Expert Systems to Generative AI — tiny steps that caused giant leaps in productivity

In the beginning, we wrote the rules by hand. The expert systems of the 1980s and 90s were magnificent and exhausting. You found the best person in the building — the diagnostician, the underwriter, the network guru — and you sat a knowledge engineer across from them for months, extracting their judgment one IF-THEN at a time. Thousands of rules, curated into a knowledge base, executed by a deterministic inference engine that never hallucinated, never improvised, never surprised you. The execution was flawless. The problem was everything else: knowledge engineering cost a fortune, the expert could never quite articulate the intuition they actually used, and the systems were brittle in a way that bordered on comic — one step outside the rulebook and the magnificent machine went silent, or confidently applied a rule that did not fit. Every new product version meant another expensive rule-surgery project. The support costs compounded, and expert systems became a cautionary tale: perfect execution, starved by its own knowledge.

The generative AI era inverted exactly one thing: where the rules come from. Instead of interviewing the expert, you curate the raw record of their work — every decision, every correction, every recovery — and let an autoregressive probabilistic model learn the patterns from the data. The rules are no longer written; they are learned. And here is the part people get wrong about how these systems run in production: the model does not execute anything. It fills in a carefully formatted card — a JSON form, a tool call — and hands it to a deterministic harness that reads the card and does the work. It reminded me of nothing so much as the mainframe punchcard. Judgment happens in the model; execution happens in code; the card is the boundary. The expert system is back, but the knowledge base is learned, and the inference engine is split in two: a probabilistic form-filler in front of a deterministic executor.

Talk is cheap, so I ran the experiment on myself. I took a small open-weights model — free to download — and trained a LoRA adapter on three months of my own agent sessions: every failure, every recovery, curated into a few thousand clean examples. The adapter training cost me under $50 in GPU time. The model itself was free, so the total cost of my custom model is $50. For this price nowadays you can get dinner in a good restaurant. But I have a model and it works. I ran it in ollama, served it locally as an endpoint, and pointed my harnesses at it — Interpreter, VSCode+RooCode. I could not tell the difference.

Your mileage may vary. But the direction is set: the cost of a custom model is now the cost of a dinner, the data is the work you already did, and the harness does not care whose card it reads

Which task actually needs AI?

AI earns its place at the workplace when a task is a workflow, not a single computable step: data collected from scattered places, analyzed, and turned into a conclusion someone can act on. That workflow exists in every industry — but I am only familiar with IT, so the table below shows how it applies in IT. The pattern I find: AI collects, analyzes, and drafts the conclusion; the human owns it.

Task / Activity	Human value add	Where AI earns / helps
Report generation from data spread across enterprise databases and files	Knows which questions matter and what the numbers mean for the business	Collects the scattered data (retrieval), analyzes it, drafts the report in the house format
Infra cost-saving initiatives (find waste, rightsize, decommission)	Owns the risk call, approves destructive actions, adjudicates exceptions	Finds waste candidates, sizes the savings, drafts the justification a manager reads
Architecture evaluation against best practices	Judgment under tradeoffs; the final call and the accountability	Checks designs against conventions, cites the violated principle, drafts the review
Incident triage & error recovery	Novel failure modes, escalation judgment, the 3 a.m. call	Recognizes known failure modes and proposes the known fix instantly (my $50 specialist)
Capacity planning	Business context — launches, seasonality, risk appetite	Crunches the utilization series, narrates trends and the outlook
Compliance & audit evidence	Interprets gray areas, signs the attestation	Gathers evidence across systems, drafts the control narratives

Read the columns as a split of labor: the middle column is judgment and accountability; the last column is collection, analysis, and drafting. That is the whole claim. The tasks where AI helps most are not the ones with the most data or the fanciest math — they are the ones where the conclusion must be composed, in a specific voice, from scattered inputs, repeatedly.

Two consequences:

1. The human column is not ceremonial. Every row ends in a person who owns the outcome — the architect's call, the manager's approval, the attestation signature. Not caution theater: the model drafts, the human decides. The day a row loses its human value-add, it stops being an AI task and becomes a cron job — and that is fine too. Automation is the final stage of a well-understood task, not a failure of AI.

2. The AI column is smaller and cheaper than advertised. Judgment + your format + repetition is exactly the profile where a small model trained on your own data beats a frontier subscription. You do not need the model that writes poetry. You need the one that knows your loop.