Insight · Reporting Workflow
Technology can help tidy data, patterns, and language consistency. Judgment, reputational sensitivity, and storytelling must still be led by a professional team.
AI has entered the reporting workflow, and pretending otherwise helps no one. The useful question is not whether to use it, but where it genuinely helps and where it quietly creates risk.
Consistency checking across hundreds of pages: terminology, figures repeated in multiple sections, bilingual alignment. First-pass tidying of raw data into readable tables. Flagging gaps against disclosure checklists. These are pattern tasks, and machines are good at patterns. Used this way, AI shortens production cycles and frees the team for higher-value work.
The risk is delegation of judgment. An AI can draft a fluent paragraph about strategy that is subtly wrong, overclaims, or contradicts a figure elsewhere in the report. Fluency disguises error. In a regulated disclosure document, one confident wrong sentence can cost more than the entire efficiency gain. The same applies to tone: reputational sensitivity, what to emphasize, what to contextualize, what a regulator will read twice, is a human call informed by experience.
The model that works is simple to state: machines propose, professionals dispose. Every AI-assisted output passes through editorial review with named accountability. Strategic narrative, materiality decisions, and leadership messaging are drafted by people, then machines help polish consistency, never the reverse.
SAMCGI uses technology this way in its reporting work: as an instrument of discipline, under a team that remains accountable for every sentence a client publishes.