Sell the grade-control case—not another shiny screen

Gross benefit = potential units × economic unit value × realized share.

Full year-one cost = full upfront deployment + annual operating cost. Net = benefit − full cost. Year-one ROI = net ÷ full cost × 100. Each cost is subtracted once.

Simple upfront payback = upfront cost ÷ (annual benefit − annual operating cost) × 12 months, only if annual net benefit is positive. It assumes steady annual realization, not a discounted cash-flow forecast. Year-one break-even units = full year-one cost ÷ realized unit value.

Measure the solution buyer’s own deployment case: compare a documented pre-deployment baseline with post-deployment rework events and costs on comparable work, and separately track productive hours if used. Count full year-one buyer cost as upfront plus annual ongoing cost; net = gross benefit − cost; ROI = net ÷ cost × 100. Realization share is the portion of the estimated gross benefit expected to be realized, not the probability that a project succeeds. FHWA notes benefits are project-dependent and identifies model accuracy, positioning environment, specifications, and training as relevant conditions.

These sources explain the economic mechanism and measurement—not Alma Hunter performance:

• FHWA: Automated Machine Guidance with Use of 3D Models (TechBrief, 2013) — Defines automated machine guidance using 3D engineered models and positioning to guide earthwork and paving equipment. Describes potential first-pass grading, reduced rework and survey effort, and possible productivity and cost benefits; explicitly says it is not ideal for every project and lists model, signal, training, and specification constraints. These are potential benefits, not guaranteed outcomes.
• FHWA: Colorado I-70 Automated Machine Guidance case study (2014) — Documents a project-specific milling application using a 3D profile and robotic total stations, including survey/model challenges and lessons on accurate initial surveys, bidding unknowns, specifications, and process preparation. Its cited test productivity and survey-labor figures are context-specific, not general forecasts.
• NIST: Measuring and improving U.S. construction productivity — Explains that construction productivity measurement is deficient and discusses measurement at task, project, and industry levels, including links to information and automation technologies. Supports emphasizing buyer-specific, project-level measures rather than unsupported broad productivity claims.