AI GTM Playbook Advisor — JayOh

Free Strategic Assessment

Find the AI Plays That Will Transform Your GTM

We'll diagnose where AI can create the most leverage across your revenue engine — then map you to the exact playbooks from our 88-play portfolio company framework.

AI Plays

Maturity Stages

~4m

To Complete

Step 1 of 10 — Context

Tell us about your company

We calibrate every recommendation to your context — size, stage, and who's driving the initiative.

Company Name

Your Name

Your Role

Annual Revenue

GTM Team Size (Sales + Marketing + CS + RevOps)

Step 2 of 10 — Infrastructure

What's in your tech stack?

Select everything you currently use. We only recommend plays that work with tools you already own.

HubSpot

Salesforce

Gong

Fathom

6sense

Apollo

ZoomInfo

Clari

Clay

Outreach

Salesloft

Lemlist

Zoom

Slack

Zapier

Make

n8n

Claude

ChatGPT / OpenAI

Descript

Step 3 of 10 — Foundation

How much of your native AI have you turned on?

HubSpot Breeze has 19 AI features. Salesforce Einstein has 16. Most companies use less than 20% of what they already pay for.

20%

You're barely scratching the surface — huge upside here.

0% — Nothing activated100% — Fully activated

Step 4 of 10 — Foundation

How clean is your CRM data?

AI is only as good as the data it reads. Be honest — this shapes every recommendation.

🔴

It's a mess

Lots of missing fields, duplicates, stale records. Less than 60% of key fields filled.

🟡

Decent but inconsistent

Core fields mostly filled, some duplicates, processes exist but aren't enforced everywhere.

🟢

Pretty solid

80%+ field completion, regular hygiene processes, minimal duplicates, governance in place.

🔵

Best-in-class

Automated enrichment, strict governance, real-time quality monitoring, >90% completeness.

Step 5 of 10 — Discovery Layer 1

Where is your team losing time on work that shouldn't require a human?

These are the tasks that feel like "real work" but are actually pattern-matching disguised as expertise. Select up to 3.

Think: what would a new hire watch a senior person do and think "why isn't a machine doing this?"

Reps manually logging calls, updating stages, writing follow-ups

Every call ends with 10-15 minutes of CRM admin. Multiply by 8 calls/day and you've lost a full selling day per week.

Marketing reformatting the same content for every channel

Blog becomes email becomes LinkedIn becomes ad copy — same message, different formats. Takes a person, shouldn't.

RevOps cleaning CRM data, merging dupes, filling missing fields

Someone spends 5-10 hours/week on data janitorial work that could be caught and fixed automatically.

Managers building forecast spreadsheets from pipeline data weekly

Every Monday: export pipeline, cross-reference notes, apply gut feel, format for leadership. Same exercise, same frustration.

SDRs researching prospects one-by-one before writing outreach

20 minutes per prospect: LinkedIn, website, news, 10-K. The research is formulaic — the personalization is where humans add value.

CS team copy-pasting account data into QBR decks

Every quarterly review: pull usage, pull support tickets, pull NPS, format slides. 3-5 hours per account, every quarter.

Step 6 of 10 — Discovery Layer 2

Where are you throwing people at a volume problem instead of building a system?

The difference between a $10M and $50M company is often whether you scale with headcount or with leverage. Select up to 3.

Think: where does "we need to hire another person" really mean "we need a better system"?

We need more SDRs to cover more accounts

Outbound is headcount-limited. The answer always seems to be "hire another rep" instead of "make each rep 3x more effective."

Content team is buried — can't produce enough for all channels

The bottleneck isn't ideas, it's production. Same team trying to serve blog, social, email, ads, events, and sales enablement.

Every deal needs a custom proposal and we can't keep up

Proposals are 80% template, 20% customization — but someone rewrites the whole thing every time.

Support tickets are growing faster than we can hire

Volume is outpacing headcount. Most tickets follow patterns a trained model could resolve — humans should handle the exceptions.

Lead qualification is manual — every MQL gets the same treatment

No intelligent routing. A Fortune 500 VP and a student both get the same follow-up email on the same timeline.

Reporting takes days because data lives in 5 different systems

Someone is the human middleware — pulling CSVs, stitching spreadsheets, formatting for leadership. Every week.

Step 7 of 10 — Discovery Layer 3

Where does critical knowledge live in one person's head?

These are the hardest bottlenecks to see — expertise that feels irreplaceable but is actually pattern-based and can be systematized. Select up to 3.

Think: what breaks when that one key person goes on vacation?

Only a few reps know how to handle competitive objections

Your best reps have competitive responses internalized. Everyone else fumbles. That knowledge should be in a system, not a brain.

Top AE's discovery process is in their head — new reps can't replicate it

The best discovery calls follow a pattern. But it takes 12+ months for new reps to develop that instinct organically.

Pipeline reviews depend on gut feel, not consistent criteria

Every manager evaluates deals differently. There's no shared scoring framework — just intuition and experience.

Nobody really knows which marketing channels drive revenue

Attribution is a black box. Decisions about where to invest are based on "last touch" or gut feel, not real multi-touch analysis.

New rep ramp takes 6+ months to full productivity

There's no system that transfers institutional knowledge. Each new hire has to learn by osmosis, shadowing, and trial-and-error.

Churn signals are invisible until it's too late

No early warning system. By the time someone notices a customer is unhappy, the decision to leave was made weeks ago.

Step 8 of 10 — Discovery Layer 4

Where is speed or personalization costing you revenue?

These bottlenecks directly impact top-line growth. Every day they persist, you're leaving money on the table. Select up to 3.

Think: where does a customer or prospect experience friction that a faster, smarter system would eliminate?

We're too slow to respond to inbound leads

Speed-to-lead is the #1 conversion driver. If your response time is hours, competitors are already in the conversation.

Customer onboarding takes too long — we lose people before value

Time-to-value is too high. Customers churn in the first 90 days because they never hit the activation milestone.

Our outreach is generic — we can't personalize at scale

Every prospect gets a version of the same template. Real personalization (research + tailored messaging) doesn't scale with humans.

Sales can't articulate competitive differentiation in real-time

Reps freeze when a prospect mentions a competitor. Battlecard info is stale or buried in a doc nobody reads.

Proposal and follow-up speed is killing our close rate

Deals stall because it takes days to turn a call into a proposal. The buyer's urgency expires while you're formatting slides.

Expansion opportunities are invisible — we don't see signals

Upsell and cross-sell depend on CSMs "just knowing." No system surfaces expansion triggers from usage, calls, or support data.

Content doesn't match where buyers are in their journey

Top-of-funnel content goes to bottom-of-funnel prospects. No intelligent mapping of content to buyer stage or persona.

Step 9 of 10 — Readiness

Where are you on your AI journey — and who's driving it?

These two factors together determine how aggressive your roadmap can be.

Your AI experience level:

🌱

Haven't started

We know AI matters but haven't done anything beyond basic ChatGPT usage.

🔍

Exploring & piloting

Running some experiments — testing an AI feature or content tool. Nothing operationalized yet.

⚙️

Some AI workflows running

We have 2-5 AI workflows live with human review. Starting to see results but not yet scaled.

🚀

Advanced

Multiple AI workflows in production, governance in place, measuring ROI, scaling across functions.

Who owns AI at your company?

❓

Nobody — it's unclear

RevOps thinks it's IT's job. Marketing thinks it's RevOps. Nobody has a mandate.

🙋

Someone's doing it informally

One person is experimenting but it's not their official mandate. No budget or authority.

👤

Assigned to a specific person/team

Named owner with explicit responsibility for AI rollout and budget.

🏢

Executive-sponsored initiative

C-level sponsor, budget allocated, cross-functional mandate, success metrics defined.

Step 10 of 10 — Priority

What's your #1 goal for the next 90 days?

Pick the one that matters most — your entire playbook anchors around this.

📈

Generate more qualified pipeline

More opportunities entering the funnel. Better outbound, better inbound, better targeting.

⚡

Make the team more productive

Same team, more output. Less admin, more selling/creating. Higher revenue per employee.

🎯

Win more deals

Better deal execution, competitive positioning, call prep, proposals, and close discipline.

🛡️

Reduce churn & expand accounts

Protect existing revenue, detect risk earlier, accelerate onboarding, unlock expansion.

✍️

Scale content production

Produce 3-5x the volume with the same team — blogs, social, emails, case studies, battlecards.

🏗️

Fix the data & systems foundation

Clean CRM data, activate existing tools, build governance before scaling AI.

🧠

Build real attribution & intelligence

Finally measure what's working. Multi-touch attribution, funnel diagnostics, board-ready reporting.

⚔️

Sharpen competitive positioning

Real-time battlecards, win/loss patterns, competitive intel flowing to reps before every call.

Building your AI strategy...

Analyzing your discovery responses against 88 AI plays and building your strategic recommendation.

Analyzing bottleneck patterns across 4 layers

Scoring AI maturity across 6 dimensions

Matching plays to your stack, pains, and goals

Generating strategic analysis & roadmap