Sourcing contacts inside an account is where most GTM teams waste a lot of time. And it usually starts with a data filter. "VP of Sales" at a 50-person company and "VP of Sales" at a 5,000-person company are different jobs. Same title, different universe. But most teams run the same filter on both, load reps with contacts that were never going to convert, and then wonder why they aren’t seeing results.

Here’s what’s actually happening: you’re outsourcing judgment to a database. And databases don’t know what good looks like at your company - you do.

Ready to get started? Grab the Contact Qualification & Enrichment skill on GitHub and put it to work right away. Otherwise, read on for how it works.

The Problem

Most teams treat contact sourcing like a data pull. They set some title and seniority filters in Sales Nav or ZoomInfo, export the list, push it to reps. Reps then spend hours clicking through profiles trying to figure out who’s actually worth reaching out to. There’s no documented logic, no consistency across the team, and the criteria live in one senior person’s head. When that person leaves or scales past the point of doing it themselves, the judgment goes with them.

What AI Makes Possible

You can extract that judgment, document it, and run it programmatically — so every contact in every account gets evaluated against the same criteria, at scale, without burning rep time.

Here’s how to do it.

Step 1: Validate Your Contact Theory Against Reality

Before you go evaluate individual people, pressure-test your assumptions about who you’re actually selling to.

Most teams have a working theory about which personas matter — job functions, seniority levels, types of roles. Write that down. Then export two things from your CRM: contacts who came inbound in the last 6–12 months, and contacts associated with closed deals. Upload both to Claude or ChatGPT and ask it to synthesize the patterns.

Example prompt:

“Here are two CSV exports: recent inbound contacts and contacts linked to closed deals. Identify which job functions, titles, and seniority levels appear most frequently. Flag any titles or functions that appear often in closed deals but not in our working persona list.”

You’re looking for surprises. Roles you didn’t expect. Functions that keep showing up in your deals that aren’t in your target list. This gives you a validated filter set — grounded in what actually worked, not just what sounds right.

Step 2: Do a Manual Review with Dictation Running

Pull contact lists from a handful of accounts — mix of segments and verticals if you have them — and have your most experienced person go through each profile on LinkedIn, out loud.

The key is capturing the why in real time. Use a dictation tool (Wispr Flow and Monologue both work well) and have them narrate as they click: what catches their eye, what makes them hesitate, who they’d reach out to first and why. Don’t filter it — you want the raw reasoning, not a polished summary.

To get the contacts: build a search in Sales Navigator using the filters from Step 1, then export the leads using a browser automation or export tool — PhantomBuster, Evaboot, and Captain Data all handle this well.

What you’re listening for:

• Language on the profile that signals forward-thinking vs. entrenched — are they describing outcomes or tasks?

• Seniority and growth trajectory — have they moved up within a company over time?

• Whether the title reflects real ownership or a narrow individual contributor role

• How company size changes what the title actually means

That last one matters more than most teams account for. A “Director of Sales” at 50 people probably owns the entire function. Same title at 2,000 people might manage one region. You may end up needing separate criteria by segment.

Step 3: Synthesize Patterns with AI

After the review, upload your notes (or the dictation transcript) to Claude and ask it to synthesize what you found.

Example prompt:

“Here are my notes from reviewing [X] contact profiles. Identify the consistent signals that made someone a ‘yes’ vs. a ‘no.’ Group them by theme — seniority signals, language patterns, role ownership, etc. Then flag any patterns that were specific to a company size or segment.”

You’re not asking AI to invent your criteria. You’re asking it to find the shape of something you already know. The output should read like a documented version of what your best reviewer has been doing in their head.

Review the synthesis carefully. Push back where it oversimplified. Add nuance where it missed it. What you end up with is a qualification framework — your actual criteria, written down, ready to be turned into a prompt.

Step 4: Build Your Classification and Qualification Agents — Separately

Here’s something learned the hard way at MedScout: don’t try to classify and qualify in one step. When you combine them, the assessment gets muddy — you end up trying to figure out what someone does and whether they’re relevant at the same time, and both answers suffer.

Split it:

Classify first

Build a prompt that maps each contact to a job function based on their LinkedIn profile. What bucket do they actually belong in — sales, ops, marketing, finance, etc.? The same title can mean different things at different companies. Getting the function right makes the fit judgment cleaner.

Example prompt:

“Based on this LinkedIn profile, classify this person’s primary job function into one of the following categories: [your function list]. Use their title, current responsibilities description, and career history. If ambiguous, default to the function most represented in their recent roles.”

Qualify second

Now that you know what they do, assess fit. Are they the kind of person who would drive or shape an evaluation of a tool like yours? The answer is a holistic judgment — seniority, scope, function ownership, how they describe their work.

Structure the output in tiers that map to action, not just quality:

• Best Fit: worth prioritizing in outreach — they’d drive or heavily influence the evaluation

• Good Fit: worth having in the CRM and engaging, but not the person you lead with

• Not a Fit: not worth the outreach

If your segments are meaningfully different (say, SMB vs. enterprise), build separate qualification prompts for each. The buying dynamics are different enough that one set of criteria won’t cover both cleanly.

Step 5: Operationalize the workflow

Once your prompts are tested and trusted, load them into a workflow automation platform. Clay, Apollo, and Cargo are all built for this kind of multi-step enrichment and qualification pipeline.

The workflow: build your contact list in your contact database → export the leads → enrich with LinkedIn profile data → run your classification prompt → run your qualification prompt → push qualified contacts to your CRM with source and date tagged.

A few things to get right on the back end:

• Deduplicate against existing CRM records before pushing new contacts

• Tag enrichment source and date on every record — you’ll thank yourself when you need to audit data quality later

• Don’t enrich every contact at every account. Enrich the people who matter at accounts that are already qualified. Sequencing matters: account qualification first, then contact qualification, then enrichment

The goal is flipping the work. Instead of each rep spending hours in Sales Nav trying to figure out who to reach out to, the system surfaces the right people with enough context for reps to react and make the final call. That time savings compounds fast.

What Good Looks Like

A well-built contact qualification agent returns something like this for each contact:

Function classification: RevOps / Sales Operations

Fit tier: Best Fit

Reasoning: Director-level with explicit ownership language — describes “building the forecasting process from scratch” and “owning the tech stack.” Company is 300 people, so this title reflects real scope. Career history shows consistent growth within the function.

Suggested action: Prioritize in outreach. Likely economic or technical evaluator.

That output is what you want reps reacting to — not raw Sales Nav profiles.

Watch-Outs

Don’t skip the manual review phase. It’s tempting to jump straight to building prompts. But your AI agent is only as good as the criteria you give it — and those criteria have to come from real judgment applied to real profiles. The manual step isn’t inefficient. It’s the input.

Segment earlier than you think you need to. If your ICP spans SMB and enterprise, don’t try to use one prompt for both. The signals that make someone a “Best Fit” at a 50-person company look different than at a 5,000-person company. Build for that early, or you’ll rebuild later.

Tools Referenced in This Guide

This guide focuses on the methodology — how to extract, document, and operationalize your team’s contact qualification judgment. The specific tools are interchangeable. Here are the categories and options mentioned:

- Contact database (search and filter potential contacts by title, function, seniority, company): LinkedIn Sales Navigator, ZoomInfo, Apollo

- Contact export (pull contact lists from your database into a workable format): PhantomBuster, Evaboot, Captain Data

- Enrichment & workflow (enrich profiles with additional data, run AI prompts at scale, manage the pipeline): Clay, Apollo, Cargo

- Dictation (capture spoken reasoning during manual review): Wispr Flow, Monologue

- AI assistant (synthesize patterns, build classification and qualification prompts): Claude, ChatGPT

Pick what works for your stack. The frameworks in this guide work across all of them.

Special thanks to the MedScout team for sharing their contact qualification workflow, which shaped several of the frameworks in this guide.

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DEAR OVEREXTENDED MANAGER: If you missed it, here’s the article on how AEs should using AI in live deals. To be clear, there are lot of great solutions out there, but I was trying to share some actionable advice that an individual could implement (aka not company-wide, no business case or new budget). Addressing the same question at the manager level, it comes down to coaching. Here are 3 ideas to get you started with any mainstream LLM:

1. Pattern Recognition Across Calls

Instead of reviewing one call at a time, upload 5–10 transcripts and ask:

• What patterns show up in lost deals?

• Where is discovery shallow?

• Are reps multi-threading effectively?

• What objections are recurring?

Most managers rely on anecdotal memory when AI can surface trends quickly. You still validate the conclusions, but you start from signal instead of guesswork.

→ Key Takeaway #1: Use AI to find patterns. Use your experience to interpret them.

2. Create Personalized Coaching Plans

After reviewing a rep’s recent calls, prompt:

• What 3 skill gaps are most evident?

• What drills or role-play scenarios would strengthen those areas?

• What metrics should I track over the next 30 days?

Instead of vague feedback (“ask better questions”), you can walk into a 1:1 with:

• Specific moments from calls

• A clear development focus

• A measurable improvement plan

That changes the quality of the conversation and allows you to scale yourself as a manager. One thing worth knowing: managers tend to lean into the back-and-forth style v. being told what to do. That’s exactly why coaching-plan workflows like this work best!

→ Key Takeaway #2: Coaching should feel hyper-customized.

3. Upgrade Your 1:1 Prep

Before your weekly 1:1:

• Input CRM notes and pipeline data (connect the data sources to make this easier if you can!).

• Ask for a summary of leading indicators.

• Identify deals at risk based on stage aging

This gives you sharper questions:

• Why has this deal stalled?

• What’s the next customer-committed action?

• Where are we single-threaded?

AI sharpens your agenda and saves you meaningful time on admin work. You can even take this a step further and start automating these workflows to land in your inbox morning of a 1:1 or summaries at the end/start of a week.

→Key Takeaway #3: Managers spend less time on administrative prep.

Until next week!

Most investor updates get skimmed for two minutes and filed away. A few get saved, forwarded, and referenced in board meetings. The difference isn’t the quality of your quarter — it’s the quality of your communication.

Here’s how to use AI to write a monthly update that builds investor trust even when the numbers are mixed.

Download the Skill on GitHub

The Problem

Founders treat investor updates like a chore and often miss including information investors need to quickly assess your situation and provide support.

Often founders write paragraphs of information without including key metrics that help round out the narrative. The data-dump version makes investors do the interpretation work themselves, which means they fill in the story with whatever assumptions they have.

What you actually want: lead with the number, add the context that matters, skip everything else.

What AI Makes Possible

AI won’t make your quarter better. But it will help you structure what you know into an update that gives your investors the right context and builds credibility over time, even when the news is mixed.

Specifically, the founder-monthly-update skill in Claude structures every update the same way, so your investors always know where to look for what they care about. It also pushes you to include the things founders tend to skip: upfront summary, the pipeline coverage ratio, the real reason the logo churned.

Step 1: Gather Your Raw Numbers First

Before you open Claude, pull everything into one place. You need:

• Revenue: Current ARR, new ARR this month, expansion, contraction, churn, as well as the plan

• Cash: Bank balance, last month burn, 3-month average burn

• GTM detail: New logos + ACV, expansions, renewals, churn with reason

• Pipeline: Current quarter target, pipeline coverage ratio, any named deals worth flagging

• Product: What shipped, what’s coming next month

• Team: Headcount, new hires, departures, open roles

You can either pull these numbers together or connect Claude to a spreadsheet and/or your systems. Don’t editorialize yet. Just get the raw inputs together. The skill will help you turn them into a narrative.

Step 2: Give Claude Your Data and Ask for the Update

Paste your raw numbers into Claude and say:

“Write my monthly investor update using the founder-monthly-update skill. Here’s my data: [paste your inputs]”

The skill will produce a complete email draft with this structure, every time:

• Subject line — metric-anchored, built to get opened

• TL;DR — 3–5 sentence narrative that hits every section at a glance

• Key Revenue Metrics — clean data block including goal and % to goal

• Cash — balance, burn, runway, flagged if elevated

• GTM Update — logos, expansions, churn with reasons, pipeline coverage

• Product — what shipped and what’s coming

• Team & Culture — hires, departures, open roles, leadership updates

The % to goal calculation matters. Investors track it. The skill always shows both your actual number and your goal — not just one or the other.

Step 3: Read the TL;DR Out Loud

Before you send anything, read the TL;DR paragraph out loud. It should answer these five questions in 3–5 sentences:

• What was your biggest win (or miss) this month?

• What’s the key product or strategic move?

• What’s the team headline?

• What’s the one honest flag — burn, pipeline, a risk?

• What’s the energy on the team right now?

If you can’t answer all five from the TL;DR alone, ask Claude to tighten it. The TL;DR does the heavy lifting. Everything below it is for investors who want the detail.

Step 4: Check the Flags

The skill will prompt you to include things that are going wrong — not because investors enjoy bad news, but because founders who only share wins lose credibility over time.

Before you finalize, verify you’ve named:

• Burn if it’s elevated (and why)

• Pipeline coverage if it’s tight (and what you’re doing about it)

• Any churn, with the actual reason — not “strategic misalignment,” the real reason

• Runway if it’s under 18 months

If all of these are genuinely positive, great — say so. But don’t skip the section because it feels uncomfortable.

What Good Looks Like

A strong investor update from a founder who closed 103% of their Q1 target might open like this:

We closed Q1 at 103% of our new business target ($X against $Y), added 5 new logos including X and Y, and made the decision to shift our entire product org toward building an <insert detail here>.. March burn was elevated at $X, and Q2 pipeline coverage is tight at ~1x against a $X target — both areas we’re focused on. The team is ready to climb a new mountain.

That’s five things in four sentences. The win, the logos, the strategic move, two honest flags, and closing sentiment. Investors can file that away and know exactly what the company stands for that month.

Investor Update Mistakes to Watch Out For

Don’t let AI write the strategic narrative.

The skill structures everything, but the company strategy section — why you’re making a big bet, what the market is telling you — has to be in your own words. Investors can tell when a founder didn’t write their own conviction.

Don’t use % to goal without showing the goal.

“We hit 87% of plan” is meaningless without knowing the plan. The skill enforces this, but double-check the metrics block before you send.

Don’t skip the Customer Love section.

One real customer quote or anecdote — the VP who told you your product changed how their team works — does more for investor confidence than three more metrics. Include it when you have it.

The Takeaway

The goal of a monthly investor update isn’t to impress your investors. It’s to keep them calibrated. Investors who are well-calibrated ask better questions, make better introductions, and better understand how to support you.

The founder-monthly-update skill gives you the structure to do that consistently — so your updates become an asset over time, not just a monthly obligation.

Download the Skill on GitHub

Paste your raw metrics into Claude and say: “Write my monthly investor update using the founder-monthly-update skill.”

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DEAR HACKATHON TO EXECUTION GAP: Running a hackathon is a great idea in theory, but turning the winning ideas into something that changes how your team works is where things break down.

We recently brought together a group of Stage 2 LPs, including Jeb Dasteel, former Chief Customer Officer at Oracle, David Hwang, CCO and Interim CRO at Grammarly, and Colin Murphy, CCO at Zendesk to dig into this exact challenge. Below is a summary of their collective takeaways. Their read: these projects fail because they weren’t designed or scoped to survive governance, resourcing, and cross-functional realities.

If you want your hackathon to produce something that actually ships, you have to build the operating model before you build the prototype.

Start with the business outcome, not the experiment

A common pattern: teams come out of a hackathon with great ideas, then realize they don’t have the business units they need (IT, data science, security) to actually unlock them. The energy dissipates, and the idea dies in a backlog somewhere. Getting those stakeholders into the conversation early, before the hackathon, not after, is what separates the experiments that ship from the ones that don’t.

Match oversight to risk

Not every AI initiative requires the same level of governance. Internal workflow automation moves much faster because the exposure is lower. Once automation touches customers directly, the oversight model changes.

Separate internal productivity use cases from customer-facing automation. Internal use cases often include knowledge retrieval, drafting communications, or demo preparation. These create quick wins and build momentum. Customer-facing automation requires deeper collaboration with security and data teams.

If governance enters the conversation after the hackathon, it feels like friction. If governance is built into the evaluation criteria from the start, it becomes part of the design.

Pressure test your foundation before you automate it

Many teams assume their documentation is ready, then discover it isn’t updated, isn’t structured, and is full of assumptions that were never written down.

Preparing for AI-powered onboarding automation often means going back and designing a prescriptive experience from scratch. That means defining the journey, creating the content, and filling in the data gaps before any automation is possible.

Many teams discover that the real work is not building the AI layer. It is cleaning up what sits underneath it. If that’s the case, take a beat to do this foundational work first.

Plan for maintenance, not just momentum

The build versus buy discussion is different today than it was even a year ago. Tools evolve quickly, and internal teams are more capable than ever. But the real question is not whether you can build something. The question is whether you are prepared to support it long term.

After the hackathon, assign a clear owner and ask the hard resourcing questions:

• Who is going to maintain this?

• Does your team have the capacity to keep up with it as the technology keeps moving?

This was the make-or-break factor that came up again and again - not whether you can get something built, but whether you have the people to sustain it. If you don’t have a clear answer, that’s worth resolving before you commit.

Until next week!

Most discovery call debriefs go like this: the AE gives a verbal summary or fills in a few CRM fields, sends a follow-up email, and the deal moves to the next stage with half the MEDDIC blank.

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The Problem

MEDDIC works as a framework. What breaks down is the debrief. Managers don’t have time to listen to every call. AEs self-report optimistically. And even when someone does listen, translating a 45-minute transcript into a crisp qualification gap analysis takes real cognitive work.

The result: deals enter the pipeline underqualified, forecasts are wrong, and reps spend cycles on accounts that aren’t closing.

What AI Makes Possible

Drop a call transcript into Claude with a structured MEDDIC debrief skill, and you get a scored qualification analysis, a sales leader commentary, a prioritized next steps list, and a ready-to-send mutual action plan email — in under two minutes.

This isn’t AI summarizing the call. It’s AI doing the analytical work a good sales manager would do: reading between the lines, flagging what wasn’t said, identifying the moment the seller should have pushed harder, and turning that into something the AE can act on immediately.

🛠️ New: Stage 2 GTM AI Skills on GitHub

We’ve launched a Stage 2 GitHub repo containing all of our GTM AI Skills — including the MEDDIC debrief skill referenced in this post — so you can fast-track these how-tos for your own team. Visit our GitHub to download the skill and plug it into your Claude workflow.

Get the MEDDIC Analyzer Skill on GitHub

The How To

Step 1: Get your transcript

Most sales teams record calls via Gong, Chorus, Fathom, or Fireflies. Export the transcript as plain text — you don’t need the recording, just the words. If you’re working from notes, that works too, but the more specific the input, the more specific the analysis.

If you have Claude connected to your recorded calls automatically, you can just ask it to run the MEDDIC analysis against your calls during a specific time and it should pull them automatically.

What to watch out for: Transcripts with heavy small talk at the top (sports, tech issues, catching up) can dilute the signal. However, this skill will skip past preamble automatically and look for the moment the substantive conversation starts.

Step 2: Run the MEDDIC debrief skill

Paste the transcript into Claude and trigger the skill. The skill will:

• Parse the transcript for deal context: stage, competitive situation, timeline, key stakeholders

• Score each MEDDIC element (Strong / Partial / Missing) with specific evidence from the call

• Render a visual scorecard so the gaps are obvious at a glance

• Write a sales leader commentary identifying the critical misses and broader patterns

• Generate 4–6 prioritized next steps ordered by urgency and deal impact

The prompt is simple:

Analyze this discovery call against MEDDIC and give me my next steps. [paste transcript]

Step 3: Review the scorecard and commentary

The scorecard is your quick read — any red (Missing) element is a live deal risk. But the real value is in the commentary. A good debrief will tell you:

• The specific moment the seller should have pushed and didn’t

• Whether your ‘champion’ is actually a champion or just a friendly coach

• How the competitive situation actually looks vs. how the AE described it

• The pattern in how this AE runs calls — not just this deal

Real example: On a recent portfolio company call, the AI flagged that the rep let the buyer name the actual decision-maker and then never asked how to get to him. The call ended with ‘let’s reconnect in May’ — which isn’t a next step, it’s a prayer. That’s the kind of miss that’s obvious in hindsight but easy to miss in the moment.

Step 4: Send the mutual action plan email

The skill generates a ready-to-send MAP email for the AE to send to their buyer contact within 24 hours. The email:

• Opens with something specific the buyer said — not generic ‘great talking to you’

• Summarizes what was covered from the buyer’s perspective, not the seller’s

• Lists what the seller is sending first, before making any asks

• Includes a clean action table with owner and target date for each next step

• Closes with a frictionless CTA — easy for the buyer to confirm

This isn’t just a nice-to-have. A MAP email forces the AE to commit to specific deliverables, gives the buyer something to react to (which flushes out ghosting early), and creates a written record of what was agreed. Deals with documented next steps close faster.

Quick Reference: What to Surface in Each Element

What Good Looks Like

A strong AI-assisted MEDDIC debrief will be uncomfortable to read. It should name things the AE didn’t want to flag — the champion who hasn’t committed to anything, the budget cycle that probably rules out this fiscal year, the ‘free competitor’ objection that was addressed technically but never economically neutralized.

It’s a great opportunity for the AE to continue to learn the art of discovery without impacting their current pipeline.

Watch-Outs

• Garbage in, garbage out: If the AE’s notes are thin or the transcript is heavily garbled, the analysis will be thin too. This skill rewards teams who record and transcribe calls.

• Don’t skip the review: AI will flag gaps based on what was said. It can’t know what the AE knows from offline conversations. Always sanity-check the debrief against the full relationship context.

• The MAP email needs a human read before sending: The skill will draft commitments based on what was discussed on the call. Make sure the AE isn’t promising something they didn’t actually agree to send.

The Takeaway

MEDDIC doesn’t fail because reps don’t know the framework. It fails because the debrief process doesn’t give it teeth — there’s no one with the time and the transcript access to do the analysis consistently, at scale, after every call.

AI closes that gap. Not by replacing sales judgment, but by doing the analytical legwork so the manager can spend their time on coaching, not call review. The sellers who adopt this workflow will have better-qualified pipelines, fewer stalled deals, and follow-up emails that actually get responses.

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DEAR CAUTIOUS CRO: AI doesn’t change the quality of your sales team. It amplifies it. Thoughtful reps get sharper. Average reps get faster at producing average work.

So the question isn’t “Should they use it?” It’s really “Where does it improve thinking inside a live deal?”

In working with our portfolio, the trend I notice: AI is most powerful when it strengthens preparation and inspection, not when it replaces judgment.

NOTE: I’m not going to comment on specific LLMs or products for the purposes of this article. This advice can just as easily be applied to Gemini, Claude, OpenAI, etc…

Let’s dig in…

Before Discovery: Raise the Quality of Preparation

Strong reps don’t walk into discovery hoping the buyer tells them what’s wrong. They walk in with a strong hypothesis based on research.

AI can accelerate the formation of that hypothesis. It can help synthesize likely business priorities, surface industry pressures, and suggest angles the rep may not have considered. That’s useful, especially for newer AEs or when the team is feeling time-constrained.

But you have to set the standard that the rep refines the thinking before it ever reaches the buyer.

If they can’t articulate why they’re asking a question, it shouldn’t be asked. AI-generated discovery questions are raw material and the rep’s job is still to connect the dots.

The real win here isn’t speed. It’s depth of preparation in less time.

Between Stages: Improve Deal Inspection

This is where AI can create real leverage.

You’ve seen deals stall in the pipeline or surprisingly go silent after weeks of engagement. Deals collapse when risk goes unnoticed. Risk comes in many forms - weak access to power, unclear decision process, lack of urgency, etc...

After a call, a rep can input their notes and ask for a structured critique. What buying gaps exist? Where is the economic buyer missing? What objections are likely to appear late? Here’s a sample prompt to get you started:

System Role: You are a senior B2B sales manager with deep expertise in enterprise deal qualification and pipeline risk assessment. You think like a skeptical sales leader preparing for a forecast call. [Add any industry or company specific insight]

Instructions: Review the call notes or transcript below and provide a structured deal inspection critique. Be direct and do not soften findings. Your job is to identify risk, not validate optimism.

Evaluate the following:

1. Access to Power - Is the economic buyer identified, engaged, and on record supporting this deal? If not, what is the path and timeline to get there?

2. Decision Process - Does the rep have a clear, confirmed understanding of how this company buys? Who is involved, what are the steps, and what could derail it?

3. Compelling Event / Urgency - Is there a real, documented reason this deal closes in the stated timeframe? Would this deal slip if the rep stopped pushing?

4. Competition and Risk - What competitive threats or internal alternatives exist? What has not been asked or answered that a competitor could exploit?

5. Buying Gaps - What objections have not surfaced yet but are likely to appear late? What does the rep not know that they should know by now?

6. Recommended Actions - List 3 to 5 specific next steps the rep should take before the next pipeline review to reduce deal risk.

End with a one-line overall risk rating: Low / Medium / High and a single sentence explaining why.

Call Notes / Transcript: [INSERT HERE]

That exercise forces clarity and introspection, and the more data you provide the better the output will be.

To be clear, this doesn’t replace your pipeline review, but can make your pipeline review sharper. Reps who consistently pressure-test their own deals show up differently in forecast conversations because they are actively thinking about risk mitigation.

Late Stage: Elevate the Champion

In enterprise sales, your champion is selling internally when you’re not in the room. We’ve been arming them with material for years and AI can help here too.

AI can absolutely help draft an executive summary, outline a business case, or structure a mutual action plan –as long as you read it, edit it and treat it this as a draft. That’s an appropriate use. It saves time on format so the rep can focus on substance.

Bonus points if you roll this out at a team or org-wide level. You can build a skill or agent trained on the best business cases (or exec summaries, or action plans, etc…) that your org has used over the years. This creates consistency for the team and removes duplicate work.

I’ll say it again…no late-stage communication should be sent without thoughtful editing. Tone, sequencing, and nuance matter more at the executive level. The rep always owns the final message.

Set the Standard Before the Tool Sets It

If you simply tell your team to “use AI,” quality will vary wildly. Instead, define what good usage looks like. For the examples shared today, you can boil it down to:

• AI drafts. The rep rewrites.

• AI surfaces risk. The rep decides action.

• AI suggests ideas. The rep owns judgment.

AI will widen the gap between thoughtful sellers and transactional ones. The reps who win will use it to prepare sharper hypotheses, identify risk earlier, and strengthen their champions, not to send faster templated emails.

As a leader, it’s on you to define what “great” looks like when your team uses AI.

Until next week!

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Most target account lists are built the same way: export from ZoomInfo, filter by firmographics, assign out to your reps. The list looks defensible, but it can be missing the most useful signals that match your ICP.

The problem isn’t the data provider. It’s that the criteria that actually determine fit — the stuff your best reps have been quietly applying for years — never makes it into the list at all. It lives in someone’s head. Filters can’t capture it. And until you write it down, you can’t train to it, scale it, or ask AI to help with it.

Here’s a process for building an account list that captures real judgment, not just spreadsheet logic.

Step 1: Acknowledge what your filters can’t do

Start by pulling your initial list from ZoomInfo, Sales Nav, or whatever data provider you use. Filter by the firmographics you know matter — size, industry, geography. This gives you a working universe.

Then sit with it honestly. There are companies on that list that don’t belong, and companies missing that should be there. That’s not a failure of the tool — it’s the ceiling of filters. Industry classifications don’t map neatly to your ICP. Revenue ranges don’t tell you anything about buying behavior. The raw list is a starting point, not a judgment.

This skill walks you through the entire process end to end. If you want to test each section first, we have included prompts to try along the way.

Prompt to try in Claude: “Here is my current ICP criteria: [paste your firmographic filters]. What are the types of companies that might pass these filters but still be a bad fit? What types of companies might not pass these filters but still be worth targeting? Explain your reasoning for each.”

This is a useful gut-check. It starts surfacing the criteria you’re already applying informally — the ones that never made it into your filter settings.

Step 2: Shadow your best rep(s) — and document what you see

This is the most important step, and the most skipped.

The team at MedScout figured this out early. Brian Aoyama and Mallory Blocker brought their entire team into a room, grabbed a few accounts from the CRM, hooked one of their best AEs up to the projector, hit record on Fathom, and asked him to walk through account evaluation out loud in real time. The team watched what tabs he opened, noticed where he lingered, and paid attention to what made him say “this is interesting” versus “this one’s not for us.”

They wrote up what they observed. Then they took it back to him and asked: what did we miss? The first pass always misses things that were instinctive. That conversation surfaced criteria he’d been applying for years without consciously thinking about them.

Once you have a recording of a session like this, feed the transcript into Claude:

Prompt to try: “Here’s a transcript of a rep walking through account evaluation. Pull out the criteria they applied, the signals they looked for, and anything they weighted heavily without naming explicitly. Flag any areas of uncertainty or unresolved questions. Structure it so we can bring it back to the rep to confirm and fill in what’s missing.”

The output of this step is a first documented draft of how your best person actually evaluates accounts — not invented top-down, but captured from real behavior. That’s worth building for two reasons: training new reps, and eventually serving as the foundation for AI to help qualify accounts at scale.

Step 3: Run your best and worst customers through the same lens

Pull at least three of your best-fit customers and three of your worst-fit or churned customers. For each one, ask: what made this a good or bad fit? What did we know — or could have known — before the deal closed?

You’re looking for the underlying traits beneath the surface patterns. Not just “they were a small company” but why that mattered. Was it budget constraints? Decision-making process? Risk tolerance? “Small company” is a data point. “Low risk appetite in a category they see as non-core” is a trait.

Prompt to try in Claude: “Here are three of our best-fit customers: [names + 2-3 sentences on each - what made them a good or bad fit, how the deal played out, anything you wish you’d known or asked about sooner]. Here are three that churned or were bad fits: [same]. Based on what I’ve shared, what traits seem to distinguish the two groups? What questions would you ask me to sharpen the distinction?”

Run this in rounds — one good, one bad, then another pair, then another. Each round tunes the lens. You end up with a set of real patterns grounded in your actual customer history, not your assumptions about who should be a good fit.

Step 4: Turn patterns into traits, and traits into signals

Now you have a list of good and bad patterns. The next step is leveling up: what are the underlying traits these patterns point to?

A trait is something like “openness to innovation” or “organizational stability” — not a single data point, but an attribute that matters for whether an account will succeed with your product. One trait might have multiple signals. Your job here is to get specific: for each trait, what would actually tell you it’s present?

Prompt to try in Claude: “Here are the patterns we identified across our best and worst customers: [list]. Help me group these into 4-6 underlying traits. For each trait, suggest 2-3 observable signals that would indicate it’s present or absent.”

This is where vague instincts become something you can actually work with — and eventually, something you can hand off.

Step 5: Map each signal to when and how you can find it

For each signal, you need to answer two questions: could you find evidence of this from outside-in research before a conversation? Or does it only surface during the sales process itself?

This matters because it shapes your list-building logic and your qualification workflow. Some signals are research-accessible — job postings, LinkedIn, news, a company’s website. Others only show up in discovery. Both are useful, but you need to know which is which before you can build a repeatable process around them.

Prompt to try in Claude: “Here are our qualification signals: [list]. For each one, help me identify: (1) whether this is findable through outside-in research or only through direct conversation, (2) where specifically you’d look to find it, and (3) what ‘good’ vs. ‘concerning’ looks like for each signal.”

The output is your qualification rubric: a structured document a new rep could follow, or that Claude could use to research and pre-qualify accounts at scale.

Ready to give it a go?

Download the skill here. It guides you through the process to produce a rubric for account qualification tailored to your business.

Download the Claude Skills

What good looks like

A finished rubric has 4-6 traits, each with 2-3 signals. For each signal, you know: what it is, why it matters to your business, what good looks like versus what’s concerning, and where to find it. It’s specific enough that two different people reviewing the same account would reach the same conclusion.

The practical test Brian and Mallory used at MedScout: could a new rep use this rubric to evaluate an account the way your best rep would? If yes, you’re done. If not, keep iterating.

Watch-outs

The first pass will feel incomplete. That’s normal — the goal of the shadowing step and the best/worst customer analysis is iteration, not perfection on round one. Don’t try to build the definitive rubric in a single session.

Don’t let the rubric drift into a yes/no checklist either. The value is in documented judgment with reasoning, not a binary score. You want to be able to defend why a signal matters and what you’re looking for — not just whether a box got checked.

The takeaway

A target account list built on filters alone is a starting point. What turns it into something your team actually trusts is the reasoning underneath it — both your ICP guidelines and the criteria your best reps apply instinctively, documented clearly enough that anyone can follow it.

That documentation is worth building for its own sake: it trains new reps, aligns the team, and creates a shared standard for what good looks like. And once it exists, it becomes the foundation for Claude to help — not replacing your judgment, but doing the research legwork against criteria you’ve already defined.

Special thanks to Brian Aoyama, VP Growth & Applied AI, and Mallory Blocker, Growth Marketing Lead, at MedScout for sharing their process and the hard-won lessons behind it.

Your rep is on a customer call and the customer asks a detailed product question. The AE doesn’t know the answer, says they’ll follow up, and then spends 20 minutes tracking down an engineer or sorting through a database to find the answer. Sound familiar?

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The problem

Your team is splitting their attention across CRMs, dashboards, Slack threads, and internal wikis — and still can’t get fast, accurate answers about your own product. Engineers field the same questions on repeat. CS teams interrupt each other to find customer-specific data. Product knowledge lives in too many places to be useful in the moment. The best information your company has is locked behind people, not accessible through systems.

The setup

A Claude Code agent that lives in Slack with read access to your codebase and key tools can answer most of those questions in under 3 minutes — while the rep is still on the call. Jeff Silver, CTO and Co-Founder at Opus, built exactly this. They call it Product Bestie.

And he’s sharing the recipe so you can build it, too.

Step 1: Find an engineering resource

If you’re a RevOps leader or revenue leader reading this and thinking I want this for my team — great, that’s the right instinct. But the actual build requires a technical founder, senior engineer, or CTO to set up. Your job is to define the use case and make the case for the investment. Their job is to wire it together.

The question to answer before you start: What tools does an engineer need to answer the questions my team asks most often? That list becomes the blueprint.

Step 2: Set up the four components

Product Bestie has four moving parts. You don’t need all of them on day one — but you do need to understand what each one does.

1. A Slack App — Create a Slack app with bot token scopes for reading mentions and posting replies. Subscribe to the app_mention event. This is what lets the agent “hear” when someone tags it.

2. An execution environment — A machine where Claude Code is installed with your codebase cloned and dependencies loaded. Opus uses Fly.io containers with pre-built “golden volumes” so agents spin up in seconds. A VM, EC2 instance, or even a local machine works for V1.

3. An agent definition file — A markdown file (.claude/agents/your-agent-name.md) that tells the agent what to do. Write it like you’re briefing a new hire: here’s how to read a Slack thread, here’s when to query the database, here’s what a good answer looks like, here are the formatting rules.

4. Skill files — Markdown files that teach the agent how to use specific tools. Opus gives theirs: a Slack CLI (to read and post messages), access to some anonymized customer data, , Datadog (for log and session search), Linear, and GitHub. Each skill file is essentially a how-to guide for the agent.

Example agent prompt structure:

Read the full Slack thread context.

Identify what question is being asked.

Check the codebase for relevant logic.

Query the production DB if needed for customer-specific data.

Post a reply in the thread. Be specific. If you found exact data, show it.

If you’re an engineer reading this, include what you did at the bottom.

Step 3: Lock down security before you go further

This is the step people skip. Don’t.

If your agent has read access to customer data — and it should, if you want it to answer real questions — you need to own the infrastructure. The reason Opus built on Fly.io instead of using Cursor or an off-the-shelf Claude integration is control: they know exactly what’s in the container, they control the secrets, and the container gets destroyed when the job is done. The key security principles your technical partner should be focusing on are: access scoping (no access to PII), secret management, and ephemeral execution.

Know what data your agent can touch. Know who can query it. Build the lockdown before you give reps access.

Step 4: Add observability so it gets smarter over time

Product Bestie isn’t a set-it-and-forget-it tool. Opus built an internal dashboard (they call it Magnum) where engineers can see everything the agent has done — every question, every response, every action. Reps can give a thumbs up or thumbs down on responses. A thumbs down prompts feedback, and the agent learns not to repeat that mistake.

This is the part that makes it genuinely useful over time instead of just a neat demo. If you’re not watching what it does, you’re not managing it. Build in the feedback loop early.

What good looks like

Here’s a real example from Opus. A CS rep was on a call with a new account, and was asked a question about a specific product feature. Rather than a “let me get back to you” they were able to ask Bestie and get an answer a few minutes later, before the call ended.

Or imagine a scenario where an AE is prepping for a renewal call and pings Bestie to surface the account’s usage trends, recent support tickets, and any feature requests from the past quarter. In just a few minutes, they have a tailored talking-points doc.

Watch-outs

• Security first, always. The moment your agent can touch production data, you’re in different territory. This is not a place to take shortcuts.

• Don’t try to measure ROI too early. The value shows up in rep confidence, faster answers, fewer interruptions for your engineers. It’s real — but it’s diffuse. If you’re managing closely enough to know your team, you’ll know if it’s working. Trust that.

Start simpler if you need to

If building on Fly.io feels like too much for right now, there’s a V1 path: Cursor connected to Slack, pointed at your codebase. You lose the tools that make Bestie extremely valuable, but you gain a fast, low-lift way to start answering product questions in Slack but without relevant customer information.

Jeff’s framing: “You can do this pretty easily if you want to just get answers from the codebase. Things get more complex when you start to introduce more tools, but introducing those tools is where the magic happens..”

Use the simpler version to prove the value internally. Once your team is hooked on getting answers in Slack, the case for the full build becomes easy to make.

The takeaway

The buy vs. build decision is changing fast. For years, SaaS tools won because internal builds weren’t worth the engineering investment. That math is shifting. When a technical founder can build a working V1 in an evening, and when the customization ceiling on off-the-shelf tools keeps frustrating your team, the calculus looks different.

For Opus, Product Bestie isn’t just about answering questions faster. It’s about building infrastructure where every employee spends less of their day searching for answers and more time actually solving customer problems. The companies that figure this out first are going to have a real productivity edge.

Special thanks to Jeff Silver, CTO and Co-Founder at Opus, for sharing how Product Bestie works and letting us build this How-To from his write-up and our conversation.

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DEAR PRIORITIZING CSAT IN AN AI-WORLD: Every founder and support leader is feeling this tension right now.

You see the opportunity. Automate repetitive tickets. Free up your team. Reallocate headcount to strategic work. Improve margins.

But you also know one bad automated experience can undo months of goodwill with a customer.

We asked Mary-Beth Donovan, former Chief Customer Officer at EDB and Stage 2 LP, to share her take on how operators should think about automation without compromising trust. Her framing was simple and practical. There are three lenses to evaluate every automation decision: operational efficiency, customer experience, and product value.

Here is how to approach it thoughtfully.

Start with capacity, not cost savings

The cleanest use case for automation is not eliminating people. It is reallocating their time.

Mary-Beth emphasized that the first win most teams should chase is freeing up one full-time equivalent from low-value interactions. If AI can consistently handle repetitive, predictable questions, your team can move upstream into more proactive and strategic conversations.

This shift matters. Customers will tolerate automation if it leads to better human engagement where it counts.

Before setting an automation target, ask:

• Which interactions are repetitive and rules-based?

• Which conversations require judgment and context?

• What higher-value work would we do if we reclaimed this time?

If automation does not unlock a better customer experience somewhere else, it is the wrong goal.

Trust and tolerance are the real metrics

Mary-Beth introduced a powerful framing around trust and tolerance.

You do not need 100 percent perfection to automate. In many environments, good enough is acceptable if you preserve trust. The danger is over-automation that traps customers in loops or prevents escalation when they need it.

Every automated workflow should clearly answer one question. When does a human step in? That moment should not feel hidden or punitive. It should feel seamless.

Practical guardrails include:

• A clear path to escalate to a human

• Monitoring negative feedback patterns

• Regular review of cases that required escalation

Automation without a visible safety net erodes confidence quickly.

You cannot automate bad documentation

One of the most common surprises teams encounter is that their knowledge base is not ready for automation.Outdated documentation, inconsistent articles, or unclear processes will surface immediately once AI begins referencing them. Automation does not fix weak foundations. It amplifies them.

As an aside, I recently had a pretty funny intersection with an AI chatbot. I asked a “how to” question, and was immediately told it wasn’t possible. After playing around for 5 minutes, I found a way to do it and replied to that effect in the chat. The AI asked “can you walk me through how you did it so I can give better feedback to the next question?”. Rather than insisting I was wrong, I was surprised and delighted to see that the company had thoughtfully architected this moment for learning and growth.

Before increasing your automation rate, invest in tightening your knowledge base and documenting edge cases. The quality of your answers will determine whether customers view automation as helpful or frustrating.

This is often less about technology and more about discipline.

Set targets, and revisit them quarterly

There is no universal “right” automation percentage. The correct level depends on your customer base, complexity of product, and support model.

Mary-Beth cautions against chasing an arbitrary number like 80 percent automation without considering the downstream impact. Instead, set a goal tied to a clear business outcome. For example, freeing up capacity for proactive outreach or improving time to resolution on complex cases.

Then measure not only deflection rates, but also customer satisfaction and expansion indicators. If satisfaction drops or renewal conversations become harder, reassess.

Automation is not a one-time project. It is an evolving balance between efficiency and experience. When done well, it does not replace your team. It elevates them.

Until next week!

The Starter Play: How to Build a Marketing Attribution Report in 10 Minutes Using Claude + HubSpot (or your CRM)

Marketing attribution has been the thing everyone wants but it’s hard to generate. You know the conversation — “we need to understand what’s driving pipeline” — but it can take weeks to pull, clean and analyze the data to get a directionally correct answer.

Here’s the thing: if your data is in HubSpot (or any CRM that has a connector to Claude), you can have a working attribution report today that tells you what marketing activities are actually correlating with closed revenue.

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Before You Start

• Claude account with the HubSpot connector enabled (this is key — without the connector, Claude can’t read your data). If you are using a different CRM, just make sure that it is also connected to your Claude account and follow these same instructions).

• HubSpot access with closed/won deal data and at least some marketing activity tracked against contacts

• A rough idea of the time window you want to analyze (last 6-9 months is a good starting point)

• Deals should have contacts associated — Claude reads contact-level properties, not just deal records

Step 1: Connect Claude to HubSpot

In Claude, open a new conversation and enable the HubSpot connector from the tools panel. You don’t need to export anything or build a prompt file ahead of time. Claude reads your HubSpot data directly once the connector is active.

Step 2: Run the Attribution Prompt

Start with this prompt, adjusted for your time window:

“Look at every deal that closed won in the last 9 months. Analyze the activity of each contact associated with those deals. Determine what marketing activities generated closed revenue. Give me an executive summary with: total revenue, average deal size, time to close, and the top marketing touchpoints correlated with winning. Then break it down by deal tier if you can.”

Claude will work through your data and return a structured report — not just text. Expect an executive summary, a breakdown by marketing activity, and a recommendations section. Brant Morton at Rally got a Markov chain analysis on his first try, which he wasn’t even expecting.

Your output looks something like this:

Step 3: Iterate on the Report

Don’t stop at the first output. Keep prompting to go deeper. Try:

“Now show me first touch and last touch attribution separately.”

“Show me the most common activity sequences for your top 20% of deals by deal size.”

“What marketing activities appeared in more than 50% of closed won deals?”

Each follow-up refines the picture. The first report is a starting point, not the final answer.

Step 4: Read the Output Critically

Claude can only read HubSpot contact properties right now — not full activity timelines or custom objects. That means it’s working from fields like first conversion, most recent conversion, and MQL date. The insights are real, but they’re directional. If your report tells you “one-on-one sales emails are your top driver,” that’s probably true — and also not very actionable. Push Claude to surface the marketing signals that aren’t obvious.

Step 5: Screenshot and Document What Surprised You

Before you close the conversation, take screenshots of the sections that surfaced something unexpected. This is your working version. You’ll build on it.

What Good Looks Like

A solid first-pass report gives you: total pipeline influenced, average deal size by segment, the top 3-5 marketing touchpoints correlated with winning, and at least one insight you didn’t already know. Brant’s report surfaced that in-person dinners were showing up in enterprise deals while content downloads were driving smaller ones — a genuine signal that shaped their demand gen priorities.

Watch-Outs

Claude is reading your HubSpot data, which means it’s only as good as what’s actually logged. If your team isn’t consistent about recording activity — or if marketing touches aren’t tracked at the contact level — the report will reflect that. Garbage in, limited insight out.

Also: custom objects in HubSpot are not currently readable by Claude’s connector. If you’ve built custom conversion tracking, you won’t see it here. That’s what Version 2 is for.

The Takeaway

This would have taken a data analyst weeks to build. You did it in one conversation. The report isn’t perfect — but it’s real, it’s based on your actual data, and it gives you something to react to. That’s more than most teams have. Start here, pressure-test the output with your sales and marketing leads, and then decide what you want to go deeper on.

The Advanced Play

How to Build a Full Marketing Attribution App Using HubSpot, Supabase, and Lovable.

Claude’s HubSpot connector is a great starting point. But it has a ceiling: 200K tokens, no custom objects, no full activity timelines. If you want attribution across your entire history — every touchpoint, every channel, every contact — you need to pull the data out of HubSpot and build something on top of it.

The good news: you don’t need a product team to do it. Brant Morton at Rally built this with a $30/month Lovable subscription, a free Supabase table, and a few weekends. It’s running in production.

This is Version 2. Do Version 1 first — it’ll tell you what questions to go answer with this.

Before You Start

• HubSpot access with API permissions (you’ll be pulling data via automation, not the Claude connector)

• A Make.com or n8n account to set up the HubSpot → Supabase workflow

• A Supabase account (free tier works to start)

• A Lovable account ($30/month starter tier is enough)

• At least one quarter of closed/won deal data in HubSpot with marketing activities logged

• Basic comfort with no-code automation — you don’t need to write code, but you need to follow a workflow

Step 1: Set Up Your Supabase Data Table

Create a table in Supabase to receive marketing touchpoint data. You’ll want columns for: contact ID, company ID, event type, event date, campaign, channel, and deal association (if any). This is your attribution database. Everything else gets built on top of it.

Step 2: Build the HubSpot → Supabase Sync in Make or n8n

Create a workflow that fires every time a marketing interaction is logged in HubSpot — email delivery, form submission, ad interaction, webinar attendance, sales call — and writes it to your Supabase table. This is the key architectural decision: instead of asking Claude to re-read everything every time, you’re building a running log that never hits a token limit.

Brant pulled in: marketing email deliveries, ad interactions, sales emails, calls, form submissions, and webinars. That gave him 442,000 touchpoints — way beyond what Claude’s HubSpot connector can handle in a single session.

Step 3: Build the Lovable App on Top of Supabase

Open Lovable and connect it to your Supabase table. Start with a prompt like:

“Build a marketing attribution dashboard connected to my Supabase table [table name]. I want to see: total touchpoints by channel, attribution by model (first touch, last touch, linear, time decay, W-shaped), and a contact-level view that shows every interaction a contact had before a deal closed.”

Lovable will build the app. Iterate from there. Brant added a company rollup view (so you can see activity across all contacts at an account, not just the one on the deal), a paid ads page pulling LinkedIn and Google Ads data, and a “levers to pull” page that surfaces recommendations by attribution model.

Step 4: Add Attribution Models

The real power is running multiple attribution models against the same dataset. Start with first touch and last touch — those are easy. Then add linear and time decay. Markov chain is the most accurate but requires a Python calculation running in the background; Lovable can set it up but you’ll need to fine-tune the logic.

The point isn’t to pick one model as “right.” It’s to see where they agree and disagree — and investigate the gaps.

Step 5: Use It to Ask Better Questions

The dashboard is a question-generator, not a final answer. Use it to surface: which two or three channels show up in almost every closed/won deal, where marketing and sales activity overlap (and where they don’t), and which touchpoints appear in enterprise deals vs. smaller ones. Brant found that in-person dinners correlated with his largest deals — something that never would have surfaced in a standard HubSpot attribution report.

What Good Looks Like

A fully working version of this gives you a live dashboard with six attribution models, contact-level activity timelines, company-level rollups, and a paid ads ROI view — all from your own data, without a third-party attribution platform. Brant built Rally’s version in a weekend

Watch-Outs

Your data quality matters more here than in Version 1 — you’re running actual statistical models. If your team isn’t logging activities consistently, the models will surface noise as signal. Audit your HubSpot data hygiene before you build.

Also: Markov chain analysis requires some tuning. Don’t trust the first output of that model without cross-referencing it against your simpler models.

The Takeaway

Three months ago, standing up a marketing attribution system like this would have required a dedicated PM, a team of engineers, and months of roadmap time. Juliette Kopecky said it clearly: at HubSpot, they had an entire product team devoted to tools like this. Brant built the equivalent over a weekend. That’s not hyperbole — that’s what this moment actually is. The question isn’t whether you can afford to build this. It’s whether you can afford not to know what’s actually driving your revenue.

Special thanks to Brant Morton, GTM Ops, and Juliette Kopecky, CMO, both at Rally, for building this in public and sharing every detail.

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DEAR OUTBOUND FATIGUE: It’s only March, and I’ve heard this sentence more times than I can count this year: “Outbound is dead.” And yet, I continue to see teams making it work.

What’s actually happening isn’t the death of outbound. It’s the death of high-volume, low-ownership outbound. The spray-and-pray motion popularized by the “sequence is the strategy” approach.

We recently got a group of GTM operators from our portfolio together to discuss what is actually working. These teams are all actively running outbound motions and had A LOT of advice to share.

Read on for my favorites:

Get Creative With Your Use of AI

One of the most actionable ideas was how they’re using AI behind the scenes. Not to crank out more generic emails, but to sharpen their thinking.

One company is feeding AI with transcripts from sales calls, customer support tickets, and closed-lost notes. Then they’re asking it to extract patterns: How does this segment describe the problem? What language do CFOs use versus operators? What objections show up repeatedly before deals stall?

The output isn’t copy-and-paste messaging, but phrasing grounded in how customers actually talk. This context is a shortcut as you lean into deeply personalized messages.

A few other ideas in this vein…

• Extract common language patterns from real customer conversations

• Identify recurring objections by segment

• Surface vertical-specific pain points from historical notes

• Draft contextual summaries that reps can refine, not blindly send

In other words, use AI to accelerate pattern recognition, not replace critical thinking.

Dramatically Narrow Focus

Instead of handing reps hundreds of accounts and calling it coverage, reduce books to a manageable number and force prioritization. When a rep owns 40–60 accounts, not 600, the dynamic changes. There’s room for planning. There’s space for research and personalization. There’s accountability to break in.

I am a huge supporter of removing decision fatigue, using data to select the right accounts, and letting reps focus on only the top-tier opportunities. Some leaders are even defining a hard cap (eg 75 accounts max) and maintaining weekly or biweekly book hygiene where reps can add/drop accounts based on engagement and fit. “One in one out” is a good rule to get started and ensure the list doesn’t grow out of control.

When you have a narrow focus, weekly reviews become less about activity volume and more about account rationale and strategy. Why this company? Why now? What’s the trigger? What will you say first?

Increased ownership will drive increased performance.

Coordinate channels tightly

Multi-channel isn’t new, but coordination is more critical than ever.

Rather than scattering touches over 60 days, several teams are running concentrated outreach sprints over a few weeks. Calls, emails, LinkedIn messages, executive outreach, physical mail, and digital follow-ups are tightly grouped. The goal isn’t to overwhelm. It’s to create a sense of momentum and urgency.

Phone calls and emails from a BDR or AE remain the baseline, but top-tier accounts are getting:

• FedEx package (category-specific materials)

• Within 24 hours: digital follow-up

• Within a week: executive LinkedIn message

Ultimately, it doesn’t matter which channel breaks through. The prospect may respond to the third touch or the seventh, to an email or a package. But the clustered timing creates cohesion that random outreach simply doesn’t.

A final note on the unglamorous but critical layer: infrastructure.

Deliverability is harder than it was even a year ago (thanks AI!) and high-performing teams are treating inbox health as a core metric. They’re monitoring bounce rates, domain reputation, reply rates by segment, and volume per sender. If your email isn’t landing, debating messaging strategy is irrelevant.

Outbound still works. But only when it’s deliberate.

Until next week!

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Turn your CRM export into a complete pipeline forecast and recommendations — in one prompt.

Play created by Jonathan Kvarfordt, Head of Marketing at Momentum.io. You can find more of his GTM AI plays at gtmaipodcast.com.

FOR: RevOps & Revenue Leaders | TIME SAVED: 6–8 Hours Per Week | OUTPUTS: 3 Deliverables

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01 — What It Does

Most revenue teams are still manually producing weekly pipeline forecasts — pulling from Salesforce, formatting spreadsheets, copying numbers into slides. The Pipeline Intelligence Engine play using Claude, replaces that process entirely. You give it your CRM data. It gives you everything you need for the forecast meeting.

The Claude skill runs three parallel analyses on your pipeline data and produces three files in one prompt:

Deliverable:

What’s Inside Pipeline Health Spreadsheet (.xlsx) Stage conversion rates, velocity metrics, deals at risk, week-over-week movement, and per-rep breakdown — all with auditable formulas.

Interactive Dashboard (.html) Funnel visualization, pipeline by rep vs. quota, coverage ratio gauge, and a sortable deals-at-risk table. Renders in any modern browser.

Coaching Priorities Document (.md/.docx) The 5 deals most likely to slip, rep performance highlights, and specific talk tracks for 1:1s — based on your actual data, not templates.

02 — Prerequisites

Before running the skill, confirm you have the following:

• CRM data access — A CSV or spreadsheet export from Salesforce, HubSpot, or any CRM. Alternatively, connect via CRM connector or MCP integration.

• Pipeline export — Include active opportunities with: deal name, stage, amount, close date, owner, created date, and last activity date. The more fields, the richer the output. If you have your CRM connected, it can pull directly from your CRM. If not, you will want to include this in your export.

• Quota context — Your current quarter’s quota target and rep roster. Add these as a reference file in your project folder — the skill gets significantly smarter with this context.

• Skill installed — The Pipeline Intelligence Engine skill folder must be uploaded to Claude Cowork. See Setup below. You can find the skill here.

Watch out: If your pipeline export is missing close dates or rep assignments, the skill will flag the gaps and work with what’s available — but stage velocity and rep analysis will be limited. Clean data going in means better coaching insights coming out.

Download Project Skill

03 — Setup

Set up takes about five minutes. You only need to do this once.

Install the Skill

1. Open Claude Cowork and go to Settings > Capabilities > Add Skill.

2. Upload the skill folder — the Play-01-Pipeline-Intelligence-Engine directory.

3. Confirm it appears in your active skills list.

Prepare Your Project Folder

Create a dedicated folder for your pipeline work. Place the following files inside:

• Your pipeline data export (CSV or .xlsx from your CRM)

• Your quota sheet or forecast template — optional but recommended

• A rep roster with names and quota targets — optional but recommended

Connect Your CRM (Optional)

If you prefer live data pulls over manual exports, connect Salesforce or HubSpot via the built-in connector or MCP integration in Cowork settings. Once connected, the skill can pull pipeline data directly — no export required.

04 — How to Use

Once the skill is installed and your data is ready, the process runs in five steps.

Step 1 — Point Cowork at Your Folder

Select your pipeline project folder in Claude Cowork. This tells the skill where to read data from and where to save the three output files.

Step 2 — Trigger the Skill

Use any of these prompts to start the analysis:

“Run the Pipeline Intelligence Engine on my pipeline data”

“Analyze my pipeline”

“Create a pipeline health spreadsheet, interactive dashboard, and coaching priorities doc from this export”

Step 3 — Answer the Setup Questions

The skill will ask a short set of questions on the first run. Have these ready:

• What CRM or data source are you using?

• What is your current fiscal quarter and quota target?

• Do you want analysis by rep, by team, or both?

• What is your average sales cycle length?

• Any specific deals or segments you want highlighted?

You can skip any questions you have already covered in your prompt or project folder context.

Step 4 — Review Your Three Deliverables

The skill generates all three files in parallel and saves them to your project folder with today’s date in the filename:

• pipeline-health-[date].xlsx

• pipeline-dashboard-[date].html

• coaching-priorities-[date].md or .docx

Review each file. If any section needs refinement — different chart format, additional rep breakdowns, more specific coaching talk tracks — ask for edits in natural language. The skill will update the relevant file.

Step 5 — Make It Recurring

Once you are satisfied with the output, say:

“Save this as a weekly workflow” or “Package this as a plugin my whole RevOps team can use”

The skill will configure the workflow to run automatically — typically every Monday morning. Your team can trigger it the same way without needing to re-enter context each week.

05 — Example Use Cases

Weekly Pipeline Review

Export your Salesforce pipeline on Monday morning, drop it in the folder, and trigger the skill. In 5 minutes, you have your health spreadsheet for the forecast call, an interactive dashboard for the CRO, and coaching talking points for every manager’s 1:1. No manual formatting, no Vlookups.

Sales Leader Forecast Prep

Before your weekly forecast meeting, run the skill on the latest pipeline pull. The coaching priorities doc tells you exactly which deals to probe and what questions to ask each rep. You walk in knowing where the risk is — not finding out in the meeting.

Board-Ready Pipeline Metrics

The board wants pipeline health data. Run the skill and hand them the dashboard — funnel conversion, coverage ratios, and velocity from your actual data, not a static slide from last month.

New RevOps Hire Onboarding

Share this skill with a new RevOps team member. They generate the same reports the senior team produces on day one. No training required on which spreadsheets to pull or how to calculate pipeline velocity.

Multi-Segment Analysis

Run it on enterprise pipeline one day, mid-market the next. Compare the dashboards side by side to see where conversion rates differ and which segment needs different coaching.

06 — Best Practices

Add Context to Your Folder

The richer your input, the richer your output. Before triggering the skill, drop in your quota sheet, rep roster, and last quarter’s pipeline data for comparison. A 10-minute setup saves you an hour of back-and-forth on the outputs.

Verify Formulas on First Run

All calculations in the pipeline health spreadsheet include auditable formulas. On your first run, spot-check two or three numbers against your CRM directly. Once you trust the math, you can move faster every week.

Iterate in Natural Language

If a chart is not quite right, or you want the deals-at-risk table sorted differently, just say so. The skill responds to plain-English requests: “Sort the deals at risk by amount descending” or “Add a tab that breaks out pipeline by close month.”

Set Up the Recurring Workflow

The skill saves 6–8 hours per week — but only if you actually run it every week. Setting up the recurring workflow removes the activation energy. Schedule it for Monday morning, before your first meeting. The outputs will be waiting for you.

Handle Edge Cases Proactively

If you are working with fewer than 20 deals, the skill shifts automatically from statistical analysis to deal-by-deal review. For multi-segment pipelines, decide upfront whether you want combined or separate analysis — combined is faster, separate is more precise for coaching.

Watch out: Never use placeholder or synthetic data with this skill. It is designed to work from your actual pipeline — and will flag if it detects anything that looks like demo data. Coaching insights that don’t come from real deals aren’t useful coaching insights.

Master Checklist

Setup

• Install the skill — Upload the Pipeline Intelligence Engine folder to Claude Cowork via Settings > Capabilities > Add Skill.

• Prepare your folder — Add your CRM export, quota sheet, and rep roster.

• Connect your CRM — Optional — link Salesforce or HubSpot for live data pulls.

Each Run

• Export pipeline data — Pull the latest from your CRM, or let the connector do it.

• Trigger the skill — Say “Analyze my pipeline” or “Run the Pipeline Intelligence Engine.”

• Answer setup questions — CRM source, quota target, analysis scope, sales cycle length.

• Review three deliverables — Spreadsheet, dashboard, coaching doc — one pass before the forecast meeting.

• Iterate if needed — Ask for adjustments in plain language.

Ongoing

• Save as recurring workflow — Set it to run every Monday morning.

• Spot-check formulas — Verify calculations against source data on first run of each quarter.

• Read the coaching doc before 1:1s — Not during — give yourself time to internalize the talking points.

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DEAR DROWNING IN EDGE CASES: I hear you! If your health score keeps crying wolf, your team stops listening. And once that happens, it’s all just noise.

We called on Parker Chase-Corwin, Stage 2 LP and fractional CS leader and advisor who has built CS, support, and services teams from Series A through Fortune 50 to shed some light on this question. His perspective? “Health scores aren’t meant to confirm what you already know. They’re meant to surface the blind spots.” The challenge, of course, is doing that when your customer base isn’t uniform.

Here’s how he recommends tackling this challenge:

Define “healthy” separately before you try to score it

Most health score problems are actually definition problems, especially if you are leveraging AI to help you with diagnostics.

If you serve SMB, mid-market, and enterprise customers, you likely have three different definitions of success (whether you’ve written them down or not). An enterprise account may be perfectly healthy with modest usage but strong executive alignment and a clear roadmap. An SMB account may only be healthy if usage is frequent and consistent. When you force those realities into one uniform red/yellow/green model, you create false positives.

Parker put it well: “You want the smallest number of inputs that reliably point to risk. Not the largest number of signals you can collect.” In practice, if you’ve got a lot of edge cases, that means creating segment-specific scorecards, even if 70% of the inputs are shared. Adjust thresholds by segment. Allow for nuance. Keep the number of signals intentionally small so you can interpret movement over time.

Account for seasonality by shifting the lens, not lowering the bar

Seasonality breaks a lot of early health score models. If you work with construction, education, retail, or any industry with predictable slow periods, your “usage = health” logic will collapse at least once a year.

Instead of letting the model panic every off-season, treat those periods as a different operating mode. When activity is expected to dip, shift what you’re measuring. During slower months, product usage may matter less than planning, responsiveness, stakeholder stability, and readiness for the busy season.

In other words, during hibernation you aren’t measuring “how much they’re doing.” You’re measuring “are we aligned and ready.”

That shift preserves signal integrity without creating noise. It also forces your team to be thoughtful about what really indicates risk versus what’s simply expected behavior.

Protect trust with simplicity and transparency

As your model evolves, resist the urge to throw in 50 signals just because you can. It’s tempting, especially with new tooling and AI layers, but more variables often create more volatility, not more accuracy.

Parker cautioned against over-complication: “When you have too many variables, it’s going to ping-pong all over the place. You want trending you can trust.” If your score is constantly bouncing because minor inputs shift daily, your team won’t know what actually matters or how to take the next best action.

Four principles help maintain trust:

1. Keep your core signals precise and stable.

2. Iterate intentionally (quarterly or semi-annually), not impulsively.

3. Keep the inputs visible so team members can reverse-engineer the score to determine which signals need to be prioritized and addressed.

4. Allow a “human override” so those employees closest to the customer can adjust based on their unique qualitative context that isn’t showing up in the signals

You can absolutely use AI to mine sentiment or identify anomalies in transcripts and emails, but the foundational rules still need to be understandable and defensible. Especially if you’re presenting health to your board, the model must clearly connect to observable behaviors, not the qualitative “they seem happy.”

A health score people understand is far more valuable than a sophisticated one no one believes.

Design the health score to trigger action

A red account without a defined motion is…nothing.

The most underutilized part of health scoring is intervention. When an account dips, how quickly do you respond? What changes? Who gets involved? One metric I love tracking is time to first intervention after a score change. If you can consistently respond within a defined window, you’ve turned a passive report into an operational lever.

This is also where Stage 2’s concept of a single behavioral anchor (the Leading Indicator of Retention from the Science of Scaling framework) becomes useful. If you can name one core behavior that healthy customers consistently demonstrate within a given timeframe, your model becomes dramatically easier to manage. The health score becomes a system that asks, “Are they building the habit that leads to renewal?” And when that habit breaks, you intervene.

Health scoring at this stage shouldn’t be about perfection. It should help your CSMs prioritize, give leadership a credible read on the base, and surface blind spots early enough to do something about them.

Until next week!

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Every year, go-to-market teams go through the same painful ritual: spreadsheets, arguments, guesswork, and a territory plan that nobody fully trusts. I sat down with the GTM team at Forter to understand how they used AI to completely reimagine their territory planning process. Forter is a global fraud prevention platform serving 500,000 merchants and 2B consumers, processing $500B GMV in 2025 alone.

I spoke with Ozge Ozcan, CRO; Parker Tipton, Head of Field Ops; Gabby Upson, RevOps; and Kris Alspach, Head of Revenue Operations, about the challenge they faced, what they built, and what it means for anyone trying to scale a GTM operation with a lean team.

The Problem: A Global Business Without a Defined ICP

When Ozge took the CRO role, and Forter appointed its new CMO, Jason Grunberg, they started to assess foundational components of the company’s GTM strategy:

Where are we actually winning?

And how can we better align our Sales, Customer, and Marketing teams to accelerate growth?

“It has been a while since we took a step back and asked what are our ICP win rates,” Ozge told me. “Where are we winning? Where are we experimenting? And where are we not winning, but we think we are — because of research bias?”

Kris, who joined in October, walked into the middle of annual planning and quickly recognized the gap. “This is a global business, and we haven’t defined the ICP,” he said. “Territory carving strategy didn’t exist before Parker and Gabby started this initiative.”

Parker added: “ICP was insider knowledge, not a system. Territory design was a manual process and trust within the organization had really eroded.”

The data situation was just as fraught. Forter’s thousands of accounts lived across disparate systems. Industry classifications had ballooned to 65+ variations. Revenue banding came from conflicting sources. And the enrichment tool they were relying on — SimilarWeb — was only covering a small fraction of all accounts, leaving a sizeable account gap with no data at all.

And the timing? They discovered all of this in the second half of the year, with annual planning deadlines looming. Territories needed to be delivered in three weeks.

What They Were Doing Before

Before this project, Gabby had owned the territory process for nearly three years. “I don’t know if I would even call it a process. Territories were ad hoc — when reps needed their books replenished, when someone was dissatisfied, when we had new headcount. It wasn’t a process.”

The workflow was entirely spreadsheet-based. Each time something changed, a new spreadsheet got created. There was no scoring model, no defensible assignment logic, no global consistency across Americas, EMEA, and APAC. Reps didn’t trust their territories, and the RevOps team spent enormous amounts of time manually rebalancing account lists without being able to clearly explain the reasoning behind decisions.

“It was always a new spreadsheet every single time one thing changed,” Gabby said. “Messy, challenging, always ad hoc.”

How They Built an AI-Powered Territory Planning Workflow

What Parker and Gabby built over the next several weeks was a fully AI-assisted ICP and territory design process. Here’s exactly how they did it.

Step 1: Pull All Account Data and Audit the Gaps

The first move was getting everything out of Salesforce and into a working environment to understand the true state of their data. This wasn’t glamorous work — it was pulling CSVs, running analysis in Google Sheets, and mapping what they actually had versus what they needed.

“The first phase really showed us where our data inequalities were and where the gaps were,” Parker explained. The gaps were significant: inconsistent industry classifications (65+ variations), unreliable revenue banding from multiple conflicting sources, and missing e-commerce GMV data — a critical metric for a fraud prevention platform focused on transactions.

Step 2: Enrich the Data with Clay

To close those gaps, the team brought in Clay, an AI-powered data enrichment platform. They integrated Clay directly with Salesforce, pulling their entire account base in to identify and fill the missing fields.

“Clay was a really big factor for us,” Parker said. “We pulled down our entire account base and used Clay’s data providers to fill the missing space — annual revenue, GMV data, employee counts — things that were all over the place or nonexistent.”

But Clay didn’t just clean the existing list. Once they had a clearer ICP definition (more on that below), they fed those criteria into Clay to find net-new accounts that matched the profile — adding several thousand accounts to their universe. “Now we have net new accounts that we did not have in our CRM that we can now target,” Parker said.

Step 3: Build the ICP Framework — with AI as a Sparring Partner

The ICP analysis itself was primarily a manual, collaborative effort between Parker, Gabby, finance, and GTM leadership. But they used general-purpose LLMs — ChatGPT, Claude, Gemini — throughout the process as what Parker called a “sparring partner.”

“We used AI for strategic reasoning — to pressure test what we were actually thinking,” he explained. “It could come up with different analysis or different thoughts that maybe we weren’t considering, or flag when we weren’t looking at the data in a specific way.”

The output was a tiered ICP: a primary target profile and a secondary profile, defined by industry, revenue band, GMV potential, and historical win rates. This was something the company had never had before in a codified form.

Step 4: Use a General LLM to Build the Territory Assignment Prompt

Rather than writing complex territory logic from scratch, Parker uploaded all of their ICP rules, quota coverage requirements, and territory classification criteria to an LLM and used a conversational back-and-forth — often via voice, not just typing — to synthesize everything into a single structured prompt.

“I try and use the conversation piece as much as possible — talking back and forth rather than typing,” Parker said. “I find it quicker, easier, and I can multitask. Even walking the dog, I’m still working.”

The prompt that came out was extensive — covering ACV/quota coverage thresholds, enterprise vs. strategic segmentation, ICP tier weighting, acceptable territory size ranges, and regional classifications for North America, EMEA, and APAC. “This would take a human being an incredibly long time to write,” Parker said. “Using tools like this, it takes me seconds.”

He also noted something that surprised the team: “I didn’t even have to explain what Carve was to the LLM. I just said, ‘We’re using a tool called Carve.’ It already knew exactly what it does and built the prompt for it.”

Step 5: Run Territory Assignment in Carve (by Gradient Works)

Carve, an AI-powered territory design tool from Gradient Works (and also a Stage 2 Portfolio company), is where everything came together. The team uploaded their enriched account CSV, fed in the prompt from Step 4, and let Carve do what would have taken days of manual work.

Here’s how Carve works in practice:

Upload your data. Bring in a CSV of your account universe (or connect directly via Salesforce integration). Define what each column means so the AI understands your schema.

Feed in your prompt. Paste in the territory logic you developed with your LLM in Step 4. Carve accepts plain natural language — no special formatting required. Gabby noted she wrote prompts “in all lowercase letters” and the tool responded correctly.

Run multiple scenarios simultaneously. You can test different territory configurations against the same data set — adjusting coverage thresholds, adding geographies, creating “to-be-hired” patches — and see how the outcomes differ without doing any manual work.

Ask exploratory questions about your data. Query conversationally: “How many apparel accounts do we have in Washington State?” This was especially valuable for answering ad hoc strategy questions from leadership without spinning up a new analysis each time.

Review AI-generated assignment explanations. Every account assignment comes with a clear rationale — “Assigned to [rep]: Geo = Enterprise East, Industry = Retail” — so you can answer rep questions and address challenges quickly and with confidence.

One dynamic the team found particularly interesting: as Parker and Gabby each ran separate projects within Carve, the tool got progressively smarter. “The questions it was asking from Gabby’s first build to my second build were much more robust and quicker,” Parker said. “It’s educating itself as we work together as a team.”

It also builds in human judgment checkpoints — surfacing ambiguous cases and asking the user to decide rather than auto-assigning — which is a meaningful trust feature. I especially like how it asks you — ‘I can make this decision, or do you want to?’ giving the human the extra reassurance and control.

Step 6: Export, Pressure Test, and Roll Out with Data-Backed Explanations

Once territories were built in Carve, the team exported results back to spreadsheets, added visuals, and brought them to leadership and the field. Because every assignment came with an AI-generated explanation, they were able to answer rep questions and address the inevitable reactions to territory changes far more efficiently than in past cycles.

“There is the analytics part of getting territories right, and then there is the emotional part,” Ozge reflected. “Having the data to explain the logic is incredibly difficult when you’ve spent days in a spreadsheet and lost the thread of why you made each decision. The team was able to answer questions much more rapidly with the AI tool.”

The Results

The numbers are striking for what a two-person RevOps team delivered:

• 65,000+ accounts enriched, scored, and assigned with defensible, explainable logic

• First-ever codified ICP at a company with sizable revenue and customer base

• First true territory model with geo-based logic, segmentation rules, and global consistency across Americas, EMEA, and APAC

• 15x quota coverage target achieved across all account books, with documented rationale for every assignment

• Delivered in approximately 6 weeks against a hard annual planning deadline

“For a two-person team like Gabby and myself, we can now do things at a level and scale that would usually take a 5, 6, or 7-person RevOps organization,” Parker said.

To put it in perspective, when I was leading teams of 600 people, territories and assignments would take us a couple of months and three people just to build the analysis and show the data to managers, so everyone felt comfortable that the opportunity was distributed fairly. Seeing this — it’s crazy.

What’s Next for Forter’s AI-Powered GTM Stack

The team isn’t stopping here. Their next phase involves two major initiatives.

Intent Data with Common Room. Parker described rolling out Common Room post-SKO to surface account-level intent signals for sellers, connected directly to SalesLoft for outreach and Salesforce as the system of record. Reps will see a composite “BIT” score — combining ICP fit and intent signals — so that when they log in each morning, the highest-priority accounts are surfaced immediately.

AI-Powered Win-Loss Analysis. Ozge’s next initiative is to run a deep win-loss analysis using existing data — without requiring field interviews. She acknowledged the prerequisite challenge: building a culture where customer call recordings in Gong or Zoom are treated as strategic competitive assets rather than surveillance.

“Customer calls and conversations are now my competitive advantage data points,” she said. “That’s a mindset shift for the field — and it’s the next hurdle we need to go through.”

Three Lessons for GTM Leaders

1. Clean data is a prerequisite, not an outcome. AI amplifies what you have. The team’s first investment was in data quality — and it unlocked everything that followed. If your data is broken, the AI output will be too.

2. A defined process beats a smarter tool. You need data that’s accurate and a workflow or process already outlined. AI doesn’t just come in and do all those things for you. The ICP framework gave Carve the rules it needed to make meaningful decisions.

3. Use LLMs as thinking partners, not just execution tools. Some of the most valuable AI use in this workflow wasn’t automation — it was using ChatGPT, Claude, or Gemini to pressure test assumptions, identify gaps in reasoning, and translate complex business logic into a clean, structured prompt. That’s a skill every RevOps leader should develop.

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Launching a new product is hard. Launching a new AI product, alongside new pricing and packaging, is even harder.

That was the challenge facing DataSnipper last year. In just one quarter, the company rolled out:

• Its first generative AI product, DocuMine

• A fully new pricing and packaging model

• Company-wide changes impacting sales, marketing, product, legal, and security

All while Carrie Bosworth, CRO, and Leah Levitte, Director of Enablement, were still relatively new in their roles.

Many teams would have solved this with volume: more training, more documentation, more tools. Instead, Carrie and Leah decided not to overwhelm the go-to-market team and make a different bet: use AI to give reps confidence at the exact moment they needed it.

Here’s the step-by-step playbook Carrie and Leah used, and how other teams can replicate it.

Step 1: Acknowledge the Real Problem (It’s Not “More Training”)

DataSnipper didn’t approach selling a brand-new product as a content gap.

By the time of launch, reps already had all the content they needed:

• Live trainings and kickoffs

• Manager enablement sessions

• Product certifications

• Long-form playbooks

• Call recordings

• Office hours

People don’t need more information,” said Leah. “They need help synthesizing it and making it relevant to their day-to-day.”

This is the first crucial insight: At scale, enablement breaks down not because reps lack information, but because they lack confidence in applying it.

Step 2: Redefine Enablement as “Training Wheels,” Not Mastery

Carrie framed the goal with a simple analogy: training wheels. Training wheels aren’t about perfection. They’re about reducing fear, enabling early wins, letting people practice without embarrassment, and building muscle memory over time.

The goal isn’t to replace rep judgment. It’s to get reps comfortable enough to show up confidently in front of customers, especially when the topic is something they are unfamiliar with, such as an AI product and new pricing.

This mindset shaped everything that followed.

Step 3: Start With One High-Impact Use Case

Rather than boil the ocean, DataSnipper focused on one product and one moment: Helping reps prepare for customer conversations about DocuMine.

That clarity and focus mattered. They didn’t try to reinvent the entire enablement stack, replace every workflow, or build an AI sales platform.

They focused on just one problem: “How do I prepare for this specific customer call with confidence?”

Step 4: Build a Custom GPT Around Your Actual GTM Reality

The core of the DocuMine enablement playbook was a custom internal GPT, trained specifically on DataSnipper’s GTM reality: product positioning, objection handling, pricing and packaging, vertical context, discovery questions, and points of view for first calls, demos, and negotiations.

What made it work wasn’t “AI.” It was specificity.

Instead of giving reps another 30-page playbook, Leah built a “call prep assistant” that responded in the exact format reps needed, for the exact situations reps faced.

Here’s how to replicate it step by step.

A. Start with the moment of uncertainty (not the entire enablement universe)

Don’t try to build an assistant for “everything sales might ask.” Pick one repeatable moment, like:

• “Help me prepare for a first call with a [vertical]”

• “I’m going into a pricing conversation, what are the landmines?”

• “I’m demoing to a CFO, what should I emphasize?”

Your goal is to reduce fear and increase preparedness right before customer conversations, when reps are actually going to use the tool.

B. Define the output format first (this is the secret)

Most internal GPTs fail because they return generic paragraphs.

DataSnipper solved that by forcing a repeatable structure. The assistant always produced a crisp briefing document with:

• strategic context

• document-heavy workflows likely in focus

• personas (end users + economic buyers)

• value mapping

• discovery questions (pain, metrics, implications)

That format made the output feel usable, not academic.

C. Write “system instructions” that don’t allow drifting

You don’t need to over-engineer the build. But you do need strong instructions.

DataSnipper’s used very clear, concrete prompts. Their instruction sequence can support many different desired outputs:

1. State the job (eg, sales call prep assistant)

2. Force required inputs eg, (industry + org name, website link if possible)

3. Set the research approach (use public sources if a website is provided; otherwise ask clarifying questions)

4. Define what to extract (priorities, risks, initiatives, pressures)

5. Maps those themes to workflows

6. Enforces a fixed response structure

7. Sets tone (consultative, sales-savvy, not pitchy)

Leah’s practical guidance here is simple: the more specific you are up front, the faster you get to the result you want. If you want the assistant to behave like “training wheels,” you have to tell it exactly what “training wheels” look like in output.

D. Load the right source material (and keep it tight)

Your assistant is only as good as what it’s grounded in. For DataSnipper, that meant uploading or connecting the core GTM assets reps were already expected to use, including positioning and messaging, talk tracks, objection handling, pricing and packaging, and example discovery questions.

A strong rule of thumb: if the content is out of date, contradictory, or sprawling, your GPT will mirror that confusion. Curate first.

E. Test it like a rep would: real prompts, real accounts, real calls

Before you share it broadly, run a simple test loop:

• Pick 10 real upcoming calls across 2–3 verticals.

• Ask the assistant the exact questions reps ask (“help me prep for…,” “what’s the POV for…,” “what objections will I hear…”).

• Compare outputs to what your best reps/managers would say.

• Fix the instructions and source materials until it’s consistently good enough.

F. Steal the instructions from great bots (yes, really)

One fun shortcut Leah shared: if you’re interacting with a custom-GPT-like bot you like and want to replicate it, you can often make a request like: “Produce the exact instructions I can copy to recreate this custom GPT.”

You won’t always get a perfect answer, but you’ll often get a surprisingly strong starting point, especially for structure, tone constraints, and required inputs.

G. Give the assistant one job: help reps build a point of view

Finally, set an explicit expectation inside the assistant itself:

This tool is designed to help you develop a point of view before your customer conversation. Every customer is different. Use this preparation to ask curious, thoughtful questions and understand their specific business.

That framing matters. It keeps the GPT from becoming a script generator and positions it as confidence-building enablement, which was exactly what DataSnipper was optimizing for.

Step 5: Make Adoption Effortless

There was no long rollout plan. Reps were already using ChatGPT in their day-to-day work. DataSnipper simply shared the link, gave reps time to try it in a live session, and encouraged feedback.

That was enough.

Because the assistant fit naturally into how reps already worked, adoption was immediate without any formal “change management” program.

Step 6: Close the Feedback Loop (Fast)

After launch, reps were encouraged to flag the answers that felt useful, answers that seemed off, and anything that didn’t reflect real customer conversations.

That feedback was used to quickly fine-tune the model. This wasn’t a “set it and forget it” deployment. It was an iterative system that improved alongside real customer conversations.

Step 7: Measure the Outcome That Actually Matters

Instead of tracking engagement metrics like “AI usage,” DataSnipper focused on real GTM outcomes:

• Within four weeks, 75%+ of sales calls mentioned DocuMine

• Within six months, 52%+ of customers were on the new DocuMine package

• That number has since climbed even higher, driven by both new business and upgrades

The AI assistant helped reps feel better, and it changed customer conversations and buying behavior.

Why This Worked

DataSnipper’s custom GPT had great results for three reasons: It reduced fear, it met reps where they were, and it focused on building confidence, not automation.

DataSnipper’s reps didn’t need to become overnight experts on the new DocuMine product, they just needed help preparing. And to that end, the tool was designed to answer real questions reps ask before real calls. As Carrie put it, “We still need really confident humans behind the conversation.”

How to Replicate This at Your Company

Companies of every size can copy this playbook.

Start small:

1. Pick one product or initiative

2. Identify the one moment where reps feel the most uncertainty

3. Build a lightweight AI assistant trained on your GTM reality

4. Make it easy to use

5. Iterate based on real feedback

The goal isn’t AI everywhere. The goal is better conversations, sooner.

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Landing your first paying customers is a big moment. It’s validating, energizing… and let’s be honest, a little overwhelming.

You’re not just building product anymore, you’re running a business. And for product-led teams, that shift raises a classic tension:

We know we should be tracking metrics, but we don’t have time to track everything, and we’re not sure what actually matters.

Here’s the good news: most early-stage companies try to track too much, too soon. The ones who scale well? They focus on a tiny handful of metrics that actually answer one question: Is the business working before revenue scales?

Let’s break that down.

The Early-Stage Trap

At Stage 2 Capital, we see early-stage teams fall into two common traps when it comes to metrics:

1. Tracking nothing, because “we’ll worry about metrics later.”

2. Tracking everything, filling dashboards with vanity numbers that feel productive but rarely change decisions.

Neither approach works.

Before you chase growth, before you optimize for efficiency, before you worry about CAC or LTV, you need to validate one thing: Are customers getting real, repeatable value from your product?

The Three Metrics That Matter

In your first 12–18 months, success = value delivery. That’s it.

So let’s keep it simple. Here’s what we recommend tracking:

1. Leading Indicator of Retention (LIR)

The best proxy for long-term value

You don’t have enough time (or customer volume) to wait for churn numbers. Instead, define a leading indicator. This is an observable behavior that signals a customer is likely to stick around.

Examples:

• For Slack: teams sending 2,000+ messages

• For a data tool: weekly active users pulling reports

• For a workflow product: creating and reusing 3+ workflows in the first 30 days

Your LIR should be:

• Measurable (yes/no per customer)

• Tied to value (not just usage)

• Predictive of retention (even if directionally at first)

Pro tip: Don’t wait for perfect. Pick a hypothesis, measure it, and refine.

2. Time-to-Value

How quickly are new customers seeing impact?

Once you know your LIR, track how long it takes new customers to get there. This is your time-to-value, and it’s an early pulse on product clarity, onboarding, and customer experience.

Early warning sign? If it takes too long for customers to get value (even if they eventually do), you’re already at risk of churn.

Shorten time-to-value before you scale. It pays dividends later.

3. Customer Health Rollup

A simple weekly gut check (in a spreadsheet is fine)

You don’t need a full analytics stack. But you do need a way to consistently ask:

• Who’s healthy?

• Who’s stuck?

• Who’s at risk?

This is less about reporting and more about confronting reality before problems compound.

What Not to Optimize (Yet)

Feeling pressure to track CAC, LTV, gross margin, burn multiple?

Ignore it. Seriously.

Those metrics matter a lot. But only later, after you’ve nailed value creation and retention first.

Optimizing too early = polishing a funnel that doesn’t work yet.

First: make it work. Then: make it efficient.

A Final Thought

Right now, your job isn’t managing a dashboard.

It’s learning faster than your problems grow.

That means choosing a few high-signal metrics, revisiting them often, and using them to drive smart decisions before growth masks the truth.

This approach is core to how we think at Stage 2 and baked into the Science of Scaling framework. Define value clearly. Measure it early. Then build on what works.

If you get this right, everything downstream gets easier.

Until next week!

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DEAR STAGE 2: I'm the CEO of a Series B company, and my board is pushing me to uplevel two of the leaders on my team as we review plans for the year. How should I think about growing talent internally versus hiring from the outside? ~WEIGHING MY OPTIONS

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DEAR WEIGHING MY OPTIONS: This one hits close to home for a lot of founders. Upleveling your team is one of the hardest (and necessary!) parts of scaling. It’s deeply personal. You’ve built something from the ground up, often shoulder to shoulder with these early leaders. But as the business grows, so do the requirements of each role on the team.

I recently had the chance to hear Henry Schuck, founder and CEO of ZoomInfo, speak candidly about this very topic during our annual summit and was wowed by the learnings. Henry built ZoomInfo from a bootstrapped, two-person operation into a public company and navigated all the common inflection points along the way. His perspective? You owe it to the business to make the hard calls when it’s time.

Henry shared that every executive on his team was a first-timer in their role when he promoted them. That gave him a front-row seat to the strengths and limitations of internal talent as the business matured. He didn’t hesitate to get close to the business when things weren’t working. And more importantly, he didn’t shy away from making leadership changes when needed.

So how should you think about internal growth vs. external hires?

Assess what the business needs next

When you’re at the Series B stage, you’re no longer just looking for people who can “figure it out.” You need leaders who can execute at the next level, with increasing scale, complexity, and scrutiny from stakeholders.

Ask yourself:

• What do we need from this function over the next 12–24 months?

• Are we trying to mature a motion, break into enterprise, stand up international, etc.?

• Does this person know what great looks like in this phase, or are they learning alongside you?

It’s OK if the answer is “they’re not there yet.” The real question is whether they can get there fast enough.

Have the courage to act when it’s clear

Henry said it best: “The minute I realized someone was no longer right for the job, every day after that was a grueling day.”

The longer you wait to make a change, the more you’re asking your company, customers, and other team members to bear the cost of that delay. And ironically, you’re not doing that exec any favors either. If they’re struggling, they feel it too.

When it’s time to make a move:

• Be clear and respectful. They helped you get here, and deserve thoughtful communication.

• Give them the dignity of a clean transition and support finding what’s next.

• Use it as an opportunity to signal your standards to the broader team.

Don’t assume external = better

External hires can bring experience, playbooks, and credibility, but only if they’re truly a fit for your company. Some common pitfalls:

• Over-hiring from large companies (they expect infrastructure that doesn’t exist yet)

• Misalignment on values or leadership style

• A lack of startup muscle (scrappy, fast, hands-on)

Internal promotions can work if:

• The leader is self-aware and eager to learn

• You’re willing to stay close and coach them through the growth curve

• You can invest in advisors, coaches or mentors to support them through the transition

• They’ve earned trust across the org

Just know that internal bets often require more hands-on support in the short term.

Hope these lessons from Henry help you make the call on what’s in store for your own team this year.

Until next week!

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For years, go-to-market strategy assumed a relatively stable buyer entry point: search.

Founders invested in SEO, content teams optimized for keywords, and awareness was measured in rankings and clicks. That model quietly broke.

At the Stage 2 Capital Summit, Sydney Sloan, CMO Advisor to G2, laid out why we are now in a different era entirely. Buyers increasingly begin their journeys inside large language models, not search engines, where visibility is determined not by who ranks, but by who gets cited.

This shift has implications well beyond marketing. It changes how companies build awareness, how trust is established, and how early-stage GTM strategy must evolve in an AI-mediated buying environment.

What follows are five themes from Sydney’s talk that every founder, revenue leader, and marketer should be internalizing now

1. Buyers Are No Longer Starting with Search, They’re Starting with Answers

The most important insight from Sydney’s talk is also the simplest: buyer behavior has changed, and it changed fast.

“In August, 50% of buyers said they start their search on an LLM,” she said. “In April, that was 29%. In four months, that changed 71%.”

This is not a marginal shift. Buyers are no longer discovering products by typing keywords into Google and clicking through content. They are asking LLMs complex, contextual questions and expecting synthesized guidance.

From a GTM perspective, this matters because awareness is no longer driven by traffic alone. If your company does not appear in the answers buyers are receiving, you may never enter the consideration set at all.

2. The Objective Has Shifted from Winning the Click to Winning the Answer

Classic SEO was built around a clear goal: rank highly and earn the click. This is no longer sufficient.

“We’re moving from this era of ‘I need to win that click’ to actually winning the answer,” Sydney explained.

Answer engine optimization (AEO) reflects this shift. Rather than optimizing for individual keywords, companies must optimize for the questions buyers are asking, and for being included in the responses LLMs generate.

This reframes content strategy from a traffic exercise into a trust and relevance exercise. The question is no longer “How do we rank?” but “When buyers ask real questions, do we show up as part of the answer?”

3. AEO Is a Real Category, and It’s Forming at Breakneck Speed

Sydney was explicit in distinguishing between closely related terms: “AEO is about ensuring your product shows up when a question is being asked. GEO is about generating content.”

That distinction matters, especially given how quickly AEO has become its own category.

“In April there were seven vendors; now there are over 100,” Sydney said.

The speed of category formation signals both urgency and noise. For most companies, the opportunity is not to build tooling, but to understand how AEO fits into their broader GTM motion.

Founders should treat AEO the same way they once treated SEO: not as a tactic, but as infrastructure for awareness in the dominant discovery channel of the moment.

4. LLMs Reward Consensus, Not Isolated Optimization

One of the most operationally important insights in Sydney’s talk is how LLMs actually determine what they surface.

“LLMs work on consensus,” Sydney said.

LLMs scan across many sources to determine the most consistent, credible answer. That means visibility is no longer driven by a single high-ranking page, but by coherence across your entire digital footprint.

“You want to ensure your content is consistent,” she said. “This includes how you describe yourself on your website, in partner portals, on G2.”

From a GTM standpoint, this pushes companies toward a more integrated view of brand, content, reviews, and distribution. Inconsistent positioning across channels does not just confuse buyers; it reduces the likelihood that LLMs will surface you at all.

5. Awareness in the AI Era Is Built on Breadth, Consistency, and Human Language

Finally, Sydney emphasized that AEO is not about gaming a new algorithm. It is about answering real buyer questions clearly and consistently, in human language, across many surfaces.

She encouraged teams to build content around personas and jobs-to-be-done, even when that content does not directly promote the product.

“Even if it’s not what you do, that’s how you build your brand,” Sydney said.

This mirrors what we see repeatedly in successful GTM strategies: durable awareness comes from being genuinely helpful before it comes from being persuasive.

What This Means for Go-To-Market Leaders

AEO is not just a marketing concern. It sits upstream of pipeline, influencing which vendors buyers ever learn about in the first place.

As AI becomes the default interface for research and discovery, go-to-market teams must rethink how awareness is earned. Winning in this environment requires:

• Understanding the real questions buyers are asking

• Ensuring consistent positioning across owned and third-party channels

• Investing in breadth, credibility, and distribution, not just rankings

As Sydney put it:

“There is no playbook. You just have to stay in the playground.”

For founders and operators building in this moment, AEO is quickly becoming a core GTM capability, not because it is new, but because buyer behavior has already moved on.

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DEAR NEW TO PARTNERING: The tension you’re feeling is real. When you’re early, and every dollar counts, supporting indirect channels can feel like a gamble, especially if it threatens to pull energy and resources from your direct sales engine. But when partnerships are approached with intention, even a lean partner motion can become a force multiplier.

I had the chance to discuss this with Theresa Caragol, founder and CEO of AchieveUnite and a long‑time ecosystem leader who has spent decades helping companies design, launch, and scale successful partnership strategies across technology, services, and global markets. Her perspective is grounded in one core belief: partnerships are not an add‑on. They are a deliberate go‑to‑market choice. Here’s how she thinks about laying the right foundation for partnerships early on:

Not All Partners Are Equal. Treat Them Accordingly

The first mistake early‑stage companies make is engaging partners without a clear strategy. Before you invest time, enablement, or executive attention, you need clarity on three things:

• Your ideal customer profile

• Your ideal partner profile

• The business motion you need from those partners (influence, co‑sell, services, revenue, or market access)

The degree to which you get this strategy right upfront is directly tied to the success you’ll see down the road (we’re talking 12-18 months later).

Included in that strategy must be a clear answer to a question many companies overlook: What’s in it for the partner? There is a value proposition for the customer, and there is a distinct business proposition for the partner. If the partner cannot clearly articulate why working with you helps their business, momentum will stall.

Only after this foundation is in place should you begin partner segmentation. Segmentation is not a nice‑to‑have or a side exercise. It is how you decide where to place your limited bets. Leading indicators of potential and indicators of performance must be defined, measured, and understood across the organization. Segmentation and success metrics go hand in hand.

Partnering is not a sales initiative

This is where many seed‑stage companies get tripped up. Partnering is not a sales experiment or a side channel; it is a fundamental way the company goes to market.

When partnering is treated as “sales versus channel,” internal conflict follows. When partnering is aligned as a go‑to‑market motion across leadership, companies are significantly more successful over time. Organizations that align early avoid the friction that slows growth and dilutes trust between teams.

Every executive should understand how partnerships fit into the company’s strategy from the beginning.

Build the Strategy and Playbook Early

You do not need a large partner program at the seed stage, but you do need a repeatable motion. Theresa recommends documenting learnings as you go:

• Who is the buyer?

• Where does the partner add influence or access?

• What does a strong introduction or co‑sell motion look like?

• What behaviors consistently move deals forward?

Ownership matters here. Someone must be accountable for the partner motion, or sellers must be enabled from the start to leverage partners effectively. In today’s era of influence‑driven selling, teaching sellers how to work with partners is often the stronger path.

Balance Short and Long Term Goals

Every company should have a partner strategy from the onset. That strategy does not need to be complex, but it does need to be intentional.

For some early‑stage companies, the partner bet may focus on influence. For others, it may be revenue, services, or supplier leverage. The key is to place two to three deliberate bets where partnerships can act as a force multiplier.

In a world driven by digital, social, and ecosystem influence, direct sales and direct marketing alone cannot reach every buyer. Partners extend reach, credibility, and context, but only when they are chosen and engaged deliberately.

This is why success should be evaluated using both leading indicators (engagement, influence, access) and lagging indicators (revenue). Remember: revenue confirms success, but it rarely tells the whole story early on.

From Direct Sales to Leverage Selling

We are moving from a world dominated by pure direct sales to one defined by co‑selling and leverage. The most successful AEs are those who understand how to use partnerships for influence, new logos, service wrap, and long‑term stickiness. Alignment is the key. Compensation, engagement timing, and clarity around who leads and who supports at each stage of the sales cycle must be explicit.

Misalignment creates confusion. Alignment creates leverage.

Partnerships at the seed stage are less about scale and more about leverage. If you place a few intentional bets early, validate them with both leading and lagging indicators, and document what works, you will know exactly when it’s time to double down.

And when that moment comes, you’ll be scaling from clarity, not guesswork.

Until next week!

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Every company wants deeper insight into why they win (and lose) deals. But actually getting that insight is a different story…and a consistent question we ask in the board room. Where are we winning and why? What can we learn that we can apply to the process or product? And can we leverage AI to get better insights faster?

I asked the CMO of Rally, Juliette Kopecky, to work on a project with me to test using AI to build this process in a way that is reliable, repeatable and actually useful. This process can also be used to understand why you lose deals, too, and ultimately combine those learnings to forecast more accurately.

Below is a recap - what wasn’t working, what Rally did with AI, and what they learned so you can implement this in your own organization.

The GTM Challenge: Win-Loss Is Critical… But Hard to Do Well

Win-loss analysis has always been a high-leverage GTM motion. It tells you why customers buy, where your product differentiates, and what is working in your process.

But despite its importance, the traditional process is riddled with problems:

1. The reasons behind wins and losses are complex.

No two deals are alike. Context, champions, competitors, timing, procurement, pricing—so many factors influence outcomes that it’s difficult for humans to abstract meaningful patterns at scale.

2. There is bias on both sides.

Buyers don’t always tell you the full truth and sellers have blind spots and emotional investment in the deal.

3. It is extremely time-consuming.

One-on-one interviews take hours to schedule, conduct, transcribe, and analyze. Most companies only do a handful per quarter—far too few to spot meaningful trends.

What Rally Tried: A Three-Phase AI-Augmented Win Analysis Workflow

To tackle the challenge, Rally broke the project into three phases—each building on the last.

Phase 1: Can AI replicate the win interview using sales calls alone?

Instead of relying on scheduled buyer interviews, Rally asked:

If we feed AI the sales call recordings, can it reconstruct what a proper win-loss interview might uncover?

They processed large volumes of sales conversations from all their won deals in a quarter and evaluated whether AI could extract insights similar to human-led interviews. Rally uses Gong to record their calls, so they tested Gong’s built in AI analysis to summarize the calls. They uploaded their own list of prompts so that the summaries were consistent from call to call.

What they learned: AI is surprisingly strong at pulling structured information out of unstructured call data. It can summarize, classify, and surface themes quickly—producing a baseline “interview-like” assessment without ever speaking to the customer.

Phase 2: Can AI identify trends across deals?

Once AI could summarize individual sales calls, the next question was scale:

Could AI detect patterns across dozens or hundreds of deals?

This phase involved aggregating insights across call transcripts—things like:

• What customer pain points resonated the most?

• What product features did customers want?

• How did the product compare to their current solution and/or competitor offerings?

• What are key steps in the process for buyers? What roles need to be involved?

Rally uploaded these summaries to build their own GPT in ChatGPT so they could run the insight analysis across all of the call summaries. They did this manually, but realized they could also automate it in the future given that Gong has an API into ChatGPT.

What they learned:
This is where AI shined. The model could sift through massive volumes of interactions and surface repeatable patterns much faster than any manual process. Rally could take these learnings and apply them back to the sales process so that they could ensure that they were following all the right steps to have the best outcome. For example, the majority of the won deals were focused on doing research with their own users, improving governance and compliance in their research practice, and speeding up the research recruitment process, so they have prioritized these use cases in discovery.

Phase 3: Can AI predict outcomes and recommend actions?

The ultimate ambition:

If AI can understand the past, can it help shape the future?

This phase can test whether AI could not only analyze trends but also predict deal risk, flag competitive steps, suggest next steps, and recommend product improvements. Rally is testing this phase now to see if they can run the first 2-3 calls once a deal reaches a certain stage to determine the likelihood of closing. To do this, you can build another custom GPT as a Forecast and load in the several attributes and prompts you want it to detect in the call. It can give you the results, as well as a forecast.

The Outcome: A Hybrid Approach Is the Winning Formula

After running all three phases, Rally reached a clear conclusion:

AI is incredible for helping us with:

• Speed

• Breadth

• Pattern recognition

• Synthesizing large amounts of data

But AI alone is not a replacement for human-led conversations.

The richness, nuance, and authenticity of a live customer interview still matter—especially for complex buying journeys.

So what’s the right approach?

AI as the starting place for analysis, humans for depth.

AI dramatically improves the win-loss process by:

• giving teams the ability to analyze more data, more often

• preparing interviewers with a stronger context

• filling insight gaps when a buyer won’t take a call

• generating trendlines that humans simply cannot see

This hybrid model creates better conversations, better enablement, and better GTM decision-making, all while saving teams enormous time.

What This Means for GTM Teams

Win-loss analysis used to be something companies did quarterly because of the time commitment. With AI, it can be continuous. And when paired with human depth and judgment, it becomes one of the most powerful GTM tools available. This project showed that the future of GTM isn’t AI or humans—it’s AI-powered humans making better, faster, more confident decisions.

My challenge to you: Run this analysis for both deals you won and deals you lost on your Q4 data. Include two slides in your Q1 Board Deck:

1. Why we win deals

   • What our CRM says

   • What AI says

   • What we learned

2. Why we lose deals

   • What our CRM says

   • What our AI says

   • What we learned

If you want to share, I would love to hear about it!