Learning path · For managers and leaders who need to adopt and spread AI across the company
12 Terms Every AI Adoption Lead Should Know
You’ve been told to 'adopt AI,' but half of what comes up in vendor meetings is unfamiliar. The problem isn’t ability. It’s vocabulary. This path covers 12 concepts you’re sure to meet in enterprise AI adoption, in the order you’ll meet them: from the fact that employees are already using AI on the quiet (shadow AI), to choosing official tools, safeguards, and automation.
Skimming each term’s one-line definition gives you the full picture in 15 minutes. Click through to the terms you want to explore for real-world examples and FAQs.
- 1Shadow AIAI tools employees use for work without IT approval or oversight. A blind spot for data leaks, and also a signal that employees want to work with AI.
- 2Enterprise AIAI services a company adopts officially, with security, admin controls, and data protection. The key difference from personal AI is that your input data is not used to train the model.
- 3AI LiteracyThe basic competence for everyone in the organization to understand how AI works and where it falls short, and to use it safely and effectively at work.
- 4AI AgentAI that goes beyond simply answering: given a goal, it makes its own plan and uses tools to carry out multi-step tasks.
- 5RAG · Retrieval-Augmented GenerationA technique where AI first retrieves external knowledge, such as internal documents and databases, before answering, then generates its response grounded in that evidence.
- 6Prompt EngineeringThe skill of designing and refining instructions (prompts) so generative AI produces the results you want. The more clearly you specify role, context, examples, and format, the better the quality.
- 7HallucinationWhen AI plausibly makes up information that isn’t true and presents it as fact. A risk you must manage when using generative AI.
- 8Human-in-the-Loop · HITLThe principle of deliberately designing human review and approval points into how an AI system operates (Human-in-the-Loop). The higher the risk of a decision, the stronger the human involvement.
- 9AI Governance · EthicsAn organization’s management framework of principles, policies, and controls for using AI safely, fairly, and responsibly.
- 10Private AI · On-Premises LLMAn approach that runs a large language model (LLM) on in-house servers (on-premises) or in a controlled private cloud, instead of sending company data to external APIs, to prevent data leaks.
- 11AI Workflow AutomationUsing AI to handle work that needs human judgment and was hard to automate before, such as email triage, document summaries, and report drafts. The next step beyond rule-based RPA.
- 12Fine-TuningFurther training an already-trained AI model on a small amount of data so it fits a specific field, task, or tone.
Related programs
📚 AX Workshop: Business Transformation with AI Agents📚 ChatGPT & AI Tools TrainingRead all 12? The next step is action
10 questions, 3 minutes. You’ll get your organization’s AI adoption stage and a recommended training direction.
🧭 Take the free AI readiness assessmentFAQ
Where should we start with AI adoption?
Start by assessing where you are today. Find out which AI tools employees already use (shadow AI) and what stage your organization’s readiness is at. Then proceed in this order: official tools, guidelines, training.
Do non-developers need to know these terms too?
Yes. These 12 are not technical terms but decision-making terms. Vendor selection, security policy, and training planning are all decided on top of these concepts.
How long does it take to read everything?
Skimming just the one-line definitions takes about 15 minutes. Reading each term’s detail page takes about an hour. It’s also a good size to split up as team study material.