Illustrative recreation of the RGM decision-support screen: a draft recommendation across price, promotion and mix, an Approve / Edit / Reject gate, and a why-this-recommendation panel
RGM·24Case file · 2024 Stevie Award

The architecture that removed three objections at once.

PMF discovery / AI architecture / Governance / Go-to-market

RoleProduct Manager & GTM Lead
OrgTCS, AI SaaS & Enterprise Solutions
Timeline2024 to 2025
Result1st enterprise deployment + 2 follow-on deals

The problem

Buyers evaluating our agentic pricing and revenue-growth-management product kept raising three objections that stalled deals: data security and residency, vendor lock-in, and “we already invested in a model”.

Discovery

I ran jobs-to-be-done interviews with Revenue Management Directors and Commercial Finance leads at 8 target accounts, and a competitive analysis of 12 incumbent tools. The consistent finding was that buyers wanted decision support. That insight drove three roadmap pivots: reporting to recommendation engine, generic analytics to CPG-vertical-specific models, and desktop-first to field-accessible mobile views.

The architecture decision

Instead of shipping a proprietary model, I argued for and designed a composable layer that orchestrates the customer's own LLM via RAG over their own data. The product's value sat in the orchestration and intelligence layer. That one design choice removed the data-residency, lock-in, and prior-investment objections.

Diagram: a composable orchestration layer (retrieval, recommendation logic, explainability, human approval gate) running on the customer's own LLM and data, with three removed objections annotated
The product runs on the customer's own model and data. Illustrative recreation.
The PMF breakthrough was realizing that our buyers wanted fewer decisions to make. Every product choice followed from that.

Governance

The agent could technically act on price, promotion, and mix, but a wrong autonomous action could breach a contract term or trigger channel conflict before anyone noticed. I designed it to monitor and recommend. Every consequential recommendation goes through a human approval gate, and the explainability was built for that gate, so an approver could interrogate a recommendation in seconds. Keeping a person in the loop was a design choice for this market.

Close-up of a recommendation card: the agent drafts, a person approves, edits or rejects
The agent drafts; a person decides, and every decision is logged with its reasoning. Illustrative recreation, sample data.

I defined success on four metrics: recommendation acceptance rate (the clearest signal of trustworthy, well-targeted feedback; high volume with low acceptance means the recommendations are noise), decision-time reduction, adoption tracked separately across rep, manager, and trade-promotion-manager roles (an aggregate number would hide a failure in any one), and margin or revenue attributable to accepted recommendations, which is the number an enterprise buyer renews on.

Scorecard of four success metrics with adoption split by rep, manager and trade promotion manager, no values shown
The four success metrics, with adoption tracked per role. Illustrative recreation; no values shown.

Go-to-market

Ideal customer: large CPG companies, $1B+ revenue, manual trade-spend processes, a Revenue Management Director with budget authority. I built competitive battlecards, ROI calculators, and capability demo scripts, and led senior-executive discovery sessions and executive presentations.

Result

1st
enterprise deployment
+2
follow-on deals
Stevie 2024
AI Product of the Year

Building AI into pricing or trade decisions? I have taken this problem from discovery to a paying enterprise customer.

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