Market-entry problem
The company wanted growth, but did not know where to start.
The first challenge was not automation. It was market-entry clarity. The company needed to understand how to approach France with a lean commercial setup, limited resources and enough structure to later scale the same model across Europe.
Operating idea: I designed and implemented the commercial department and the AI engine myself, so the technology supported real roles, real workflows and real commercial decisions.
End-to-end ownership: this was not only project management. I designed the department, built the agents, structured the orchestrator, deployed the system and made the operating model usable by the commercial team.
What changed
From market uncertainty to a lean commercial operating system.
Compact proof points showing how the commercial department, roles, AI agents and expansion logic were structured.
3-partdepartment model: sales director, operator and AI engine
Agenticcommercial engine built around specialized Copilot agents
Francefirst market-entry logic before broader European scale
Europesame lean operating model reusable for expansion
Lean department design
The department was designed and implemented around people plus agents.
The design avoided a heavy sales organization. I built a lean structure where a sales director owns commercial judgment, an operator runs the execution workflow and the AI commercial engine organizes research, CRM data, RFQs, documents and priorities.
Prospecting agentIdentifies market targets, segments and entry opportunities.
CRM agentStructures account data, interaction history and next actions.
RFQ agentSupports request analysis, response preparation and requirements logic.
Document agentGenerates commercial documents, briefs and reusable materials.
Priority agentRanks opportunities and allocates work to the operator.
Control agentHelps the sales director see pipeline, actions and execution status.
Commercial engine workflow
The system I built organized the work of the operator and the sales director.
The AI engine was not positioned as a replacement for sales judgment. I implemented it as an execution layer that reduces manual friction, organizes data and helps a lean team act with more discipline.
Market research
Collect companies, sectors, signals and possible entry points for France.
Target selection
Prioritize accounts by fit, relevance, timing, data quality and commercial potential.
CRM structuring
Organize accounts, contacts, interaction history, missing data and next actions.
RFQ support
Help analyze requests, assemble evidence, prepare responses and structure requirements.
Document generation
Create commercial briefs, outreach material, internal notes and client-facing documents.
Priority allocation
Organize the operator’s work and surface decisions for the sales director.
What I built from A to Z
Designed and implemented the department, the agents and the orchestrator.
I led the project from end to end. I structured the operating model around the minimum viable commercial team: one sales director, one operator and one AI commercial engine. The goal was to give the company a way to start in France without waiting for perfect data, a large budget or a full sales organization.
I defined how the roles interact, what each agent does, how the orchestrator organizes the workflow, how the operator uses the outputs, how priorities are allocated and how the sales director keeps decision control. Then I developed and deployed the first working version using the company’s Microsoft stack: Copilot Pro and Copilot Studio.
Execution focus: create a working lean commercial department that could operate France first, then reuse the same system, roles and technologies for European expansion.
What this proves
Commercial execution can be designed and implemented as an operating system.
This proof matters because it shows the ability to personally design, build, deploy and orchestrate a department, roles, responsibilities, workflows and AI agents as one executable system.
Transferable pattern: relevant when a company wants to enter a market with limited resources, incomplete data and no clear commercial starting point.
Related system
Explore the architecture behind this proof.
This proof is directly connected to the AI Execution Systems page because the commercial engine follows the same workspace logic: agents, workflows, prioritization, document generation and operating control.
System architecture
AI Execution Systems Built
Use this case as the commercial-engine proof behind the system architecture: agents for prospecting, CRM, RFQs, document generation and priority allocation, personally built and orchestrated into a lean department operating model.