What's the difference between "RevOps in 2025" and "RevOps in 2026" as described in this cheat sheet — isn't it just adding AI tools on top of existing processes?

The shift isn't about adding tools — it's about who executes the work. The 2025 model has RevOps designing manual processes that reps may or may not follow; the 2026 model deploys agents that run those processes automatically, with RevOps owning the orchestration layer. The cheat sheet is explicit that this also changes the data model: you move from structured-only reporting to a unified layer that includes unstructured data, which is what makes agents actually useful.

Do I need to be using Salesforce to apply what's in this cheat sheet?

Salesforce is central to the agent workflows shown — the CRM Hygiene Agent and Deal Inspection Agent both pull directly from Salesforce opportunity records using field conditions like stage, close date, and last activity. That said, the maturity model, metrics, and team structure sections apply regardless of your CRM. If you're not on Salesforce, you'll need to adapt the agent logic to whatever data model your CRM uses.

Which GTM workflows should I automate with agents first, and which should stay human-led?

The cheat sheet maps 18 specific agents across the bow-tie funnel, but the clearest starting points are the ones with structured triggers and low-stakes outputs — CRM hygiene and deal inspection are both shown with full workflow logic. Keep humans in the loop anywhere judgment is required at the moment of consequence: pricing exceptions, churn conversations, executive relationships. The AI Ops Manager role in the 2026 team structure owns exactly this decision.

What does my Salesforce data need to look like before I start deploying agents?

The cheat sheet sets a hard threshold: CRM Hygiene Score above 90% completeness and accuracy before you scale agents. The CRM Hygiene Agent workflow itself flags opportunities with empty Next Step fields, past close dates, and no activity in 14+ days — which tells you what fields matter most. If those core fields are unreliable, every agent downstream will produce bad outputs, and the cheat sheet calls this out directly as the gate, not a nice-to-have.

How do I know if the agents I deploy are actually working, or just running without doing anything useful?

The cheat sheet includes a dedicated AI & Agent Metrics section with specific targets: Task Completion Rate above 85%, Accuracy above 90% on review, and Human Override Rate below 15% and falling over time. If your override rate is high or flat, that's a signal the agent logic or the underlying data is off. Track Agent-Influenced Pipeline and Agent-Influenced Revenue in dollars to connect agent activity to business outcomes, not just operational throughput.

How often should I review the agents running in production once they're live?

The Deal Inspection Agent in the cheat sheet runs weekly, the day before pipeline review — that cadence is a reasonable baseline for any agent touching active pipeline. For hygiene and data quality agents, daily triggers make sense because stale data compounds fast. Beyond individual agent schedules, treat your AI & Agent Metrics as a monthly ops review item so you can catch drift in accuracy or adoption before it becomes a bigger problem.