REAL AI Adoption Framework

The Four Pillars of REAL

Our framework divides AI adoption into four complementary areas, each addressing critical success factors

📋

Recommend

Targeted AI adoption. Identify strong use cases, create an AI Menu, and clarify organizational AI policy to guide employees on best practices.

✓

Evaluate

Ensure good returns from AI tools through rigorous testing. Apply AI Usefulness Test and maintain human-in-the-loop protocols for quality assurance.

⚙

Adapt

Re-engineer enterprise processes to integrate AI directly into operational systems. Evolve roles and skills to match the new technology landscape.

🧠

Learn

Build broad workforce understanding through AI literacy programs. Teach prompt engineering and AI-Assisted Thinking to engineers, planners, and operators across the organization.

Why REAL for Infrastructure?

Infrastructure companies are not technology companies. They're bound by regulations where reliable practices are paramount. The REAL Framework recognizes these constraints while preparing organizations for sustainable, scalable AI integration that delivers measurable operational benefits without sacrificing risk management.

“We created the REAL framework to be practical and implementable by critical infrastructure companies, where managing risk is key.”

Renbo Huang

Creator, REAL AI Adoption Framework

“In our view, AI and digital enablers have the potential to significantly enhance and inform human opinion. Through the REAL implementation framework, we implement a true interface between human capital and the vast potential of AI.”

Mehdi Danesh

Managing Partner, Resolute

Understanding AI Technology

Know the capabilities and limitations of modern AI to make informed deployment decisions

Two Key AI Types

Large Language Models (LLMs)

How they work: Use neural networks to predict the next "token" (word). When paired with vector search, they can search by meaning rather than specific keywords.

Neural Networks: Computers trained through mathematical optimization
Vector Databases: Search by meaning and similarity
Natural language input/output

Machine Learning (ML)

How it works: Algorithms that learn patterns from data. Neural networks are one important family, while Deep Learning uses many layers for complex relationships.

Pattern recognition from historical data
Can reflect biases in training data
Excellent for predictive tasks

AI Strengths

Summarization: Process large datasets quickly and extract meaning
Brainstorming & Research: Generate comprehensive initial ideas and frameworks
Search by Meaning: Find relevant information using semantic similarity
Translation & Code Generation: Convert between languages and coding languages
Pattern Recognition: Detect anomalies and defects in large datasets
Behavior Modeling: Learn from past decisions and personalize responses

AI Limitations

Hallucinations: Makes up information when taken beyond training data
Limited Reasoning: Can break down problems but struggles with deep deductive reasoning
No Real-Time Learning: Cannot immediately incorporate new information permanently
Image Details: Generates statistical averages, not accurate specific details
Formatting Blindness: Ignores document formatting that may convey meaning
File Type Constraints: Cannot read proprietary industry-specific file formats

The Integration Layer Opportunity

LLMs are emerging as an application integration layer. Through technologies like Model Context Protocol (MCP), AI can communicate between different applications using natural language instead of precise APIs. This enables easier integration, vendor flexibility, and simplified agentic AI deployment.

AI Adoption Path

Move progressively from augmentation to automation, building organizational confidence at each stage

Human Adoption Journey

The path from AI introduction to full automation is graduated and should be carefully evaluated at each stage:

Phase 1
Task Augmentation

Users in Control

Break activities into small logical tasks. AI executes tasks, which users review and integrate into the larger activity. Users remain fully in control of decisions and execution.

Phase 2
Full Augmentation

Quality Gatekeeper

AI runs complete activities up to execution/delivery. Users review AI results as a second opinion or quality check before final execution or delivery.

Phase 3
Automation (Agentic)

AI Independent

AI runs and executes complete activities without user intervention. Only deployed after proven accuracy over weeks or years at previous stages.

Phased Implementation

Phase 1
Develop AI Trust

Building Internal Capability (Weeks 1-6 Months)

Determine areas of positive ROI using interviews and activity mapping
Design AI strategy aligned with enterprise strategy
Build confidence with well-established use cases
Educate workforce on AI fundamentals and capabilities
Establish AI policy clarifying usage and data confidentiality
Select foundational AI platforms and tools

Success Metrics: Internal understanding, AI proficiency, policies and strategies developed

Phase 2+
Strategic AI

Delivering Customer Value (6+ Months)

Implement one strategic AI focus aligned with corporate strategy
Deploy to specific use cases within infrastructure lifecycle
Reinforce AI usage through integration and training
Measure and optimize for business impact

Success Metrics: Speed to resolve, reliability, cost savings, customer satisfaction

Strategic AI Focus Options

Choose a clear, focused course aligned with your corporate strategy to deliver differentiated customer value

💰 Cost Efficiency

Automate high-volume transactions, documentation review, and routine customer requests. Reduce manpower needs and lower cost-to-serve.

⚡ Speed of Service

Digitize end-to-end processes, enable self-service, and pre-populate forms. Minimize handoffs and customer effort through AI-driven workflow automation.

✓ Reliability & Quality

Use AI as a quality assistant through anomaly detection, predictive maintenance, and compliance reviews. Augment human decision-making and reduce errors.

🔄 Real-Time Operations

Optimize outcomes in real-time using IoT and smart technologies. Anticipate customer needs and adapt operations before issues arise.

📈 Growth & Scaling

Accelerate product development, minimize employee workload through AI assistance, and build scalable infrastructure for future growth.

Key Principle

Rather than pursuing broad "AI transformation," successful infrastructure companies choose one clear, focused strategic direction that aligns with corporate strategy and delivers measurable customer value. This focus allows for deeper integration, better resource allocation, and more sustainable competitive advantage.

Key Implementation Practices

Proven approaches to ensure sustainable and scalable AI adoption

The AI Menu: Guiding Safe Usage

Create a sanctioned set of AI tasks with clear use cases, maturity levels, and limitations. This gives employees clear direction on where to start and what AI can safely accomplish.

                    Maturity Levels
                    A - Very Reliable: High accuracy, use as final deliverable without review
B - Usable: Accurate with limited customization, suitable for low-scrutiny work
C - Draft/Prototyping: Prototyping only, requires full human review and verification
F - Unreliable: Do not use for this activity

                

AI Usefulness Test

Before deploying AI, test whether it provides genuine advantage over non-AI alternatives:

Does the feature truly leverage AI strengths? Would it work without AI?
For predictions: Can simpler rules achieve the same result?
For accuracy tasks: Is occasional error (90-95% accuracy) acceptable?
For execution: Does the system need to handle unexpected events?
For interactions: Does this improve human-to-human relationships or replace them?

Quality Assurance for AI

Unlike traditional software, AI testing must emphasize error discovery and edge cases:

Conduct scenario-based validation to identify error boundaries
Test performance under abnormal conditions
Use human-in-the-loop protocols for complex decisions
Consider parallel diagnosis (human and AI together)
Use multiple LLMs to fact-check each other
Document limitations and create job aids for common pitfalls

Building Organizational AI Literacy

Educate teams on how modern AI works and why it sometimes fails
Teach prompt engineering and AI thinking to relevant staff
Provide AI policy training to ensure compliance and safe usage
Create feedback mechanisms to continuously improve AI implementations
Share success stories and lessons learned across the organization

Avoiding Common AI Pitfalls

Learn from others' experiences to accelerate your successful AI adoption

The Three Major Pitfalls

Complacency

Over-reliance on AI can lead people to trust AI when their expertise is critical. AI is limited by training data and poor at rare edge cases.

Solution: Maintain human expertise. Use AI as augmentation, not replacement, in high-stakes decisions.

Over-Summarization

Over-using summarization features leads to poor understanding of core data and can miss critical edge insights.

Solution: Use AI summaries as starting points. Always review original data for complex decisions.

Maintainability Debt

As humans rely on AI-generated code, designs, and text, understanding and maintaining outputs becomes difficult.

Solution: Develop in small chunks. Maintain human understanding. Provide thorough review and testing.

Key Safeguards

Learning: Educate on how AI works and why it fails
Implementation QA: Test edge cases and error scenarios thoroughly
Output Evaluation: Compare AI outputs with human outputs, especially early on
Chunked Development: Break large tasks into subtasks for better control
Clear Policy: Define what data can and cannot go into AI tools