Which of HubSpot's September 2026 updates actually change how a RevOps team works? Adam Statti sorts through seven areas of the platform, from Revenue Hub to the data model.
What you'll find inside
Useful as a quick checklist for HubSpot teams deciding which of this month's releases to test first, especially around quoting, contracts and duplicate management.
Read the full article here for more perspective on the new quoting and contract tools, plus more input on the data management changes.
Do sales teams still need to open Salesforce at all? Gerry Marletta looks at Salesforce Headless 360, where AI agents query the CRM through Model Context Protocol instead of the interface.
What you'll find inside
Marletta lists five practical uses: CRM structure diagnostics in minutes, pulling data from several systems without a formal integration, filling CRM fields from call notes, autonomous call prep for reps, and continuous pipeline monitoring. He pairs them with five risks: field overload (with 300+ fields, AI cannot tell which "amount" or "open" field matters without guidance), over-agreeable answers without guardrails, unchecked write-backs, inconsistent results on repeated queries for metrics like pipeline value, and point solutions limited to proprietary fields. On write access, AI should never move a deal to lost, should rarely move one to won outside contract signature, and should move earlier stages only depending on the complexity of the sales motion.
A fresh angle on AI inside the CRM: instead of waiting for a perfectly clean instance, start with documented instructions and read-only workflows, and add write access last.
Discover the full article here for more perspective on where write-backs get risky, plus more input on handling field overload.
Why does only about half of the sales force hit quota? SalesGlobe's 2026 research on sales compensation points to how, and when, quotas get set.
What you'll find inside
A solid reference point for anyone planning next year's quota rollout, since the gap between 66% and 41% attainment is a concrete number to bring into the conversation.
Get the complete report here for more perspective on quota timing, plus more input on how teams are using AI in compensation.
How does a public company run 113 AI agents without adding headcount? Meagan Eisenberg, CMO at Samsara, explains how she rebuilt the go-to-market function around AI.
What you'll hear
Eisenberg starts with change management: a 2.5-week boot camp trained half the organization and gave people hands-on practice before any cost controls were introduced. From there Samsara built 113 agents across events, sales, support and creative work, most of them running in Slack, with agent-building opened up across departments instead of centralized. She reports flat headcount alongside growth in pipeline and revenue, ABM landing pages that went from two to three weeks down to under 30 minutes, content output of 10 pieces a day, and a rebrand campaign cut from six months to 3.5 weeks. The conversation also covers hiring for AI fluency and positioning AI as a competitive advantage rather than a threat.
Worth a listen for marketing and RevOps leaders planning an AI rollout, because the sequence Samsara describes puts training ahead of cost controls.
Listen to the full episode here for more perspective on how Samsara trained its team, plus more input on opening agent-building to every department.
Is a buyer signal the same thing as a sale? Jenna Chambers, formerly VP of Marketing at DemandScience, argues go-to-market planning should start from how buyers actually buy.
What you'll hear
A reminder that a signal is a starting point rather than a verdict, and that planning from buyer behavior can change how brand and demand programs fit together.
Hear the complete conversation here for more perspective on buyer behavior, plus more input on merging brand and demand gen.
Is maxing out token usage a sign of AI maturity in RevOps? Avtar Varma, VP RevOps at Chainguard, says it is only the first of three stages.
What you'll hear
Varma lays out three stages of enterprise AI adoption: token maxing first, then centralizing functional skills and templates, and finally a dedicated AI operations team. He explains why usage leaderboards work as vanity metrics and why hackathons are a better way to reward adoption, and he names meeting preparation as a practical, high-impact use case. The conversation also covers where an AI ops team should sit, who owns the data infrastructure agents depend on, cutting cost with open-source models, and why go-to-market teams are a good place to prove AI ROI.
Helpful for teams unsure where they sit on the AI adoption curve, since the three stages give them a way to place their own organization and decide what comes next.
Discover the full episode here for more perspective on the three adoption stages, plus more input on structuring a central AI ops team.
Tuesday, October 13, 2026, 1:00 PM ET | Speakers: Apurva Shukla (Sumble), Matthew Volm (RevOps Co-op, moderator)
Are AI agents solving your data problems, or just acting on them faster? This RevOps Co-op session with Sumble makes the case for fixing GTM data before scaling automation.
What you'll see
A timely shift in the question for teams about to scale agents: not which agent to buy, but whether the underlying records can support it.
Register for the full session here for more perspective on the third era of GTM data, plus more input on lineage and governance.
By: RevOps Co-op ft. Openprise
Wednesday, October 14, 2026, 1:00 PM ET | Speakers: Jackson Mattox (Cockroach Labs), Dom Freschi (Openprise), Matthew Volm (RevOps Co-op, moderator)
What does an AI-powered GTM stack look like once it is running in production? Cockroach Labs shows how its team built one for account prioritization and outbound.
What you'll see
The session covers what is working, what is not, and what the team has learned about signals, AI governance and cost. Topics include keeping signal-to-noise under control as AI generates more scores, governing AI at scale, and orchestrating data and cost while keeping control of the system.
A look inside a live AI GTM stack from the team running it, useful for anyone comparing their own approach to signals, governance and cost.
Save your seat for the full session here for more perspective on signal-to-noise, plus more input on AI governance and cost control.
Wednesday, October 14, 2026, 4:15 PM ET / 1:15 PM PT | Speaker: Stephanie Martin, Head of RevOps at Lative
How many revenue plans are built on assumptions nobody tested? Stephanie Martin walks through a six-step framework for planning capacity, headcount, quota and pipeline.
What you'll see
A new way to frame the annual plan: as a living process that gets tested, simulated and tracked, rather than set once and revisited at the next kickoff.
Join the full session here for more perspective on scenario simulations, plus more input on tracking plans after they are set.