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About the Author

About the Author

ORCID

Mark Julius Banasihan designs decision governance systems for high-stakes, regulated environments where speed, accountability, and defensibility must coexist.

His work addresses a persistent failure mode in complex organizations: decisions that are technically sound yet operationally fragile because their rationale, evidence, and risk posture cannot be reconstructed under scrutiny. This failure becomes more pronounced as artificial intelligence accelerates analysis and execution without comparable discipline in decision ownership and documentation.

Mark approaches AI governance from a decision-first perspective. Rather than treating AI as an autonomous actor or a productivity layer, he designs bounded analytical systems that strengthen human judgment, preserve explicit accountability, and produce audit-ready decision artifacts at the moment choices are made. His frameworks prioritize clarity over automation, restraint over novelty, and structure as a means of protecting expert judgment rather than replacing it.

RGDS (Regulated Gate Decision Support) emerged from this perspective. It is presented as a reference model for organizations operating in environments shaped by regulatory review, inspection risk, and irreversible downstream consequence. The work integrates decision science, systems thinking, and organizational design to address a difficult question with practical implications: how to accelerate decisions without weakening trust in how those decisions were reached.

Mark’s contribution is not centered on tools, models, or platforms. It lies in making decision logic explicit, evidence-linked, and reviewable so organizations can move faster precisely because they are more disciplined. His work is intended for practitioners, regulators, and leaders who recognize that governance maturity is inseparable from execution, and that credibility is built through structure rather than assertion.

RGDS is shared as an independent research effort. It is meant to be tested, challenged, and adapted in real operational settings. Its value is measured by whether it helps organizations reduce irreversible mistakes, defend decisions under scrutiny, and preserve human authority in an AI-accelerated environment.

Research and Practice Orientation

Mark’s approach to AI governance draws from decision science, systems theory, and organizational psychology rather than model optimization alone. His work emphasizes:

  • Explicit decision ownership and scope
  • Evidence classification and traceability
  • Bounded use of AI with enforced abstention
  • Governance that remains valid even if AI is removed

The objective is consistency under pressure, not sophistication for its own sake.

Core Areas of Focus

Decision Architecture and Non-Agentic AI Governance

Designing decision-first frameworks for regulated, phase-gated workflows where AI provides analytical support while human accountability remains singular and explicit.

Applied Research Translation

Converting published research into bounded, falsifiable decision inputs with clear constraints, failure modes, and oversight points.

AI-Assisted Workflow Design

Reducing friction in evidence synthesis, review, and alignment without forcing adoption patterns that conflict with how expert teams actually work.

Executive Decision Communication

Structuring complex insights so leaders can act with clarity, understanding what was learned, what decision is required, and what risk exists if action is deferred.

Working Philosophy

  • People define correctness. Systems exist to preserve it.
  • Structure protects judgment under time pressure.
  • Technology should adapt to human work, not the reverse.
  • Skepticism in regulated environments is rational and deserves respect.
  • Explicit governance is more defensible than implicit consensus.

Engagement

Mark is open to focused consulting, implementation partnerships, and collaborative research related to decision governance and regulated AI adoption. He views RGDS as a foundation rather than a product, intended to evolve through real-world application and scrutiny.

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