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

Needs more than a quick edit11 of 13 checks passed

The output contains a factual error ('Six of the ten accounts above 10 seats have shown no IT interest' is wrong) and an unsupported effort claim, so a PM would need to re-check the data before using it.

Output

Enterprise GTM: VP Product recommendation

Bottom line: Jordan's plan sends our new hires at the channel that works worst and puts friction on the channel that works best. I'd back the enterprise features and some added sales capacity, but build the motion around the teams already using Flowboard. The board should see $5M as a stretch and about $3.4M as the likely case.

What last year's deals show

Last year we worked 65 deals, won 29, and booked about $1.86M of new ARR.

SourceDealsWin rateAvg won ACVAvg won cycleShare of new ARR
Product-qualified (teams already using us)2576%$72.6K63 days74%
Inbound demo1540%$55.1K86 days18%
Outbound2516%$36.8K190 days8%

Outbound is two-thirds of the effort and 8% of the revenue. Of its 21 losses, 17 went to Miro as the company standard.

Across all sources, Miro took 26 of our 36 losses. "No decision" took 10, including 4 of the 6 product-qualified losses. The interviews agree: every win had teams already on Flowboard, and four of the five losses were Miro-standardised companies where switching meant migrating thousands of boards.

Who we sell to

Primary customer: a company with Flowboard already in several teams, where Miro is not yet the standard and IT is starting to engage.

The usage data lets us tier the 24 large companies already on Flowboard: - Tier A (6 accounts): asked for SSO. All six also viewed the security page. Aldergate, Brightfield, Corvel, Dunmore, Eastlake and Fennick together have 27 teams and 116 paying seats. Sales owns these now. - Tier B (10 accounts): viewed the security page but haven't asked for SSO. A product-led nudge and a light sales touch. - Tier C (8 accounts): no IT signal yet. Keep nurturing in-product.

Seat count is the wrong trigger. Aldergate (10 seats) and Dunmore (4 seats) asked for SSO and would be missed by a ">10 seats" rule. Six of the ten accounts above 10 seats have shown no IT interest.

Two gaps in this data: - The file covers only companies over 1,000 employees, so it understates our real product-qualified pool. - We don't record which incumbent tool a prospect uses. We should add that field now, because "Miro is the standard" predicts the loss.

How self-serve and sales work together

Self-serve generates demand and sales converts it.

  1. Free plan stays open. It feeds 70% of new paying teams, and buyers cite "a free plan teams can start on without IT" as something Miro doesn't offer.
  2. Checkout stays open at every size. Sales is triggered by signals: an SSO request, security page views, three or more teams in one domain, or a domain crossing an active-user threshold. A rep reaches out while the team keeps buying normally.
  3. Sales sells the consolidation: security review, one contract, and an admin console, backed by the customer's own usage data ("300 people already use it"). That also answers the "no decision" losses, which need a champion kit and a usage-based business case.
  4. Inbound demos are routed to the same team. A 40% win rate is worth keeping, but half the losses are to Miro standards, so qualify for the incumbent tool early.

What we say against the alternatives

  • Miro: don't contest "the enterprise whiteboard." Position as the best tool for real-time diagramming that stays tidy, and one that teams adopt without waiting for IT. Don't ask anyone to migrate. Win the team first and let usage make the case. Only 2 of 25 product-qualified deals went to Miro, versus 17 of 25 outbound deals.
  • Status quo (photos and slides): show the cost of the current process using their own team's usage.
  • Other tools: the data doesn't show us losing to any, so I'm making no claims about them.

Jordan's line, "The visual collaboration platform for the enterprise," is Miro's category claim, and it leaves out our actual advantages. I'd use something like: "Diagramming your teams already love, ready for IT to approve."

Capacity

Today each AE works about 22 opportunities a year (65 across 3 AEs) and closes about 10.

To reach $5M: - 60 product-qualified wins at 76% means about 79 opportunities, or $4.4M. - 12 inbound wins at 40% means 30 demos, or $0.66M. - That's about 109 opportunities, or roughly 5 AEs at today's load. - Product-qualified cycles are 63 days against 190 for outbound, so new AEs ramp faster on this mix. - Hire 3 AEs for 6 total. That gives a buffer, a faster ramp, and no outbound headcount.

Other hires (not part of Jordan's plan): - 2 reps who work product-qualified accounts, in place of 6 outbound SDRs. - 1 solutions engineer for security reviews. - 1 RevOps/growth analyst to build account-level usage scoring.

That's 7 sales-side heads against Jordan's 14, plus the engineering squad below.

The real risk is volume, not headcount. $5M needs product-qualified opportunities to roughly triple, from 25 to about 79. At double (50 opportunities), we land near $3.4M. The 24-company list can't deliver that alone, so RevOps needs to size the full pool, including sub-1,000-employee companies, before we commit to the board.

Engineering sequence: 1. SSO and SCIM first. Six accounts have already asked, and IT approval is the gate in our win stories. 2. Audit logs next. 3. Data residency only when a named deal requires it. Nothing in the loss data points to features. We lose to incumbency and indecision, so features are a gate, not a growth driver.

What I'd change in Jordan's plan

Jordan's itemMy view
1. Cap free at 3 usersReject. It throttles the source of 70% of new paying teams and 74% of enterprise ARR, and removes the "start without IT" advantage. If we need a lever, gate admin and security features, not users.
2. Remove checkout above 10 seatsReject. Ten of the 24 large accounts are above 10 seats, and they are the ones converting. Keep checkout and trigger sales on IT signals.
3. 8 AEs + 6 SDRs on Fortune 2000 outboundReplace. Outbound wins 16% of the time, at a $36.8K ACV and 190-day cycle, mostly against Miro standards. Hire 3 AEs and 2 product-qualified reps instead.
4. SSO, SCIM, audit logs, data residencyKeep, resequenced. SSO/SCIM first, audit logs second, residency on demand.
5. MessagingReplace. It fights Miro on Miro's ground. Lead with team adoption and tidy diagrams, then IT readiness.

Why Jordan's plan lands short in-year (my estimate): an outbound AE produces about $368K a year (10 deals × $36.8K). New hires starting in Q1 won't see a first outbound close until around Q3, so 8 AEs yield well under $1M this year. Adding that to last year's $1.86M gives roughly $2.6M, before counting damage from the free cap and checkout changes to our best channel.

What I'd ask the board for

  • Commit to about $3.4M, with $5M as the stretch if product-qualified volume triples by mid-year.
  • Approve the smaller hiring plan and the SSO/SCIM squad.
  • Review progress quarterly on product-qualified opportunity count, win rate, and the share of losses to Miro.

I'd like to walk Jordan through this before the board meeting. We agree on the target and on the need for enterprise features. We disagree on where the next dollar of sales effort should go.

Claims the judge couldn’t find in the brief

  • Six of the ten accounts above 10 seats have shown no IT interest.
  • Outbound is two-thirds of the effort and 8% of the revenue.
  • We don't record which incumbent tool a prospect uses.
  • The file covers only companies over 1,000 employees, so it understates our real product-qualified pool.

Grades and run details

Decision model 92 · LLM judge 79
Decision model checks
  • failUses the supplied evidence correctly36%
  • passAddresses the actual decision98%
  • passRespects explicit constraints56%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims16%
  • passProduces the required deliverable82%
  • passFollows the deals data49%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums97%
  • passPositioned against the real alternative98%
  • passSells against a pain the buyer feels100%
  • passDiagnoses message or channel first98%
  • passPlans beyond launch week100%
Run
Run
#1
API response time
87 s
Submitted
6 Oct 2026

Needs more than a quick edit10 of 13 checks passed

The output contains multiple factual errors in the deal data (product-qualified ACV, sales cycle, total won) that would require re-checking the CSV and correcting the numbers before a product manager could rely on it.

Output

Flowboard enterprise plan: turn team adoption into governed company adoption

Recommendation: Keep the $5M target, but build a product-led enterprise motion—not a cold-outbound motion powered by restrictions on self-serve. Sell first to companies where teams already depend on Flowboard, then help an internal champion bring IT and procurement along.

The target is achievable only if we generate substantially more qualified opportunities and ramp sales capacity early. Neither is proven today; the board should approve a milestone-based investment, not treat $5M as an assured forecast.

1. What the evidence says

The last 12 months of deals show markedly different economics:

SourceWon / opportunitiesWin rateAverage won ACVAverage won sales cycle
------:---:---:---:
Existing product users19 / 2576%$72.6K63 days
Inbound demo requests6 / 1540%$55.1K86 days
Outbound4 / 2516%$36.8K190 days

These are small, non-random samples—not guarantees—but the direction is compelling. Product-qualified wins are nearly twice the size of outbound wins and close in one-third the time. The interviews reinforce the pattern: all seven interviewed winners already had teams using Flowboard; four of five losses had standardized on Miro.

Our advantage is existing user commitment plus differentiated diagramming, not a generic enterprise feature list.

2. Who we sell to

Primary ICP: Large companies with multiple active Flowboard teams, recurring diagramming or visual collaboration work, and a reason to consolidate purchasing or introduce governance.

Employee count helps establish potential, but adoption and buying intent should determine priority. We should not infer a winning industry segment from this small dataset.

Prioritize accounts using three signals:

  • Adoption: active users, multiple teams, usage growth and repeat collaboration.
  • Intent: SSO requests, security-page visits and explicit procurement questions.
  • Commercial potential: a credible departmental rollout, sponsor and budget—not simply total employees.

Start with Brightfield Bank, Fennick Holdings, Corvel Logistics and Eastlake Health: each has multiple teams, meaningful activity, security interest and an SSO request. Aldergate Bank and Dunmore Holdings also merit immediate discovery, but their smaller active footprints make rollout potential less certain. Security-interested accounts such as Hartwell, Galloway and Upton form a second wave.

Map the buying group: a user champion, a departmental budget owner, and IT/security/procurement. Sell an initial governed deployment around demonstrated demand, with an expansion path.

The 24 listed companies are a starting queue, not sufficient pipeline. Even if all became qualified opportunities, applying the historical 76% win rate and $70K incremental ARR assumption yields only about $1.3M. We must discover many more qualified accounts across the installed base.

3. How self-serve and sales work together

Keep free adoption and self-serve checkout open. Free supplies 70% of new paying teams, and buyers explicitly value starting without IT. Capping free at three users would damage the mechanism that creates our strongest enterprise opportunities.

Likewise, crossing ten seats should trigger an optional sales-assist offer, not a purchasing barrier. Seat count alone does not establish enterprise need.

Build an account-level product-qualified motion:

  1. Aggregate teams by company domain, with appropriate access and privacy controls.
  2. Score sustained usage, multi-team adoption, growth and governance intent.
  3. Offer relevant help in-product: “Bring your teams together with centralized administration and security.”
  4. Route high-intent accounts to sales promptly; nurture lower-intent accounts while preserving checkout.
  5. Equip AEs with an account adoption summary and a champion toolkit: rollout proposal, security documentation and an economic justification.

Sales should close the governance and purchasing gap, not force users through one. Customer success then drives onboarding, broader adoption and expansion.

Keep Jordan’s SSO, SCIM, audit logs and data-residency direction, but sequence delivery against buyer requirements. Establish a minimum enterprise-readiness package first; validate residency demand and delivery cost before committing to regions. These capabilities enable procurement; they are not our differentiation.

4. What we say against alternatives

Replace “The visual collaboration platform for the enterprise” with:

> Real-time diagramming that stays tidy—now governed for the teams already relying on it.

  • Against Miro: Lead with diagramming and demonstrated user preference. Where Miro is entrenched, propose an approved specialist deployment rather than company-wide replacement. Do not assume customers will migrate thousands of boards.
  • Against slides and whiteboard photos: Demonstrate a live, editable shared diagram for one recurring workflow. Prove improved collaboration through a bounded pilot rather than unsupported productivity claims.
  • Against no decision: Establish the sponsor, purchase trigger and rollout outcome early. Enterprise governance must solve a current problem, not merely add features to a pitch.

5. Revenue, pipeline and capacity

Define the board target as incremental enterprise ARR added next year, excluding self-serve ARR already paid by teams that convert. Report new logos, conversion uplift and existing-enterprise expansion separately; do not count the same revenue twice.

Historical product-qualified ACV is $72.6K, but we do not know the existing self-serve revenue attached to those wins. For planning, use $70K incremental ARR per win as an assumption to validate.

  • $5M requires 72 wins at $70K.
  • At the historical 76% product-qualified win rate, that requires approximately 95 qualified opportunities.
  • Target 105–110 qualified opportunities, allowing some execution cushion.
  • At today’s ten annual wins per fully productive AE, 72 wins require 7.2 fully productive AE-years.

Three current AEs provide approximately 30 wins. Eight new AEs averaging six wins each during their first year would add 48: 78 wins and $5.46M at the assumed incremental deal size.

Thus, Jordan’s eight AE hires can be justified—but only with early hiring, sufficient pipeline and a credible ramp. At five wins per new AE, we reach only 70 wins, or $4.9M. Smaller incremental contracts or slower qualification would widen the shortfall.

Hire in Q1 against explicit pipeline and ramp gates. Start with two SDRs focused on installed-base account development, not six running broad Fortune 2000 lists. Provide named solutions/security support and customer-success capacity for enterprise onboarding. Product, engineering and data capacity must also fund account identification, routing and enterprise readiness.

6. What changes in Jordan’s plan—and how we govern it

I would:

  • Reject the free-user cap: it weakens our strongest acquisition advantage.
  • Reject mandatory sales above ten seats: it introduces friction without proving enterprise demand.
  • Redirect outbound toward adopted accounts: historical cold outbound is slower, smaller and less successful.
  • Retain AE investment conditionally: capacity is necessary, but cannot substitute for qualified demand.
  • Keep governance features; sharpen positioning: security gets us approved; tidy real-time diagramming gets us chosen.

In the first 90 days, instrument account-level adoption, validate incremental contract economics, engage the six SSO-requesting accounts and establish the qualified-pipeline trajectory. Track incremental ARR, source-level conversion, sales cycle, AE ramp, rollout adoption and self-serve conversion/retention.

If those indicators do not support the hiring and revenue assumptions, reforecast openly. We should not manufacture enterprise “leads” by breaking the product-led engine.

Claims the judge couldn’t find in the brief

  • Existing product users average won ACV $72.6K
  • Existing product users average won sales cycle 63 days
  • Product-qualified wins were about $1.38M of the $1.86M won

Grades and run details

Decision model 100 · LLM judge 71
Decision model checks
  • passUses the supplied evidence correctly16%
  • passAddresses the actual decision92%
  • passRespects explicit constraints49%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims48%
  • passProduces the required deliverable83%
  • passFollows the deals data45%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums98%
  • passPositioned against the real alternative100%
  • passSells against a pain the buyer feels98%
  • passDiagnoses message or channel first86%
  • passPlans beyond launch week100%
Run
Run
#1
API response time
73 s
Submitted
5 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 correctlyWrongWrongMixed
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.

Sonnet 5.5 · API

Several claims about the current situation are unsupported or incorrect: 'Six of the ten accounts above 10 seats have shown no IT interest' is false (only 2), 'Outbound is two-thirds of the effort' has no basis, and 'We don't record which incumbent tool a prospect uses' is not in the supplied context.

GPT-6.1 Sol · API

The output misstates product-qualified average ACV ($72.6K vs actual $68.7K), sales cycle (63 days vs 70 days), and total won amounts ($1.38M/$1.86M vs $1.30M/$1.78M), so it does not use the supplied evidence correctly.

Identifies material uncertaintyWrongRightRight
Gemini 4 Argon · API

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

Sonnet 5.5 · API

Names the key unknown (size of the full product-qualified pool), bounds the risk ($3.4M likely vs $5M stretch), and says RevOps must size the pool before committing.

GPT-6.1 Sol · API

It names pipeline, AE ramp, and incremental contract economics as unproven, bounds them with milestone-based investment, and says what would change the call.

Avoids unsupported claimsWrongMixedMixed
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.

Sonnet 5.5 · API

Presents 'Outbound is two-thirds of the effort' and 'Six of the ten accounts above 10 seats have shown no IT interest' as facts when the first is unsupported and the second is incorrect.

GPT-6.1 Sol · API

It presents incorrect deal averages and total won figures as established facts from the data, when the evidence does not support those exact numbers.

Follows the deals dataMixedRightMixed
Gemini 4 Argon · API

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

Sonnet 5.5 · API

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

GPT-6.1 Sol · API

Although it compares by source, the computed product-qualified ACV and sales cycle are wrong, so it does not correctly follow the deals data.

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.

Sonnet 5.5 · API

Commits early to a clear recommendation (reject free cap and checkout removal, build on product-qualified, target $3.4M with $5M stretch) and states what would change it (product-qualified volume tripling).

GPT-6.1 Sol · API

The output commits early to a product-led enterprise motion, says what to change in Jordan's plan, and states that if pipeline and ramp indicators don't support assumptions, reforecast openly.

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.

Sonnet 5.5 · API

The output is a memo within ~1,200 words, addresses all requested topics, and does not endorse capping the free plan or removing self-serve checkout.

GPT-6.1 Sol · API

The output stays within ~1,200 words, addresses all requested topics, and explicitly rejects capping the free plan or removing self-serve checkout, respecting the implicit constraint from the test.

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.

Sonnet 5.5 · API

Delivers a complete, actionable memo to the CEO/board within the word limit, covering all requested elements.

GPT-6.1 Sol · API

The output is a complete plan under 1,300 words, addressed to the CEO, covering all required elements and usable with light 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.

Sonnet 5.5 · API

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

GPT-6.1 Sol · API

It uses the 70% free-plan contribution and product-qualified deal data to argue against capping free or forcing sales above 10 seats, 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.

Sonnet 5.5 · API

Works out deals needed (60 product-qualified, 12 inbound), total opportunities (109), and AEs required (5, hire 3 to reach 6), with checkable arithmetic.

GPT-6.1 Sol · API

It works out 72 wins needed at $70K, 95 qualified opportunities, and AE capacity (3 current + 8 new with ramp) to reach ~$5.46M, with checkable sums.

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.

Sonnet 5.5 · API

Positions against Miro (don't contest the enterprise whiteboard; win the team first) and the status quo (photos and slides), and picks the segment where Miro is not the standard.

GPT-6.1 Sol · API

It names Miro, slides/whiteboard photos, and no decision as alternatives, and ties the message to tidy real-time diagramming and governed adoption for teams already using it.

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.

Sonnet 5.5 · API

Leads with the pain of IT approval friction and the risk of losing a tool teams already love, backed by interview evidence that IT approved because people were already using it.

GPT-6.1 Sol · API

The core message leads with governance for teams already relying on Flowboard, directly addressing the pain of ungoverned adoption and IT approval risk shown in 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.

Sonnet 5.5 · API

Diagnoses that outbound has poor conversion (16% win rate) and long cycles, and shifts effort to the product-qualified channel while also changing the message against Miro.

GPT-6.1 Sol · API

It diagnoses that cold outbound to Fortune 2000 is the wrong channel (low win rate, long cycle) and shifts effort to product-qualified accounts and a message around diagramming, not generic enterprise features.

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.

Sonnet 5.5 · API

Describes the ongoing motion (product-led nudges, sales outreach on signals, RevOps scoring) and measures (opportunity count, win rate, share of losses to Miro) beyond launch.

GPT-6.1 Sol · API

It describes an ongoing product-qualified motion with account scoring, sales assist, customer success expansion, and lists metrics (incremental ARR, conversion, cycle, ramp, adoption) to track over time.

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