GlobalProposals and quotes
Organize requirements, retrieve approved information, prepare drafts, and review them before sending.
Commercial AI case studies
Commercial AI case studies for proposals, CRM, sales knowledge, and opportunities.
Case studies of companies applying artificial intelligence to proposals, CRM, sales knowledge, and opportunity management with approved sources, clear boundaries, and human review.
E2M contributes assessment, functional design, requirements, and decision criteria; it can direct pilots and govern adoption. Technical delivery belongs to client IT, existing vendors, or specialist partners. The cases distinguish advisory work, documented client use, and possible future phases.
Understand the problemUnderstand the methodReview the evidenceEvaluate the next step
A stable taxonomy
Four case types, organized by commercial challenge.
The collection will grow by adding companies within these four categories. We do not organize cases by industry; we show the process improved and the judgment used to improve it.
CRM and sales follow-up
Summaries, next steps, ownership, and commercial continuity without relying on individual habits.
Sales knowledge
Product, service, process, and prior-experience information made available with context and references.
Customers and opportunities
Account preparation, missing-information detection, and prioritization using defined commercial rules.
Featured cases
Advisory cases grounded in real commercial workflows.
Four companies show how to structure specialized information, maintain follow-up continuity, and preserve professional decision-making.
Proposals and quotes
01
A1 Security Supply: complex requests converted into reviewable commercial files.
Product references, manufacturers, project requirements, and open questions are organized before the specialist validates the response. AI prepares; the accountable professional decides.
ChallengeUneven information distributed across the catalog, forms, and team knowledge.
InterventionAssessment and functional design of an assisted workflow for reviewable proposals.
OutcomeGreater consistency, traceability, and human control before responding.
CRM and sales follow-up
02
Moldblade: industrial opportunities converted into context, owners, and next actions.
Commercial activity, technical projects, and the international network are organized around a shared CRM model. AI prepares and proposes; Moldblade validates every decision and commitment.
OpportunityTurn scale and distributed knowledge into shared commercial continuity.
InterventionAssessment, functional architecture, governance, and a roadmap for IT or a partner.
OutcomeA defined system for recording, measuring, and automating in phases without losing professional control.
Sales knowledge
03
AIM Electronic: engineering knowledge prepared for a new agentic phase.
Machinery, consumables, manufacturer documentation, and process experience are organized to prepare technical inquiries with context. AIM retains validation; E2M defines the functional architecture and criteria for a second agentic coordination phase.
OpportunityMake existing technical knowledge more accessible and reusable.
InterventionFunctional design of the file, sources, data, and review criteria.
Next phaseDefinition of agentic functional architecture, permissions, and professional review.
Customers and opportunities
04
Segurmer: portfolio reviews prepared with context and professional validation.
Authorized information, relevant dates, pending documentation, and next steps are gathered before the conversation. AI organizes; the professional interprets, advises, and decides.
OpportunityPrepare complete reviews without manually rebuilding the entire context.
InterventionAnalysis and functional design of the review file, sources, permissions, and review.
OutcomeGreater continuity, traceability, and professional control before acting.
Editorial standards
What an E2M case study must demonstrate.
We publish enough context to understand the process and enough boundaries to avoid presenting AI as an uncontrolled promise.
Direct experience
What E2M assessed, recommended, or designed, and what the client decided.
Verifiable evidence
Observable outcomes without inventing savings or percentages.
Human review
What remained dependent on an accountable professional.
Clear boundaries
What belongs to the technical implementer and which decisions remain with the client.
Next step
Do you have a similar process?
Tell us where information is lost, what repeats, and who must validate the result. We will assess whether a Commercial AI Diagnostic or Pilot is the appropriate next step.