AI terminology shouldn't be a barrier to adoption. Here's a plain-language guide to the concepts your leadership team and employees need to understand.
Software that can learn from data, recognize patterns, and make decisions, helping your teams work faster and smarter without replacing human judgment.
Context:When a business says it's 'using AI,' it usually means tools powered by machine learning that assist with tasks like writing, analysis, or automation.
The engine behind tools like ChatGPT and Claude. LLMs understand and generate human language, enabling everything from drafting emails to analyzing reports.
Context:LLMs are trained on massive amounts of text data. Popular LLMs include GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google), and Copilot (Microsoft).
An AI system that can take actions on your behalf: researching prospects, scheduling meetings, or processing data across multiple steps autonomously.
Context:Unlike a chatbot that just answers questions, an agent can use tools, make decisions, and complete multi-step workflows with minimal human input.
AI that doesn't just answer questions but actively works through multi-step tasks, making decisions and using tools along the way to complete real business workflows.
Context:This is the direction AI is heading: from passive Q&A to active execution. Governance and guardrails become critical as AI takes on more autonomous work.
A standard that lets AI models connect to your existing tools and data sources (your CRM, email, databases) so AI works with your systems, not in isolation.
Context:Think of MCP as a universal adapter. Without it, AI tools operate in a silo. With it, they can read your Salesforce data, check your calendar, and update your project management tool.
Reusable instructions that tell AI exactly how to perform a specific task. Think of them as playbooks your teams can use without needing to be prompt engineers.
Context:A well-crafted skill might be 'Research this company and draft a personalized outreach email.' Teams share and refine these over time.
A sequence of AI-powered steps that automate a business process from lead research to proposal drafting to follow-up scheduling, end to end.
Context:Workflows connect multiple AI actions into a pipeline. A sales workflow might: research a prospect, enrich their data, draft an email, and log everything to CRM.
The process of preparing your people, processes, and workflows to effectively use AI tools. It goes beyond deployment to build the literacy and habits that drive real adoption.
Context:Most AI initiatives fail not because of bad technology, but because teams were never enabled to use it. Enablement closes that gap.
The foundational understanding of what AI can and cannot do, how it works at a high level, and where it fits in your specific role and workflows.
Context:AI literacy is not about becoming a data scientist. It's about knowing when to use AI, when not to, and how to evaluate its output critically.
The policies, guardrails, and oversight structures that ensure AI is used safely, securely, and in alignment with your organization's values and compliance requirements.
Context:Good governance answers: What can AI access? Who approves its actions? How do we audit what it did? What happens when it makes a mistake?
The practice of crafting specific instructions (prompts) to get the best possible output from an AI model. It's part art, part science.
Context:While prompt engineering skill helps, the goal of AI enablement is to build reusable skills and templates so every team member doesn't need to be an expert prompter.
A structured database that maps relationships between entities like people, companies, concepts, and places. Search engines and AI systems use knowledge graphs to understand context and connections between information.
Context:Google's Knowledge Graph powers the information panels you see when searching for companies or people. When AI systems answer questions about a business, they often pull from knowledge graph data to understand what that business does, who runs it, and how it connects to other entities.
A technique where AI retrieves relevant information from your data before generating a response, making outputs more accurate and grounded in your actual business context.
Context:RAG is how companies make AI 'know' their internal data without retraining the model. It pulls from your documents, knowledge base, or CRM before answering.
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