Accurate quantity takeoff determines the reliability of construction cost estimates from tender pricing to project budgeting, yet the shift from manual to digital and AI-assisted workflows raises practical questions about estimator control, error classification, and output validation. Traditional manual quantity takeoff using drawings and spreadsheets remains common in residential construction, especially in small and mid-sized firms, but it is time-consuming and vulnerable to drawing interpretation, scale, unit conversion, and omission errors. BIM-based and digital takeoff workflows improve consistency by connecting model geometry, two-dimensional drawings, classification systems, and cost data. More recently, AI-assisted drawing recognition and quantity extraction tools have introduced automated first-pass measurement support. However, AI-assisted estimation remains dependent on drawing quality, training data, scope definition, local rate assumptions, and estimator judgment. This paper proposes and demonstrates a comparative methodology for evaluating manual, BIM/digital, and AI-assisted quantity takeoff workflows for residential building cost estimation. A clearly labelled illustrative residential case study is used to compare quantity deviation, cost variance, time effort, error types, and human correction requirements. The paper proposes an Estimator-Controlled AI-Assisted Quantity Takeoff Framework that positions AI as a reviewable support tool rather than a replacement for estimators. The contribution is a practical human-in-the-loop validation framework for estimators, contractors, BIM/VDC teams, and construction management researchers working in Indian and similar developing construction markets.