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AI Use Cases

OpenAI's analysis of over 600 enterprise AI deployments found that nearly all use cases fall into six primitives. These primitives describe the type of work AI does, not the tools or platforms involved. Understanding them helps you classify your own workflows and find the right building blocks faster.

The six primitives and department examples in this section are adapted from OpenAI's Identifying and Scaling AI Use Cases and made platform-agnostic.

Connecting to the Framework

The Business-First AI Framework helps you find opportunities (Step 1 — Analyze) and deconstruct them into building blocks (Step 2). Use these six primitives to classify the opportunities you find — they tell you what type of AI work each opportunity involves, which makes choosing the right building blocks easier.

The Six Primitives

Primitive What AI Does Typical Building Blocks Example
Content Creation Drafts, edits, translates, repurposes Prompt, Context, Skill, Project First-draft blog posts in brand voice
Research Searches, synthesizes, structures information Prompt, Context, Agent, MCP Multi-source competitive analysis
Coding Generates, debugs, ports, explains code Prompt, Context, Agent Python scripts for non-coders
Data Analysis Harmonizes data, identifies trends, visualizes Prompt, Context, Skill Expense analysis across sources
Ideation & Strategy Brainstorms, plans, gives feedback, models scenarios Prompt, Context, Project Campaign ideation with constraints
Automation Executes repeatable routine tasks with minimal human involvement Skill, Agent, MCP Weekly competitive update pipeline

Content Creation

AI drafts, edits, translates, and repurposes content across formats. This is the most common entry point for teams adopting AI — nearly every department produces written content, and AI can handle first drafts, formatting, and adaptation between audiences.

Content creation works best when you provide context (brand voice, style guides, examples) so the AI produces output that matches your standards rather than generic copy.

Content Creation detail

Research

AI searches, synthesizes, and structures information from multiple sources. Research use cases replace the hours spent gathering, reading, and summarizing — the AI handles the collection while you focus on judgment and decision-making.

Research primitives are particularly powerful when combined with MCP connections to external data sources, letting the AI pull from your actual tools rather than just web search.

Research detail

Coding

AI generates, debugs, ports, and explains code. This primitive isn't limited to software engineers — it extends to anyone who needs to create scripts, formulas, queries, or technical artifacts as part of their work.

Coding use cases range from simple formula generation (Excel, SQL) to full application development with agents that plan, write, test, and iterate autonomously.

Coding detail

Data Analysis

AI harmonizes data from multiple sources, identifies trends, and produces visualizations. Data analysis use cases turn raw information into structured insights — the AI handles cleaning, formatting, and pattern recognition while you direct the analysis.

This primitive often pairs with coding (generating analysis scripts) and research (interpreting results in context).

Data Analysis detail

Ideation & Strategy

AI brainstorms ideas, plans approaches, provides feedback, and runs scenario analysis. This is the most collaborative primitive — AI serves as a thinking partner rather than an executor, helping you explore possibilities you wouldn't consider alone.

Ideation works best in project workspaces where the AI has persistent context about your goals, constraints, and past decisions.

Ideation & Strategy detail

Automation

AI executes repeatable routine tasks with minimal human involvement. Automation is the highest-autonomy primitive — once configured, these workflows run on schedule or in response to triggers, producing consistent results without manual intervention.

Automation typically builds on the other primitives. A content creation workflow becomes automation when it runs on a schedule. A research workflow becomes automation when it monitors sources continuously.

Automation detail

Classifying Your Use Cases

When a use case seems to span multiple primitives, identify the primary output to pick the right one:

If the main output is... The primary primitive is... Even though it also involves...
A written document Content Creation Research to gather source material
A structured summary of findings Research Content creation to format the report
A working script or application Coding Research to find the right approach
Charts, trends, or insights from data Data Analysis Coding to build the analysis
A plan, list of options, or recommendation Ideation & Strategy Research to inform the strategy
A pipeline that runs without you Automation Any of the above as individual steps

Most real workflows combine two or three primitives. The classification helps you find the right starting point and examples — not a rigid box.

Primitives vs. Building Blocks

These are different lenses on the same work:

Concept What it answers Where to learn more
Primitives (this section) What type of work is AI doing? You're here
Building Blocks How do you implement it? Agentic Building Blocks
Autonomy Levels How much AI involvement? Business-First AI Framework

Primitives help you browse use cases by category. Building blocks help you assemble the implementation. Autonomy levels help you decide how much control to hand over. All three work together.