uncertainty95%
  • partialAvoids unsupported claims23%
  • passProduces the required deliverable88%
  • passFollows the deals data41%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums97%
  • passPositioned against the real alternative97%
  • passSells against a pain the buyer feels100%
  • passDiagnoses message or channel first98%
  • passPlans beyond launch week100%
Run
Run
#1
API response time
54 s
Submitted
8 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 4

Uses the supplied evidence correctlyWrongRightWrong
Gemini 4 Argon · API

Multiple facts are incorrect (PQL average ACV, outbound win count, Miro loss count), so not every statement about the current situation is supported by the evidence.

GPT-6 Astra · ChatGPT

All factual claims about the current situation are directly from the supplied context or follow from arithmetic on that data.

Haiku 5.5 · API

The median cycle for product‑qualified deals is stated as 66 days but the data gives 62, and the claim that no deal cited data residency is unsupported.

Identifies material uncertaintyWrongRightRight
Gemini 4 Argon · API

Does not name specific unknowns that could change the decision or explain how they would be resolved.

GPT-6 Astra · ChatGPT

Names demand as the largest unresolved issue, bounds the gap, and specifies how to resolve it within 30 days and monthly reviews.

Haiku 5.5 · API

It identifies unknowns (e.g., why no‑decision losses stall, Miro import feasibility) and says how to resolve them.

Avoids unsupported claimsWrongRightWrong
Gemini 4 Argon · API

States interpretations and forecasts (e.g., 'self-serve is our enterprise CAC engine', 'Jordan's plan will starve our self-serve engine') as fact without hedging.

GPT-6 Astra · ChatGPT

Hypotheses and forecasts are clearly labelled, and confident claims are supported by the evidence.

Haiku 5.5 · API

It presents the incorrect median cycle and the ‘no deal cited data residency’ statement as fact without support.

Follows the deals dataMixedRightRight
Gemini 4 Argon · API

Computes win rate, deal size, and cycle by source and builds the motion on product-qualified accounts.

GPT-6 Astra · ChatGPT

Computes win rate, deal size, and cycle time by source and builds the motion on product-qualified accounts.

Haiku 5.5 · API

The plan computes win rate, size and cycle by source and builds the motion on product‑qualified accounts.

All got right 9

Addresses the actual decisionRightRightRight
Gemini 4 Argon · API

Commits clearly to a product-led sales motion early in the memo, addressed to the CEO, even if it does not explicitly state what would change the call.

GPT-6 Astra · ChatGPT

Commits to a clear answer (adoption-led enterprise sales, keep self-serve open) and states what would change it (demand not supporting the model).

Haiku 5.5 · API

The output commits to a clear plan ($3.0M committed, $5M stretch with gates) and states what would change it.

Respects explicit constraintsRightRightRight
Gemini 4 Argon · API

Memo is under 1,300 words, addressed to CEO, and the plan does not endorse capping the free plan or removing self-serve checkout.

GPT-6 Astra · ChatGPT

Respects the 1,300-word limit, addresses all requested elements, and is framed for the CEO/board.

Haiku 5.5 · API

The memo is within 1,300 words, addresses all requested topics, and is addressed to the CEO.

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

Produces a complete memo covering who we sell to, self-serve/sales interplay, positioning, capacity, and changes to the CRO plan, within length and for the CEO.

GPT-6 Astra · ChatGPT

The output is a complete, actionable plan in memo form, within the word limit, covering all requested topics.

Haiku 5.5 · API

It delivers a complete, actionable memo in the requested form, suitable for the CEO with minimal edits.

Protects the self-serve engineRightRightRight
Gemini 4 Argon · API

Uses the 70% free-plan contribution and product-qualified deal data to show the cost of capping, and keeps self-serve open.

GPT-6 Astra · ChatGPT

Uses the 70% free-to-paid stat and product-qualified deal data to argue against capping the free plan, and keeps self-serve open.

Haiku 5.5 · API

It explains why capping the free plan and removing checkout would hurt the pipeline and keeps self‑serve open.

Does the capacity sumsRightRightRight
Gemini 4 Argon · API

Calculates ~70 deals needed at $71k, pipeline needed, and AE capacity with checkable sums.

GPT-6 Astra · ChatGPT

Works out deals needed (69-80), qualified opportunities (134), and AE-years (8) with checkable arithmetic.

Haiku 5.5 · API

The output works out the deals needed (~69), pipeline required (~90 PQOs), and AE capacity to reach $5M.

Positioned against the real alternativeRightRightRight
Gemini 4 Argon · API

Names the real alternatives (Miro company standard, status quo/no decision) and ties message to tidy diagramming and frictionless adoption.

GPT-6 Astra · ChatGPT

Names Miro, photos/slides, and doing nothing as alternatives, and ties the message to real-time tidy diagramming and starting without IT.

Haiku 5.5 · API

It names Miro and ‘no decision’ as the real alternatives and ties the message to design adoption and IT controls.

Sells against a pain the buyer feelsRightRightRight
Gemini 4 Argon · API

Leads with pain of messy diagrams and unmanaged shadow IT, supported by buyer quotes from the interviews.

GPT-6 Astra · ChatGPT

The message addresses the governance and control pain that IT buyers feel when teams adopt tools without oversight, supported by SSO requests and security-page visits in the data.

Haiku 5.5 · API

The message is built on buyer pain (IT losing control, design team refusal) evidenced by the interviews.

Diagnoses message or channel firstRightRightRight
Gemini 4 Argon · API

Identifies that outbound fails because it targets Miro-entrenched accounts (channel misalignment) and misaligned messaging, and pivots effort to product-qualified accounts.

GPT-6 Astra · ChatGPT

Diagnoses outbound's poor results as a channel problem (cold outreach to Miro-entrenched accounts) and shifts effort to product-qualified accounts.

Haiku 5.5 · API

It diagnoses that the outbound channel is underperforming (low win rate, high loss to Miro) and shifts effort to warm product‑qualified accounts.

Plans beyond launch weekRightRightRight
Gemini 4 Argon · API

Describes an ongoing land-consolidate-close loop with PQL scoring and product-led SDRs, not a one-time launch.

GPT-6 Astra · ChatGPT

Describes ongoing measurement (free-to-paid, pipeline, conversion, monthly reviews) and a 30-day validation plan beyond the initial launch.

Haiku 5.5 · API

The plan includes ongoing routing, quarterly gates, and measures (PQOs, win rate, SSO closures) beyond launch.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.8100.02None
2Opus 5.5withClaude94.185.22None
3GPT-6 LunawithAPI88.589.32None
4GPT-6.1 SolwithAPI93.878.02None
5Sonnet 5.5withAPI89.977.72None
6Haiku 5.5withAPI88.077.72None
7Gemini 3.8 FlashwithAPI72.181.92None
8Gemini 4 ArgonwithAPI74.070.32None
9Gemini 3.5 Flash-LitewithGemini70.063.52None

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.

Task score against relative cost for each setup. The table below has the same figures.607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 98, 81× the cheapestOpus 5.5 · Claude: task score 90, 75× the cheapestGPT-6 Luna · API: task score 88, the cheapestGPT-6.1 Sol · API: task score 84, 16× the cheapestSonnet 5.5 · API: task score 80, 46× the cheapestHaiku 5.5 · API: task score 80, 3.3× the cheapestGemini 3.8 Flash · API: task score 76, 11× the cheapestGemini 4 Argon · API: task score 69, 18× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 66, 3.5× the cheapestGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeGPT-6 Luna · APIGPT-6.1 Sol · APISonnet 5.5 · APIHaiku 5.5 · APIGemini 3.8 Flash · APIGemini 4 Argon · APIGemini 3.5 Flash-Lite · Gemini
Nobody beats these on both score and cost
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API881.0×
Haiku 5.5 · API803.3×
Gemini 3.5 Flash-Lite · Gemini663.5×
Gemini 3.8 Flash · API7611×
GPT-6.1 Sol · API8416×
Gemini 4 Argon · API6918×
Sonnet 5.5 · API8046×
Opus 5.5 · Claude9075×
GPT-6 Astra · ChatGPT9881×

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

About the task

The PM job

Taking a product or add-on to market with marketing and sales.

Why it matters

Launch plans fail by trying to reach everyone, by selling upside nobody feels, and by treating launch day as the whole plan.

What good looks like

  • One primary segment: those who value the difference most
  • Positions against what customers do today
  • Sells against a pain the buyer feels, with evidence
  • Diagnoses whether the problem is the message or the channel
  • Plans beyond launch week

Deliberately not measured

  • Creative copywriting quality
Capability tested

Go-to-market judgement

The failure we’re looking for

Every channel, every segment, and nothing after launch day

Grading

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