A working deterministic system is doing its job. An ETL that produces the same output every cycle, a workflow that hits its deadlines, a process that survives audit — these don’t want a model inside them. Variability where the business needs predictability becomes a risk.
But every system has human layers — the points where someone has to think, decide, write, judge. That’s where the work slows down and where quality varies. That’s where AI belongs.
In a document review workflow, the human work is finding cross-references — where one document cites, contradicts, or overlaps with another. AI scans the corpus, surfaces matches with a one-line summary of each, and lets the reviewer read down a table instead of opening every section. The reviewer still decides what matters; the AI compresses the search.
In a categorisation pipeline, the human work is classifying loose, inconsistent records into a stable taxonomy. A model trained on past mappings predicts the category and a confidence score; the operator confirms or corrects. This isn’t GenAI — it’s classical machine learning — but the pattern is the same: the system handles the bulk, a human handles the edges.
In a metric authoring workflow, the human work is translating a rule from English into code. AI does that translation in seconds. The contributor reviews and ships.
In a reporting cadence, the human work is the executive summary. Structured outputs go through a prompt; the AI drafts the summary; a reviewer adjusts before it ships.
The pattern is the same: AI augments the human layer, not the deterministic core. It catches what humans miss, drafts what humans would otherwise write from scratch, classifies what’s repetitive, surfaces what they didn’t know to look for. The system around it stays predictable.
Done well, the AI feels like a knowledgeable colleague at the contributor’s shoulder — improving the output, not replacing the contributor.