• Version
• Download 33
• File Size 1.75 MB
• File Count 1
• Create Date April 27, 2026
• Last Updated April 27, 2026

AI Target Operating Model

The AI Target Operating Model (AI TOM) provides a future‑state blueprint for how organisations can operate with AI‑enabled. It integrates customer insight, work orchestration, finances and organisational design into a single, coherent operating system for modern enterprises.

What the AI TOM Enables

• Continuous flow of work through clearly defined cadences that eliminate departmental hand‑offs and reduce delays.
• Customer‑centred operating structures, built around real end‑user journeys rather than internal functions.
• AI‑supported decision‑making, using machine learning, neural networks, NLP, deep learning, fuzzy logic and expert‑system concepts as foundational knowledge domains.
• Work orchestration as the “golden source” of all work, maintaining a full chain of custody from insight to outcome.
• Financial agility, replacing traditional CapEx/OpEx constraints with long‑horizon strategic budgeting tied directly to ROI and value for money.
• Operational agility, ensuring optimisation continues after delivery through lifecycle‑based impact tracking.
• Organisational structures built for adaptability, using Customer Journey Groups and a new HR data model centred on individuals rather than roles.

Why Organisations Use the AI TOM

The AI TOM helps enterprises:

• Reduce waste and increase clarity by defining expected outcomes before work begins
• Align customer, business and technical priorities through shared OKRs
• Bridge business, strategy, solution and delivery work in a single orchestration layer
• Enable real‑time reporting and dependency mapping
• Support large‑scale transformation with measurable impact

It is designed for organisations seeking high‑agility, AI‑enabled, customer‑driven operating models.

Download Includes

• AI Target Operating Model (AI TOM) overview
• Core principles and structures
• Customer Journey Group model
• Work Orchestration Lifecycle
• Financial and operational frameworks
• LLM workflow location
• AI‑related conceptual foundations (ML, NLP, NN, expert systems, fuzzy logic)