📚 Academic Citation: Ivchenko, O. (2026). The Small Model Revolution: When 7B Parameters Beat 70B. Research article: The Small Model Revolution: When 7B Parameters Beat 70B. ONPU. DOI: 10.5281/zenodo.18832650 Abstract The prevailing assumption in enterprise AI procurement has been that larger models deliver proportionally superior outcomes — that scaling parameters translates linearly into business value….
Category: Cost-Effective Enterprise AI
40-article series on cost-effective AI implementation in enterprise
Model Benchmarking for Business — Beyond Academic Metrics
📚 Academic Citation: Ivchenko, O. (2026). Model Benchmarking for Business — Beyond Academic Metrics. Research article: Model Benchmarking for Business — Beyond Academic Metrics. ONPU. DOI: 10.5281/zenodo.18827617 Abstract Academic AI benchmarks — MMLU, HumanEval, GSM8K — dominate public leaderboards but systematically misalign with enterprise purchasing decisions. This article constructs a business-centric benchmarking framework that integrates…
Autonomous Systems Economics: Replacing Human Labor with Compute
📚 Academic Citation: Ivchenko, O. (2026). Autonomous Systems Economics: Replacing Human Labor with Compute. Cost-Effective Enterprise AI Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18822768 Abstract 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…
Enterprise AI: A Comprehensive Guide to Navigating Complexity and Avoiding the 80% Failure Rate
📚 Academic Citation: Ivchenko, O. (2026). Enterprise AI: A Comprehensive Guide to Navigating Complexity and Avoiding the 80% Failure Rate. Cost-Effective Enterprise AI Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18772218 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,…
Multi-Provider Strategies: Avoiding Vendor Lock-in While Maximizing Value
📚 Academic Citation: Ivchenko, O. (2026). Multi-Provider Strategies: Avoiding Vendor Lock-in While Maximizing Value. Cost-Effective Enterprise AI Series. Odesa National Polytechnic University. DOI: Pending Zenodo registration Abstract 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…
Specialized vs General Models — When to Use Domain-Specific AI
📚 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 particular industries and tasks. This article…
Open Source LLMs in Production — Llama, Mistral, and Beyond
📚 Academic Citation: Ivchenko, O. (2026). Open Source LLMs in Production: Llama, Mistral, and Beyond. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18741621 Introduction Throughout my career deploying AI systems at enterprise scale, I have observed a fundamental shift in how organizations approach large language model (LLM) infrastructure. The emergence of high-quality open…
OpenAI vs Anthropic vs Google: Enterprise Provider Comparison 2026
📚 Academic Citation: Ivchenko, O. (2026). OpenAI vs Anthropic vs Google: Enterprise Provider Comparison 2026. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.PENDING Author: Oleh Ivchenko Affiliation: Lead Engineer, a major technology consultancy | PhD Researcher, ONPU Series: Cost-Effective Enterprise AI (Article 10/40) Published: February 2026 Abstract The enterprise AI landscape in 2026…
The Model Selection Matrix: Matching LLMs to Enterprise Use Cases
📚 Academic Citation: Ivchenko, O. (2026). The Model Selection Matrix: Matching LLMs to Enterprise Use Cases. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18714060 Abstract Selecting the appropriate large language model for enterprise applications requires balancing performance requirements, cost constraints, latency expectations, and compliance mandates. After deploying over 50 AI systems across finance,…
Failure Economics — Learning from $100M+ AI Project Disasters
📚 Academic Citation: Ivchenko, O. (2026). Failure Economics — Learning from $100M+ AI Project Disasters. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18679509 Abstract The economics of AI failure receive far less systematic attention than the economics of AI success. This is a dangerous asymmetry. Between 2016 and 2025, documented AI project failures…