AI-Native Foundations

Foundations

AI-Native

An organization that embeds AI into its operating model, workflows, and decisions by design, rather than bolting AI onto existing processes.

Artificial Intelligence (AI)

Systems that perform tasks normally requiring human intelligence, such as understanding language, recognizing patterns, and making predictions.

Machine Learning

A branch of AI where systems learn patterns from data to make predictions or decisions without explicit programming.

Generative AI

AI that creates new content — text, images, code — by learning patterns from large datasets.

Large Language Model (LLM)

A generative AI model trained on vast text to understand and produce human-like language.

Concepts

Prompt

The input or instruction given to a generative AI model that shapes its output.

Tokens

The chunks of text an LLM processes; models have limits on how many they can handle at once.

Training Data

The data a model learns from; its quality and representativeness shape the model's behavior and biases.

Hallucination

When generative AI produces confident but false or fabricated information, requiring human verification.

Context Window

The amount of text a model can consider at once; larger windows allow more context but have limits.

AI-Native Model

AI-Native Operating Model

Structuring people, processes, and technology so AI is integral to how value is created and delivered.

Human-AI Collaboration

Designing work so AI augments people — accelerating tasks — while humans provide judgment and accountability.

Augmentation vs. Automation

AI can augment human work (assist and accelerate) or automate it (replace steps); most value comes from thoughtful augmentation.

AI Fluency

The broad capability across an organization to use AI effectively and responsibly in everyday work.

Use Cases

Identifying Use Cases

Finding where AI can add the most value by improving speed, quality, insight, or experience.

High-Value Use Cases

Applications with strong impact and feasibility — such as drafting, summarizing, analysis, and support — prioritized first.

Prioritizing Use Cases

Evaluating candidate uses by value, feasibility, and risk to sequence adoption.

Proof of Concept

A small experiment to validate an AI use case before scaling investment.

Responsible AI

Responsible AI

Using AI ethically, transparently, fairly, and securely, with human accountability for outcomes.

Bias and Fairness

AI can reflect and amplify bias in data; responsible adoption tests and mitigates unfair outcomes.

Data Privacy and Security

Protecting sensitive data and avoiding leakage when using AI tools, especially public ones.

AI Governance

Policies, roles, and guardrails that guide how AI is adopted and used across the organization.

Adoption

Starting the AI Journey

Beginning with awareness, small wins, and responsible guardrails, then scaling what works.

Change and Culture

Adopting AI is a change effort requiring new skills, mindset, and ways of working, not just tools.