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 16 graded outputs by 8 models. 50% were usable with at most a quick edit.

Reliably right

  1. Positioned against the real alternative100% pass
    It names the real alternatives (accountant-run payroll with mistakes, owner spreadsheet with risk) and ties the message to confidence and accuracy that those alternatives lack.
    GPT-6 Astra · ChatGPT · Payroll, for the people who fear it
  2. 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
  3. Does the capacity sums100% pass
    Works out deals needed (69-80), qualified opportunities (134), and AE-years (8) with checkable arithmetic.
    GPT-6 Astra · ChatGPT · Enterprise, without killing the engine

Where it slips

  1. Avoids unsupported claims47% 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
  2. Uses the supplied evidence correctly52% 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
  3. Identifies material uncertainty73% pass
    The plan does not name specific unknowns that could change the decision, nor does it say how they would be resolved or what would trigger a pivot.
    Gemini 3.8 Flash · API · Payroll, for the people who fear it

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 · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit11 of 13 checks passed

The output is a complete, actionable plan; the one factual error (5 of 6 no-decision losses) is minor and can be corrected with a quick edit without redoing any reasoning.

Output

Flowboard Enterprise Go-to-Market: VP Product Version

To: CEO | cc: Jordan Hale, CRO

Summary

Our enterprise deals come from teams already using Flowboard. Last year, deals with existing teams closed 3x faster, at 2x the contract size, and at nearly 5x the win rate of outbound deals. Jordan's plan invests most heavily in outbound, our weakest motion, and caps the free plan that creates our strongest one. I recommend we build the enterprise motion on product usage instead.

On the evidence we have, that motion supports about $3.4M of new enterprise ARR, with a funded path to $5M if product-qualified pipeline grows as expected. We should give the board that range rather than commit to $5M.

What the data shows (65 enterprise deals, last 12 months)

SourceDealsWin rateAvg contract (won)Days to close (won)Main loss reason
Teams already using Flowboard2576% (19)$72.6K63No decision (5 of 6)
Inbound demo request1540% (6)$55.1K86Miro standard (7 of 9)
Outbound2516% (4)$36.8K190Miro standard (17 of 21)
  • Where the revenue came from: existing-user deals produced $1.38M of the $1.86M won.
  • Where we lose: 26 of 36 losses were to companies already standardised on Miro, and outbound found most of them.
  • What buyers told us: all 7 interviewed winners already had teams on Flowboard. All 4 Miro losses came down to the cost of migrating thousands of boards. When buyers described what we do better, they named the free plan teams can start on without IT.

Who we sell to

Primary target: companies with 1,000+ employees where two or more teams already use Flowboard. - Champion: the team lead whose people "refused to give it up." - Approver: IT or security, who signs off once enough people are already using the product.

The current list of large companies using Flowboard (24 accounts) splits into three tiers:

TierSignalAccountsAction
1Viewed security page and asked for SSOAldergate, Brightfield, Corvel, Dunmore, Eastlake, Fennick (6)AE engaged within 2 weeks
2Viewed security page, no SSO requestGalloway, Hartwell, Iverson, Juniper, Kestrel, Northcote, Oakhurst, Pemberton, Upton, Yardley (10)Sales-assist outreach; in-product enterprise trial
3Usage onlyLinton, Marlowe, Quayside, Redfern, Stanway, Thornbury, Varden, Whitmore (8)Product-led nurture until they cross a threshold

Who we don't target: companies standardised on Miro with no Flowboard usage. That describes most of the outbound pipeline, and we lost 17 of those 21 outbound deals.

Data gap to close before the board meeting: 24 accounts can't produce the roughly 50 product-qualified opportunities this plan needs. We need the full pool of qualifying accounts, including companies under 1,000 employees that look like past enterprise buyers. The capacity numbers below depend on that pool existing.

How self-serve and sales work together

1. Self-serve stays open. - No seat cap on checkout. - The free plan keeps unlimited users. - It feeds 70% of new paying teams and is the source of nearly every enterprise win.

2. Product defines a "product-qualified account" (PQA) and routes it to sales. - Trigger: a 1,000+ employee domain with any of these: - 2+ teams, - 25+ active users, - a security-page view, - an SSO request. - Product owns the scoring. Sales owns the response time. - Routing is by account signal, not by team size. A 15-seat team at a 60-person agency stays self-serve.

3. Enterprise features are the upgrade path. - Admin controls (SSO, SCIM, audit logs) appear in-product with a "talk to us / start a trial" path. - This gives IT a reason to engage without blocking the teams that are already working.

4. Fix the "no decision" problem. Five of our six losses on existing-user deals ended in no decision. The champion needs help getting IT to approve, not more prospecting. We will provide: - a security review pack, - an admin console demo, - a business case built from the account's own usage data.

5. No channel conflict. - AEs get credit for self-serve revenue that consolidates into their enterprise contract. - Self-serve keeps its own targets below the enterprise threshold.

What we say against the alternatives

Headline: "The diagramming tool your teams already chose — now ready for IT."

  • Against Miro: don't pitch a company-wide replacement. Buyers have told us migration cost decides those deals, and we lose.
  • Lead on what buyers said we do better: real-time diagramming that stays tidy.
  • Lead on proof of adoption: "300 people are already on it."
  • Where Miro is the standard, test positioning Flowboard as the diagramming tool alongside it. Treat this as a hypothesis and measure it on 5–10 deals before scaling.
  • Against whiteboard photos and slides (no decision): the free plan lets a team start this week without an IT project. IT controls come later, when the usage justifies them.
  • Against "the enterprise platform": that phrase is Miro's ground, and claiming it invites the head-to-head comparison we lose.

Capacity

Pipeline and revenue. These assume historical win rates and contract sizes hold.

SourceOpportunitiesWinsARR
Existing users (2x last year, via PQA routing)5038$2.76M
Inbound (better routing and messaging)2510$0.55M
Targeted outbound (only accounts not standardised on Miro)152–3$0.10M
Total90~50~$3.4M

People: - AEs: about 50 deals at roughly 10 per ramped AE needs 5–6 AE-years. - We have 3 AEs. Hire 4 more in Q1. - I'm assuming about a one-quarter ramp, which is plausible with 63-day cycles but needs validating with Jordan. - Sales engineer (1): owns security reviews and the no-decision problem. - Account manager (1): owns expansion across our 46 enterprise customers. - Product: a PQA scoring and routing squad, plus the enterprise feature roadmap. - Total: about 7 hires, versus 14 in Jordan's draft.

Path to $5M (the remaining ~$1.6M) has two options: 1. Stretch pipeline. At 76% win rate and $72.6K average, $5M needs about 90 existing-user opportunities. We should only fund this against evidence: - Checkpoint: if Q1 produces 15+ product-qualified opportunities, hire 3 more AEs in Q2. 2. Expansion. If the board counts expansion of existing enterprise customers as new ARR, those 46 accounts (~$2.9M base) are a real source. The board needs to settle that definition.

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

1. Cap the free plan at 3 users. → Drop it. - The free plan feeds 70% of new paying teams and seeds almost every enterprise win. - Buyers named it as a reason they chose us over Miro. - A cap forces conversations with teams that haven't adopted yet, and that kind of deal is where we lose most often.

2. Route every team over 10 seats to sales and remove checkout. → Route by account signal; keep checkout. - With 31,000 paying teams, a seat-count rule would bury AEs in small-company teams that will never be $64K deals. - It would also add friction to the self-serve revenue we already have.

3. Hire 8 AEs and 6 SDRs on Fortune 2000 outbound. → Hire 4 AEs, 1 sales engineer, and 1 account manager; no outbound SDR team. - Outbound wins 16% of the time at $36.8K over 190 days. - Reaching $5M through outbound would take about 850 opportunities. - An AE hired in Q1 on a 190-day cycle closes little or nothing in-year, so most of that headcount pays off in the following year. - If we want SDRs at all, use 1–2 to work product-qualified accounts, not cold lists.

4. Enterprise features (SSO, SCIM, audit logs, data residency). → Agree, with sequencing. - Ship SSO, SCIM, and audit logs first. Six Tier 1 accounts are asking for SSO now. - Build data residency against named deals. The bank and health accounts on our list are the likely candidates.

5. Messaging: "The visual collaboration platform for the enterprise." → Change it. - It positions us head-on against Miro on Miro's strongest ground. - Lead with the diagramming quality and bottom-up adoption that buyers say actually win the deal.

Where Jordan and I agree: we need more sales capacity and real enterprise features. We differ on where the pipeline comes from.

I'd like to walk Jordan through these numbers before the board meeting so we present one plan. That plan would be built on how our customers actually buy.

Claims the judge couldn’t find in the brief

  • Five of our six losses on existing-user deals ended in no decision.

Grades and run details

Decision model 96 · LLM judge 86
Decision model checks
  • passUses the supplied evidence correctly19%
  • passAddresses the actual decision80%
  • passRespects explicit constraints40%
  • passIdentifies material uncertainty96%
  • partialAvoids unsupported claims35%
  • passProduces the required deliverable45%
  • passFollows the deals data100%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums92%
  • passPositioned against the real alternative99%
  • passSells against a pain the buyer feels79%
  • passDiagnoses message or channel first98%
  • passPlans beyond launch week100%
Run
Run
#1
Time to output
1.5 min
Submitted
5 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 correctly31%
  • passAddresses the actual decision91%
  • passRespects explicit constraints29%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims52%
  • passProduces the required deliverable82%
  • passFollows the deals data100%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums98%
  • passPositioned against the real alternative100%
  • passSells against a pain the buyer feels60%
  • passDiagnoses message or channel first82%
  • passPlans beyond launch week100%
Run
Run
#1
API response time
73 s
Submitted
5 Oct 2026

Needs more than a quick edit7 of 13 checks passed

The win rate and deal count numbers are factually wrong, so a PM would need to re-analyze the deals data and correct the pipeline and capacity estimates before using this.

Output

Memo: Alternative Enterprise Go-To-Market Strategy

To: CEO, Board of Directors From: VP of Product Subject: Response to CRO Enterprise Plan & Alternative Go-To-Market for $5M New ARR

---

Executive Summary

Jordan Hale’s proposed enterprise plan relies on a playbook built for a different kind of software: high-friction outbound sales into the Fortune 2000, artificial self-serve restrictions, and generic enterprise positioning.

A rigorous analysis of our historical deal data and active user base reveals a stark reality: Outbound cold enterprise sales do not work for Flowboard. When we sell outbound, we lose 76% of the time—almost exclusively to entrenched incumbents like Miro. Conversely, our product-qualified leads (PQLs) convert at an exceptionally high rate and command an average contract value (ACV) exceeding $70,000.

To hit our $5M new ARR target next year without burning cash or destroying our self-serve engine, we must abandon cold outbound, protect our organic adoption loops, and build an enterprise motion centered around bottom-up land-and-expand.

---

1. Who We Sell To (The Ideal Customer Profile)

Jordan wants to target cold Fortune 2000 logos via outbound SDR armies. The data shows this is a trap.

Out of 65 deals analyzed over the last 12 months, Outbound deals had a win rate of only 24% (6 won, 19 lost), with an average sales cycle of nearly 180 days. When we go outbound, we face entrenched enterprise incumbents (Miro) where companies have already standardized their toolsets and board repositories. Switching costs are insurmountable for a cold prospect.

On the other hand, Product-Qualified (PQL) deals had a win rate of 62% (16 won, 10 lost) and an average sales cycle of just 65 days.

Our ICP is not the cold enterprise logo; it is the "Shadow IT" enterprise. We sell to companies where employees are already using Flowboard organically. Our data shows dozens of mid-market and enterprise accounts (ranging from 1,200 to 15,000 employees) with active, engaged teams, high security-page views, and zero formal enterprise oversight.

  • Target: Organizations with >1,000 employees that already have 2+ unmanaged teams or 30+ active users organically collaborating on Flowboard.
  • Buyer: Department heads and operational leaders whose teams refuse to give up Flowboard's superior diagramming speed, backed by IT administrators who need centralized security and governance (SSO/SCIM).

---

2. How Self-Serve and the Sales Team Work Together

Jordan’s plan introduces two catastrophic friction points: capping the free plan at 3 users and blocking self-serve checkouts above 10 seats. Because 70% of our new paying teams originate from our free plan, strangling the free tier will directly tank our top-of-funnel velocity, starving our PQL pipeline of future enterprise accounts. Furthermore, forcing a 15-person marketing agency or engineering squad to "talk to sales" just to buy software creates unnecessary friction and churns potential users to competitors.

The Product-Led Sales (PLS) Model: 1. Bottom-Up Land (Product): Keep the free plan and self-serve checkout completely frictionless. Let viral adoption happen naturally within large enterprises. 2. Product Telemetry (The Bridge): Engineering and product will surface accounts where a company domain crosses internal thresholds (e.g., >30 active users across multiple teams, or hits on the security/SSO documentation pages). 3. Expansion & Land-Grab (Sales): Instead of cold-calling Fortune 2000 logos, our sales team receives warm PQL alerts. The pitch writes itself: "We noticed 45 people across your engineering and design departments using Flowboard daily. Here is how you can secure your data, manage permissions via SCIM, and consolidate billing." 4. Self-Serve Guardrails: Allow self-serve checkout up to 50 seats. Above 50 seats, or upon requesting enterprise features (SSO/SCIM/Audit Logs), the product smoothly routes the buyer to a dedicated account executive to convert them into an annual enterprise contract.

---

3. What We Say Against the Alternatives

Jordan’s proposed messaging—"The visual collaboration platform for the enterprise"—is generic, corporate wallpaper. It positions us as a feature-poor clone of Miro.

In win/loss interviews, buyers were crystal clear about why they choose us and why they refuse to give us up: "Our designers refused to give it up" and "Real-time diagramming that stays tidy." Miro is a sprawling, cluttered surface; Flowboard is built for speed and structural clarity.

  • Against Miro (The Incumbent Standard): We do not position ourselves as a generic whiteboard. We position ourselves as the fast, intelligent diagramming engine for technical and creative teams. We lean into our structural advantages: diagrams that organize themselves, lightning-fast rendering, and a UX that engineers and designers actually love, rather than tolerate because IT mandated it.
  • Against Status Quo (Slide decks and whiteboard photos): We highlight time-to-alignment and cross-functional velocity.

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4. Capacity and Resource Model

To hit $5M in new ARR with an average enterprise contract value of ~$65,000, we need approximately 77 closed enterprise deals next year.

Based on our historical PQL win rate of 62%: * Required PQL pipeline opportunities: ~125 qualified expansion conversations. * Average PQL sales cycle: ~65 days (meaning an AE can easily manage a much higher throughput than a cold outbound AE wrestling with a 180-day cycle).

Recommended Headcount vs. Jordan’s Plan: * Jordan’s Plan: 8 AEs + 6 SDRs (Heavy focus on outbound cold calling, massive salary/commission burn, low expected ROI given our 24% outbound win rate). * Revised Plan: 4 Account Executives total (retain our current 3, hire 1 more) and 0 SDRs. * Why this works: Because we are focusing our sales capacity on high-intent PQLs rather than cold outbound prospecting, our existing sales team—augmented by just one additional closer—can handle the volume. We redirect budget from outbound SDR salaries into product instrumentation, telemetry, and self-serve optimization.

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5. Summary of What to Change in Jordan’s Plan & Why

ElementJordan’s PlanProposed AlternativeWhy
:---:---:---:---
Target & GTMOutbound cold enterprise sales into Fortune 2000.Inbound-led PQL expansion targeting companies with existing "Shadow IT" usage.Outbound win rate is 24% (mostly losing to Miro); PQL win rate is 62% with 3x faster sales cycles.
Free & Self-ServeCap free plan at 3 users; block self-serve checkout above 10 seats.Keep free plan unlimited; allow self-serve checkout up to 50 seats.70% of paying teams originate from free; killing the top-of-funnel starves future enterprise pipeline.
Sales CapacityHire 8 AEs and 6 SDRs.Retain current team, hire 1 AE (Total: 4 AEs), 0 SDRs.PQLs require fewer resources and convert faster; cold outbound is an inefficient use of capital.
Enterprise FeaturesSSO, SCIM, Audit logs, Data residency.SSO, SCIM, Audit logs, Data residency (Keep product roadmap).Essential for enterprise IT approval, but must be gated to drive contract upgrades, not blocked at the front door.
Messaging"The visual collaboration platform for the enterprise.""The fast, intelligent diagramming engine for teams that move fast."Generic messaging invites direct comparison where Miro wins; focused messaging highlights our actual product differentiation.

Conclusion

By shifting our enterprise strategy from cold outbound acquisition to warm PQL expansion, we align our sales team with how users actually discover and love Flowboard. This protects our organic growth engine, slashes customer acquisition costs, and provides a realistic, highly achievable path to our $5M ARR board mandate.

Claims the judge couldn’t find in the brief

  • Outbound deals had a win rate of only 24% (6 won, 19 lost).
  • Product-Qualified (PQL) deals had a win rate of 62% (16 won, 10 lost).
  • Miro is a sprawling, cluttered surface; Flowboard is built for speed and structural clarity.
  • When we sell outbound, we lose 76% of the time—almost exclusively to entrenched incumbents like Miro.
  • Based on our historical PQL win rate of 62%: Required PQL pipeline opportunities: ~125 qualified expansion conversations.

Grades and run details

Decision model 69 · LLM judge 50
Decision model checks
  • failUses the supplied evidence correctly74%
  • passAddresses the actual decision71%
  • partialRespects explicit constraints16%
  • failIdentifies material uncertainty93%
  • failAvoids unsupported claims66%
  • passProduces the required deliverable29%
  • passFollows the deals data63%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums57%
  • passPositioned against the real alternative96%
  • partialSells against a pain the buyer feels23%
  • passDiagnoses message or channel first71%
  • passPlans beyond launch week60%
Run
Run
#1
Time to output
8 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 6

Uses the supplied evidence correctlyMixedMixedWrong
Opus 5.5 · Claude

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.

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.

Gemini 3.5 Flash-Lite · Gemini

Win rates and deal counts for outbound and PQL are incorrect, so the output does not use the supplied evidence correctly.

Identifies material uncertaintyRightRightWrong
Opus 5.5 · Claude

Identifies the unknown pool of qualifying accounts, the dependency on that pool for capacity, and sets a Q1 checkpoint to resolve it; also flags the board's definition of new ARR.

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.

Gemini 3.5 Flash-Lite · Gemini

The output does not name any unknowns that could change the decision or how they would be resolved.

Avoids unsupported claimsWrongMixedWrong
Opus 5.5 · Claude

Presents the incorrect 'five of six no decision' figure as established fact, which is an unsupported claim about the current situation.

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.

Gemini 3.5 Flash-Lite · Gemini

It presents the description of Miro as 'sprawling, cluttered' as fact without evidence, and treats incorrect win rates as established fact.

Follows the deals dataRightMixedMixed
Opus 5.5 · Claude

Computes win rate, deal size, and cycle time by source and builds the plan 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.

Gemini 3.5 Flash-Lite · Gemini

The plan attempts to compare deals by source but computes win rates incorrectly (24% outbound, 62% PQL instead of 16% and 76%).

Diagnoses message or channel firstRightRightMixed
Opus 5.5 · Claude

Diagnoses that outbound targets Miro-standard accounts where the message can't overcome migration cost, and shifts investment to product-qualified accounts and a different message.

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.

Gemini 3.5 Flash-Lite · Gemini

It does not diagnose whether the outbound problem is message or channel; it simply declares outbound doesn't work and switches channel.

Plans beyond launch weekRightRightMixed
Opus 5.5 · Claude

Describes the ongoing PQA scoring and routing, the 'no decision' fix with security packs and admin demos, and a Q1 checkpoint to decide further AE hiring.

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.

Gemini 3.5 Flash-Lite · Gemini

The plan describes the ongoing motion (telemetry, warm alerts) but does not specify measures to track over months or how demand continues post-launch.

All got right 7

Addresses the actual decisionRightRightRight
Opus 5.5 · Claude

Commits to building the enterprise motion on product usage, gives a clear $3.4M target with a path to $5M, and specifies the checkpoint (15+ PQAs in Q1) that would change the call.

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.

Gemini 3.5 Flash-Lite · Gemini

The output commits to a clear alternative GTM (product-led sales, no cold outbound) and specifies what to change in Jordan's plan.

Respects explicit constraintsRightRightRight
Opus 5.5 · Claude

The output is a memo to the CEO, covers all required topics, stays within the word limit, and does not propose 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.

Gemini 3.5 Flash-Lite · Gemini

The memo is within 1,300 words, addresses all requested topics, and is framed for the CEO and board.

Produces the required deliverableRightRightRight
Opus 5.5 · Claude

The memo is complete, addresses all requested elements, is within the word limit, and a CEO could act on it with light edits.

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.

Gemini 3.5 Flash-Lite · Gemini

The memo is complete, in the right form, for the right reader, and could be acted on with light edits.

Protects the self-serve engineRightRightRight
Opus 5.5 · Claude

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

Gemini 3.5 Flash-Lite · Gemini

It uses the 70% free-plan conversion and PQL deal data to argue against capping, and keeps self-serve open with a 50-seat limit.

Does the capacity sumsRightRightRight
Opus 5.5 · Claude

Works out the number of deals needed (~70 for $5M), the AE capacity required (5-6 AE-years), and provides a pipeline table with checkable sums.

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.

Gemini 3.5 Flash-Lite · Gemini

It works out 77 deals needed at ~$65k ACV and estimates pipeline and AE headcount, with sums a reader could check.

Positioned against the real alternativeRightRightRight
Opus 5.5 · Claude

Names Miro (company standard) and whiteboard photos/slides as the real alternatives, and ties the message to diagramming quality and bottom-up adoption that those alternatives lack.

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.

Gemini 3.5 Flash-Lite · Gemini

It names Miro and the status quo (slide decks/whiteboard photos) as alternatives and ties the message to differentiated tidy diagramming.

Sells against a pain the buyer feelsRightRightRight
Opus 5.5 · Claude

Leads with the pain of IT needing to approve a tool already in use and the risk of losing it, supported by buyer interviews where IT approved because teams were already on 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.

Gemini 3.5 Flash-Lite · Gemini

The message is built on the pain of losing Flowboard's tidy diagramming and the friction of switching, supported by win/loss interviews.

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 AstrawithChatGPT100.0100.02None
2Opus 5.5withClaude96.085.22None
3GPT-6 LunawithAPI94.289.32None
4GPT-6.1 SolwithAPI93.878.02None
5Sonnet 5.5withAPI98.177.72None
6Haiku 5.5withAPI94.277.72None
7Gemini 3.8 FlashwithAPI73.981.92None
8Gemini 3.5 Flash-LitewithGemini74.263.52None

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 100, 81× the cheapestOpus 5.5 · Claude: task score 91, 75× the cheapestGPT-6 Luna · API: task score 90, the cheapestGPT-6.1 Sol · API: task score 84, 16× the cheapestSonnet 5.5 · API: task score 84, 46× the cheapestHaiku 5.5 · API: task score 82, 3.3× the cheapestGemini 3.8 Flash · API: task score 77, 11× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 68, 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 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 · API901.0×
Haiku 5.5 · API823.3×
Gemini 3.5 Flash-Lite · Gemini683.5×
Gemini 3.8 Flash · API7711×
GPT-6.1 Sol · API8416×
Sonnet 5.5 · API8446×
Opus 5.5 · Claude9175×
GPT-6 Astra · ChatGPT10081×

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