Individual AI Productivity
Equip employees with AI assistants and tools that improve everyday knowledge work, creativity and decision support.
- AI assistants
- Knowledge work
- Personal productivity
The EAOC Framework
Build the organizational capability that makes AI valuable.
Enterprise AI Operating Capability connects strategy, execution, governance and adoption into a repeatable system for translating AI opportunities into sustainable business outcomes.
A working definition
What is Enterprise AI Operating Capability?
An enterprise's repeatable ability to identify, prioritize, implement, govern, adopt and continuously improve AI-enabled ways of working that deliver measurable business outcomes.
Four AI capability areas
Four ways AI can become part of the enterprise.
Organizations may begin in different places, but sustainable value requires a connected view of personal productivity, embedded business AI, intelligent operations and governance.
AI Embedded in Business
Integrate AI into business processes, products and solutions to improve experiences, decisions and outcomes.
- Business processes
- Products & solutions
- Decision intelligence
AI-Powered Operations
Apply AI and automation to IT operations and service delivery to make services more proactive, predictive and resilient.
- Service delivery
- Intelligent operations
- Self-Driving IT
04
AI Governance
Cross-cutting across all three AI domains
Establish the accountability, risk practices and controls required to use AI responsibly and with confidence.
- Responsible AI
- Risk & security
- Policy & assurance
How the model fits together:
These remain four distinct enterprise AI capabilities. The first three describe where AI is applied, while AI Governance operates across all three. The EAOC domains below describe what the organization must build to scale them responsibly and realize value.
Four enabling EAOC domains
The organizational foundations that make these AI capabilities scalable.
01
Strategy & Value
Align AI ambition, investment and use-case priorities with measurable business outcomes.
- Business alignment
- Use-case portfolio
- Value realization
02
Technology & Data
Create trusted data foundations, scalable architecture and disciplined lifecycle practices.
- Data readiness
- Architecture & integration
- AI lifecycle
03
Governance & Trust
Embed accountability, responsible AI, security and assurance into how AI operates.
- Responsible AI
- Risk & security
- Accountability
04
People & Adoption
Redesign work, build skills and create the conditions for sustained behavioral adoption.
- Work redesign
- AI literacy
- Change & adoption
The operating loop
From opportunity to continuously improving value.
Every use case moves through a governed lifecycle with clear ownership, evidence and outcome measures.
01Discover
02Prioritize
03Design
04Validate
05Deploy
06Adopt
07Improve
Maturity journey
Progress from experimentation to an AI-native enterprise.
Experimenting
Isolated pilots, local enthusiasm and limited enterprise coordination.
Establishing
Initial ownership, standards, governance and shared foundations.
Scaling
Repeatable lifecycle practices and cross-functional adoption.
Optimizing
Portfolio-level value management and continuous improvement.
AI-native
AI embedded into operating models, workflows and decisions.
Make the framework practical