Category: Cost-Effective Enterprise AI

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

Buy vs Build in 2026: Why CIOs Are Choosing Integrated Agentic Ecosystems

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19005352  39stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed36%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,031✓Minimum 2,000 words for a full research article. Current: 2,031
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19005352
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (30 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The classic "build vs buy" dilemma in enterprise software has been resolved for most AI deployments in 2026 — not by a clear winner, but by a third option that renders the original question obsolete. As Gartner projects worldwide AI spending at $2.5 trillion in 2026, enterprises are abandoning bespoke AI moonshots in favour of orchestrated integration across incumbent vendor ecosystems. This ar...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19005352 39stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed36%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,031✓Minimum 2,000 words for a full research article. Current: 2,031
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19005352
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (30 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

Why Companies Don’t Want You to Know the Real Cost of AI

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18944159  40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted39%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,796✓Minimum 2,000 words for a full research article. Current: 2,796
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18944159
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]28%✗≥60% of references from 2025–2026. Current: 28%
[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 (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The current landscape of artificial intelligence pricing operates on a fundamental deception: what consumers pay bears almost no relationship to what the technology actually costs. This paper explores the economic mechanics behind platform subsidisation, the strategic motivations for concealing true costs, and the implications for enterprises building AI-powered products. Drawing on platform ec...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18944159 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted39%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,796✓Minimum 2,000 words for a full research article. Current: 2,796
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18944159
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]28%✗≥60% of references from 2025–2026. Current: 28%
[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 (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

The Subsidised Intelligence Illusion: What AI Really Costs When the Platform Isn’t Paying

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943388  32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,738✗Minimum 2,000 words for a full research article. Current: 1,738
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943388
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[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 (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI adoption has accelerated dramatically, yet fundamental cost misperceptions persist. This paper demonstrates that consumer subscription plans for frontier AI models (Claude Max at $100/month, ChatGPT Plus at $20/month) represent heavily platform-subsidised pricing that bears no relation to actual inference economics. Through detailed token consumption analysis and API pricing calcu...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18943388 32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,738✗Minimum 2,000 words for a full research article. Current: 1,738
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943388
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[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 (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

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

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18916800  39stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,178✓Minimum 2,000 words for a full research article. Current: 3,178
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18916800
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
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...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18916800 39stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,178✓Minimum 2,000 words for a full research article. Current: 3,178
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18916800
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

Open-Source vs Proprietary LLMs: Real Enterprise Economics

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18894954  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted19%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic19%○≥80% from journals/conferences/preprints
[f]Free Access31%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,188✓Minimum 2,000 words for a full research article. Current: 2,188
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18894954
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]41%✗≥60% of references from 2025–2026. Current: 41%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
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...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18894954 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted19%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic19%○≥80% from journals/conferences/preprints
[f]Free Access31%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,188✓Minimum 2,000 words for a full research article. Current: 2,188
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18894954
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]41%✗≥60% of references from 2025–2026. Current: 41%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

Bridging the Gap: Startup Workflows for AI Productivity Integration

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18868149  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,814✓Minimum 2,000 words for a full research article. Current: 2,814
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18868149
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]35%✗≥60% of references from 2025–2026. Current: 35%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[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 ...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18868149 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,814✓Minimum 2,000 words for a full research article. Current: 2,814
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18868149
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]35%✗≥60% of references from 2025–2026. Current: 35%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

Fine-Tuned SLMs vs Out-of-the-Box LLMs — Enterprise Cost Reality

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18838660  36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,010✓Minimum 2,000 words for a full research article. Current: 2,010
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18838660
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]28%✗≥60% of references from 2025–2026. Current: 28%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[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'...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18838660 36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,010✓Minimum 2,000 words for a full research article. Current: 2,010
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18838660
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]28%✗≥60% of references from 2025–2026. Current: 28%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

The Small Model Revolution: When 7B Parameters Beat 70B

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18832650  31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,209✓Minimum 2,000 words for a full research article. Current: 2,209
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18832650
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]23%✗≥60% of references from 2025–2026. Current: 23%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
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...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18832650 31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,209✓Minimum 2,000 words for a full research article. Current: 2,209
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18832650
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]23%✗≥60% of references from 2025–2026. Current: 23%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
Read more

Model Benchmarking for Business — Beyond Academic Metrics

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18827617  15stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted0%○≥80% from verified, high-quality sources
[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 Access14%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[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
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
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...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18827617 15stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted0%○≥80% from verified, high-quality sources
[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 Access14%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[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
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (5 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
Cost-Effective Ent…Read More
Read more

Autonomous Systems Economics: Replacing Human Labor with Compute

Posted on by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18822768  22stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic5%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[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
[g]Code—○Source code available on GitHub
[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...

Show moreHide
Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18822768 22stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted15%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic5%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[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
[g]Code—○Source code available on GitHub
[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%)
Cost-Effective Ent…Read More
Read more