qaszsxMost AI projects don’t fail because the technology is weak — they fail because the business model is missing.
In our previous article, How to Identify an AI Use Case in 7 Steps, we mapped the value stream, diagnosed workflows, defined the problem, and shaped the core AI solution. These outputs give us clarity, but they don’t yet answer the question leadership cares about most:
“Is this use case actually viable?”
This is where many AI initiatives lose momentum. Teams understand the problem and the potential solution, but they haven’t translated it into a business model — the data it depends on, the capabilities required, the systems it touches, the people involved, and the outcomes it will produce.
AI Business Modelling fills this gap.
The AI Business Model Canvas (AI-BMC) — developed and validated with World AI Council members— is a simple, executive-friendly tool that transforms your identified use case into a structured business model. It’s the bridge between idea and strategy, helping you answer the only question that matters at this stage:
“Is this use case viable enough to move into readiness assessment and strategic planning?”
To make this practical, we’ll use our host partner, Victoria Royal Investment (VRI), as the running example. VRI is a real-estate development company in Cyprus managing multiple residential and commercial projects. Like many real estate developers, they face a common challenge: fragmented financial visibility and no real-time way to forecast cashflow across multiple projects.
We’ll show how their AI Use Case Discovery outputs feed directly into the AI-BMC to create a complete, executive-ready picture. You can review their full AI Use Case Statement in the previous article here.
Note: This version of the framework was updated on December 4th, 2025 in collaboration with the Chief AI Officer Nov. 2025 Cohort, incorporating insights from World AI Council members.
Introducing the AI Business Model Canvas (AI-BMC)
Once an AI use case is identified, the next step is to understand what it will take to bring the solution to life. The AI Business Model Canvas (AI-BMC) gives you that clarity on a single page. Developed with input from World AI Council members, the AI-BMC is a lightweight but powerful tool that turns your use case into a structured business model executives can evaluate quickly.
The canvas blends information from two sources:
1. Inputs from Your AI Use Case Discovery
Value Stream
Selected Workflow (using TRACE)
Problem Statement
These ensure your business model is grounded in real operational friction, not abstract brainstorming.
2. Eleven Business Model Blocks
These describe the essential elements needed to understand feasibility and value at a high level:
Value Proposition
Data
Capabilities
Team
Key Metrics
Systems & Platforms
Stakeholders
End Users
Key Dependencies
Estimated Cost
Business Outcome
Together, these elements create the 360° view needed to answer the key leadership question:
“Is this use case strong enough to move into readiness assessment and strategic planning?”
Before we break down the blocks, here is the full AI-BMC layout to anchor the rest of the article.
Figure 1. The AI Business Model Canvas (AI-BMC) — a one-page strategic framework validated with World AI Council members. It combines AI Use Case Discovery inputs with the core business elements needed to evaluate feasibility and value.
The 11 Blocks of the AI Business Model Canvas
Before we dive into the table, it’s important to understand how the AI-BMC actually turns an AI idea into a strategic business model. The canvas is intentionally simple: each block answers a specific question about feasibility, value, or adoption — and together, they create a complete 360° view of your use case.
To keep this practical and grounded in reality, we’ll continue with our host partner, VRI. By mapping their financial-operational challenges onto the canvas, you’ll see clearly how each block works and how the model can be applied to any industry or organization.
We’ll move through the canvas block by block — starting with the value proposition at the center, then working down the left side (data, capabilities, team, and key metrics), followed by the right side (systems and platforms, end users, stakeholders, and key dependencies), and finally completing the bottom section with the cost–benefit analysis. The goal here isn’t perfection; it’s clarity. At this stage, the AI-BMC gives you a directionally correct strategic picture — something leadership can evaluate before committing to deeper readiness assessment or architectural planning.
Below is the complete breakdown, organized in the same sequence you will follow when completing your own canvas — with each block illustrated using an example from VRI.
AI-BMC Breakdown: Definitions + VRI Example
Block | What It Means | VRI Example |
|---|---|---|
Value Proposition | The core benefit the AI solution delivers — the meaningful improvement in speed, visibility, accuracy, or decision-making that justifies the initiative. | Real-time financial visibility and predictive forecasting that replaces manual spreadsheets and enables faster, more confident decisions on project launches and resource planning. |
Data | The information the AI needs, where it currently lives, and whether it’s accessible in a usable form. This defines feasibility. | Bank transactions, unit sales, customer payment schedules, construction invoices, and partner obligations — scattered across spreadsheets, bank portals, and shared drives. |
Capabilities | The AI abilities required to solve the problem (e.g., forecasting, reasoning, classification, anomaly detection, summarization). Not the algorithms — the functions. | Cashflow forecasting, anomaly detection, reasoning over financial documents, summarizing insights, and generating liquidity or investment recommendations. |
Team (Talent Needed) | The roles and expertise required to design, build, deploy, or maintain the solution. Think capabilities, not full-time headcount. | AI Engineer, Data Analyst, Dashboard Developer, Financial SME, and part-time Project Manager. |
Key Metrics | The indicators that prove the AI is delivering value — operational, financial, accuracy-based, or adoption KPIs. | Hours saved, forecast accuracy improvement, reduction in financial delays or penalties, and faster decision cycles for new projects. |
Systems & Platforms | The systems, tools, or data sources the AI must connect to or rely on — early identification of integration points. | Bank portals, spreadsheets (Google Drive), financial tracking files, and BI dashboards (Power BI / Tableau). |
Stakeholders / Departments | The internal groups that influence, approve, benefit from, or are affected by the AI solution. | Finance, Operations, Executive Leadership, external accountants, and advisors. |
End Users / Customers | The people who will directly use or benefit from the AI solution — critical for adoption and design. | CEO, Finance Manager, Project Managers; indirect users include investors and external partners. |
Key Dependencies | High-level feasibility conditions that must exist — NOT risks. Includes data availability, system access, SME input, and workflow clarity. | Access to bank exports, structured financial spreadsheets, SME involvement from finance, and ability to centralize scattered files. |
Estimated Cost | A directional estimate of the investment needed — development effort, data preparation, talent, tools. Not a full financial plan. | $32K–$55K total, covering engineering, data structuring, dashboard build, and AI Copilot development; optional monthly API/tooling costs. |
Business Outcome | The measurable organizational value — efficiency, financial gain, risk reduction, decision-making improvements. | 180–240 hours saved annually; $10K–$25K in avoided financial risk; 25–40% better forecasting accuracy; $50K–$100K in annual business value. |
How It All Comes Together
When you fill out these blocks, the AI-BMC becomes a strategic snapshot of your use case — clear, structured, and ready for leadership review. For VRI, the AI-BMC transformed a single workflow bottleneck into an actionable business model defining the data, users, value, capabilities, and outcomes required to build their AI Financial Copilot.
What Happens After the AI-BMC?
The AI-BMC gives you a complete, high-level business view of your AI use case — but it’s only the first filter. Once the canvas is complete, the next step is to evaluate whether the organization is genuinely ready to implement the solution.
This is where the AI Readiness Assessment (AIRA) comes in.
AIRA looks at the organization across five areas — organization, people, technology, environment, and economics — to identify maturity levels, gaps, and early constraints that must be addressed before development begins. Think of the AI-BMC as the “what” and AIRA as the “are we ready?”
After readiness comes Governance Modelling, where we define how the solution will operate safely and responsibly, followed by Future Foresight, which ensures the use case is designed with long-term evolution in mind.
We’ll cover all of this in detail in our upcoming article on the AI Readiness Assessment Framework.
For now, the key point is simple:
The AI-BMC tells you whether a use case is worth pursuing.
AIRA tells you whether you’re ready to pursue it.
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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