Section 3
Guiding Principles
The ten principles that govern how AI4EA is applied.
Principle 1
Business Value Before Technology
Begin with a business problem, opportunity or strategic objective rather than a model or technology.
Principle 2
Architecture Before Scale
Experiments can be rapid; scalable enterprise AI requires deliberate architecture.
Principle 3
Data and Knowledge Are Enterprise Assets
AI effectiveness depends on accessible, trusted, contextualized and governed enterprise information.
Principle 4
Govern by Design
Security, privacy, responsible AI, compliance and auditability are designed in, not added later.
Principle 5
Human Accountability Remains Explicit
Automation must not obscure accountability; oversight should match impact, autonomy and risk.
Principle 6
Evaluate Continuously
Quality, safety, cost and performance must be evaluated beyond initial testing.
Principle 7
Design for Portability and Change
Minimize unnecessary dependency on individual models, vendors and protocols.
Principle 8
Scale Through Reuse
Prefer reusable platforms, patterns, services, standards and controls.
Principle 9
Economics Are an Architecture Concern
Consumption, compute, retrieval and infrastructure economics influence architecture decisions.
Principle 10
Measure Outcomes, Not AI Activity
Models, agents and pilots are activity measures; enterprise outcomes are success measures.