As AI continues to reshape industries and redefine how organizations operate, it’s more important than ever to speak a common language. The AI Adopter Lexicon is a curated glossary designed for business leaders, innovators, and policymakers attending ALL IN 2025. Whether you're building, adopting, or scaling AI solutions, this guide helps clarify key terms and concepts that are driving AI transformation in Canada and around the world.
AI Adopters
Companies that leverage AI-driven solutions to enhance their core services or operations.
AI Ethics
The principles guiding how AI should be developed and applied, addressing issues such as bias, discrimination, transparency, and user consent.
AI Governance
The policies, frameworks, and oversight mechanisms that guide how AI is developed and used responsibly within an organization or ecosystem.
AI Integration
The process of embedding AI solutions into existing systems, workflows, or business functions to increase efficiency, innovation, or insight.
AI Providers
Organizations whose primary business is the development or enablement of AI-based products, services, and infrastructure.
AI Readiness
An organization’s level of preparedness — in terms of strategy, data, infrastructure, culture, and talent — to successfully adopt and scale AI technologies.
AI Regulation
Governmental or institutional rules and standards that govern the development, use, and impact of AI technologies to ensure public safety, trust, and fairness.
AI ROI (Return on Investment)
A measure of the value generated by AI initiatives in relation to their cost, often used to justify investment and scale adoption.
AI Talent Gap
The shortage of qualified professionals needed to develop, deploy, and manage AI systems — a growing concern across industries.
AI-as-a-Service (AIaaS)
Cloud-based platforms that provide AI capabilities on demand, allowing businesses to access advanced tools without needing in-house development.
Bias Mitigation
Techniques and practices used to detect and reduce unintended biases in AI systems, ensuring fairness across different user groups and populations.
Custom-Built AI
Tailored AI systems designed to support or replace specific job functions. These solutions integrate into existing workflows and offer specialized capabilities that off-the-shelf products cannot match.
Edge AI
AI computation performed locally on devices (e.g., sensors, smartphones, vehicles) rather than in the cloud. It supports real-time decision-making and protects data privacy.
Explainability (XAI)
The ability to make an AI system’s decisions understandable to humans, critical for trust, oversight, and compliance — especially in regulated sectors.
Forecasts
Short-term outlooks based on current data trends, used to anticipate likely outcomes under present conditions.
Foundation Models
Large-scale AI models trained on broad datasets that can be adapted to perform a wide range of tasks across industries and domains.
General Purpose (Off-the-Shelf) AI Products
Pre-built AI tools designed for rapid deployment to solve common business needs — such as automation, data analysis, and customer engagement.
GenAI (Generative AI)
AI systems that can create original content — such as text, code, images, or music — using advanced models like large language models (LLMs).
Human-in-the-Loop (HITL)
An approach that includes human oversight or intervention in the AI decision-making process, used to improve accuracy, ethics, and accountability.
Intelligence (AI) for Business
The application of AI technologies to improve business operations across sectors such as logistics, marketing, IT, finance, and customer service.
Large Language Models (LLMs)
Deep learning models trained on massive text datasets to understand and generate natural language. They power many GenAI applications.
“Made in Canada” AI
AI systems, products, and services developed by Canadian companies, designed for global use and innovation leadership.
Model Drift
The decline in a model’s performance over time due to changes in the data or context compared to what it was originally trained on.
Projections
Longer-term analyses that explore multiple hypothetical future scenarios to understand a range of potential outcomes.
Responsible AI
The design and use of AI in ways that are ethical, transparent, inclusive, and aligned with human values and societal impact.
Synthetic Data
Artificially generated data that mimics real-world information and is used for training or testing AI models when real data is limited, sensitive, or biased.
