Decision latency, not tooling, is the binding constraint
Organisations that have invested in data infrastructure and AI tooling are discovering that neither generates competitive advantage if the decision process they feed is structured for weekly or monthly cycles.
Decision processes still run on reporting cadences, not signal speed
Most enterprise decision processes were designed for the reporting cadence of the organisation, not for the latency of the problem. A customer churn signal that surfaces in a real-time data platform gets escalated to a weekly operations meeting, reviewed at a monthly business review, and actioned at the next quarterly planning cycle. By that point, the signal is historical context, not an actionable prompt.
D2 (Digital Cognitive Organisation) describes the organisation as a system of designed decisions, not a hierarchy of roles. In that frame, decision latency — the time between a signal arriving and a decision being made and executed — is the primary operating variable. Technology reduces the cost of generating signals. It does not reduce decision latency unless the decision process is redesigned to match.
The emerging shift is practitioners recognising that data platform investments and AI tooling are generating diminishing returns not because the tools are inadequate but because the decision architecture they feed is unchanged. Redesigning decision loops — shortening the cycle, clarifying authority, defining the trigger conditions that initiate a decision without escalation — is the execution move that releases the value already invested in data and AI infrastructure.
Loop redesign is a process task, not a technology purchase
For transformation practitioners, the implication is concrete: decision loop redesign is a process design task, not a technology task. It does not require new tooling; it requires mapping the existing decision process for a given domain, identifying where latency is introduced (escalation points, approval sequences, reporting cycles), and redesigning those steps with explicit trigger criteria and authority assignments.
The organisations that are making this shift are not buying decision intelligence platforms. They are doing process design work on existing decision structures — starting with the highest-frequency decisions in their operations and removing the escalation steps that were introduced for consistency, not because the decision genuinely required committee input.
Faster time-to-action without a new stack is the tell
Watch for: organisations reporting shorter time-to-action on operational signals without changes to their data or AI stack. That is the signature of decision loop redesign rather than technology investment. Also watch for: new practitioner roles with titles like "decision architecture," "decision operations," or "signal-to-action design" — these are indicators that the function is being explicitly staffed rather than absorbed into existing roles.
Watch against: "AI decision support" investments that add intelligence to existing escalation processes without changing the escalation logic. These extend the latency chain rather than shorten it.
“Technology reduces the cost of generating signals. It does not reduce decision latency unless the decision process is redesigned to match.”
Strip one high-frequency decision down to its real authority
Select one high-frequency operational decision in your domain — a pricing adjustment, an inventory reorder, a resource allocation. Map the current cycle from signal to action. Count the escalation steps. For each step, ask: is this step here because the decision genuinely requires committee input, or because that was the default process design? Remove the steps that are default. Define explicit trigger criteria and authority assignments for what remains. Run the redesigned loop for 30 days and measure the latency reduction. That 30-day result is the model for the rest.
Sources
- 01dtmi-content-generator/references/shared-6xd-canonical.md
