AI in project controls: what actually works, and what is theatre
Forecasting a slipping work front is a genuinely useful application of machine intelligence. Asking a chatbot to write your monthly report is not.
What works: extraction and extrapolation
Reading a 4,000-line BOQ out of a badly formatted PDF and normalising its units is drudgery a machine does well — provided a planner reviews it line by line before it becomes live scope. Extrapolating run-rates into a forecast finish date is arithmetic the model just explains well.
What is theatre: confident narratives with no data behind them
A model will happily write a polished paragraph about a project it knows nothing about. If a risk narrative is not grounded in validated quantities and dated progress, it is prose, not analysis — and it is dangerous precisely because it reads well.
Confidence must be visible
Two weeks of DPR history cannot forecast a two-year package. Any honest system states how much data its forecast rests on and lowers its own confidence when the history is thin.
Keep the human sign-off
Nothing from a model should reach a client, a lender or an auditor without a qualified engineer's review. Used that way, AI removes hours of clerical work. Used the other way, it manufactures liability.
Plan-Sarthi puts these practices into one workspace — validated DPRs driving the S-curve, critical path, earned value and forecast automatically.
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