Programmers has developed a powerful tool to help businesses envision, implement, and go further with AI – the AI Canvas.

Inspired by the Business Model Canvas, this tool simplifies AI planning by breaking it into key components, allowing teams to structure and refine their AI strategies with confidence. It is an integral part of our process when working closely with clients to identify AI use cases, detail specific initiatives, and guide pilot projects through successful development.

If you haven’t downloaded the AI Canvas yet, you can get it for free here.

We are excited to share this resource with you, and hope it will help your organization better define AI solutions, foster alignment across teams, and improve communication throughout the implementation process.

This article provides a detailed guide on how to use the AI Canvas effectively. If you need expert support in piloting AI applications, developing an implementation strategy, or integrating AI into your existing systems and processes, our team is here to help. Contact us to explore how we can support you in your AI journey.

• What is the AI Canvas?
• How to Use the AI Canvas
  • AI Goals
  • Key Events
  • AI Skills
  • Decision Rules
  • Integrations
  • Data Points
  • Four Pillars of AI
  • Level of Delegation
• What Is an Example of a Finalized AI Canvas?
• Example: AI Canvas for Predictive Maintenance

1. What is the AI Canvas?

The AI Canvas provides a structured way to plan, evaluate, and refine AI initiatives, ensuring they align with business priorities and technical feasibility. By mapping out key components, organizations can align stakeholders, clarify goals, and ensure AI initiatives are designed to address real business challenges. It is a strategic tool designed to guide teams through the AI development process by helping them:

• Align AI projects with business goals
• Identify and structure AI use cases
• Define data, integration, and capability requirements
• Ensure successful deployment and long-term impact

By leveraging this framework, organizations can create AI-driven solutions that not only work in theory but also deliver real-world value.

2. How to Use the AI Canvas

The AI Canvas is structured into three key areas:

1. Business Context

Aligns the AI initiative with organizational goals, processes, and decision-making frameworks, ensuring the AI is built to solve a real business problem and deliver measurable value.

2. Data & Integrations

Covers essential data inputs and sources, system connections, and technology ecosystem to support AI functionality. Defining these elements early guarantees smooth implementation and scalability.

3. AI Capabilities

Defines the intelligence, functionality, level of autonomy, and decision-making rules of the AI system.

By structuring the AI Canvas (and implementation of your AI solution) this way, organizations can systematically design AI solutions that are not only technically feasible but also strategically aligned with business needs.

Below, we will describe each section of the AI Canvas and provide guidance on how to fill them out effectively. Each section plays a crucial role in shaping a well-defined AI solution, ensuring that it aligns with business goals, integrates seamlessly with existing systems, and leverages the right AI capabilities.

2.1 AI Goals

A well-defined goal ensures alignment with business needs and guides the entire AI development process. Every other section in the AI Canvas relies on this definition—if the goal is unclear, the success of the AI initiative may suffer. Think of the goal as the direction of propulsion for your AI’s output.

2.2 Key Events

Understanding the key events that trigger AI processes ensures seamless workflow integration and confident decision-making.

Defining Relevant Events:

• Think process-first – Identify where AI fits in existing operations.
• Consider inputs and triggers – What action, system event, or user interaction activates the AI?
• Map AI’s role – How does AI react or contribute to each event?

Event Examples:

• AI Chatbot for Customer Service
  Event: A customer submits a support ticket → AI analyzes the issue and provides an automated response.
• AI for Inventory Management
  Event: Stock levels drop below a threshold → AI predicts demand and suggests replenishment.
• AI for Predictive Maintenance
  Event: A machine sensor detects abnormal vibration → AI predicts possible failure and triggers a maintenance alert.

2.3 AI Skills

AI skills define what tasks the system can perform, the level of intelligence it possesses, and how it interacts with data and users. These capabilities range from basic automation to advanced reasoning and adaptation. Properly defining AI skills ensures that the solution meets business requirements while maintaining efficiency, accuracy, and scalability.

Here is a comprehensive table of the skills your AI could have:

How to Use This AI Skills List in Your AI Canvas:

When filling out the “AI Skills” section, ask:

• What specific tasks will my AI perform?
• How will it process data (text, images, sound)?
• Will it generate, predict, recognize, recommend, or automate something?

AI Skill Examples:

• AI for Customer Support Chatbots → NLP (Intent Recognition, Sentiment Analysis, Text-to-Speech).
• AI for Fraud Detection in Banking → Predictive Analytics (Anomaly Detection, Risk Assessment).

2.4 Decision Rules

Decision rules define how the AI makes choices based on the data it processes. AI decision-making requires well-defined rules to ensure reliability, consistency, and compliance with business policies. Decision rules govern how AI processes data and makes choices, balancing automation with human oversight.

What Are Decision Rules?

Guiding Questions:

• What criteria will the AI use to make decisions?
• Should decisions be rule-based, AI-driven, or hybrid?
• Are there any constraints or ethical considerations the AI must follow?
• What level of human oversight is required?

Decision Rule Examples:

• AI for Loan Approvals
  Decision Rule: Approve applications if credit score > 700, income stability is verified, and no high-risk flags are detected.
• AI for Predictive Maintenance
  Decision Rule: If vibration sensor readings exceed threshold X for Y minutes, trigger a maintenance request.
• AI for Customer Support
  Decision Rule: If sentiment analysis detects negative feedback, escalate the case to a human agent.

How to Apply Decision Rules in Your AI Canvas:

As you fill in this section, define the rules that will govern your AI’s decisions. If your AI lacks clear rules, consider whether human intervention or additional guardrails are needed to ensure accuracy, fairness, and compliance.

2.5 Integrations

In this section, define the systems and platforms that your AI will need to connect with in order to function effectively once in production. Seamless integrations ensure that the AI can access necessary data, interact with other tools, and be a cohesive part of your organization’s ecosystem.

Defining Integrations:

Integrations are the connections between the AI and other systems or platforms that it interacts with to achieve its goals. These can include:

• Data Sources – Databases, data lakes, or IoT devices providing the information the AI needs.
• Software Systems – Applications like ERP, HRMS, or customer service platforms the AI will interact with or augment.
• External APIs – Third-party services the AI will access, such as payment gateways, weather data, or AI model APIs.
• User Interfaces – Platforms where users interact with the AI, such as web portals, mobile apps, or dashboards.

Guiding Questions:

• What existing systems does your AI need to connect with for data or functionality?
• Are there any third-party platforms or APIs that the AI must interface with?
• How will data be shared between systems? Will it be real-time, batch, or on-demand?
• Does the AI need to push results or actions to other systems (e.g., create tasks in a CRM or trigger a workflow)?
• How will you manage security, data privacy, and permissions when integrating the AI?

Integration Examples:

• AI for Marketing Automation
  Integrations: CRM system (for customer data), email marketing platform (for campaign management), and social media tools (for targeting and analytics).
• AI for Inventory Management
  Integrations: ERP system (for product and order data), supplier databases (for stock updates), and warehouse management system (for order fulfillment).

How to Apply Integrations in Your AI Canvas:

As you fill in this section, list out the specific platforms, systems, and data sources that your AI will need to integrate with. Ensure these integrations are clearly defined, and plan for their implementation and maintenance as part of your AI deployment strategy.

2.6 Data Points

Data is the foundation of AI systems, influencing accuracy, performance, and decision-making capabilities. Clearly defining data requirements ensures that AI models are trained and deployed using high-quality, relevant datasets.

Defining Data Points:

Data points refer to the specific pieces of information that the AI system will process. These can come from various sources and be categorized as:

• Structured Data
  Organized, easily searchable data in databases, spreadsheets, or APIs.
  Ex: Customer demographics, sales transactions, sensor readings, timestamps.
• Unstructured Data
  Complex, non-tabular data that requires processing before use.
  Ex: Emails, social media posts, call transcripts, scanned documents, images, videos.
• Real-Time vs. Historical Data
  Some AI models work with live/real-time data streams (e.g., IoT sensors), while others analyze past records (e.g., maintenance logs) to identify patterns and make predictions.
• Internal vs. External Data
  AI solutions may rely on company-owned data (e.g., ERP, CRM) or third-party sources (e.g., public datasets, market trends).

Guiding Questions:

• What specific data does the AI need to function correctly?
• Where does this data come from (e.g., internal databases, external APIs, user inputs)?
• Is the data structured or unstructured? Does it require preprocessing?
• Will the AI rely on historical trends, real-time feeds, or a mix of both?
• Are there any data privacy or compliance considerations (e.g., GDPR, HIPAA)?

Data Point Examples:

• AI for Customer Support Chatbots
  Data Points: Previous customer interactions, support ticket history, sentiment analysis of messages, chatbot conversation logs.
• AI for Predictive Maintenance
  Data Points: Machine temperature readings, vibration levels, maintenance logs, error reports, IoT sensor data.
• AI for Fraud Detection
  Data Points: Transaction amounts, device location, user behavior patterns, flagged past fraud cases.

How to Apply Data Points in Your AI Canvas:

List the key data points your AI will process. Ensure they align with the AI’s objectives and that the data is accessible, reliable, and sufficient to support accurate AI decision-making. If data gaps exist, consider how to acquire or generate the missing information.

2.7 Four Pillars of AI

Any effective AI solution should ideally meet four essential criteria:

Together, these pillars create a robust framework for AI that maximizes its potential and impact. Here’s how these pillars create a strong, dependable foundation:

How to Apply These Four Pillars in Your AI Canvas:

Assess whether your AI solution meets all four pillars. If any are missing, reconsider the approach. 

If the AI lacks autonomy -> Refine its automation capabilities.

If the AI is not scalable -> Consider architectural changes to support future growth.

If the AI provides insights but is unable to drive decisions -> Rethink how AI integrates into workflows.

Alignment with these four pillars concretes a well-rounded, future-proof AI system.

2.8 Level of Delegation

AI solutions vary in their level of autonomy and human involvement, ranging from fully supervised systems to completely autonomous decision-makers. Understanding these levels helps define the appropriate balance of control and automation for your AI solution.

Here are the four levels of AI delegation:

1. Supervised AI

The AI requires human intervention to initiate tasks and interpret results. It acts as a support tool, providing insights that help humans make decisions.
Example: An AI-powered analytics dashboard that generates reports but requires analysts to interpret the findings and take action.

2. Assistant AI

The AI automates task execution and interpretation, reducing human effort but still requiring oversight. It enhances efficiency while keeping humans in the loop.
Example: A virtual assistant that schedules meetings, suggests responses to emails, and executes basic automated workflows based on user preferences.

3. Consultative AI

The AI autonomously executes tasks and provides decisions, but humans validate its outputs before full implementation. It combines automation with human judgment.
Example: An AI-based fraud detection system that flags suspicious transactions and recommends actions, but a human reviews and approves the final decision.

4. Autonomous AI

The AI operates entirely independently, initiating tasks, executing actions, and making decisions without human intervention. It enables seamless, real-time operations.
Example: A fully automated stock trading system that analyzes market conditions, executes trades, and adjusts strategies in real time.

How to Apply Levels of Delegation in Your AI Canvas:

Define the appropriate level of delegation for your AI solution:

Does your AI need human validation before acting? → Consider Supervised or Consultative AI.

Should the AI act independently to optimize efficiency? → Assistant or Autonomous AI might be the right fit.

If the level of delegation doesn’t align with your AI’s goal or business
requirements, rethink how tasks, decisions, and oversight should be structured for the best balance between automation and control.

3. What Is an Example of a Finalized AI Canvas?

To help you understand how to fill out the AI Canvas, here’s a real- world example of an AI solution for Predictive Maintenance.

This AI is designed to minimize unplanned equipment downtime by analyzing sensor data, predicting failures, and recommending maintenance actions before issues occur. As you go through this example, pay attention to how each section is structured: clear definition of business objectives, identification of necessary AI capabilities, decision rules, data points, integrations, etc.

Use this as a reference to guide your own AI project, ensuring that your solution is well-defined, actionable, and aligned with business needs. If you’re ready to accelerate AI adoption, download the AI Canvas or contact us to explore how our experts can support your AI transformation.

3.1 Example: AI Canvas for Predictive Maintenance