AI content production that does not compromise your organization’s reputational position
Supervised workflows · Proprietary editorial criteria · Guaranteed institutional coherence
Producing content with language models without specific editorial criteria for institutional communications creates reputational incoherence risk: texts that do not reflect the organization’s voice, statements that contradict its public position, or materials that circulate with undetected errors. Blum Digital PR defines supervised workflows and quality criteria adapted to each organization.
What is included
Editorial criteria for AI production
Definition of criteria that AI-generated content must meet before publication: coherence with the organization’s public position, verifiable factual accuracy, institutional tone and voice, and human review requirements according to content type and channel.
Workflows with structured oversight
Design of the generation, review and approval processes for AI content. Defines who oversees what, at which point in the workflow and with what criteria. Every editorial decision is documented: if a text goes out with an error, the process can show exactly where it broke down.
Usage guides by content type
Specific documentation by format: press releases, institutional statements, social media content, internal reports, executive presentations. Each guide defines which parts of the process can rely on AI and which require mandatory human authorship or review.
Review of existing materials
Audit of already published or in-production content to identify reputational inconsistencies introduced by unguided AI use. Includes prioritized correction recommendations.
For whom
- Communication agencies producing AI content for clients with public profiles who need documented quality criteria.
- Corporate communications departments where several team members use AI in an uncoordinated way.
- Organizations that have received negative feedback on the quality or coherence of their content after adopting AI tools.
- Communications directors who want a reference framework before scaling AI use in production.
Next step
The service starts by understanding how the team works today: what gets generated with AI, who reviews it and with what criteria. If there are no documented criteria, that is where the problem starts — and where the work begins.
