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    Execution Drift Control

    The capacity to detect and correct divergence between intended and actual execution state before it compounds into structural failure.

    Concept origin: Execution Drift Control is dimension 4 of the Execution Integrity Score framework, developed by Duena Blomstrom and Dave Ballantyne as part of the Human Debt™ research programme.

    What is Execution Drift Control?

    Execution Drift Control is the capacity of an organisation to detect and correct divergence between what was intended and what is actually happening across AI-assisted workflows. It encompasses the mechanisms — structural, behavioural, and technical — by which drift is noticed, measured, and interrupted before it becomes irreversible.

    What causes execution drift?

    Drift begins when AI-generated outputs are accepted without verification, creating a gap between the model's version of reality and the organisation's actual operational state. Under AI acceleration, this gap widens faster than traditional management processes can detect it. Local coherence — the sense that one's own work is on track — masks systemic drift until it surfaces as visible failure.

    How is Execution Drift Control scored?

    Scored 1–5 as dimension 4 (d4) of the Execution Integrity Score v1.0. A score of 1 indicates no systematic mechanism exists to detect divergence between intended and actual execution. A score of 5 indicates continuous, cross-functional drift detection with verified correction loops and documented recovery timelines.

    What is the relationship between Execution Drift and Execution Debt®?

    Execution Debt® accumulates when drift goes uncontrolled. Each uncorrected divergence creates a compounding liability — decisions made on false premises, resources deployed against misread signals, and teams coordinating around a fiction of execution that leadership cannot directly verify. Execution Drift Control is the primary structural defence against Execution Debt® accumulation.