AI Economics Research

Idea and Motivation

Enterprise AI investment decisions are driven by optimism rather than rigorous economics. Technology vendors, consultancies, and research labs publish success metrics from controlled environments. Organizations deploying at scale face fundamentally different problems: hidden costs, coordination complexity, regulatory compliance expenses, and talent scarcity driving wage inflation. The published literature on AI ROI is thin, dominated by case studies from well-resourced technology companies and pharmaceutical firms. What is largely absent: honest, reproducible economic models for typical enterprise AI projects.

This series was born from a pragmatic observation. Over more than a decade in enterprise architecture and AI deployment, the cost structure of real projects rarely matches initial estimates. Data acquisition and labelling consume 40–60% of budgets. Infrastructure costs scale unpredictably. Model retraining and operational maintenance are persistent drains. Regulatory compliance in sectors like healthcare, finance, and utilities adds 20–30% overhead. Organizations systematically underestimate the human cost: data engineering, operations, integration work, and continuous monitoring.

The research question is straightforward: can we build economic frameworks precise enough to predict AI project costs and benefits with reasonable accuracy? Not post-hoc case study narratives, but frameworks that practitioners can apply prospectively to guide real investment decisions.

Goal

The series aims to construct a comprehensive, reproducible economic science of enterprise AI. This means moving beyond vendor marketing and anecdotal success stories to develop formal cost accounting, ROI models, risk quantification methods, and investment decision frameworks grounded in large-scale deployment data.

The goal is to enable organizations to answer these questions with evidence: How much will this AI initiative actually cost? What is the realistic time to positive ROI? What are the material risk factors and their economic impact? How should we compare AI projects against alternative uses of capital? Which AI approaches (build vs. buy, on-premises vs. cloud) optimize cost-benefit trade-offs for our specific context?

Scope

The series covers 44 articles across five thematic phases, focusing on economic analysis of enterprise AI from foundations through decision-making frameworks.

Focus

The primary research focus is on rigorous quantification of AI project costs and benefits. Rather than broad surveys of AI technology, the series maintains a disciplined economic lens: what are the material cost drivers? How do they scale with project size and scope? Where do published estimates systematically diverge from observed reality?

Special emphasis is placed on cost structures that are often invisible in technology literature: data engineering (the largest operational cost in most AI projects), regulatory compliance, integration overhead with legacy systems, continuous retraining requirements, and organizational change management. The series treats these not as secondary considerations but as first-order economic phenomena that determine project viability.

Real options analysis is explored as a decision framework for AI investments with uncertain outcomes. Capital budgeting methods adapted from traditional infrastructure investment are evaluated for their applicability to technology projects with shorter lifecycles but higher failure risk.

Limitations

Scientific Value

The series makes four contributions. First, it introduces cost accounting rigor to enterprise AI, moving from anecdotal case studies to reproducible, quantitative economic models. Second, it documents the substantial gap between published AI project timelines and observed reality, identifying systematic estimation errors in technology literature. Third, it develops capital budgeting frameworks adapted to AI’s unique risk and uncertainty characteristics. Fourth, it provides a structured repository of real-world cost data and investment decision frameworks that researchers and practitioners can extend and refine.

The Risk Calculator tool—an interactive implementation of the series’ cost models—demonstrates practical applicability of the research: practitioners can input project parameters and receive estimated cost distributions and ROI projections based on the series’ models.

Resources

• AI Risk & Economics Calculator→
• Zenodo Collection→
• Series DOI: 10.5281/zenodo.18665628→
• Author ORCID: 0000-0002-9540-1637→

Status

Ongoing. 44 articles published to date. The series is active and expanding. New articles are published regularly covering emerging cost structures in the AI market, updates to capital budgeting frameworks, and new case studies from diverse industries. The Research Risk Calculator is maintained and updated alongside the series’ latest models.

Contribution Opportunities

Researchers and practitioners are invited to contribute in the following directions:

• Cost data collection: Conduct structured surveys or analysis of AI project costs across organizations. Anonymised, aggregated cost data strengthens model calibration.
• Industry-specific economics: Extend the framework to specific sectors (healthcare, financial services, manufacturing, energy) with detailed cost structure analysis.
• Real options modeling: Develop and test real options frameworks for AI platform investments where timing and scale flexibility create strategic value.
• Regulatory cost mapping: Quantify compliance costs across jurisdictions and AI application domains (autonomous systems, algorithmic decision-making, etc.).
• Longitudinal studies: Track AI projects from inception through operational phase, documenting actual vs. projected costs and enabling model refinement.
• Calculator extension: Contribute domain-specific cost calculators or integration with enterprise financial planning tools.