Most companies begin their AI journey with the wrong question:

“Where can we use AI?”

That’s how budgets get burned, pilots drag on forever, and impressive demos never make it into real operations.

The truth is simple:

👉 AI is not the starting point. The business is.

Every successful AI initiative begins long before a model, dataset, or tool enters the conversation. It starts by understanding:

This guide walks you through a clear, structured, and battle-tested method used by enterprise AI leaders and top CAIOs to identify real, high-value, high-feasibility AI use cases.

It’s not theoretical. It’s the exact, practical approach organizations use to uncover millions of dollars in opportunities by mapping value streams, exposing workflow bottlenecks, defining problems clearly, and shaping AI solutions that actually deliver ROI.

Follow these seven steps, and you won’t just generate AI ideas. You’ll identify the right use cases — grounded in real value, ready to build, and aligned with measurable business impact.

The output of this exercise will become the key input for the AI Business Modelling Canvas (AI BMC), which we’ll cover in a future article. There, we’ll analyze your selected use case across data, capabilities, metrics, stakeholders, end users, costs, and benefits. For now, your only goal is to identify the right use case—nothing more.

Let’s dive in.

Before identifying any AI use case, you must understand how value actually flows across your organization.

Common examples of value streams include:

These are end-to-end journeys. These are where AI creates the biggest impact.

Use this simple sequence to define your value stream clearly:

Finalize the map: Trigger → Stages → Outcome

This is the foundational output you will use in all following steps.

Once you’ve mapped the value stream, then you break it into workflows

Once you’ve defined the value stream, the next step is to zoom in on one workflow inside it.

If the value stream is the journey, workflows are the roads that move value from point A to point B.

Most workflows are messy, hidden, fragmented, or full of handoffs.

If you can’t see the workflow clearly, you can’t fix it — and you definitely can’t automate or apply AI to it.

We do that using a simplified TRACE mindset (Trigger → Route → Annotate → Check → Escalate). Use TRACE to surface the real friction.

TRACE is our workflow-mapping formula. It turns vague processes into a clear, diagnosable system. We have a full separate article explaining it step-by-step.)

TRACE = Trigger → Route → Annotate → Check → Escalate

A TRACE map highlights:

This makes invisible work visible.

Examples of quantifiable workflow problems:

This is the level of clarity AI needs to deliver meaningful value.

By now, you’ve mapped:

Now you must turn the workflow friction into a clear, concise, measurable problem statement.

This step matters because:

👉 If you can’t state the problem clearly, you can’t build an AI use case.

AI does not solve vague frustrations like:

The problem must be formulated precisely, based on real numbers from Step 2.

A good problem statement answers four questions:

A well-formed problem statement is short, factual, and measurable.

Examples:

Your goal is to convert the Step 2 bottlenecks into one crisp problem sentence.

Expected Output for Step 3

A single sentence that captures:

This becomes the problem input for Step 4: Value Proposition.

Once you have a clear problem statement (Step 3), the next step is to define the value proposition — the improvement your organization wants to achieve.

👉 The value proposition simply describes the business value you want to create.

It turns the problem into a clear “better future state.”

This step ensures you don’t jump straight from “problem” into “AI” without defining what “better” actually looks like.

The value proposition should answer three questions:

To make it practical, use the 6 AI Value Objectives as your guide:

The value proposition should link the problem to one or more of these objectives.

Expected Output for Step 4

A clear 1–2 sentence value proposition that defines:

This sets the stage for Step 5:

Defining the AI solution (capabilities) that make this value possible.

Once you know the value you want to create, the next step is to translate it into a simple description of what AI should actually do.

You are simply describing the role of AI, the data it uses, and the type of help it provides.

Think of it this way:

👉 If AI were a smart colleague working alongside you, what would you want it to look at, and what work would you want it to handle?

To keep this easy and structured, we use a simple table — the AI Solution Canvas. This canvas forces clarity without requiring technical knowledge.

You fill out 5 fields — that’s it.

This canvas centers the conversation on value and function, not technology.

It also ensures every use case has a clear identity and purpose before any technical decisions are made.

By the end of this step, you should have a completed AI Solution Canvas that clearly states:

This provides a clean bridge into Step 6: Feasibility Check, where we test whether the solution can actually be built with the data and technology available.

Now that you know what AI should do (Step 5), the next question is:

👉 Can we actually build this with the data and technology we have today?

This step prevents you from designing a beautiful AI concept that collapses during execution. It turns the AI solution from an idea into something realistic.

You don’t need to be an engineer. You just need to answer two questions:

To make this simple, we use the Feasibility Check Canvas — a short table that surfaces the essentials.

Fill out these 5 fields.

Data Feasibility Checklist

You can build the use case now if:

If data is missing but easily collected → “needs prep work.”
If data can’t be collected → “not feasible yet.”

Tech Feasibility Checklist

You can build it now if:

If not, you may need small infrastructure steps first.

Your final output should clearly show:

After this, you are ready for Step 7: Estimate the Operational Impact — the final step before moving into the AI Business Model Canvas.

Now that your use case is feasible, the final step is to estimate its potential operational impact — the real-world improvement the organization should expect.

👉 This step simply quantifies how much better the workflow becomes when AI is added.

To make this simple, we capture impact in a short 4-row table:

This gives a clear, structured impact snapshot that any executive can understand.

At the end of Step 7, the candidate should produce one completed Impact Estimation Canvas, showing:

This completes the 7-step journey.

✔ Step 1 — Map the Value Stream: Understand how value moves end-to-end across the organization.
✔ Step 2 — Map the Workflow & Diagnose Bottlenecks: Make invisible work visible and quantify the friction.
✔ Step 3 — Formulate the Problem Clearly: Write one sharp, measurable problem statement.
✔ Step 4 — Craft the Value Proposition: Define the improvement the business wants and why it matters.
✔ Step 5 — Describe the AI Solution (AI Solution Canvas): Specify what AI should look at, what job it performs, and how humans will use it.
✔ Step 6 — Check Feasibility: Confirm you have the data and technology needed to build it.
✔ Step 7 — Estimate Operational Impact: Quantify expected improvements in time, cost, quality, and decision-confidence.

🚀 AI only creates value when it’s anchored in real business problems.

Follow these seven steps, and you won’t just identify use cases — you’ll identify the right ones, with clarity, feasibility, and measurable impact.

Here’s how the full 7-step framework comes together in a real use case from Victoria Royal Investment.

1. Value Stream

Financial Visibility & Decision Readiness
End-to-end journey that gives management a clear, real-time view of cash position to decide when and how to launch new development projects.

2. Key Workflow Mapped

Sales Data → Client Payments → Bank Updates → Obligations → Consolidation → Management Report

Bottlenecks identified:

3. Problem Statement

Cashflow consolidation takes hours, relies on multiple spreadsheets, and provides no forecasting, leaving management without real-time insight to make confident project-launch decisions.

4. Value Proposition

Provide real-time financial visibility and predictive insights so management can act confidently, launch new projects at the right time, and avoid cashflow-related risks.

5. AI Solution Canvas

6. Feasibility Check

7. Expected Operational Impact

Final Use Case Summary (One Sentence)

Build an AI Financial Copilot that consolidates financial data, forecasts cashflow, detects risks early, and guides management on the optimal timing for launching new real-estate projects.

The combined outputs from Steps 1–7 become the inputs for the AI Business Model Canvas (AI BMC), where the use case is analyzed 360° across: data, capabilities, key metrics, stakeholders, end users, systems and platforms, estimated cost, and business outcome. That will be covered in a future article.

Sam Obeidat is a senior AI strategist, venture builder, and product leader with over 15 years of global experience. He has led AI transformations across 40+ organizations in 12+ sectors, including defense, aerospace, finance, healthcare, and government. As President of World AI X Ventures, a global corporate venture studio, Sam works with top executives and domain experts to co-develop high-impact AI use cases, validate them with host partners, and pilot them with investor backing—turning bold ideas into scalable ventures. Under his leadership, World AI X has launched ventures now valued at over $100 million, spanning sectors like defense tech, hedge funds, and education. Sam combines deep technical fluency with real-world execution. He’s built enterprise-grade AI systems from the ground up and developed proprietary frameworks that trigger KPIs, reduce costs, unlock revenue, and turn traditional organizations into AI-native leaders. He’s also the host of the Chief AI Officer (CAIO) Program, an executive training initiative empowering leaders to drive responsible AI transformation at scale.

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