The Path to AI Product
Decode AIPM Jargon and Avoid the Road to Perdition
#beyondAI
How can we demonstrate our expertise to our environment before taking action? By communicating in a way that prevents misunderstandings.
As an AI Product Manager, I believe that understanding terms deeply before using them is a sign of professionalism. If we want people to trust us, we should do our homework and know what we are talking about.
Using terms just because others do can lead to embarrassing situations when someone asks a term-related question and the stuttering begins.
It quickly erodes trust.
But this is not the only reason you should learn the jargon. It helps us build a robust mental model of relationships between terms. Once you have this network of interconnected terms at the top of your mind, you can easily navigate the entire AI product lifecycle. Understanding that an AI use case is different from an AI solution, which is again distinct from an AI product, can significantly influence how we approach and manage AI projects.
This is what this issue is all about. Building a robust understanding of key AI Product terms.
You will learn about:
Why Clear Terminology Matters in AI Projects
The Path to AI Product
Step-by-Step breakdown
What is an AI Product
What is an AI Solution
What is an AI System
What is an AI Model
What is an AI Use Case
Take away
Happy reading 🛋️
Why Clear Terminology Matters in AI Projects
Precise terminology is the cornerstone of clear and effective communication within teams and with stakeholders. Misunderstanding terms can lead to misaligned expectations and project goals, and no one wants to see a project fail due to a simple misunderstanding.
In my opinion, there's nothing more frustrating than a project failing because of a miscommunication. It is just ridiculous.
Unfortunately, this happens more often than you'd think. I can recall at least two projects from my early career where a simple misuse of terms was the turning point between success and failure.
In one of my early projects, I discussed an AI use case with a stakeholder who misinterpreted it as an AI product. I intended to convey that we were exploring potential applications of AI to solve a specific problem, but the stakeholder assumed we had a fully developed AI product ready for deployment. This misunderstanding led to unrealistic expectations, disappointment, and ultimately, the project's failure because the stakeholder's expectations were not met.
This experience taught me a valuable lesson: never assume that those outside our field will understand our terminology. Now, I always make sure to explain key terms clearly and provide a glossary after each kickoff meeting so that stakeholders can refer to it later. This practice has proven to be extremely helpful.
In another project, I used the term AI solution when describing a prototype we were developing. My team understood this to mean a final, ready-to-deploy solution, while I was referring to an early-stage proof of concept. This miscommunication led to significant delays, as the team focused on refining the prototype beyond the intended scope instead of progressing to the next development phase.
Regardless of who misunderstood what, it's the AI Product Manager's duty to ensure that everyone is on the same page.
Efficient Resource Allocation
Differentiating between these terms also helps in proper resource allocation. An AI use case might require exploratory research and experimentation to validate its feasibility, whereas developing an AI system demands more technical resources and expertise to build and deploy the model sustainably. Scaling to an AI product involves even more resources, including user experience design, ongoing support, and ensuring governance compliance. This ensures that resources are allocated efficiently and appropriately across different stages.
Measuring Success with Appropriate Metrics
Moreover, each term corresponds to different success metrics. An AI use case might be measured by its potential impact or feasibility, an AI system by its technical performance, an AI solution by its problem-fit, and an AI product by its market adoption and user satisfaction. Understanding these differences ensures that appropriate KPIs are set and monitored, which is critical for assessing progress and success accurately.
I hope this provides enough motivation for everyone to accept that learning the jargon is important. But if not, just imagine your doctor not knowing the difference between a flu and a bacterial infection 🩺 🤒.
Let’s dig into the first of potentially many jargon-explaining issues. Let's understand the differences and the relationships between AI Use Case, AI Models, AI Systems, AI Solutions, and AI Products.
The Path to AI Product
Before we dive into defining each term in the AI Product concept map and exploring their relationships, let’s take a step-by-step approach to understanding the diagram. By applying logical rules, we can learn how to accurately interpret the diagram and grasp the connections between each component.
As a computer scientist, I like to regularly apply the theoretical toolbox learned in university. For this, I would like to utilize the calculus of propositional logic 🤓
1. Term: AI Use Case
Requires: Nothing directly.
Applied Rule: Identity (P; P is true)
Insight: An AI Use Case can exist independently and does not require any other elements to be defined.
2. Term: AI Model
Requires: AI Use Case, Data
Applied Rule: Conjunction (P and Q; P is true and Q is true)
Insight: An AI Model requires a specific AI Use Case and relevant Data to be developed and trained.
3. Term: AI System
Requires: AI Model, Other Components
Applied Rule: Conjunction (P and Q; P is true and Q is true)
Insight: An AI System needs a developed AI Model and additional components to be operational and effective.
4. Term: Problem
Requires: Nothing directly.
Applied Rule: Identity (P; P is true)
Insight: A Problem can exist independently and does not require any other elements to be defined.
5. Term: AI Solution
Requires: AI System, Problem, Other Components
Applied Rule: Conjunction (P and Q and R; P is true, Q is true, and R is true)
Insight: An AI Solution requires an operational AI System, a defined Problem, and other necessary components to address the problem effectively.
6. Term: Business Opportunity
Requires: Nothing directly.
Applied Rule: Identity (P; P is true)
Insight: A Business Opportunity can exist independently, but recognizing it leads to the development of an AI Product that addresses specific market needs or problems.
7. Term: AI Product
Requires: AI Solution, Business Opportunity, Other Components
Applied Rule: Conjunction (P and Q and R; P is true, Q is true, and R is true)
Insight: An AI Product can be successfully developed if there is a viable AI Solution, an identifiable Business Opportunity, and all other necessary components are in place.
Applying Transitivity and Other Logical Rules
And by applying more logical rules, we can derive important insights. Let’s try this out (this approach involves a mix of formal logic and commonsense reasoning, often referred to as non-monotonic reasoning).
Problem Must Be Addressable by an AI Use Case:
Insight: A Problem must be addressable by an AI Use Case because an AI Solution requires an AI System, which requires an AI Model, which in turn requires an AI Use Case. If there is no AI Use Case, it means the Problem cannot be solved with AI.
Business Opportunity Indirectly Requires AI Use Case (When Related to AI Products):
Insight: A Business Opportunity related to AI products indirectly requires an AI Use Case, as it necessitates an AI Product, which in turn requires an AI Solution, AI System, AI Model, and ultimately an AI Use Case. However, not every business opportunity requires an AI solution, as some can be addressed with other technologies.
AI Product Requires Data:
Insight: An AI Product requires Data, as it relies on an AI Solution, which depends on an AI System and AI Model, ultimately requiring Data.
AI System Requires Both AI Model and Other Components:
Insight: An AI System is dependent on both an AI Model and other components (such as hardware or software infrastructure) to function effectively. Without either, the system cannot operate.
AI Solution Needs Continuous Data Supply:
Insight: Since AI Solutions rely on AI Models, which in turn rely on Data, an effective AI Solution requires a continuous and high-quality data supply to remain accurate and functional over time.
Effective AI Use Cases Drive AI Product Viability:
Insight: The presence of well-defined AI Use Cases directly impacts the viability and success of an AI Product. Without clear use cases, it is difficult to justify the development of AI Models and Systems, ultimately affecting the feasibility of creating a valuable AI Product.
Other Components are Critical Across Multiple Stages:
Insight: Other Components are crucial at various stages of the AI product development process, from AI Systems to AI Solutions to the final AI Product. Their role is pervasive and essential for seamless integration and functionality.
Validated AI Use Cases Lead to Business Opportunities:
Insight: Validated AI Use Cases can highlight new Business Opportunities. As AI Use Cases demonstrate successful problem-solving capabilities, they can reveal untapped markets and customer needs, driving new business ventures.
😍 Isn't it beautiful how everything fits together? Each piece connects perfectly, creating a clear path from identifying a problem to developing a successful AI product. Understanding these connections helps us tackle AI product development confidently, showing how all the elements work together to create effective, marketable solutions.Pretty cool, right? And a bit nerdy, too—but hey, isn't nerdy the new sexy? 🤓Now let’s see what the terms actually mean. We'll start with my definition of an AI product, as it encompasses all the other terms. From there, we'll break them down step by step, starting with the AI Product and working our way to the AI Use Case.
What is an AI Product
If we put together everything we have learned about AI products based on the logical derivation of the concept map, we could summarize AI products as follows:
📙 Long Version: An AI product must originate from a validated problem and business opportunity, which necessitates a corresponding AI use case. This use case requires the development of an AI model based on data, deployed within an AI system, and supported by essential components. These elements together form the AI solution, which must then be integrated with other components necessary for a complete product offering. If any link in this chain is missing or unvalidated, the AI product cannot be successfully developed or implemented.But this is everything but an elevator pitch. My personal definition, which I use often, sounds like this:
📙 Short Version: An AI product provides a desired AI solution to a problem in a way that people are willing to pay for it so that the product can be offered sustainably.When using this definition I always emphasize the importance of in a way, because this is the most tricky part.
Interpretation of "in a way":
Effectiveness: The AI product must effectively address the problem, which is ensured by starting with a clear problem and developing an AI use case.
User Experience: The solution must be user-friendly, trustable, and secure, supported by the AI system and essential components.
Value Proposition: The AI product must deliver clear benefits that customers find valuable, making them willing to pay for it.
Market Fit: The AI product should meet market needs, validated by the initial business opportunity.
Sustainability: The AI product should be scalable and maintainable, integrating all necessary components for long-term viability.
And many more ways…
What is an AI Solution
An AI Solution is a complete set of tools and technologies that work together to solve a specific problem. It includes the AI model, AI system, and other necessary components that address the problem effectively. Think of it as the complete package that does the job you need it to do. An AI solution brings together the various elements required to turn raw data into actionable insights or automated actions that solve the problem at hand.
What is an AI System
An AI System is the infrastructure that supports the AI model. It includes all the hardware and software needed to run the AI model. For example, it could be the cloud platform where the AI model is deployed and the software environment that allows the model to function properly. The AI system ensures that the AI model can operate efficiently, process data, and deliver results in real-time or as needed.
What is an AI Model
An AI Model is a mathematical representation created based on data to solve a particular problem. It is trained using data to recognize patterns and make predictions or decisions. The AI model is the core of any AI product, as it is the part that learns from data and makes intelligent decisions. For instance, an AI model could be a machine learning algorithm trained to detect fraud in financial transactions. This model, when integrated into an AI system and supported by necessary components, forms the backbone of the AI product. That’s also why an AI Product is named an AI Product 😉
What is an AI Use Case
An AI Use Case is a specific scenario or application where AI can be applied. It defines how AI will be used and what problem it aims to solve. For example, using AI to automate customer support by creating a chatbot is an AI use case. Identifying the right use case is crucial because it directs the development of the AI model and ensures that the AI product addresses a real need or opportunity in the market.
Take Away
If I were to tell aspiring AI Product Managers or companies who want to build their own AI products one crucial thing, it would be: The path to an AI product can quickly become the Road to Perdition.
I know, the movie Road to Perdition might not be the best analogy for this context, as it is a crime drama dealing with themes of revenge, loyalty, and father-son relationships, which are quite different from the challenges faced in AI product development. But the title itself conveys a powerful message about the potential for a promising journey to take a dark turn if not handled with care.
In the realm of AI product development, the journey is fraught with its own set of challenges and potential pitfalls. I often describe the complexity of building AI products using the six dimensions of the Double Trio. This framework highlights the various factors you need to be aware of, from data quality and model accuracy to user experience and regulatory compliance.
Given these complexities, don't let a simple misunderstanding be the reason for failure. I promise you there will be plenty of other challenges waiting for you 🎢. The journey won’t be boring 🥱.
JBK 🕊
Question: Where should we start? Do you have an answer to the question in the concept map diagram The Path to AI Product?
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