Mapping Your Path to AI Success: Value Chains and Use Case Primitives

Introduction

You know that AI can help your business. But how do you find the exact spots where it will make the most difference? How do you move from a general idea like AI for sales to a clear project? This is where good frameworks help. Mapping your value chain and using use case primitives are two strong tools. They help you structure your AI opportunities. They turn vague ideas into clear, actionable plans. This is the bridge between thinking about AI and actually making it work.

Value Chain Mapping

Generative AI helps your business by making intelligence stronger. The next step is to find out where that intelligence is in your organization. Mapping your value chain does this. It helps you see your organization as a series of activities that create value. This is better than just seeing departments on an org chart. Once you see this clearly, you can find where AI can have the biggest impact.

The Value Chain Mindset

A value chain is the full system of how your organization creates and gives value. It goes from the initial idea to what the customer gets. Every part of this chain has people, processes, and data. In each part, there are repetitive tasks, decisions, and knowledge. AI can support all these points.

Why Mapping Matters

Many AI projects fail. They start in one team without seeing how that work connects to the larger system. When you map the value chain, you find:

Where things get stuck or are difficult.
Which tasks are done many times across different departments.
Which parts rely heavily on human knowledge or documents.
Where information is hidden or not used enough.

These are the places where Generative AI can really help.

Breaking Down the Chain

You can put almost any organization into three main areas:

Core Operations: These processes directly deliver your main product or service. Examples are making products, logistics, or product design. Look for manual document work, complex decisions, or repetitive communication.
Support Functions: These keep the business running but do not directly deal with customers. Examples are HR, finance, or legal. Look for policy questions, process documents, or internal training material.
Customer-Facing Functions: These involve direct contact with customers. Examples are marketing, sales, or customer service. Look for a lot of text-based messages, personalization needs, or customer feedback.

GenAI can summarize data, draft documents, answer questions, or personalize content in all these areas.

How to Map Your Chain (Step-by-Step)

You do not need complex software. A whiteboard or a spreadsheet works well. Here is how to do it:

List your key activities: Start with the customer and work backward. For example, customer purchase leads to marketing, then production, then supplier management, then finance.
Add supporting processes: Include HR, legal, IT, and training.
Under each activity, note the key things produced: These are reports, emails, presentations, decisions, or data.
Mark pain points: Where are the slow or manual tasks? Where do people copy and paste data often?
Highlight decision points: Where are decisions made based on data or documents?
Mark dependencies: Which steps rely on knowledge that is only in peoples heads or in many different folders?

The result should be a flow of boxes. These boxes show where work slows down, repeats, or relies on text and human thought. This is your AI heatmap.

Spotting AI Leverage Points

Once your map is ready, look for patterns like:

Repetition: The same report or summary made many times a year.
Complexity: Decisions that need many documents or data sources.
Delay: Time wasted waiting for information.
Variation: Where quality depends on the person doing the task.

Generative AI often brings value when at least two of these overlap. For example, weekly project reports take 6 hours each, vary in quality, and use scattered updates. This is a good GenAI opportunity.

Connecting Value Chain Mapping to Strategy

Link your map back to your strategic goals. Which processes make you different from competitors? Which are important but get stuck by manual work? Which create the most problems for customers when they are slow? If a process meets one of these points and also has clear problems, it is a good place for AI.

Use Case Primitives: Turning Ideas into Structured Opportunities

You have found places where AI could help. Now you need a clear way to describe each AI opportunity. This is so it can be discussed, tested, and compared. This is what Use Case Primitives are for.

What Use Case Primitives Are

A use case primitive is a basic building block. It is a small piece of information that shows how an AI system fits into a workflow. Instead of vague ideas, primitives help you write exactly what happens, when, and why it matters. They are a common language between business and technical teams.

Why Primitives Matter

Without a shared structure, AI idea sessions quickly become long lists that no one can evaluate. With primitives, every idea becomes a short, standard card. Anyone can understand it. They help you:

Capture opportunities quickly.
Find duplicate ideas.
Estimate if an idea can be done and its impact.
Choose which ideas to work on first.

They connect strategy talk to implementation talk.

The 15 Core Primitives

These primitives define each AI opportunity. They describe the situation, the goal, and the effort needed.

Here are the 15 Use Case Primitives that help define each AI opportunity:

1. Actor / User
- What it defines: Who performs or benefits from the task
- Example: Proposal writer
2. Trigger / Event
- What it defines: What starts the process
- Example: New RFP email arrives
3. Input
- What it defines: Data or artifacts needed
- Example: RFP PDF + client data
4. Output
- What it defines: What is produced
- Example: Draft proposal document
5. Success Criteria
- What it defines: How success is measured
- Example: Time to first draft reduced by 80%
6. Value Driver
- What it defines: Why this matters to the business
- Example: More bids submitted, higher win rate
7. Frequency & Scale
- What it defines: How often it happens
- Example: 40 proposals/month
8. Data Quality & Availability
- What it defines: How usable and accessible the data is
- Example: Partial data in CRM
9. Process Context
- What it defines: Where this fits in the workflow
- Example: Sales → Proposal → Legal → Submit
10. Integration Points
- What it defines: Systems to connect
- Example: CRM, Office 365
11. Constraints
- What it defines: Security, regulation, or technical limits
- Example: Confidential client data
12. Human-in-the-Loop (HITL)
- What it defines: Where people must review or approve
- Example: Proposal writer + legal check
13. Ownership & Stakeholders
- What it defines: Who owns and funds it
- Example: Proposal team; Head of Sales
14. Risks & Mitigations
- What it defines: Main risks and controls
- Example: Hallucination → human review
15. Level of Effort / Feasibility Notes
- What it defines: Rough estimate of technical complexity
- Example: Medium engineering, high data effort

Each primitive entry is short. But together, they give a full picture of the use case.

How to Capture Primitives in Practice

Here is a simple way to capture them:

Pick one process area. This could be customer support or marketing content.
Ask your team about their biggest time problems.
For each good idea, fill out a Use Case Primitive Card. Get as much detail as you can.
Do not worry about being perfect at first. You can improve it later.

The goal is to get enough structure to compare ideas well.

Worked Examples

Let us look at some examples.

Example 1: Proposal Drafting Assistant

Actor: Proposal writer
Trigger: New RFP email arrives
Input: RFP document, past winning proposals, client profile
Output: First draft proposal + executive summary
Success Criteria: Draft time cut from 6 hours to 1 hour
Value Driver: More proposals submitted per week
Frequency: 40/month
Data Quality: 80% structured; missing history for some clients
Process Context: Sales → Proposal → Legal review → Submit
Integrations: Email, CRM, Microsoft Word
Constraints: Client confidentiality, legal compliance
HITL: Proposal team edits draft before submission
Ownership: Head of Sales
Risks: Hallucinated content — mitigate with source linking
Feasibility: Medium effort (needs document parsing and templating)

Example 2: Customer Support Summarization

Actor: Customer service representative
Trigger: Support ticket resolved
Input: Chat transcript, customer details
Output: Summary note and follow-up email
Success Criteria: Reduce after-call documentation time by 70%
Value Driver: Agents handle more cases per shift
Frequency: 500+ tickets/day
Data Quality: Structured and available via CRM
Process Context: Chat → Resolution → Summary → Close ticket
Integrations: Zendesk, Slack
Constraints: GDPR compliance
HITL: Agent reviews AI summary before submission
Ownership: Head of Customer Experience
Risks: Incorrect summary tone — mitigate with style fine-tuning
Feasibility: High feasibility, low engineering effort

Example 3: Marketing Content Generator

Actor: Marketing copywriter
Trigger: New product or campaign launch
Input: Product details, audience persona, brand tone guide
Output: Blog post, email copy, and social captions
Success Criteria: Cut campaign content prep time from 5 days to 1
Value Driver: Faster go-to-market, consistent brand tone
Frequency: 10–15 launches per quarter
Data Quality: Readily available (internal data + templates)
Process Context: Product brief → Copy generation → Review → Publish
Integrations: CMS, Google Docs
Constraints: Brand compliance rules
HITL: Copywriter approves/edits AI draft
Ownership: Marketing Director
Risks: Tone inconsistency — mitigate with fine-tuned prompt templates
Feasibility: High — ready to pilot immediately

How to Use These Cards

Once you have cards for each potential use case, you can:

Group them by business function (HR, Sales, Operations, etc.)
Score them on how much impact they will have and how easy they are to do.
Pick the top ones to test or try out.

Conclusion

Mapping your value chain gives you a clear view of your operations. It is the base for finding where AI can make an impact. This is not guesswork. It uses structure. When you can see your work as a flow of knowledge and decisions, finding good AI opportunities becomes simple. Then, using Use Case Primitives helps you define these opportunities. They turn ideas into structured plans that can be measured and acted on. These tools together give you a clear path to AI success. They bridge the gap between AI potential and real business results.