Full Operational Capability

Make AI a dependable part of daily operations.

Why this Phase exists

FOC is reached when AI is no longer treated as a special initiative, but as a normal part of how the organization operates. Systems are reliable, users are enabled, and governance mechanisms are in place to manage performance, risk, and change.

At FOC, AI delivers consistent value and survives personnel changes, leadership shifts, and evolving business conditions.

Many organizations mistake IOC for FOC - often leading to silent failure months later.

Where this phase fits in the journey

Discover
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Pilot
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IOC
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FOC
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Scale
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Frameworks for this phase

AI Altitude Model

AAM

The AI Altitude Model (AAM) is a staged operating framework that helps organizations understand where they are in their AI adoption journey and what is required to safely progress from experimentation to full operational control and sustained value realization.

Explore Framework

Flight Crew Operating System

FC-OS

The end-to-end operating system that governs how organizations discover, deploy, adopt, and scale AI.

Explore Framework

AI Adoption Operating Model

AAM

A structured model that maps AI initiatives from experimentation to sustained operational impact.

Explore Framework

OODA Loop

OODA

A continuous decision loop to observe, orient, decide, and act on AI performance in real operations.

Explore Framework

Operational Viability Index

OV

A composite index that measures whether an AI initiative is ready to operate sustainably.

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Control Tower Governance Model

CT

A centralized governance model that manages AI execution, risk, and value realization.

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Champions Program

CHAMP

A structured network of internal champions that drive adoption inside the business.

Explore Framework

Briefings to master this phase: 

How you know you're in this phase

  • AI solutions operate as part of standard business workflows
  • Governance, security, and compliance are embedded, not bolted on
  • Business leaders treat AI capabilities as dependable infrastructure
  • Operational teams trust outputs enough to act on them

What must be true to move forward

  • AI performance is stable, predictable, and continuously monitored
  • Ownership, funding, and accountability are institutionalized
  • Training and onboarding support sustained usage
  • AI is no longer viewed as “new” or experimental