Doctoral Research · UCAM — Universidad Católica San Antonio de Murcia

S4IL — Research Prototype

A practitioner-led proof of concept for the AI-Augmented Organizational Memory framework.

S4IL (System for Innovation and Institutional Learning) is an AI-augmented Innovation Management System developed by the researcher as part of the doctoral thesis submitted to UCAM — Universidad Católica San Antonio de Murcia. It serves as a Phase 4 technical instantiation of the AI-OM framework, demonstrating the feasibility of core architectural components identified in the literature review.

Research Context

Role in the thesis. S4IL is positioned as a Design Science Research (DSR) Level 1 artifact (Gregor & Hevner, 2013) — a situated technical instantiation that complements the Level 2 nascent design theory embodied in the AI-OM framework.

Methodological framing. Phase 4 of the four-phase research design employs technical action research (Wieringa, 2014) and reflective practice (Schön, 1983). The researcher acts simultaneously as developer and academic investigator.

Purpose in interviews. During expert validation interviews (Phase 3), S4IL is demonstrated to provide participants with a tangible reference point for evaluating the AI-OM framework. The demonstration is standardised across all interviews to ensure consistency.

Core Components Demonstrated

1. Retrieval-Augmented Generation (RAG)

Semantic search over organisational knowledge repositories, enabling retrieval of contextually relevant documents and insights rather than keyword-only matching. Operationalises the knowledge retrieval mechanism identified in Walsh and Ungson (1991) and extended through AI capabilities (Lata, 2025).

2. Multi-Agent Orchestration

Coordination of specialised AI agents across different knowledge domains (market signals, project history, organisational memory). Addresses the cross-functional knowledge synthesis gap identified in the literature (Barros, 2025; Ampel and Ullman, 2025).

3. Structural Memory with Interpretive Context

Knowledge retention that preserves not just data but the reasoning and context behind decisions. Implements the organisational memory “retention bins” concept (Walsh and Ungson, 1991) augmented with computational memory layers.

4. ROI² Data Infrastructure

Measurement architecture supporting the dual constructs of Innovation Readiness and Innovation Growth. Demonstrates the feasibility of operationalising the ROI² model proposed in the conceptual framework.

The S4IL Investment Cycle

S4IL organises innovation management into four investment phases, each corresponding to a distinct organisational question:

Phase	Question	Key Functions
1. Where to Play	Which market signals should inform our portfolio?	Signal scanning, foresight assessment, opportunity mapping
2. How to Win	Which ideas merit structured investment?	Challenge-based idea collection, evaluation, strategic alignment
3. How to Deliver	How do we manage innovation as a portfolio discipline?	Project management, milestone tracking, ROI² measurement, structured exit (Purgatory)
4. How to Get Adopted	How do we ensure organisational uptake?	Adoption campaigns, stakeholder engagement, behavioural nudges

An AI-OM Orchestrator layer operates across all four phases, providing semantic search, knowledge extraction, and memory proposals. The Organisational Memory module retains and surfaces knowledge from all phases, including lessons from projects that were discontinued.

What Participants Will See

During your interview, the researcher will walk you through a standardised demonstration covering:

Signal scanning and foresight — How S4IL ingests and surfaces external market signals.
Knowledge retrieval — How the RAG system retrieves contextually relevant organisational knowledge.
Project lifecycle and Purgatory — How knowledge is preserved when projects are discontinued.
Memory Map — How organisational knowledge connections are visualised.
ROI² dashboard — How Innovation Readiness and Innovation Growth are tracked.

The demonstration lasts approximately 15–20 minutes and is followed by structured evaluation questions.

Important. Your evaluation of the AI-OM framework is independent of S4IL. The framework is a theoretical contribution that stands on its own merit. S4IL demonstrates that the framework’s core mechanisms are technically feasible, but your expert assessment focuses on the framework’s relevance, coherence, completeness, and necessity for Saudi RDI organisations.

Transparency

Researcher positionality. Yann Rousselot-Pailley is both the doctoral researcher and the developer of S4IL. This dual role is acknowledged in the thesis methodology (Chapter 3, §3.9.4) and managed through:

Structural separation of conceptual validation (Phase 3, based on your independent expert judgement) and technical demonstration (Phase 4, based on artifact functioning).
A reflexive journal maintained throughout the research process.
Peer debriefing with the academic supervisor, Dr. Vimala Sanjeevkumar.
Open-ended questions designed to elicit critical feedback, not confirmation.

Status note. S4IL is a research prototype, not a commercial product. It may be developed into one in the future, but no such decision has been made and no commercial offering currently exists. Your participation is an evaluation of a research artifact only, and your responses will not be used for marketing or sales purposes.