Paper Number
ECIS2026-2201
Paper Type
SP
Abstract
Small and medium-sized enterprises (SMEs) face growing pressure to respond quickly and accurately to Requests for Quotation (RFQs) while keeping their pricing competitive. This action design research project develops a decision support system enhanced with AI for RFQ pricing at HS, a Greek medical manufacturer. The system connects enterprise resource planning (ERP) data with AI recommendations using a graduated autonomy framework. It starts with human oversight and can evolve toward semi-automation once the company trusts its reliability. Following Action Design Research methodology, we work through repeated cycles of building, testing, and evaluating with company stakeholders. The research tackles practical needs like reducing response time, improving pricing consistency, and reducing dependence on individual experts. It also addresses theoretical gaps about AI-enhanced decision support in resource-limited environments. We expect to contribute design principles for implementing graduated autonomy in SME contexts and practical insights into how humans and AI work together effectively.
Recommended Citation
Chatziioannou, Christos and Ogunseye, Shawn, "Designing An AI-Augmented Decision Support System For RFQ Pricing In Smes: An Action Design Research Study" (2026). ECIS 2026 Proceedings. 9.
https://aisel.aisnet.org/ecis2026/bus_analytics/bus_analytics/9
Designing An AI-Augmented Decision Support System For RFQ Pricing In Smes: An Action Design Research Study
Small and medium-sized enterprises (SMEs) face growing pressure to respond quickly and accurately to Requests for Quotation (RFQs) while keeping their pricing competitive. This action design research project develops a decision support system enhanced with AI for RFQ pricing at HS, a Greek medical manufacturer. The system connects enterprise resource planning (ERP) data with AI recommendations using a graduated autonomy framework. It starts with human oversight and can evolve toward semi-automation once the company trusts its reliability. Following Action Design Research methodology, we work through repeated cycles of building, testing, and evaluating with company stakeholders. The research tackles practical needs like reducing response time, improving pricing consistency, and reducing dependence on individual experts. It also addresses theoretical gaps about AI-enhanced decision support in resource-limited environments. We expect to contribute design principles for implementing graduated autonomy in SME contexts and practical insights into how humans and AI work together effectively.
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