AI Economics: TCO Models for Enterprise AI — A Practitioner’s Framework

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18616503  63stabilfr·wdophcgmx
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Total Cost of Ownership (TCO) analysis for enterprise AI systems presents unique challenges that traditional IT TCO frameworks fail to address adequately. This paper presents a comprehensive TCO model specifically designed for AI implementations, drawing on my fourteen years of enterprise software experience and seven years of AI research at a leading technology consultancy. I propose a four-ph...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18616503 63stabilfr·wdophcgmx
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AI Economics: Economic Framework for AI Investment Decisions

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18616115  63stabilfr·wdophcgmx
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Enterprise artificial intelligence investments present unique economic challenges that traditional capital budgeting frameworks fail to adequately address. This article develops a comprehensive economic framework specifically designed for AI investment decisions, integrating uncertainty quantification, option value analysis, and dynamic portfolio optimization. Drawing from fourteen years of sof...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18616115 63stabilfr·wdophcgmx
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AI Economics: Risk Profiles — Narrow vs General-Purpose AI Systems

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665626  55stabilfr·wdophcgmx
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Enterprise AI systems exhibit fundamentally different risk profiles depending on their architectural paradigm. This paper presents a comprehensive economic analysis comparing narrow AI systems—purpose-built for specific tasks—with general-purpose AI (GPAI) systems, particularly large language models and foundation models that have proliferated since 2022. Drawing from 14 years of enterprise sof...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665626 55stabilfr·wdophcgmx
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AI Economics: Structural Differences — Traditional vs AI Software

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665628  34stabilfr·wdophcgmx
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In March 2022, a senior architect at a Fortune 500 financial services firm stood before his team with a troubling admission. His organization had spent $47 million over three years building what they called "the most sophisticated fraud detection system in the industry." The system worked—brilliantly, in fact—catching 23% more fraudulent transactions than their previous rule-based approach. But...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665628 34stabilfr·wdophcgmx
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Enterprise AI Risk: The 80-95% Failure Rate Problem — Introduction

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665630  58stabilfr·wdophcgmx
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Enterprise artificial intelligence initiatives fail at rates between 80% and 95%—a staggering statistic that dwarfs failure rates in traditional software development. Despite billions in investment, most AI projects never reach production, and those that do often fail to deliver promised business value. This failure epidemic is not primarily caused by limitations in machine l[REDACTED]g algorit...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665630 58stabilfr·wdophcgmx
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Data Mining Chapter 4: Taxonomic Framework Overview — Classifying the Field

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665633  45stabilfr·wdophcgmx
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The proliferation of data mining techniques over the past three decades has created an urgent need for systematic organization and classification of methodological approaches. This chapter establishes a comprehensive meta-taxonomic framework for understanding, categorizing, and relating the diverse landscape of data mining methods. We propose a three-dimensional classification scheme that organ...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18665633 45stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Anticipatory Intelligence: State of the Art — Current Approaches to Predictive AI

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665635  61stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 2026

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18665635 61stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Medical ML: Open Questions for Future Research — A Medical AI Research Agenda for Ukrainian Healthcare

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665637  21stabilfr·wdophcgmx
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[r]References2 refs○Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (1 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

After twelve weeks examining machine l[REDACTED]g applications in medical imaging diagnosis, significant knowledge gaps remain that demand systematic investigation. This concluding article synthesizes open research questions emerging from our comprehensive review, organized across seven priority domains: generalization and distribution shift, algorithmic fairness and bias mitigation, human-AI c...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665637 21stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665637
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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Score = Ref Trust (1 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Training Curriculum for Medical AI — Healthcare Professional Development Framework

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665639  16stabilfr·wdophcgmx
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[t]Trusted0%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
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The rapid proliferation of AI-enabled medical devices—exceeding 1,200 FDA authorizations as of 2026 with 80% targeting radiology—has outpaced the educational infrastructure needed to prepare healthcare professionals for effective utilization. A 2026 survey revealed that approximately 24% of radiology residents report having no AI/ML educational offerings in their residency programs, despite the...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18665639 16stabilfr·wdophcgmx
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Medical ML: Clinical Protocol Templates for ML-Assisted Medical Imaging Diagnosis

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730553  40stabilfr·wdophcgmx
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The deployment of machine l[REDACTED]g algorithms in clinical radiology represents one of the most significant technological transformations in modern healthcare. With over 1,200 FDA-authorized AI medical devices and hundreds of CE-marked solutions available globally, healthcare facilities face a critical challenge: translating technological capability into reliable, safe, and efficient clinica...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730553 40stabilfr·wdophcgmx
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