AI-AUGMENTED PRE-SALES: DESIGNING AND EVALUATING AN AGENTIC RFP WORKFLOW IN HEALTHCARE AND LIFE SCIENCES
| dc.contributor.author | Budnichenko, Svitlana | |
| dc.date.accessioned | 2026-06-08T09:11:28Z | |
| dc.date.available | 2026-06-08T09:11:28Z | |
| dc.date.issued | 2026-05 | |
| dc.description.abstract | This study investigates the design and evaluation of an AI-augmented Request for Proposal (RFP) response workflow within the Healthcare and Life Sciences (HC&LS) pre-sales practice of a global IT services firm. Using a Design Science Research methodology combined with a quasi-experimental before-and-after evaluation, the study develops a Minimum Viable Prototype (MVP) comprising three coordinated AI layers — Research Agent's Cluster, Prototype Brief Writer, and Proposal Narrative Writer agent — embedded within a multi-agent full architecture and orchestrated via the n8n workflow automation platform. The workflow incorporates Retrieval-Augmented Generation (RAG) using a Supabase pgvector knowledge base constructed from 192 classified HC&LS proposals from the organization's knowledge base, of which 32 are fully enriched with ten structured fields and indexed via cosine similarity search. The MVP reduced the active effort required to produce a first reviewable draft by approximately 80–85%, compressing the elapsed cycle from a typical 5–7 day process to same-day draft availability, while achieving a proposal quality score of 25/30 (83%) on a six-dimension evaluation rubric. Findings show that AI creates the greatest pre-sales value when embedded in a redesigned end-to-end workflow as an augmentation layer, not when applied as isolated task automation. Six design principles are derived: augmentation over automation, RAG-based knowledge grounding, early-stage AI prototyping, modular agent architecture, human-in-the-loop validation, and incremental adoption. The study contributes both a functional prototype and a generalizable framework for AI-enabled workflow redesign in pre-sales contexts. | |
| dc.identifier.citation | Budnichenko, Svitlana. (2026). AI-AUGMENTED PRE-SALES: DESIGNING AND EVALUATING AN AGENTIC RFP WORKFLOW IN HEALTHCARE AND LIFE SCIENCES. Kyiv: American University Kyiv. URI: https://er.auk.edu.ua/handle/234907866/188 | en |
| dc.identifier.uri | https://er.auk.edu.ua/handle/234907866/188 | |
| dc.language.iso | en_US | |
| dc.publisher | Manuscript | |
| dc.subject | AI-augmented workflow | |
| dc.subject | RFP response | |
| dc.subject | pre-sales | |
| dc.subject | Healthcare and Life Sciences | |
| dc.subject | agentic AI | |
| dc.subject | Retrieval-Augmented Generation | |
| dc.subject | Design Science Research | |
| dc.subject | human-AI collaboration | |
| dc.subject | n8n orchestration | |
| dc.subject | proposal quality | |
| dc.title | AI-AUGMENTED PRE-SALES: DESIGNING AND EVALUATING AN AGENTIC RFP WORKFLOW IN HEALTHCARE AND LIFE SCIENCES | |
| dc.title.alternative | AI-ОРІЄНТОВАНИЙ ПРЕ-СЕЙЛС: ПРОЄКТУВАННЯ ТА ОЦІНЮВАННЯ АГЕНТНОГО РОБОЧОГО ПРОЦЕСУ ОБРОБКИ RFP У СФЕРІ ОХОРОНИ ЗДОРОВ'Я ТА НАУК ПРО ЖИТТЯ | |
| dc.type | Thesis |