Revenue Strategy · GTM Architecture
I rebuild broken revenue systems by changing what gets measured.
Operator across Goldman Sachs, AWS, Ford, and Autodesk. I build the measurement layer that ties go-to-market execution to revenue outcomes, and the seller-facing systems that deliver it in the flow of work.
Portland Metro Area, Oregon · Senior Manager at Autodesk
§ 01 · Positioning
Most revenue problems are measurement problems first.
- 3× win rate · 2.4× revenue
- On the field motion I redesigned at Ford.
- 60K+ sellers
- Global GTM platform I owned as PM at Amazon Web Services.
- 9 yrs
- Goldman Sachs · AWS · Ford · Autodesk.
For nearly a decade I have built the infrastructure underneath revenue teams: the routing, the instrumentation, the reporting, and the operating system — REOS — that lets distributed go-to-market organizations see what is working and act on it. I came up through product and platform roles at Goldman Sachs, AWS, Ford, and Autodesk, consistently owning the layer between the tools a revenue org runs and the decisions its leaders make.
My current focus is measurement under uncertainty. Agentic AI is entering go-to-market faster than anyone can evaluate it, and most teams measure it on adoption rather than revenue. At Autodesk I am running a phased controlled evaluation across a 16,000-seller org: human baseline first, a defined revenue metric up front, the agentic layer introduced as a treatment, and a real decision at the end. I am an AI-fluent operator, not an AI engineer.
§ 02 · Impact
Systems that moved the number.
Revenue lift from the Virtual Showroom rollout — and 3x faster deal cycles versus the national peer group
Ford
Win-rate improvement on the same program
Ford
Annual OPEX savings from a global GTM tech-stack consolidation across 3,000+ locations
Ford
Users on the AI Knowledge Graph I owned — LLMs auto-resolved ~85% of frontline queries
Ford
Sellers under the phased, controlled evaluation of agentic AI I designed and run
Autodesk
From kickoff to a live CRM and enablement platform for a global sales org
Autodesk
§ 03 · How I operate
Measurement and motion. One system, two views.
Enablement is shifting from reactive content support to a measured system that follows the seller across the full deal lifecycle. Meet sellers where they work — with the content they need, when the deal needs it — and instrument every step so leaders can correlate behavior with revenue.
Following one recurring example: Acme Corp · Enterprise Platform · $485K · competitor present.
Part A · The motion · seller-flow exhibit
Embedded in the seller's stackThe seller journey, embedded — not a portal sellers leave their work to visit.
Hover any stage to preview · click to lock the example open.
- 01EmailIndustry POV — Enterprise Platformssent
- 02LinkedInComment on Sarah's repostqueued
- 03CallTalk-track: 45-sec openernext
Illustrative · "Acme Engage" stands in for the sales-enablement layer embedded inside the sequencer at the SDR/BDR stage.
Illustrative · generic CRM stand-in for how the AE sees the right next move inside their opportunity view.
- ✓Technical deep dive
- ✓Security & procurement review
- Executive alignment callthis wk
- Mutual close plan signed+10 d
Illustrative · "Acme Rooms" stands in for the digital sales room that hosts the buyer-side of the deal.
Illustrative · generic CS stand-in for how onboarding, expansion, and renewal signals return to the CRM.
Examples are illustrative simulations using a fictional "Acme Corp" enterprise deal. Tooling shown is representative, not vendor-specific.
Part B · The measurement spine · REOS
Captured at every stage. Returned to the system. Correlated to revenue.
Part C · Diagnostic
Four enterprise GTM traps. What would you do?
Each question is a real choice senior operators get wrong. There's no scoring — answer four, then see how Ram actually moved on one of them.
What would Ram do? · Step 1 of 4
Diagnostic · no scoringThe trap
Activity is up. Adoption is up. Win rate is flat.
The field is being judged on activity and tool adoption. What do you change first?
Methodology, behavior cloning, and org moves all look defensible. None of them work until you change what the program is judged on.
§ 04 · Experience
Track record.
Where the work happened
- −30%
Internal GTM platform · 60,000+ technical sellers
- Owned roadmap and telemetry; sustained executive funding by tying platform usage to GTM productivity.
- AI/NLP search optimization cut global support ticket volume 30% and lifted engagement 25%.
- Embedded knowledge and analytics directly into seller workflows.
- 3×
Global GTM systems · 3,000+ locations
- Redefined a national field motion around a single revenue metric; 3× win rate, 2.4× revenue, 3× faster deal cycles vs. peer group.
- $4M+ annual OPEX savings from a global GTM tech-stack consolidation.
- Internal AI Knowledge Graph for 200K+ users; auto-resolved ~85% of frontline queries.
- 16K
REOS · operating model for 16,000 sellers
- Authored REOS — measurement spine connecting CRM telemetry, in-workflow nudging, and revenue outcomes.
- Phased, controlled evaluation of agentic AI against a human baseline across 16,000 sellers.
- Stood up CRM and revenue-enablement stack in under 7 weeks with a 9-person hybrid team.
- Nov 2025 – PresentAutodeskRemote
Senior Manager, Enablement Platforms
Authored REOS, a three-layer architecture connecting CRM telemetry, in-workflow nudging, and revenue measurement — adopted as the operating model for VP-level GS&O product reviews. Lead a 9-person hybrid team that stood up the CRM and revenue-enablement stack in under 7 weeks. Structured and run a multi-quarter, phased evaluation of agentic AI (an agentic workflow tool) across a 16,000-seller GTM org, sequencing A/B cohorts to isolate revenue contribution against a human baseline. Deployed an internal LLM automation suite that eliminated quarterly content audits and freed 120+ analyst hours per quarter.
- Jul 2023 – Dec 2025Ford Motor CompanyRemote
Manager, GTM Enablement & AI Strategy
Orchestrated a global GTM systems rationalization across 3,000+ locations, generating $4M+ in annual OPEX savings. Owned an internal AI Knowledge Graph serving 200,000+ users that auto-resolved ~85% of frontline queries and removed 1,000+ hours of monthly support overhead. Led the GTM rollout of the Virtual Showroom — 2.4x revenue, 3x win rates, and 3x faster deal cycles versus the national peer group. Engineered in-CRM workflow deflections that absorbed 66% of tier-1 requests, accelerated resolution by 90%, and lifted CSAT by 23 points in a single quarter.
- Jun 2021 – Jul 2023Amazon Web ServicesDallas, TX
Product Manager
Directed the product roadmap and telemetry strategy for an internal GTM platform serving 60,000+ technical sales users, sustaining executive funding by tying platform usage to GTM productivity. Led an AI/NLP search optimization that cut global support ticket volume by 30% while lifting platform engagement by 25%. Embedded knowledge resources and GTM analytics directly into sales workflows to remove context-switching from complex technical deals.
- Jul 2017 – Jun 2021Goldman SachsSalt Lake City, UT
Platform Operations Manager, Trading Systems
Ran mission-critical trading infrastructure across Linux/UNIX/Oracle environments at 99.9% uptime for global trading desks. Pioneered early technical enablement workflows — restructured L1/L2 support architectures and self-service documentation to cut issue resolution time by 40%.
Education · M.S., Management Information Systems, University of Texas at Arlington · B.E., Mechanical Engineering, Amrita Vishwa Vidyapeetham
§ 05 · Approach
Measure first. Automate what the measurement justifies.
- 01Instrument the funnel.
- 02Set the human baseline.
- 03Introduce the treatment to a comparable cohort.
- 04Measure the delta against revenue, not adoption.
- 05Decide: expand, adjust, or stop.
§ 06 · Field notes
Working in public.
Short notes on the same problem from different angles: structured content as infrastructure, the translation layer between behavior and pipeline, and the measurement model agentic AI actually needs.
- 2w · LinkedIn№ 01
Structured content is infrastructure. The evaluation model is the strategy.
The Salesforce acquisition of Contentful is a real infrastructure milestone for the agentic era. It's also only half the battle.
It validates a thesis my team has been building around: structured content is now a core GTM requirement, not a marketing asset. Engineering teams are rushing to make data modular and portable enough for AI agents to consume. That's a critical technical step, but from a commercial standpoint it's only table stakes.
The real challenge — and where most GTM organizations will stumble — is measuring what happens after the agent consumes the content. If an AI agent delivers information ten times faster but doesn't measurably shift attach rates, cycle times, or win rates, you didn't build a strategy. You built a faster pipe.
Structuring the data is a technical milestone. Tying it to verifiable commercial outcomes is the actual transformation.
#RevOps#GTM#Agentforce#Salesforce - 3mo · LinkedIn№ 02
The translation layer between seller behavior and pipeline movement is the seat that matters.
RevOps can see the pipeline. They can tell you what's stuck, what's aging, what's converting. What they usually can't tell you is why.
Enablement can coach the behavior. They can build the skills, train the methodology, develop the content. What they usually can't do is prove any of it changed a deal outcome.
Both functions are increasingly getting the same question from leadership: are our sellers doing the things that actually move deals, and can you prove it?
Neither side can answer that alone. The real leverage is in the translation layer between seller behavior and pipeline movement. Whoever builds that connection — whether it sits in RevOps, Enablement, or something new — has the most valuable seat in the GTM org.
#RevOps#SalesEnablement#GTMStrategy#RevenueEnablement - Method note · Ram Chennuru№ 03
Designing a controlled evaluation of agentic AI against a revenue metric.
Most GTM teams are deploying agentic AI faster than they can evaluate it, and almost all of them are measuring the wrong thing. Adoption tells you the feature shipped. It does not tell you it worked.
Start with the metric, not the tool. Establish the human baseline first against that metric, at the segment level, over a stable window. Skipping that is the most expensive mistake — any later improvement gets credited to the tool when it may belong to seasonality or a comp change.
Introduce the agentic layer as a treatment to a defined cohort that resembles a comparable cohort left on the baseline. Hold the rest constant so the delta can be assigned to the intervention. Name the confounders. Be honest about which is which.
Run it small and run it yourself. End on a decision: expand, adjust, or stop. A study that produces a dashboard instead of a decision has failed.
#AgenticAI#RevenueMeasurement
§ 07 · Contact
Get in touch.
Selectively in conversation about Director and Head of Revenue Operations, GTM Strategy, and Revenue / Sales Enablement roles. Always happy to compare notes with operators working the same problems.