Enterprise growth intelligence
Partner Intelligence Engine
A decision workflow that translates partner, account, and market signals into prioritized enterprise growth moves.
Challenge
Partnership teams had rich but fragmented data across accounts, campaigns, sales notes, and market signals. The problem was not dashboard scarcity; it was the absence of a shared intelligence layer that could explain where to focus, why it mattered, and what should happen next.
Approach
Designed a structured intelligence model around partner fit, account overlap, buying triggers, campaign readiness, and execution confidence. The workflow turns fragmented inputs into ranked plays, partner briefs, account talking points, and executive-ready decision memos.
Impact
Operationalized a repeatable growth review that helps leadership evaluate partner opportunities with better evidence, faster prioritization, and stronger alignment across sales and ecosystem teams.
Partner opportunities ranked by fit, evidence, timing, and execution path.
Raw ecosystem inputs translated into account narratives and recommended plays.
A repeatable leadership rhythm for opportunity quality and next-best action.
System diagram
Partner Intelligence Engine as an operating flow.
Executive memo
The boardroom version: decision, operating model, impact.
Where should leadership focus partner investment when activity volume is high but opportunity quality is uneven?
A signal-to-decision workflow that scores partner fit, account overlap, timing, execution confidence, and next-best action.
Creates a repeatable weekly review rhythm for prioritizing partner motions and reducing wasted field effort.
A ranked set of partner plays with evidence, owner, commercial rationale, and next action.
Risks reduced
Business value
The commercial reason this work matters.
Buyer problem
What the market needed to understand.
Ecosystem leaders cannot see which partners deserve attention now.
Revenue teams have partner overlap data but no shared decision framework.
Executives need clearer evidence before committing resources to joint motions.
System Thesis
Enterprise partnership work fails when teams cannot separate strategic signal from activity noise. The engine was shaped around a simple rule: every recommendation needs a traceable reason, a commercial implication, and a next action.
The result is a reusable decision layer that gives teams a common language for partner fit, opportunity timing, and sales motion readiness.
Design Moves
Mapped the decision flow from executive planning to field execution, then defined the signals needed at each step.
Designed content and data structures that support ranked opportunities, partner briefs, account-level talking points, and leadership summaries without requiring a database in v1.
What It Changed
The work turned partner planning from an intuition-heavy discussion into a more disciplined intelligence review. Teams could see why an opportunity mattered, what evidence supported it, and what motion should happen next.
My role
The direct contribution.
Artifacts produced
The work products that made the strategy usable.
Operating model
How the system moves from signal to action.
Collect signals from accounts, partners, campaigns, and market context.
Rank opportunities by commercial relevance, execution readiness, and timing.
Package the recommendation into a concise brief with evidence and next action.
Review weekly with revenue, partner, and leadership stakeholders.