Sales Operations
& Business Analyst.

CRM layouts had accumulated fields that were unused, rarely used or no longer aligned with current commercial processes.

Reviewed 585 fields across five Salesforce objects, identified low-use fields through historical usage analysis and coordinated multi-region validation to define simpler future-state layouts.

I recommended removing or consolidating low-use fields, separating reporting needs from the user layout and translating the findings into future-state proposals for stakeholder validation.

Sales users needed faster access to trusted operational knowledge while avoiding unsupported or hallucinated answers.

I began experimenting with GPT tools in 2022. Since then, I’ve applied generative AI to knowledge management, documentation and user enablement, including an internal assistant designed to make trusted information easier to find.

Designed a tightly scoped AI assistant within an approved knowledge boundary, with explicit response rules and a refusal path for unsupported assumptions.

I defined the use case, knowledge boundaries and response rules, then refined the experience around user needs.

Inconsistent CRM processes and unclear ownership made commercial reporting harder to trust.

Combined reporting analysis, CRM data review and stakeholder feedback to identify operational gaps behind the numbers.

I reviewed CRM data, supported reporting and worked with stakeholders to trace operational gaps back to their causes.

Teams need a proportionate way to move from a business problem to a measurable operational outcome without losing traceability.

I developed a reusable operating model for evidence-led analysis across CRM and commercial operations work.

The playbook is organised around four modes — Understand, Decide, Deliver and Learn — with the depth of analysis sized as Lite, Standard or Full.

I defined the traceability chain from Problem to Objective, Requirement, Acceptance, Test and Measure, so decisions remain connected to evidence and outcomes.