Business-First AI Framework¶
The Problem¶
Most AI adoption starts with the technology — "we have ChatGPT, where should we use it?" This leads to shallow adoption and misses the workflows where AI creates real leverage. And even when people identify the right workflow, there's a gap between the idea and making it real — without a repeatable process to follow, they get stuck and stop. The Business-First AI Framework closes that gap: audit your workflows, identify where AI creates the most value, deconstruct those workflows into building blocks, then build.
How It Works¶
The framework is facilitated by three skills — reusable AI routines that guide you through each step interactively. You don't need to figure out the right questions to ask or remember the methodology — the skill drives the conversation, probes for details, and produces structured outputs you carry to the next step. You bring the domain knowledge about your work; the AI handles the process.
| Step | Skill | What it guides you through |
|---|---|---|
| 1. Analyze | analyzing-workflows | Auditing your workflows and surfacing AI opportunities |
| 2. Deconstruct | deconstructing-workflows | Breaking a workflow into steps, decisions, data flows, and failure modes |
| 3. Build | building-workflows | Designing the AI implementation and constructing platform artifacts |
Get the skills: Install the Business-First AI plugin in Claude Code (/plugin install business-first-ai@handsonai) or download the skill folders from GitHub. See How to Add Skills to Your Platform for Claude Code, Cursor, Codex CLI, Gemini CLI, and VS Code Copilot.
The Framework¶
Three steps, each with a structured process:
Step 1: Analyze Workflows¶
Find which workflows are candidates for AI.
Before you can apply AI to anything, you need to know where it fits. Step 1 is a structured audit of your workflows that produces a prioritized list of opportunities across three categories:
- Deterministic Workflows — Repeatable processes with clear inputs and outputs that AI can execute reliably with little supervision
- Collaborative AI — Tasks where you and AI work together in real time (drafting, brainstorming, reviewing)
- Autonomous Agents — Goal-driven workflows where AI plans and executes steps autonomously
The audit uses a three-step process: scan what AI already knows about your work, interview you to fill gaps, then produce a categorized report with specific opportunities and actionable first steps.
Facilitated by the analyzing-workflows skill. See Analyze Workflows for details and the Skills building block for cross-platform installation.
Step 2: Deconstruct Workflows¶
Give your workflow clear structure through deep decomposition.
Once you've identified a workflow worth automating, Step 2 deconstructs it so you understand every step, decision point, and dependency. You describe your workflow — rough and incomplete is fine — and the model interviews you to surface every hidden step, data handoff, and failure mode.
The deconstruction uses the five-question framework to break down each step:
- Is this step actually multiple steps bundled together?
- Are there decision points, branches, or quality gates?
- What data flows in and out?
- What context, documents, or reference materials does this step need?
- What happens when this step fails?
The deliverable is a Workflow Definition — a structured breakdown of your workflow into refined steps, with decision points, data flows, context needs, and failure modes captured for every step. The context needs and failure modes captured here directly inform design decisions in Step 3.
Facilitated by the deconstructing-workflows skill. See Deconstruct Workflows for details and the Skills building block for cross-platform installation.
Step 3: Build AI-Powered Workflows¶
Design your AI implementation, construct the components, and run the workflow.
Step 2 produces a Workflow Definition — the analysis. Step 3 is where the building-workflows skill turns that analysis into a working AI workflow. The skill guides you through three parts:
3.1: Design — The skill confirms your platform, then extracts tool integrations, trigger/schedule, and constraints directly from the Workflow Definition — presenting a single confirmation block instead of asking individual questions. Based on the confirmed analysis, it recommends an execution pattern (Prompt → Skill-Powered Prompt → Single Agent → Multi-Agent) and interaction mode, classifies each step on the autonomy spectrum, maps AI building blocks, identifies skill candidates, and documents agent blueprints when needed. Produces an AI Building Block Spec — a platform-agnostic blueprint that tells the model exactly what to build in 3.2.
3.2: Construct — The skill tells the model what to build (the specs from Design); the model researches your platform's current tools and conventions via web search and generates the actual artifacts — context, skills, prompts, agents, MCP connections — in whatever format your platform requires. Only the components your execution pattern needs are built.
3.3: Run — The skill produces a Run Guide tailored to your platform and technical comfort level: what was built and where it lives, step-by-step setup instructions, a guided first-run test with sample input, and next steps for ongoing use.
Facilitated by the building-workflows skill. See Build Workflows for the full guide, including pattern-specific construct paths and three worked examples across the autonomy spectrum.
Key Concepts¶
Quick reference for the framework's vocabulary:
Three Opportunity Categories¶
| Category | Description | Example |
|---|---|---|
| Deterministic Workflow | Repeatable process AI executes with minimal supervision | Formatting reports, processing forms |
| Collaborative AI | Human and AI work together in real time | Co-writing, brainstorming, code review |
| Autonomous Agent | AI plans and executes steps autonomously | Competitor monitoring, research → report pipelines |
Five-Question Framework¶
Used to decompose each workflow step:
- Discrete steps — Is this one step or multiple bundled together?
- Decision points — Any if/then branches, quality gates, or judgment calls?
- Data flows — What goes in? What comes out? Where from and where to?
- Context needs — What documents, files, or reference materials are required?
- Failure modes — What happens when this step fails?
Seven AI Building Blocks¶
| Block | What It Is |
|---|---|
| Model | The AI engine that processes inputs and generates outputs |
| Prompt | A well-crafted instruction that tells the model what to do |
| Context | Background information, reference docs, or examples the model needs |
| Skill | A reusable routine the model can invoke — give it inputs, it follows a defined process, it produces consistent outputs |
| Agent | An autonomous AI that plans, uses tools, and executes multi-step work |
| MCP (Model Context Protocol) | A connector that lets AI access external tools, services, or databases on your behalf |
| Project | A persistent workspace grouping prompts, context, skills, and agents |
For detailed definitions and cross-platform examples, see Agentic Building Blocks.
Six Use Case Primitives¶
When classifying opportunities from Step 1, it helps to know what type of AI work each one involves. The six use case primitives — Content Creation, Research, Coding, Data Analysis, Ideation & Strategy, and Automation — provide a classification system for the opportunities you identify.
Autonomy Spectrum¶
| Level | Description |
|---|---|
| Human | Requires human judgment, creativity, or physical action |
| Deterministic | Follows fixed rules; AI executes reliably with minimal supervision |
| Semi-Autonomous | AI does most of the work; human reviews at key checkpoints |
| Autonomous | AI executes end-to-end, including decisions and tool use |
Execution Pattern Spectrum¶
Every AI workflow falls somewhere on this spectrum. The right pattern depends on what your workflow actually needs:
| Pattern | Description | Signals |
|---|---|---|
| Prompt | Single self-contained prompt, all logic inline | Sequential steps, human drives the process and provides all inputs |
| Skill-Powered Prompt | Prompt invoking reusable skills | Repeatable sub-routines, moderate complexity |
| Single Agent | One autonomous agent with tool access | Tool use, autonomous decisions, multi-step reasoning |
| Multi-Agent | Specialized agents in a pipeline | Multiple expertise domains, parallel execution, review gates |
Getting Started¶
- Analyze Workflows — identify your best AI candidates
- Pick your highest-impact opportunity — don't try to pursue everything at once
- Deconstruct the workflow — break it into discrete steps
- Design your AI workflow — choose an execution pattern, classify steps, map building blocks
- Build the components your execution pattern requires
- Test on a real scenario and iterate