What's still unknown. The honest version, dated.

The construct's empirical depth is still narrow.

The SRD source paper v0.4 is an architecture-driven reformulation: it settles the object model and the system-dynamics lineage against two prospective applications (Case A, a federated industrial holdings group; Case B, a tier-2 South African bank) and a structured peer-review pass. v0.4 strengthens the construct's architecture, not its evidence base; the empirical programme is consolidated onto the v1.0 path.

Lineages and mechanisms flagged for absorption.

Absorbed in v0.4: System Dynamics (Forrester 1961; Senge 1990; Sterman 2000). The argument the candidate flagged is now load-bearing in the source paper §3: the SRD framework is structurally system-dynamics-in-measurement-vocabulary. The t-state space is stocks; the t-intervals are delays; the diagnostic matrix is a phase plane. v0.4 promotes System Dynamics to the primary theoretical lineage, bridged from Galbraith's information-processing theory. The fuller simulation programme it opens (engagement-time what-if simulation) is named as a v1.0+ open question, deliberately deferred on tooling and empirical grounds.

v0.5 absorption candidate: High-Reliability Organisations (Weick & Sutcliffe 2007; LaPorte & Consolini 1991; Weick & Roberts 1993). Detection-side antecedents of t₀→t₁ under uncertainty. Flagged in Olivier 2026 §3 as a v0.4+ workstream; absorption pending an application context that surfaces high-reliability or crisis-response framing.

Positional-harm candidate: a second mechanism for the "AI makes it worse" verdict. As specified, the verdict fires on a temporal-conversion harm: AI accelerating detection (t₀→t₁) into a chain that cannot commit (t₁→t₂) or absorb (t₂→t₃). A second, positional harm appears where AI-generated output succeeds technically but sits high on the value chain, close to the customer: commoditised generation homogenises the output and erodes differentiation and authenticity. SRD measures temporal distance; this introduces a value-chain-position moderator (proximity to the customer, in the Wardley sense) where the homogenisation harm rises with proximity. Absorption pending an engagement that makes the positional harm measurable, and a differentiation/imitability lineage to anchor it.

Prior-art demarcation candidate: differentiation, not absorption. SRD's two nearest published priors both sit in the Enterprise Engineering Series, and each is demarcated rather than absorbed. Dietz & Mulder (the formal prior; already grounds the Foundations) model an organisation as a state-space over a discrete time scale and an explicit set of delays (the closest formal cousin to SRD's stock-and-interval space) but bound their account at the committed coordination act, leaving the antecedent sensing moment outside the field. Kulkarni et al. (2023; the motivational prior) state SRD's premise almost verbatim (the right intervention at the right time against an ever-shortening window) but answer it with a generative enterprise digital twin, and treat AI as the remedy. SRD's contribution is the measurement object: temporal-distance-as-diagnostic, the convert / commit / absorb verdicts, and the reading in which AI can make the gap worse. The vocabulary and the formalism are conceded; the diagnostic axis is not.

Theoretical extension is engagement-driven. Lineages get absorbed when an application surfaces a gap the current theory cannot explain, and the nearest neighbours get demarcated, not annexed.

Open questions the Playbook does not yet have a position on.

1. Cross-cultural validity

   All applications to date are South African. Whether the diagnostic protocol holds in cultures with materially different decision cadences (Japanese ringi, Northern European consensus, US-public-company quarterly) is empirically open.

2. AI-era window-contraction parameters

   §A.3 places signals against a window. How fast windows are actually contracting in different sectors is unmeasured. The Triad assumes contraction; quantification is pending.

3. Sector specificity

   Financial services applications dominate the empirical record. Sector-specific calibration libraries are not yet built; the Playbook treats this as a v0.8+ workstream.

4. Single-analyst confidence intervals

   Most engagements run with one analyst. The relationship between single-analyst measurement and the inter-analyst reproducibility standard in Part C is named but not yet quantified.

Two doors.