Category: Capability-Adoption Gap

The Shadow IT AI Layer: Unauthorized Copilot and ChatGPT Usage in the Capability Gap

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21254149  64stabilfr·wdophcgmx
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Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The diffusion of generative AI copilots such as Microsoft Copilot and OpenAI ChatGPT has produced a new class of unsanctioned employee tool usage—commonly labeled shadow AI. This article provides a systematic quantification of shadow AI adoption across Fortune 500 enterprises and evaluates its net effect on the organizational capability gap. Employing a mixed‑methods design that integrates a la...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21254149 64stabilfr·wdophcgmx
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[h]Freshness [REQ]82%✓≥60% of references from 2025–2026. Current: 82%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
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Procurement AI Paradox: Enterprise Buying Cycles vs Model Deprecation Velocity

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21150688  56stabilfr·wdophcgmx
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[h]Freshness [REQ]86%✓≥60% of references from 2025–2026. Current: 86%
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[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise adoption of artificial intelligence suffers from a structural mismatch between procurement timelines and model release frequencies. This article quantifies the misalignment between 18‑24 month enterprise buying cycles and 6‑month AI model deprecation cycles, identifies established mitigation strategies, and evaluates their effectiveness using empirical metrics. We formulate three res...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21150688 56stabilfr·wdophcgmx
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[t]Trusted87%✓≥80% from verified, high-quality sources
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]86%✓≥60% of references from 2025–2026. Current: 86%
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[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Second-Order Gap: When Adopted AI Creates New Capability Gaps

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19556491  40stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

When organizations successfully adopt AI systems, they often discover that adoption creates as many problems as it solves. This phenomenon—the second-order gap—occurs when AI adoption reveals or generates new capability deficiencies that organizations had not anticipated. This article examines the mechanisms driving second-order gap formation, quantifies their prevalence across enterprise conte...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19556491 40stabilfr·wdophcgmx
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[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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Closing the Gap: Evidence-Based Strategies That Actually Work

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Gap Research by Oleh Ivchenko  67stabilfr·wdophcgmx
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[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

Evidence-based strategies transform AI adoption from aspiration into measurable outcomes Stabilarity Research Hub April 2026 DOI: 10.5281/zenodo.19117123 Abstract The capability-adoption gap in artificial intelligence is well-documented but poorly addressed. While organizations invest heavily in AI development and deployment, measurable adoption rates consistently lag behind projected capabilit...

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Gap Research by Oleh Ivchenko 67stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
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[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
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[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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Measuring Adoption Velocity: Metrics and Benchmarks Across Industries

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19423051  55stabilfr·wdophcgmx
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Adoption velocity — the rate at which organisations move from AI awareness to scaled deployment — has emerged as a critical differentiator between enterprises that extract compounding value from artificial intelligence and those perpetually stuck in pilot limbo. In the previous article, we established that the training gap is the primary human-side barrier to AI deployment; here we turn to meas...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19423051 55stabilfr·wdophcgmx
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[l]Academic35%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
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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]67%✓≥60% of references from 2025–2026. Current: 67%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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The Training Gap: When AI Capability Outpaces Workforce Readiness

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19420224  49stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19420224
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

The gap between what AI systems can do and what organizations can operationally deploy continues to widen — driven not only by technical integration challenges but increasingly by workforce unreadiness. This article examines the training gap as a structural component of the capability-adoption gap, analyzing why AI upskilling initiatives consistently fail to produce durable competency gains. Dr...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19420224 49stabilfr·wdophcgmx
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[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
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[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access74%○≥80% are freely accessible
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[w]Words [REQ]2,062✓Minimum 2,000 words for a full research article. Current: 2,062
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19420224
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]75%✓≥60% of references from 2025–2026. Current: 75%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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All-You-Can-Eat Agentic AI: The Economics of Unlimited Licensing in an Era of Non-Deterministic Costs

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19371258  59stabilfr·wdophcgmx
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[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
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[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,384✓Minimum 2,000 words for a full research article. Current: 2,384
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19371258
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

The transition from deterministic SaaS workloads to non-deterministic agentic AI systems has fundamentally disrupted enterprise software pricing. Traditional per-seat licensing assumed predictable, bounded resource consumption per user. Agentic AI violates this assumption: autonomous agents consume 5-30x more tokens than simple chatbots, exhibit unpredictable usage patterns, and chain multiple ...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19371258 59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources10%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
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[f]Free Access81%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,384✓Minimum 2,000 words for a full research article. Current: 2,384
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19371258
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code✓✓Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (46 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)
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Adoption Friction Taxonomy: Categorizing the Barriers Between AI Capability and Enterprise Deployment

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19219179  50stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
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[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,105✓Minimum 2,000 words for a full research article. Current: 2,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19219179
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]43%✗≥60% of references from 2025–2026. Current: 43%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

The gap between what AI can do and what organizations actually deploy continues to widen in 2026. While previous articles in this series quantified the magnitude of this gap across sectors, the underlying friction mechanisms remain poorly categorized. This article introduces a four-quadrant Adoption Friction Taxonomy (AFT) that classifies eight empirically identified barrier categories along tw...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19219179 50stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,105✓Minimum 2,000 words for a full research article. Current: 2,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19219179
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]43%✗≥60% of references from 2025–2026. Current: 43%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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The Monitor Shows What Nobody Wants to See: AI Is Here, It Is Eating Jobs, and We Can Only Watch

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18993208  49stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]2,916✓Minimum 2,000 words for a full research article. Current: 2,916
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18993208
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Odesa National Polytechnic University, Department of Economic Cybernetics · PhD Candidate, ML in Pharma Economics

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.18993208 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]2,916✓Minimum 2,000 words for a full research article. Current: 2,916
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18993208
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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