Lonny Sternberg has spent 13+ years building scalable revenue engines, from zero-to-one startups and a NASDAQ listing to VC- and PE-backed companies at $350M+ scale, and now leads RevOps at Veriforce. He joins Janis to lay out his operating thesis: in the age of AI, clarity beats speed, and clarity comes from a loop of governance, context, systems and rhythm. Together they dig into shared definitions, context capture that doesn't depend on reps updating the CRM, and why change management still decides whether any of it sticks. Lonny closes with his unpopular opinion on why RevOps must be in the room before the decision, not after the cleanup.

We cover:

• Why clarity, not focus or time, is the new bottleneck for RevOps
• The loop system: governance, context, systems and rhythm
• Governance as shared definitions, not rules: ARR, ACV, SQL-to-SQO conversion and who owns which number
• Why agents and MCP layers either accelerate a thoughtful process or accelerate a disaster
• When everyone becomes a data analyst with Claude and brings their own metrics to the meeting
• Building the context layer: CRM, first- and third-party data and governance combined
• Automating context capture, because reps shouldn't have to update the CRM
• Surfacing the right context for the right person: call prep, live calls and objection handling
• Meeting ICs where they already work: Slack, Teams, Zoom and email
• Why MEDDIC-style methodologies fail when they feel like extra data entry for reps
• Change management and enablement that empowers instead of disciplines
• Rhythm and cadence: backing into the end state instead of ticking weekly checkboxes
• Unpopular opinion: RevOps belongs in the decision early, not in the cleanup afterwards

Lonny Sternberg: https://www.linkedin.com/in/lonnysternberg/

Veriforce: https://veriforce.com/

Janis Zech: https://www.linkedin.com/in/janiszech/

Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/

WeFlow: https://www.weflow.ai/

Community: https://www.weflow.ai/community

RevOps Letter: https://www.weflow.ai/revops-letter

Chapters:

(00:00:00) Intro
(00:01:41) Lonny Sternberg's path through RevOps
(00:04:54) Clarity and the loop system
(00:06:01) Governance, context, systems, rhythm
(00:09:39) Shared definitions and metrics
(00:11:30) Agents accelerate clarity or chaos
(00:14:48) Building the context layer
(00:18:00) Surfacing context by role
(00:21:38) Meeting reps where they work
(00:23:10) The MEDDIC data-entry trap
(00:24:30) Change management and enablement
(00:26:03) Rhythm and cadence
(00:29:56) Unpopular opinion: RevOps seat at the table
(00:31:53) Is the function shifting?

Ravi Parimi has spent 20+ years in revenue operations and GTM strategy, from AWS, VMware and Cisco to Zendesk and ServiceTitan, plus a stint as executive in residence advising a16z portfolio companies. He joins Janis and Philipp to explain why the agent-first mindset breaks in production and why workflows, not agents, are the unit of design. Together they dig into human-agent handoffs, permission scopes and guardrails, and why RevOps has to run agents like a product after launch. Ravi closes with the unpopular opinion that most teams badly underestimate the operating model change ahead.

We cover:

- Why an agent-first mindset works for experimenting but fails in production

- Workflow as the unit of design: inputs, outputs, people and systems

- Lead routing as the test case: account disputes, strategic accounts and other edge cases

- Why deterministic rules matter if you want agents without hallucinations

- Treating agents as digital workers in the talent mix: what agents do best vs. humans

- Autonomy with a scope, like a driver's license

- How agent speed changes your org design and the humans in the loop

- Running agents like a product: roadmap, workflow v1/v2/v3, benchmarks and release cycles

- Agent ops: owning speed, quality, cost and latency after launch

- Why agents need sandboxes, testing and security layers before production

- Why modern revenue organizations will run as systems of agents and humans

- Why solution and field engineering leaders could be tomorrow's CROs

- Why judgment and taste become the premium skill when agents give the answers

Ravi Parimi: https://www.linkedin.com/in/rparimi/

Janis Zech: https://www.linkedin.com/in/janiszech/

Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/

WeFlow: https://www.weflow.ai/

Community: https://www.weflow.ai/community

RevOps Letter: https://www.weflow.ai/revops-letter

Chapters:

(00:03) Ravi Parimi's path through RevOps

(02:59) What's wrong with agent-first

(06:54) Jobs to be done and lead routing

(09:56) Why the workflow mindset is missing

(13:54) Agents as digital workers

(15:44) Autonomy, scope and permissions

(17:13) Rethinking workflows and org design

(18:58) Running agents like a product

(21:11) Testing, sandboxes and guardrails

(24:13) The future of RevOps and the CRO

(28:48) Field engineers as future CROs

(32:01) Judgment, taste and revenue architecture

(34:26) Unpopular opinion: operating model

Janis Zech, CEO and founder of WeFlow, joins Kelly Lewis and Tiffany Jones on The Revenue Insiders to explain why AI in go-to-market fails without a clean data foundation. He walks through automated activity capture, AI meeting prep and coaching scorecards on MEDDIC/MEDDPICC, and why accurate forecasts depend on deal intelligence. The hosts push back on critical thinking, business context and vibe coding. Janis leaves listeners with one question for annual planning: are you AI-first or problem-first?

We cover:

- The three problems behind weak AI results in go-to-market: siloed data, non-selling time and unpredictable quota attainment

- Why manual data entry doesn't work and what to automate instead

- Design principles for data capture: integrate the signal systems, map everything back to the CRM, stay headless

- Why activity has to be mapped to opportunities, not just accounts

- AI meeting prep that cuts 15 minutes to 5 and lives in the calendar, Slack or email

- Coaching scorecards that auto-populate MEDDIC/MEDDPICC from every email, call and deal room

- Automation vs. human in the loop: why reps still need to understand the framework

- Why enablement and critical thinking matter more once AI does the data entry

- From system of record to system of intelligence to engagement orchestration

- Why AI forecasting failed without data pipelines, and how to give CROs multiple forecast numbers

- Feeding AI your business context: personas, industries, buying cycles

- Mining transcripts and emails for the real close-lost reasons

- Why vibe-coding your own GTM stack is slower and costlier than it looks

Original episode: https://www.therevenueinsiders.com/podcast/episode/39359e05/ai-first-or-problem-first-the-revops-question-for-2027

Kelly Lewis: https://www.linkedin.com/in/kellylewis418

Tiffany Jones: https://www.linkedin.com/in/tiffanyelizabethjones

The Revenue Insiders: https://www.therevenueinsiders.com

Janis Zech: https://www.linkedin.com/in/janiszech

Philipp Stelzer: https://www.linkedin.com/in/philippstelzer

WeFlow: https://www.weflow.ai

Community: https://www.weflow.ai/community

RevOps Letter: https://www.weflow.ai/revops-letter

Chapters:

(00:01) Janis's path to WeFlow

(03:43) Three problems in go-to-market

(05:48) What's different from Gong and Clari

(07:08) Design principles for data capture

(12:55) AI coaching on MEDDPICC

(15:40) Automation vs. human in the loop

(19:45) Enablement in an AI world

(23:34) System of intelligence

(24:52) Forecasting needs deal intelligence

(26:48) Feeding AI your business context

(30:58) Mining transcripts for real answers

(32:14) Vibe coding vs. long-term partnership

(35:07) AI-first or problem-first?