Illustrative application case study
A research-based example of how GAP would structure an AI growth programme. This is a proposed delivery scenario, rather than a completed GAP client engagement.
Grow a service business without allowing administration to absorb the team
An illustrative business services company has a healthy opportunity pipeline but slow proposal preparation, fragmented delivery knowledge and inconsistent handovers. Experienced people spend time finding information and correcting drafts instead of serving clients. Unbilled work and delayed invoicing put pressure on cash.
Connect winning work with profitable delivery
GAP would map enquiry-to-proposal and project-to-cash workflows, linking CRM, approved knowledge, project records and finance. Establish response times, review effort, proposal quality, utilisation, project margin and unbilled work before choosing a pilot.
A focused proposal and handover pilot
An assistant could retrieve approved credentials and relevant project examples, identify requirements and missing information in a request for proposal, and prepare a source-linked draft and handover checklist. Pricing, staffing, contractual commitments and final submission remain subject to named commercial approval.
The pilot would use an approved content library and role-based permissions. Test representative requests, conflicting sources and missing information; require references to supporting material and escalation where evidence is insufficient. Technical specialists configure retrieval, access and integrations.
GAP’s role in delivery
GAP would lead opportunity selection, the investment case, process design, commercial controls, implementation coordination, user adoption and benefits measurement. Deliverables include the workflow specification, working pilot, evaluation report, approval process and operating guide, followed by a recommendation on rollout.
Prove the benefit
Measure total proposal effort including review and rework, time to respond, factual and scope errors, conversion, delivery margin and cash collection. Faster drafting creates capacity; additional revenue requires suitable opportunities and delivery capability. Track attributable incremental gross profit separately from capacity value and include ongoing tool and support costs.
A second improvement stage
Once the first workflow is proven, reporting support and project-to-cash exceptions can be assessed. AI may help identify missing information and prepare commentary, while billing, financial totals and approval decisions remain tied to validated records and controlled calculations.
Published industry evidence
Microsoft’s LTM example describes an assistant retrieving approved sales material and drafting proposal responses. Its C.H. Robinson story describes AI handling emailed customer requests and accelerating quotation responses. These independently published projects demonstrate relevant applications; they are not GAP assignments.
Microsoft: LTM sales enablement · Microsoft: C.H. Robinson customer requests