Resource
🤖 GTM AI Roadmap Starter Kit
3/25/2026
A working document for ops and RevOps leaders building or refining their first AI roadmap. Use this to inventory use cases, score them, refine them, and walk into planning conversations with a defensible (and exciting!) prioritization.
This is a starting point. Your list will look different depending on your stack, your stage, and what your GTM team actually needs. The point is to have a framework before someone else decides for you.
A Note on the Politics of Owning This
I do want to address something that folks have been asking me about: a lot of ops professionals aren't sure whether they should step in to own this at all. And that hesitation is understandable -- stepping in visibly can read as territorial, and the last thing you want is a "who asked you?" from your CRO right as you're trying to build credibility.
Here's how I think about it:
If ops doesn't step in, someone else fills the vacuum that a lack of ownership creates. It usually defaults to whoever is most enthusiastic about AI at any given moment -- which tends to be a sales leader who just saw a vendor demo, or engineering, who will ship something technically impressive that doesn't account for how the data is structured or how the GTM teams work. They simply do not have the context we do.
While it’s key to work alongside partners like engineering (who can typically ship sophisticated solutions more quickly than we can), we are the key players on the business logic piece.
Position yourself as the person solving a problem nobody else wants to deal with, not the person grabbing territory. There's a meaningful difference between walking into your CMO's office and saying "ops should own AI for GTM because I feel like it should" versus walking in and saying "I've noticed we have three AI tools that may be producing contradictory outputs on the same accounts, and I want to put a framework in place so we can catch that before it hits pipeline." The first sounds like a turf claim. The second sounds like someone doing their job extremely well and builds trust.
If you're struggling to find the entry point, look for the moment where something has already visibly broken OR when there’s a glaring opportunity for AI to help. A failed AI implementation. An agent running on stale data. A rep complaining that the scoring doesn't match what they're seeing in the field. Those moments create political permission that you can't manufacture on your own -- and they're almost always available if you're paying attention.
One more thing on this: don't just show up with a governance model. Show up with ideas. Proactively bringing concrete AI use cases to your GTM leaders -- before they ask -- signals that ops understands the business and not just the systems.
As I build out our roadmap at Vector, I came in with a working list of use cases I had already been researching: a sales call analyzer, a discovery pre-read bot that preps reps before calls, a signals AI that scores and recommends accounts, a pipeline review prep bot, even pulling Pylon tickets and Slack channels to surface product feedback trends. Some will make the cut, some won't -- but the act of walking in with that list reframed the entire conversation. It stopped being "ops, can you implement what we want" and started being "ops, what do you think we should build? When can we start?”
The alternative is becoming the team that cleans up after everyone else's AI experiments. 😭 And let me be clear — we will not avoid addressing AI one way or another in our roles…if we don’t proactively lead the journey, we will end up just cleaning up the mess or enabling others and getting little to no credit.
📋 TL;DR
Sadly, most GTM AI (or GTM in general) roadmaps are built around whoever asked the loudest. This kit gives you a scoring model to evaluate every use case against six consistent criteria and a pre-populated list of real use cases to start from.
The Six-Question Scoring Model
For each proposed AI initiative, score one point per question it satisfies. Be honest -- partial credit doesn't help you prioritize.
# | Question | What You're Testing |
1 | Does this meet users where they already live? | Will reps or marketers actually use it, or does it require a new login and a new habit? |
2 | Does this democratize visibility without creating new math? | Can someone get the answer they need without having to open a separate dashboard or running an additional query? |
3 | Does this make manually collected data usable? | Does it unlock value from calls, notes, tickets, or Slack conversations that currently go to waste? |
4 | Does this make the GTM motion meaningfully more efficient? | Are you speeding up execution, or just changing what the team can see? Execution matters more than just visibility in most cases. |
5 | Does this create internal visibility for the ops function? | Does it put ops in the room at a moment that matters to leadership -- pipeline reviews, forecasting, QBRs? |
6 | Does this reduce the operational burden on the ops team itself? | Can ops also govern a growing AI ecosystem if it's still running manual processes? |
Priority tiers:
- Score 4–6 → Build now 🟢
- Score 3 → Build when data or capacity allows 🟡
- Score 0–2 → Deprioritize -- document the rationale 🔴
GTM AI Use Case Inventory
This is a working list of real use cases to start from. They are pulled from my own v1 brainstorming. 😅 Add your own, remove what isn't relevant, and score each one against the six questions above. The scores below are illustrative -- your context will change them.
Use Case | Description | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Total | Tier | Notes |
Sales call analyzer | Transcribes and analyzes calls for coaching, objection patterns, competitor mentions | 1 | 1 | 1 | 1 | 0 | 1 | = 5 | 🟢 | High value if calls are currently going unreviewed |
Sales call prep bot | Researches contact and company before a call, surfaces relevant context in the CRM or Slack | 1 | 1 | 0 | 1 | 0 | 1 | = 4 | 🟢 | Scores higher if reps are currently doing this manually |
Discovery pre-read bot | Pulls together a briefing doc before discovery calls -- deal history, stakeholders, open questions | 1 | 1 | 1 | 1 | 0 | 1 | = 5 | 🟢 | Strong candidate -- high manual effort, high sales impact |
Signals AI | Scores accounts based on intent, engagement, and fit signals and surfaces recommendations to reps | 1 | 1 | 0 | 1 | 1 | 0 | = 4 | 🟢 | Requires clean data foundation -- assess readiness first |
Sales pipeline review prep bot | Pulls deal summaries, flags at-risk opportunities, and preps a review report automatically | 1 | 1 | 1 | 1 | 1 | 1 | = 6 | 🟢 | High ops visibility -- puts you in the pipeline review conversation |
RevOps bot | Answers common RevOps questions from sales and marketing (routing logic, lifecycle stages, process docs) | 1 | 1 | 0 | 0 | 1 | 1 | = 4 | 🟢 | Great for reducing ops support burden if ticket volume is high |
Sales manager activity monitor | Watches deal stages, call activity, and rep behavior and surfaces anomalies to managers | 1 | 1 | 1 | 1 | 1 | 0 | = 5 | 🟢 | High value at scale -- assess whether managers will actually use it |
Product feedback aggregator | Reads Pylon tickets and Slack channels and surfaces the largest product feedback trends | 0 | 1 | 1 | 1 | 1 | 1 | = 5 | 🟢 | Strong cross-functional value -- good way to expand ops influence into CS and Product |
Content bot for marketing | Drafts marketing content based on briefs, brand guidelines, and campaign context | 1 | 0 | 0 | 1 | 0 | 1 | = 3 | 🟡 | Lower ops ownership -- marketing should drive, ops enables |
Personalized landing page builder for ABM | Dynamically builds landing pages personalized by account or segment | 0 | 0 | 0 | 1 | 0 | 0 | = 1 | 🔴 | Probably requires significant data and engineering lift -- deprioritize until foundations are solid |
Competitive intel bot | Monitors sources and surfaces competitive intelligence to reps and marketing | 1 | 1 | 0 | 1 | 0 | 0 | = 3 | 🟡 | Good backlog candidate -- useful but not ops-critical |
Reddit comment bot | Monitors Reddit for brand/competitor mentions and surfaces relevant threads, tags users to contribute to topic | 0 | 0 | 0 | 1 | 0 | 0 | = 1 | 🔴 | Deprioritize -- low ops relevance, high noise risk |
Step-by-Step: How to Run This Process
- Add every proposed or in-flight AI initiative to the inventory table. Include things that have been informally requested but not yet scoped. If it's been mentioned in a meeting, it belongs on the list. BTW — you can have Claude help you build your own, or you could set it up in Google Sheets/Excel.
- Score each use case against the six questions. Mark 1 if the use case clearly satisfies it, 0 if it doesn't or if you're genuinely unsure. Adjust the illustrative scores above based on your org's specific context.
- Sort by score and assign priority tiers. Scores of 4–6 go to the top. Scores of 3 sit in a backlog. Scores of 2 or lower get explicitly deprioritized.
- Document rationale for anything you're deprioritizing. One sentence per use case is enough. This protects you when someone circles back and asks why their request didn't make the cut.
- Present the scored roadmap to your CRO or CMO. Frame it as the logical, priority-based model it is, not a personal ranking.
- Revisit quarterly. A use case that scored a 2 in Q1 because the data wasn't ready might score a 5 in Q3 after a cleanup project. This is a living document, not a one-time exercise.
Templates
📂 AI Initiative Intake Form
Use this when a stakeholder submits a new AI request. Get the context you need to score it without scheduling a meeting.
AI Initiative RequestRequested by:[Name / Team]Use case description:[What should the AI do?]Which team(s) will use this?[Who are the end users?]Where are they currently spending time on this manually?[Quantify if possible] What data does this rely on?[CRM fields, call recordings, product usage, etc.]What does success look like in 90 days?[Metric or outcome]Is there an existing tool or vendor in mind?[Yes / No / TBD]
📂 Executive Briefing
Use this when presenting to your CRO, CMO, or leadership team.
What we're building and whyWe've evaluated [NUMBER] proposed AI initiatives against a consistent set of criteria focused on GTM impact, data readiness, adoption risk, and ops capacity. Here is the recommended priority order:Tier 1 — Build now:[Use case] -- Score [X/6] -- [One sentence rationale] [Use case] -- Score [X/6] -- [One sentence rationale]Tier 2 — Build when ready:[Use case] -- Score [X/6] -- [One sentence rationale]Deprioritized for now:[Use case] -- Score [X/6] -- [One sentence rationale]We will revisit this quarterly as data infrastructure and team capacity evolve.
✍️ Pro Tips (From the Field)
- Score use cases before scoping them. Most teams spend weeks scoping something before anyone asks whether it should be built at all. The scoring model takes 10 minutes. Do it first.
- The "meets users where they live" question will kill more bad ideas than any other. If the answer involves a new login or a new habit -- especially for sales -- the adoption math rarely works out, no matter how good the technology is.
- The pipeline review prep bot is the highest-leverage starting point for most ops teams. It scores a 6, it puts you in the room for the most important recurring leadership conversation, and it directly reduces the manual reporting burden on ops. If you're not sure where to start, start there.
- Your use case list is itself a political asset. Walking into a planning meeting with a pre-scored inventory of ideas signals that ops understands the GTM motion -- not just the systems. It reframes the conversation from "ops, can you implement what we want" to "ops, what do you think we should build?"
- Don't let perfect data readiness be the reason you never start. Some use cases require a clean data foundation before they'll work well. Note that in the tracker and flag it -- but don't let it stall the whole roadmap. Identify which use cases can move forward now and which ones are waiting on a specific prerequisite.
If you read up to this point, you rock. Let me know if you have any feedback! 🤘
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