Practical AI is Data Plumbing, Not Magic

Real AI impact rarely looks like autonomous systems replacing human experts; it looks like solving brutal data plumbing problems under crisis conditions. Anthropic's report on deploying Claude during the Ebola outbreak in the Democratic Republic of the Congo illustrates what practical utility actually looks like in the field.

The operational bottleneck was not a lack of diagnostic algorithms. It was field teams working late into the night manually extracting case counts out of PowerPoint slides sent over WhatsApp to assemble the daily situation report. By using an LLM to parse those unstructured formats, verify changes against yesterday's logs, and draft the sitrep in under an hour instead of all day, WHO AFRO freed up epidemiologists to run multiple disease models instead of relying on just one due to time constraints.

Lowering the friction on bioinformatics pipelines so local labs can sequence genomes without complex terminal syntax is equally telling. While the hype cycle obsesses over full autonomy, the deployments that genuinely matter look like this: removing friction from messy, high-stakes pipelines while keeping domain experts in control.