How to use Tree of Canvas to ensure Enterprise AI Product Success – Part 1

Introduction

AI Solutions (consisting of AI Products and Services) are transforming every industry and sector, creating new opportunities and challenges for businesses. However, according to a recent report by McKinsey, only 15% of Enterprise AI initiatives deliver on their intended outcomes, while 85% either fail or fall short of expectations.

Hence it behooves AI Product Leaders to examine ways to increase the chances of success for their AI Solutions. ??

Based on some creative thinking and implementation experiences, I introduce a powerful tool called The Tree of Canvas, which can help design, align, and execute your AI initiatives effectively and improve the chances of success of the AI initiatives.

Part 1 of the article series introduces the Tree of Canvas in context of AI Initiatives, why it is needed and the value addition it can bring.

Part 2 dives into the Tree of Canvas, details of its components and how to use it in the context of Enterprise AI Initiatives.

Part 3 details the application and implementation of the Tree of Canvas to an actual use case of an AI Initiative at a Large Insurance company.

In this article, I will start off by exploring some of the common pitfalls that derail AI Solutions initiatives and the need for the Tree of Canvas.

Why do AI Initiatives fail?

There is no simple answer to this question, as AI Initiatives can face various technical, organizational, and ethical challenges along the way. However, based on my experience and research, I have identified some of the most prevalent factors that contribute to AI failures, such as:

Lack of clear vision and alignment: Many AI Initiatives start without a well-defined problem statement, value proposition, or success criteria, leading to confusion and misalignment among stakeholders. Moreover, many AI Initiatives fail to align with the overall business strategy and goals, resulting in wasted resources and missed opportunities. Often there are multiple stakeholders and teams deployed for any AI Initiative, so the inter-organizational and inter-team alignment is often not optimal enough to ensure smooth progress and eventual success of the AI Initiatives.

Lack of data quality and availability: Data is the fuel for AI, but not all data is created equal. Poor data quality, such as missing values, outliers, noise, or bias, can negatively impact the performance and reliability of AI models. Moreover, data availability can be a major bottleneck, as acquiring, labeling, and integrating data from different sources can be costly and time-consuming. Poorly defined Data Strategy often does not address data issues or is not effective to ensure that Data is available at right time, right place, and the right quality,

Lack of technical expertise and infrastructure: Developing and deploying AI solutions requires a high level of technical expertise and infrastructure, which many organizations lack or struggle to access. Finding and retaining talent with the right skills and experience can be challenging, especially in a competitive market. Moreover, building and maintaining the necessary infrastructure, such as cloud services, hardware, and software, can be complex and expensive. Even if an Enterprise has all the teams and skilled resources, they often are not in concert with each other and are at cross purposes.

Lack of user adoption and trust: Even if an AI Solution is technically sound and aligned with the business objectives, it can still fail if the end users do not adopt or trust it. User adoption and trust depend on several factors, such as usability, transparency, explainability, fairness, and security. If users do not understand how the AI Solution works, why it makes certain decisions, or how it affects them, they may reject or misuse it.

Lack of governance and ethics: AI Solutions can have significant social and ethical implications, such as privacy, bias, accountability, and human rights. However, many AI initiatives do not have adequate governance and ethics frameworks in place, exposing them to legal, regulatory, and reputational risks. Moreover, many AI initiatives do not monitor or evaluate their impact on the stakeholders and the environment, leading to unintended or harmful consequences.

How can the Tree of Canvas help?

The Tree of Canvas is a framework that I have developed and used to help AI Product Leaders overcome these challenges and increase the chances of success for their AI initiatives. Tree of Canvas is based on the idea of using multiple canvas artifacts, each focusing on a different aspect or level of the AI solution and connecting them in a hierarchical structure. Each canvas artifact is a one-page visual aid that helps you capture and communicate the key elements of overall AI solution in a concise and structured way.

By using Tree of Canvas, you can:

• Define and refine your vision and value proposition for your AI Solution end-to-end and align it with the business strategy and goals. Establish alignment and common understanding of vision and goals between work streams, teams and most importantly stakeholders.
• Identify and prioritize the data sources and requirements for your AI Solution and assess the data quality and availability.
• Select and validate the appropriate technical approach and architecture for your AI solution, and plan the infrastructure and resources needed.
• Design and test the user interface and experience for your AI solution, and ensure the usability, transparency, explainability, fairness, and security of your AI model.
• Establish and implement the governance and ethics principles and practices for your AI Solution and monitor and evaluate its impact and performance.

Using the Tree of Canvas, we can realize the following advantages and value additions:

Comprehensive Solution Mapping

Holistic View: By breaking down the AI Solution into multiple canvas artifacts, each focusing on a different aspect, the Tree of Canvas provides a holistic view of the project. It allows leaders and teams to see both the big picture and the finer details, ensuring all components are aligned and interdependent aspects are considered.

Strategic Alignment: It facilitates the alignment of AI initiatives with broader business strategies by visually connecting each element of the solution back to overarching goals and objectives.

Enhanced Collaboration and Communication

Improved Communication: The visual and structured nature of the canvas artifacts makes it easier to communicate complex AI concepts and project statuses across different teams and stakeholders, enhancing transparency and understanding.

Cross-functional Collaboration: By providing a common framework that can be understood by both technical and non-technical stakeholders, the Tree of Canvas fosters cross-functional collaboration, essential for the success of AI initiatives.

Efficient Problem-Solving and Decision-Making

Systematic Problem Identification: The hierarchical structure helps in systematically identifying challenges and bottlenecks at different levels of the solution, from high-level strategy to specific technical issues.

Informed Decision-Making: With a clear view of how different elements of the AI solution interact and contribute to the overall objectives, leaders can make more informed decisions regarding resource allocation, prioritization, and strategic direction.

Agile and Scalable Approach

Agility in AI Project Management: The modular nature of the Tree of Canvas allows for agility in project management, enabling teams to iterate on specific components of the AI solution without losing sight of the overall structure.

Scalability: As the AI initiative grows, new canvas artifacts can be added to the tree, allowing the framework to scale and adapt to the evolving needs of the project.

Knowledge Management and Continuity

Effective Knowledge Sharing: The framework acts as a knowledge repository, capturing critical information about the AI solution that can be easily shared and accessed by team members.

Continuity in Project Execution: It ensures continuity in project execution, even as team members change, by providing a clear and comprehensive documentation of the AI initiative's development process and current state.

What are the components of Tree of Canvas?

Enterprise AI Canvas: This Canvas helps you define and communicate the vision and strategy for your AI solution at the enterprise level, and how it aligns with the business objectives and goals.

AI Product Canvas: The AI Product Canvas addresses the unique challenges and considerations of AI products and captures the key components and value propositions of a product in a concise, structured format. It is designed to ensure alignment between the product's features, target audience, and business objectives.

AI Business Canvas: This Canvas helps you define and communicate the value proposition and business model for your AI solution at the initiative level, and how it creates and delivers value to the customers and stakeholders.

Machine Learning Canvas: This Canvas helps you define and communicate the data and technical requirements and approach for your AI solution at the machine learning level, and how it solves the problem and achieves the desired outcome.

Deep Learning Canvas: This canvas helps you define and communicate the data and technical requirements and approach for your AI solution at the deep learning level, and how it leverages the power and complexity of neural networks.

Data Product Canvas: This canvas helps you define and communicate the user interface and experience for your AI solution at the data product level, and how it engages and satisfies the users and builds trust and loyalty.

AI Project Canvas: This canvas helps you define and communicate the governance and ethics framework and practices for your AI solution at the project level, and how it manages and mitigates the risks and ensures accountability and responsibility.

How to use Tree of Canvas?

The Tree of Canvas is designed to be flexible and adaptable to different contexts and scenarios. You can use the whole framework or select the Canvas artifacts that are most relevant and useful for your AI solution. You can also modify or extend the canvas artifacts to suit your specific needs and references.

The general steps for using The Tree of Canvas are:

• Choose the canvas artifacts that you want to use for your AI solution and order them in a logical and hierarchical way.
• Fill out each canvas artifact with the relevant information and details,
• Validate and refine each canvas artifact with your team, stakeholders, customers, and users, using feedback, testing, and experimentation.
• Connect and align the canvas artifacts with each other, ensuring consistency and coherence across the different levels and aspects of your AI solution.
• Communicate and share your canvas artifacts with your team, stakeholders, customers, and users, using storytelling, visualization, or other methods.

Conclusion

The Tree of Canvas is a powerful tool that can help you design, align, and execute your Enterprise AI Initiatives effectively. By using The Tree of Canvas, you can avoid some of the common pitfalls and challenges that cause AI Initiatives to fail and increase the chances of success for your AI Solution. In the next article, I will provide more details and examples of how to use each canvas artifact in Tree of Canvas, and how they can help you create and deliver value with AI Initiatives in flight.