Enterprise Work, Decisions and AI Participation

Enterprise Work, Decisions and AI Participation reconstructs how work actually reaches an outcome. It maps formal and informal AI use, decision points, hidden effort and reversibility. Goal-directed work is assessed through bounded goals and permitted action space rather than forced into a fixed sequence.

CONNECT THIS TOPIC TO AI TRANSFORMATION

Start AI workflow transformation with the work and decisions involved. Identify where AI can contribute and what remains under human accountability.

Explore the holistic AIDIVE framework

Why this matters

Documented processes can miss the judgement and coordination that make work function. Understanding that reality helps distinguish an automation opportunity from a problem of information, policy, authority or capacity.

Questions to bring to the work

Where does an AI contribution become an owned decision, action or business-state change?
What delay or hidden work must we understand before proposing automation?

Explore this chapter

I.1.1The Lived Flow and Where It Diverges from the Documented Process

Reconstruct real cases with the people doing the work, including informal coordination, exceptions and workarounds absent from the published procedure.

I.1.2Bounded Goals and Permitted Action Space

For runtime-composed work, inspect the approved goal and permitted actions alongside the actual trajectory rather than imposing a fixed path.

I.1.3Where AI Already Participates and How Deeply

Map how deeply deployed, embedded, experimental and informal AI participates, from preparing information to triggering or executing business actions.

I.1.4Value Connection Points, Decisions and the Path to Action

Locate where an AI contribution reaches an owned decision or state change and can therefore influence the end-to-end operating outcome.

I.1.5Friction, Hidden Work and Reversibility

Trace the causes of waiting, clarification, duplicate checks and reconciliation, and consider reversibility before choosing an automation response.

What useful evidence looks like

Representative end-to-end cases showing triggers, participants, AI involvement, decisions, actions and outcomes.
A map of value connection points, bounded goals, permitted actions and material friction or reversibility constraints.

Move from understanding to practice

AR-I01

AR-I04 · Decision Point and Decision-to-Action Mapping

Framework source

Editorial adaptation from the supplied AIDIVE Version 1 master documents.

AIDIVE Integration Dimension Version-1.docx · I.1 Enterprise Work, Decisions and AI Participation; AIDIVE Integration - Practices & Playbook Version-1.docx · AR-I01, AR-I02, AR-I03, AR-I04

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