The Six Pillars of AI Outcomes

What are the Six Pillars of AI Outcomes?

The Orion AI Outcomes (OAIO) methodology is structured around six pillars. Each pillar addresses a critical dimension of enterprise AI adoption, ensuring organizations build AI capabilities that are valuable (Pillar 1), grounded in quality data (Pillar 2), trusted (Pillar 3), adopted (Pillar 4), economically sustainable (Pillar 5), and implementation-ready (Pillar 6).

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The Six Pillars

Pillar 1: Value & Adoption

Focus: Strategic alignment and opportunity prioritization

Key Question: Where is AI worth applying?

This pillar moves organizations from scattered experimentation to evidence-based prioritization. Teams identify high-value AI use cases, assess feasibility, and create a prioritized roadmap based on business impact and technical readiness.

Deliverables: Opportunity inventory, prioritization matrix, adoption roadmap

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Pillar 2: Data Readiness

Focus: Data quality, accessibility, and governance

Key Question: Is the data ready to support AI?

AI systems are only as good as their data. This pillar assesses data availability, quality, and governance readiness for prioritized use cases. It identifies gaps and creates remediation plans before AI implementation begins.

Deliverables: Data inventory, quality assessment, remediation plan

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Pillar 3: AI Protection

Focus: Trust boundaries and governance frameworks

Key Question: How do we ensure AI is used safely?

This pillar establishes the guardrails for responsible AI use. It defines acceptable use policies, data classification rules, approval workflows, and monitoring practices that build organizational trust in AI systems.

Deliverables: AI governance policy, acceptable use guidelines, risk controls

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Pillar 4: Experience Design

Focus: Human-AI interaction patterns

Key Question: How will people actually use this?

Technology that isn't adopted creates no value. Common adoption failures include: AI tools feeling foreign to existing workflows, users not trusting AI recommendations, interaction requiring too much effort, value not being visible to users, and senior experts feeling threatened rather than empowered. This pillar designs the user experience, identifies adoption personas, and creates training and change management plans that drive real usage.

Deliverables: User journey maps, adoption personas, training plan

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Pillar 5: FinOps

Focus: Cost modeling and economic controls

Key Question: What will this cost and how do we control it?

AI costs can spiral without proper controls. This pillar models implementation and operational costs, establishes budget controls, and creates financial governance that keeps AI investments economically sustainable.

Deliverables: Cost model, budget controls, financial governance framework

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Pillar 6: Exit & Handoff

Focus: Engineering-ready documentation

Key Question: What do builders need to implement this?

The final pillar compiles everything needed for implementation: technical specifications, integration requirements, vendor evaluation criteria, and success metrics. This ensures a clean handoff to engineering or implementation partners.

Deliverables: Technical specifications, integration requirements, vendor criteria

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How the Pillars Work Together

The six pillars are designed to be executed in sequence, with each pillar's outputs feeding the next:

From	To	Handoff
Pillar 1	Pillar 2	Prioritized use cases define data assessment scope
Pillar 2	Pillar 3	Data classification informs governance policies
Pillar 3	Pillar 4	Trust boundaries shape user experience design
Pillar 4	Pillar 5	Adoption scope determines cost modeling
Pillar 5	Pillar 6	Financial constraints inform technical specifications

The six pillars ensure that AI initiatives are valuable (Pillar 1), grounded in quality data (Pillar 2), trusted (Pillar 3), adopted (Pillar 4), economically sustainable (Pillar 5), and implementation-ready (Pillar 6).

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Why Six Pillars?

Most AI initiatives fail not because of technology, but because of gaps in one or more of these dimensions:

• No clear value - AI experiments without business alignment
• Poor data - Models trained on incomplete or inaccurate data
• No governance - Shadow AI creating security and compliance risks
• Low adoption - Powerful tools that nobody uses
• Cost overruns - AI spending without financial controls
• Implementation gaps - Strategy that can't be executed

The OAIO six pillars address each failure mode systematically, ensuring organizations build AI capabilities that actually work.