Category: Capability-Adoption Gap

AI Onboarding Economics: The Hidden Cost of Getting Teams to Actually Use AI Tools

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21634045  65stabilfr·wdophcgmx
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Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The diffusion of artificial intelligence tools across enterprise environments promises productivity gains, yet many organizations encounter a stark discrepancy between technical capability and actual adoption. This article investigates the economic and sociotechnical dimensions of AI onboarding, framing the challenge as a multi‑dimensional cost problem that includes explicit monetary expenditur...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21634045 65stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21634045
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]91%✓≥60% of references from 2025–2026. Current: 91%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Data Readiness Gap: How Incomplete Data Infrastructure Blocks AI in Production

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21503339  66stabilfr·wdophcgmx
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[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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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 (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Incomplete data infrastructure continues to block a substantial share of enterprise AI initiatives, with recent analyses indicating that 60‑80 % of projects fail to reach production because of fragmented pipelines, missing metadata, and insufficient data quality controls. This article synthesizes evidence from 25 peer‑reviewed studies published between 2025 and 2026 to quantify the economic and...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21503339 66stabilfr·wdophcgmx
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[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
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[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,976✗Minimum 2,000 words for a full research article. Current: 1,976
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21503339
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[h]Freshness [REQ]80%✓≥60% of references from 2025–2026. Current: 80%
[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 (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Adoption Latency Benchmarks: Time-to-Value Across Industry Verticals in 2025

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Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21421816  67stabilfr·wdophcgmx
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[f]Free Access100%✓≥80% are freely accessible
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[h]Freshness [REQ]75%✓≥60% of references from 2025–2026. Current: 75%
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[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence (AI) is increasingly viewed as a strategic lever for value creation, yet organizations struggle to translate experimental projects into measurable returns on investment (ROI). This article investigates the latency — defined as the elapsed time from project approval to the first observable quantifiable benefit — across four major industry verticals: financial services, lo...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21421816 67stabilfr·wdophcgmx
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[t]Trusted92%✓≥80% from verified, high-quality sources
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[f]Free Access100%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]2,199✓Minimum 2,000 words for a full research article. Current: 2,199
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21421816
[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 Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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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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[f]Free Access100%✓≥80% are freely accessible
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[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)
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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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
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[w]Words [REQ]1,105✗Minimum 2,000 words for a full research article. Current: 1,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21254149
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[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)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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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[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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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%)

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
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
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[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]992✗Minimum 2,000 words for a full research article. Current: 992
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21150688
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]86%✓≥60% of references from 2025–2026. Current: 86%
[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 (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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[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
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[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
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[w]Words [REQ]1,003✗Minimum 2,000 words for a full research article. Current: 1,003
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19556491
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code✓✓Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[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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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,003✗Minimum 2,000 words for a full research article. Current: 1,003
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19556491
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code✓✓Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[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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[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]2,587✓Minimum 2,000 words for a full research article. Current: 2,587
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts2✓Original data charts from reproducible analysis (min 2). Current: 2
[g]Code✓✓Source code available on GitHub
[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
BadgeMetricValueStatusDescription
[s]Reviewed Sources33%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]2,587✓Minimum 2,000 words for a full research article. Current: 2,587
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts2✓Original data charts from reproducible analysis (min 2). Current: 2
[g]Code✓✓Source code available on GitHub
[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
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic35%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,116✓Minimum 2,000 words for a full research article. Current: 2,116
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19423051
[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%)

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
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic35%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,116✓Minimum 2,000 words for a full research article. Current: 2,116
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19423051
[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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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access74%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[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%)

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
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access74%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[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%)
Capability-Adoptio…Read More
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