Why AI Is Giving Your Teams Different Numbers | Jose Forte
RevOps manager José Forte of Infraspeak explains how AI can multiply confident but inaccurate reporting when teams lack shared data definitions, permissions, and governance. Speaking with host Sajeel Qureshi, he shares practical ways to build a source of truth, earn adoption, scale RevOps, and protect time for strategic work.
key topics
José Forte’s recent RevOps mistake: approaching AI vertically instead of first setting shared rules, context, and measurement across teams.
Why AI’s confident-sounding answers can conceal incomplete data, inaccurate reports, and misunderstood relationships between objects.
How Infraspeak manages access to sensitive actions and distributes updated Claude plugins with common rules and context.
Why a source of truth requires data to converge, regular housekeeping, and automation when errors become recurring.
How enablement and global artifacts can guide teams toward shared performance indicators without forbidding individual AI experimentation.
Why a common unit of measurement matters when plans move from revenue goals to leads and MQLs across acquisition teams.
How junior analysts can use AI to build faster while still learning to validate outputs, spot patterns, and understand the funnel.
How RevOps earns influence through quick wins, representation at leadership tables, and alignment across company-wide operations.
How José documented more than 140 support tickets in a week to make the case for senior hires and reduce repetitive requests.
José’s career practices include consulting, contributing to HubSpot Community, setting time limits, and staying involved long enough to build influence.
Chapters
00:00 The AI governance mistake: solving for each vertical separately
02:28 How confident AI outputs exposed data-quality gaps
04:01 Permissions and shared Claude plugins as safeguards
05:39 Why fast-growing teams postpone structure and security
07:14 Building a source of truth and automating recurring errors
09:11 Enablement and global artifacts align performance numbers
11:15 One unit of measurement connects goals to acquisition
12:16 What AI changes for junior analysts
15:47 Earning trust and a voice at the leadership table
18:08 Eight years at Infraspeak through changing growth stages
21:02 Managing support load: the 140-ticket week
24:12 Winning adoption for global AI tools
25:12 Write tasks down, plan ahead, and stay curious
25:47 Consulting and HubSpot Community broaden perspective
Keywords
RevOps, AI governance, revenue operations, data quality, source of truth, reporting accuracy, Claude, HubSpot, Infraspeak, RevOps career
key topics
José Forte’s recent RevOps mistake: approaching AI vertically instead of first setting shared rules, context, and measurement across teams.
Why AI’s confident-sounding answers can conceal incomplete data, inaccurate reports, and misunderstood relationships between objects.
How Infraspeak manages access to sensitive actions and distributes updated Claude plugins with common rules and context.
Why a source of truth requires data to converge, regular housekeeping, and automation when errors become recurring.
How enablement and global artifacts can guide teams toward shared performance indicators without forbidding individual AI experimentation.
Why a common unit of measurement matters when plans move from revenue goals to leads and MQLs across acquisition teams.
How junior analysts can use AI to build faster while still learning to validate outputs, spot patterns, and understand the funnel.
How RevOps earns influence through quick wins, representation at leadership tables, and alignment across company-wide operations.
How José documented more than 140 support tickets in a week to make the case for senior hires and reduce repetitive requests.
José’s career practices include consulting, contributing to HubSpot Community, setting time limits, and staying involved long enough to build influence.
Chapters
00:00 The AI governance mistake: solving for each vertical separately
02:28 How confident AI outputs exposed data-quality gaps
04:01 Permissions and shared Claude plugins as safeguards
05:39 Why fast-growing teams postpone structure and security
07:14 Building a source of truth and automating recurring errors
09:11 Enablement and global artifacts align performance numbers
11:15 One unit of measurement connects goals to acquisition
12:16 What AI changes for junior analysts
15:47 Earning trust and a voice at the leadership table
18:08 Eight years at Infraspeak through changing growth stages
21:02 Managing support load: the 140-ticket week
24:12 Winning adoption for global AI tools
25:12 Write tasks down, plan ahead, and stay curious
25:47 Consulting and HubSpot Community broaden perspective
Keywords
RevOps, AI governance, revenue operations, data quality, source of truth, reporting accuracy, Claude, HubSpot, Infraspeak, RevOps career
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