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PREreview of "Build, Buy, or Delegate: A Transaction-Cost Model of Generative AI Capability Sourcing in Small and Medium-Sized Enterprises in the United States and Switzerland"

Preprint DOI: 10.20944/preprints202610.1635.v1

Author: Viktoriya Sydoruk (Preprints.org, Oct 2026)

Reviewer: Julian Rodriguez, Jr. FRSA (Royal Economic Society / Royal Society of Arts)

What the Paper Gets Right

Most research on AI in small businesses asks a simple question: "Are firms adopting AI?" Sydoruk asks the much better question: "How are they actually sourcing it?"

Using transaction cost economics, the paper breaks down three choices for small-to-medium enterprises (SMEs): building in-house, buying off-the-shelf SaaS, or delegating to a managed service provider. Comparing the US and Switzerland, the paper delivers three solid findings:

1. Building in-house is a money loser for almost every SME. It only makes financial sense if a firm is automating over 5,000 hours of tasks a year.

2. Labor costs dictate the model: Standardized SaaS wins in the US (lower labor costs/flexible rules), while outsourcing to a managed provider wins in Switzerland (high wages/strict privacy rules).

3. The wage tipping point is tight: The switch from SaaS to managed delegation happens within a narrow labor cost band of roughly $60–$65 an hour.

Key Blindspots to Address Before Publication

1. Delegating Execution Is Not Delegating Risk:

   The paper assumes delegating AI to a third-party managed provider insulates an SME from local compliance costs. In reality, handing off execution doesn't hand off liability. As shown in research on regulatory friction and systemic risk (Rodriguez, 2026; The Zero-Defect Paradox), outsourcing complex automated tools introduces principal-agent opacity and hidden compliance friction that can quickly wipe out expected labor savings.

2. The Hidden Costs of SaaS Lock-In:

   Treating off-the-shelf SaaS as a flat, predictable cost ignores how fast AI APIs break, change terms, or depreciates features. Factoring vendor lock-in and switching costs into the model would give a much more accurate picture of the real wage tipping point.

Final Take

A sharp, practical paper that moves past AI hype and focuses on real firm economics. Highly recommended for publication once the author incorporates third-party compliance friction into the model.

Sources & References

• Sydoruk, V. (2026). Build, Buy, or Delegate: A Transaction-Cost Model of Generative AI Capability Sourcing in Small and Medium-Sized Enterprises in the United States and Switzerland. Preprints.org. DOI: 10.20944/preprints202610.1635.v1.

• Rodriguez, J., Jr. (2026). The Zero-Defect Paradox: Behavioral Collapse and the Residual Cost of Systemic Friction in Regulatory Environments. ResearchGate / Zenodo.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.