> ## Documentation Index > Fetch the complete documentation index at: https://b2-b-playbook.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # AI use-case selection **Last reviewed:** 2026-08-30 · **Reading edit:** 2026-09-06 Start with a task that is slow, costly, or frequently done badly, rather than with a new AI product. Compare an AI approach with simpler alternatives, estimate the benefit and effort, and choose a small test your team can actually adopt. Name the work problem; Compare possible fixes; Choose a bounded test *Reading guide: name the work problem → compare possible fixes → choose a bounded test.* ## Use this when * The calendar is a pile of AI pilots with no shared problem statement. * Every function wants a different copilot, and none of them move the quarter’s number. * You are about to buy from a community list of “GTM AI tools.” * The team is tired of tool-of-the-month. ## Do not use this when * There is no ICP and no number. Stay in [ICP](../01-strategy-and-buyers/icp.md) and [GTM planning](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/gtm-planning). * You already picked the problem and need stages, gates, and recovery. That is [AI workflow](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/ai-workflow). * You need a bake-off once the job is named. That is [MarTech governance](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/martech-governance). * You need a custom GPT’s instruction skeleton. That is [ai-teammate-brief.md](../../templates/ai-teammate-brief.md)—after the job exists. ## A few useful terms | Word | Meaning here | | ----------------- | ----------------------------------------------------------------------------------- | | **Problem** | What is actually stopping the quarter—not a nice-to-have. | | **Possibilities** | Every way to solve it, including *not* AI (hire, process, stop the work). | | **Payoff** | If the bet works, the business impact as a number you will defend—not “efficiency.” | | **Probability** | Honest chance it works *here*, given buyers, data, and skill. | | **Perspiration** | Total load: your team **and** RevOps, enablement, product marketing, legal. | | **Priority** | A conversation: expected value versus that load. Not a fake 7.3. | ## Keep this in mind **Pick the problem first.** If you cannot name it in one sentence the CRO would recognize, you are shopping. A possibility that is only “try this model” is not a possibility. It is a catalog. ## How to do it ### Step 1: Name the business problems Three at most. Pipeline missing, quality rotting, hiring gap, cycle slipping, onboarding stalling—**this quarter**. Then ask why until you have a cause you can act on, not a slogan. “We need AI” is not a cause. “Qualified people never reach the case-study stage because we only post the role” is. ### Step 2: Compare AI and non-AI options For each problem: outreach, a human hire, a process change, a compensation change, a page, an agent, a stop-doing. If the only row is a vendor, you jumped. ### Step 3: Estimate benefit, confidence, and effort **Payoff.** Is this a 5% lift you will not feel, or a change that would rewrite the plan? Demand a unit: pipeline, cycle days, hours returned *that you will reinvest*, win rate on a named slice. “Reps will be more productive” is not a payoff. **Probability.** Proven in a company like yours, or a speculation you are taking because the upside is large enough. Write which. **Perspiration.** Map every other team that must make this a top-three priority. A “one director, one month” project that also needs dashboards, training, and a narrative is a four-team project. A useful comparison—not a law: *(payoff you believe × probability you believe) / perspiration*. If the high-payoff bets all have terrible probability given current load, **change the problem**. That is the framework working. ### Step 4: Check whether the team can adopt the change The technology is usually less scarce than change. Principles that keep the frontline: * Start with work that makes the job easier, not a keynote agent. * Prefer tools that live where the team already works. A sixth surface is how you lose them. * Size enablement to the change: a Slack note, a written standard plus meeting time, or formal training. Most companies under-staff the last two. * One bet at a time with enough attention to finish. Random experiments train people to tune out. When the bet is chosen, it becomes a row in the [experiment ledger](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/experimentation): hypothesis, decision, kill date. ## Worked example (illustrative) Sales-assist cybersecurity. Not Owner.com, not your vertical. | Field | Fill | | ------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | Problem | Pipeline quality: first meetings from content do not match the ICP; AEs burn the week. | | Why | We do not brief the AE on stack and trigger; we book anyone who raises a hand. | | Possibilities | (1) Human SDR writes a one-pager. (2) Pre-call brief workflow with a review gate. (3) Tighten the form. (4) Stop the content CTA. | | Payoff | If briefs cut bad first meetings by a third, AE capacity returns to the ICP slice—enough to matter this quarter. Not “150% pipeline.” | | Probability | Medium: we already have call notes; ICP file is stale. | | Perspiration | Ops must land the brief in the CRM; AEs must open it; marketing must stop celebrating raw bookings. | | Choice | (2) plus (3). Not a new chat SKU. | ## Copy: selection card (fill) * Company problem (this quarter): * Root cause we can act on: * Possibilities (include at least one non-AI): * Payoff in a number we will defend: * Probability and why: * Perspiration by team (not only the sponsor): * Priority call (do / wait / pick another problem): * Experiment row we will open if we do: Working file: [ai-use-case-score.xlsx](../../templates/ai-use-case-score.xlsx). ## Before you start * [ ] The problem is a company goal, not a vendor category. * [ ] At least one possibility is not software. * [ ] Payoff is a number, not “AI transformation.” * [ ] Perspiration names other teams. * [ ] Frontline can see how this makes the week easier. * [ ] We are not running three AI bets with no owner. * [ ] A community logo sheet did not pick the winner. ## Metrics | Metric | Diagnostic use | | ----------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | | Bets finished vs started | Attention vs fatigue | | Problems killed because the math failed | Framework vs shopping | | Frontline still using the thing at day 60 | Adoption vs launch | | Experiment rows with a decision | Selection connected to [experimentation](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/experimentation) | Do not count tools trialed, or a CRO’s public “zero bad AI bets” story, as your selection system. ## Common mistakes * Starting from a demo. * Incremental busywork dressed as payoff. * Ignoring other teams’ calendars. * Adding a surface the team will not open. * Switching tools before one bet embeds. * Copying another company’s hiring-sprint tree as your problem. * Pasting a Pavilion AI spreadsheet into the stack. ## What to read next Where the motion sits on the ladder is [GTM AI maturity](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/gtm-ai-maturity). How the chosen job is staged is [AI workflow](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/ai-workflow). How you try to disprove it is [experimentation](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/experimentation). Which SKU may do the job is [MarTech governance](https://b2-b-playbook.mintlify.app/playbooks/09-operations-pipeline-and-measurement/martech-governance). Instructions for a teammate come last: [ai-teammate-brief.md](../../templates/ai-teammate-brief.md). ## Sources and evidence boundary This is an owner-maintained operating synthesis. It is not a decision-science textbook, not employment advice, and not an endorsement of any CRO’s win rate. Problem-first selection, a full possibility set, payoff × probability versus organizational load, and adoption as the scarce skill are distilled from a public write-up of a use-case method ([Cannonball GTM, *The Five P’s Framework*](https://cannonballgtm.substack.com/p/the-five-ps-framework-how-to-pick?ref=b2b-playbook), discussing a talk by Kyle Norton). That essay is a **method prompt**, not a source to copy. Owner.com performance claims, “zero bad bets,” Opportunity Solution Tree examples, and enablement slogans in the piece are **not** this library’s facts. A circulated community list of AI products is not a substitute for Step 1. A survey in which more firms report using AI than attributing EBIT to it ([McKinsey, *The State of AI*](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai?ref=b2b-playbook)) is a reminder to pick a P\&L problem—not a reason to import their 2026 percentages as your business case. *** Copyright © 2026 Ivan Xu. All rights reserved. See the [copyright and reuse terms](https://github.com/weilun88313/B2B-Playbook/blob/main/LICENSE). Canonical source: [github.com/weilun88313/B2B-Playbook](https://github.com/weilun88313/B2B-Playbook)