Tool 03
The CRM is not dead — it is becoming the context layer your agents depend on. This diagnostic does not score how much AI you use. It reads whether your stack can be trusted to feed automation: one system of record per job, owned and current data, signals that arrive in time, and people who can build the plumbing.
Describe your stack
Name the tools you actually use, layer by layer. Write-ins welcome.
Describe how it runs
Ownership, definitions, freshness, and what automation is already allowed to write.
Read the diagnosis
Overlaps, gaps, strengths and the areas worth exploring next.
Everything runs in your browser. Nothing is sent anywhere, and no email is required.
Step 3 — your reading
Pick bounded workflows with clear inputs and reversible outputs, and instrument them before you widen the scope.
System of record integrity
85
Is there one authoritative place to read from?
Context quality
64
Is the data owned, defined and current?
Signal timeliness
98
Does what happened reach the system in time to act on?
Build capability
35
Can you build and instrument the plumbing yourselves?
These bands are directional, derived from the rules below — not a benchmark against other companies. Every finding names what it saw.
Step 1
Select everything in use, including anything you are trying to sunset. Overlap is the point — we want to see it.
Accounts, opportunities, the truth agents read from.
Who the account is, and whether that stays true.
Where GTM data can be joined, modeled and governed.
The metric definitions humans and agents both quote.
What actually happened in the conversation.
Where inbound intent enters and how it is scored.
How decisions move between systems.
Retention signal, usage signal, escalation signal.
Step 2
Answer these the way you would to a colleague, not to a board. The honest answer is the useful one.
Who owns CRM data quality?
Not who complains about it — who is accountable for it.
Do you have GTM engineering capability?
Someone who can build the plumbing, not just configure a tool.
Does a warehouse mirror your CRM?
Agents and analysts need a place to join CRM data to everything else.
Are lifecycle and stage definitions written down?
If a stage means different things to two reps, it means nothing to a model.
How current is the average account record?
Your honest guess is more useful here than a report.
Do AI features write into your CRM today?
Anything that creates or updates records without a person typing it.
Is there human review before automated outreach goes out?
The difference between leverage and scaled mistakes.
What we saw
Gap
What we saw. No in-house or partner build capability today.
Why it matters with agents. Without someone who can build and instrument the plumbing, AI ambitions become vendor demos.
Next move. Start with one part-time GTM engineer or partner scoped to a single workflow end to end.
Step 4
Ranked by how much they would change your answer. Each one is a diagnosis worth doing properly.
No GTM engineering capability
Start with one part-time GTM engineer or partner scoped to a single workflow end to end.
Choosing tools
There is no AI-RevOps leaderboard, and anyone selling you one is selling you something. What separates a stack that can feed agents from one that cannot is whether definitions are reusable outside the tool that drew them.
Looker
LookML is a governed semantic layer with version control and an API — the closest fit out of the box.
Omni
Warehouse-native modeling with a shared metrics layer; lighter to adopt, newer ecosystem.
Power BI
Strong semantic models via DAX and Fabric; governance depends heavily on how the tenant is run.
Tableau
Excellent exploration, weaker shared definitions — pair it with dbt metrics rather than defining logic in workbooks.
Domo
Fast to stand up with ETL bundled in; logic tends to live inside Domo, which limits reuse elsewhere.
Sigma
Spreadsheet-familiar on top of the warehouse; good adoption path, model the metrics upstream.
Enrichment
Programmatic and composable — you should be able to write your own logic, chain providers and re-run on a schedule.
Clay and Common Room are built this way. A single static database is a snapshot, not a pipeline.
Orchestration
Observable and reversible — every automated write should be logged, attributable and undoable.
Workato, Tray and n8n expose run history; chained Zaps and one-off scripts usually do not.
Modeling
Definitions live in one reviewable place, upstream of every consumer.
dbt on top of the warehouse is the durable pattern; logic buried in dashboards cannot be reused by an agent.
Explore another model