Clear definitions of AI terms for business leaders. No jargon, just practical explanations you can use.
By Matt Monihan | Updated January 2026
Unauthorized use of AI tools by employees without company oversight or approval. This happens when employees use ChatGPT, Claude, or other AI tools for work tasks without IT or management awareness.
Why it matters:
Shadow AI creates data security risks when employees paste sensitive company or customer data into public AI platforms. It also creates compliance issues and potential liability.
Related: AI Policy, How to create an AI policy
An automation design pattern where AI handles routine tasks but humans review and approve critical decisions before they are executed. The AI does the heavy lifting; humans make the final call.
Example:
An AI drafts customer responses, but a human reviews and approves each one before sending. The AI saves time; the human ensures quality and catches errors.
Related: Autonomous Agent, AI Modalities
A three-step AI implementation methodology developed by AutomationTactics based on 15+ years of implementation experience. Most companies fail at AI because they skip to automation without doing the foundational work.
| Step | What it means |
|---|---|
| 1. Document | Write down how your processes actually work before trying to automate them |
| 2. Train | Build AI literacy so your team understands what AI can and cannot do |
| 3. Automate | Build targeted automations for documented workflows with trained teams |
Core principle:
You cannot automate what you have not documented, and you cannot deploy tools your team does not understand.
Related: Full methodology, Process Documentation
A structured assessment of organizational readiness for AI implementation. Evaluates capabilities across multiple areas to identify gaps and prioritize improvements before investing in AI tools.
The AutomationTactics framework covers eight capability areas:
Related: View the full AI Maturity Map
A cross-functional governance body responsible for overseeing AI initiatives within an organization. Makes decisions about AI policy, tool approval, and implementation priorities.
Typical members:
Related: How to form an AI steering committee
A formal document defining how an organization adopts, uses, and governs AI technologies. Sets clear boundaries, establishes accountability, and ensures AI use aligns with company values and legal requirements.
Policy vs. Governance:
An AI policy is the written rules. AI governance is the broader system of oversight (steering committee, approval workflows, monitoring) that enforces those rules.
Related: How to create an AI policy, Shadow AI
Software that automates repetitive, rule-based tasks by mimicking human interactions with computer systems. Clicks buttons, fills forms, copies data between systems.
RPA works best for:
RPA does not require AI, but modern RPA platforms often include AI capabilities for handling exceptions or processing unstructured data.
Related: RPA Deployment
A technique that combines AI language models with external knowledge sources. Before generating a response, the AI retrieves relevant information from company documents, databases, or other sources.
Why it matters:
RAG reduces hallucinations (made-up information) by grounding AI responses in actual data. It allows AI to answer questions about your specific company information without expensive model training.
Related: Document Intelligence
An AI assistant that works alongside humans in their existing applications. The AI suggests content, answers questions, and automates tasks while the human remains in control and makes final decisions.
Common examples:
Related: Human-in-the-Loop, AI Modalities
An AI system that can execute multi-step tasks independently, making decisions and taking actions without human intervention at each step. More capable than copilots but requires more robust guardrails.
Important consideration:
Autonomous agents are powerful but risky without proper controls. Most organizations should start with human-in-the-loop approaches and graduate to autonomous agents as they build trust and guardrails.
Related: Autonomous Agents deep dive, Human-in-the-Loop
Written records of how business processes work, including step-by-step instructions, decision points, exceptions, and responsible parties. The essential foundation for any automation initiative.
Why it comes first:
You cannot automate what is not documented. Attempting to automate undocumented processes leads to automating chaos at computer speed.
Coordinating multiple automated tasks, systems, and human touchpoints into a unified process. Ensures handoffs happen correctly, exceptions are routed appropriately, and the overall workflow completes successfully.
Example workflow:
Customer submits form (trigger) > AI extracts data > System creates record > Manager receives approval request > Upon approval > AI generates response > Email sends to customer
Related: Workflow Engines, Monday.com Partnership
AI terminology can be confusing. A 30-minute conversation can clarify how these concepts apply to your specific business.
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