Category: Cost-Effective Enterprise AI

40-article series on cost-effective AI implementation in enterprise

Agent Cost Optimization as First-Class Architecture: Why Inference Economics Must Be Designed In, Not Bolted On

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18916800  39stabilfr·wdophcgmx
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Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In 2026, inference costs account for 85% of enterprise AI budgets, yet most agentic system architectures treat cost optimization as an operational afterthought rather than a foundational design constraint. This paper argues that agent cost optimization must be elevated to a first-class architectural concern — embedded in system design decisions from the ground up alongside correctness, reliabil...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18916800 39stabilfr·wdophcgmx
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Open-Source vs Proprietary LLMs: Real Enterprise Economics

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18894954  34stabilfr·wdophcgmx
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Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The choice between open-source and proprietary large language models (LLMs) is one of the most consequential economic decisions facing enterprise technology leaders in 2026. While proprietary APIs from OpenAI, Anthropic, and Google offer immediate access to frontier capability with zero infrastructure overhead, the true total cost of ownership (TCO) diverges sharply from sticker pricing at scal...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18894954 34stabilfr·wdophcgmx
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Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Bridging the Gap: Startup Workflows for AI Productivity Integration

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18868149  34stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Startups occupy a paradoxical position in the 2026 AI landscape: unburdened by legacy infrastructure, yet resource-constrained in ways that make AI adoption both essential and precarious. Gartner projects that 40% of enterprise applications will incorporate task-specific AI agents by end of 2026, up from less than 5% in 2025 — a near order-of-magnitude leap that compresses traditional adoption ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18868149 34stabilfr·wdophcgmx
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Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Fine-Tuned SLMs vs Out-of-the-Box LLMs — Enterprise Cost Reality

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18838660  36stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The dominant model-selection question in enterprise AI has shifted from "which large language model?" to "should we be using a large language model at all?" This article provides a rigorous economic analysis of fine-tuned small language models (SLMs) versus out-of-the-box large language models (LLMs) for enterprise deployment, drawing on empirical benchmarks from the LoRA Land study, Predibase'...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18838660 36stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Small Model Revolution: When 7B Parameters Beat 70B

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18832650  31stabilfr·wdophcgmx
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Score = Ref Trust (18 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The prevailing assumption in enterprise AI procurement has been that larger models deliver proportionally superior outcomes — that scaling parameters translates linearly into business value. This assumption is wrong, and the evidence in 2026 is now overwhelming. A fine-tuned Phi-3-mini model beat GPT-4o on six of seven financial NLP benchmarks at an inference cost of $0.13 per million tokens ve...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18832650 31stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Model Benchmarking for Business — Beyond Academic Metrics

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18827617  15stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
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Score = Ref Trust (5 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Enterprise procurement of large language models (LLMs) continues to rely on academic benchmarks — MMLU, HumanEval, HellaSwag — that were designed for research comparisons rather than business decision-making. This article demonstrates why these metrics systematically mislead enterprise buyers and proposes the Business-Oriented Model Evaluation (BOME) framework, which centres on four operational...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18827617 15stabilfr·wdophcgmx
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[w]Words [REQ]1,821✗Minimum 2,000 words for a full research article. Current: 1,821
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18827617
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (5 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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Autonomous Systems Economics: Replacing Human Labor with Compute

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18822768  22stabilfr·wdophcgmx
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[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18822768
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]24%✗≥60% of references from 2025–2026. Current: 24%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (13 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The fundamental economic question facing enterprises in 2026 is not whether autonomous systems can replace human labor, but when the compute-labor cost crossover makes replacement economically rational. This article examines the economics of autonomous system deployment across warehouse robotics, transportation, and knowledge work domains. Analysis of real-world implementations reveals that lab...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18822768 22stabilfr·wdophcgmx
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[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
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[l]Academic5%○≥80% from journals/conferences/preprints
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[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,792✗Minimum 2,000 words for a full research article. Current: 1,792
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18822768
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]24%✗≥60% of references from 2025–2026. Current: 24%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (13 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Enterprise AI: A Comprehensive Guide to Navigating Complexity and Avoiding the 80% Failure Rate

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18772218  30stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted19%○≥80% from verified, high-quality sources
[a]DOI2%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access8%○≥80% are freely accessible
[r]References53 refs✓Minimum 10 references required
[w]Words [REQ]4,590✓Minimum 2,000 words for a full research article. Current: 4,590
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18772218
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]15%✗≥60% of references from 2025–2026. Current: 15%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Executive Summary: Despite unprecedented investment and executive enthusiasm, 80-85% of enterprise AI projects fail to deliver meaningful business value. This comprehensive analysis examines the technical, organizational, and economic factors driving this failure rate, drawing from academic research and industry studies. We present evidence-based frameworks for total cost of ownership (TCO) ana...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18772218 30stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted19%○≥80% from verified, high-quality sources
[a]DOI2%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access8%○≥80% are freely accessible
[r]References53 refs✓Minimum 10 references required
[w]Words [REQ]4,590✓Minimum 2,000 words for a full research article. Current: 4,590
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18772218
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]15%✗≥60% of references from 2025–2026. Current: 15%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Multi-Provider Strategies: Avoiding Vendor Lock-in While Maximizing Value

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18769559  24stabilfr·wdophcgmx
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Enterprise adoption of large language models (LLMs) has introduced a new dimension of vendor lock-in that differs fundamentally from traditional software dependencies. Unlike switching ERP systems or databases—where migration paths are well-understood—LLM provider transitions involve prompt re-engineering, model behavior differences, and hidden integration costs that can reach six figures even ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18769559 24stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]2,547✓Minimum 2,000 words for a full research article. Current: 2,547
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18769559
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
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Specialized vs General Models — When to Use Domain-Specific AI

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18743173  45stabilfr·wdophcgmx
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Academic Citation: Ivchenko, O. (2026). Specialized vs General Models — When to Use Domain-Specific AI. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18746111 Abstract The enterprise AI landscape is undergoing a fundamental shift from general-purpose large language models (LLMs) to domain-specific language models (DSLMs) optimized for particula...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18743173 45stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
[b]CrossRef9%○≥80% indexed in CrossRef
[i]Indexed19%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access51%○≥80% are freely accessible
[r]References53 refs✓Minimum 10 references required
[w]Words [REQ]4,232✓Minimum 2,000 words for a full research article. Current: 4,232
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18743173
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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