AI Apps are domain-infused, AI/ML-powered applications that continuously learn and adapt with minimal human intervention in helping non-technical users manage data and analytics-intensive operations to deliver well-defined operational outcomes.
I originally introduced the idea of a “Data Product Development Canvas” as one of the capstone deliverables (the other being the data science Hypothesis Development Canvas) for my “Thinking Like a Data Scientist” methodology. Several folks and students tested the canvas and gave me great feedback. The most critical feedback was to focus on the ultimate end deliverable: creating a user-friendly, AI-powered app, or “AI App,” enabling non-data scientists to leverage data and analytics to deliver a well-defined outcome.
My favorite “AI App” is the Uber app. The Uber app leverages data and analytics to provide a frictionless experience for riders and drivers, such as matching them based on location, time, and preferences, estimating fares and trip duration, optimizing routes and traffic conditions, and personalizing recommendations and promotions.
Other examples of “AI Apps” include:
- Apple Maps (Google Maps, Waze) leverages data and analytics to deliver a well-defined operational outcome. Apple Maps uses data to provide a convenient and efficient experience for users who need to navigate from one place to another, such as finding the best route, estimating travel time and distance, avoiding traffic jams and road closures, and discovering nearby places and services.
- Netflix leverages data and analytics to deliver personalized and relevant entertainment experiences for users. Netflix uses machine learning algorithms to analyze user preferences, behavior, and feedback and provide recommendations for movies and shows that users may like. Netflix also uses artificial intelligence to optimize its streaming quality, content production, and marketing strategies.
- Spotify leverages data and analytics to deliver customized and engaging music experiences for users. Spotify uses machine learning algorithms to understand user tastes, moods, and contexts and provide recommendations for songs, playlists, podcasts, and artists that users may enjoy. Spotify also uses artificial intelligence to create personalized playlists, discover new music, and enhance sound quality.
- Google Photos leverages data and analytics to organize and manage users’ photos and videos. Google Photos uses machine learning algorithms to recognize faces, objects, places, and events in users’ photos and videos and group them into albums, stories, and collages. Google Photos also uses artificial intelligence to edit, enhance, and share users’ photos and videos.
AI Apps require blending five critical disciplines – Data Management, Data Science, Application Development, Design Thinking, and Customer Experience – to create an AI App that can continuously learn and adapt to deliver meaningful, relevant, responsible, and ethical business or operational outcomes. AI Apps provide the following benefits, unlike one-off (orphaned) analytic models:
- AI Apps are operational assets that can continuously learn and adapt with minimal human intervention.
- AI Apps are economic assets that can be shared, reused, and refined across multiple use cases at decreasing marginal costs.
- AI Apps can be strung together to address complex value chain processes when the AI Apps are built on a common architecture that facilitates the sharing, reuse, and continuous refinement of the enterprise’s data, KPIs, features, ML models, and user interface.
Let’s dive into explaining the AI App Product Development canvas. There is a lot to explore!
AI Apps Development Canvas Version 2.0 – Page 1
Figure 1 shows page 1 of the AI App Development Canvas.
Figure 1: Data Product Development Canvas – Page 1
Page 1 of the AI App Development Canvas covers the following information:
(0) AI App Description. Product Name (Eventa?), Author, Completion or Update date, and Version number.
(1) Business Problem & Ideal Outcome. What is the business/operational problem or opportunity we are trying to address, and what are the Ideal Outcomes?
(2) Business Value. What are the potential benefits (financial, operational, customer, environmental, employee) from addressing this Business Problem?
(3) Potential Impediments. What are potential impediments (technology, people skills, organization, timing) in addressing this Business Problem?
(4) Business Entities. What are the business entities (human and device/equipment) around which we seek to uncover predicted propensities?
(5) Targeted Users and Desired Outcomes. Who are the intended users, and what are their desired outcomes? Note: This critical section will require considerably more space than what is provided in the canvas.
(6) Upstream System Dependencies. What is the upstream system, application, or organizational dependencies for the AI App?
(7) Downstream System Dependencies. What is the downstream system, application, or organizational dependencies for the AI App?
(8) Key Decisions. What are the targeted users’ critical decisions or actions enabled by the app?
(9) KPIs and Metrics. What are the KPIs and metrics against which the targeted users will measure the effectiveness of the AI Apps?
(10) Analytic (Predictive) Scores. What Analytic Scores are required to power the recommendations that support the targeted users’ critical decisions?
(11) Prescriptive Recommendations. What prescriptive recommendations are required to support the targeted users’ critical decisions?
(12) User Gains (Benefits). What benefits (or gains) would users experience in achieving the desired outcome?
(13) User Pains (Impediments). What impediments (pains) have users experienced to achieve the desired outcome?
AI Apps Development Canvas Version 2.0 – Page 2
Figure 2 shows Page 2 of the AI App Development Canvas.
Figure 2: Data Product Development Canvas – Page 2
Page 2 of the Data Product Development Canvas collects the following information:
(14) Data Sources. What data sources are required to support the app?
(15) Data transformations. What data transformations & enrichments are required for feature engineering?
(16) Machine Learning (ML) features. What ML features are required for the ML models?
(17) Model Performance. What are the ML model accuracy and processing time requirements?
(18) Model Monitoring/Observability. How will the model be instrumented and monitored to manage model drift and drive continuous model learning and refining?
(20) Data Pipeline Performance. What is user environmental, architecture, and technical requirements?
(21) API Requirements. What are API requirements for integrating the analytic results into the management and operational systems?
(22) App UI / UEX. How should analytic results (visualizations) be presented to the users so that they are understandable and actionable?
(23) Privacy. What personal data needs to be captured to deliver desired outcomes and their privacy considerations (GDPR, HIPAA, CCPA, FRCA)?
(24) App Feedback. How will user feedback and app performance be captured and fed back into the AI App and its enabling analytic models?
AI App Development Canvas Summary – Part 1
I will test the AI App Development Canvas to define the requirements and specifications for a “Local Events Marketing Optimization” app. This test will be part of my ongoing series on integrating GenAI into my “Thinking Like a Data Scientist” methodology, which I have discussed in my blog posts (listed below).
Let’s see what GenAI / Bing can do…
Integrating Generative AI into the “Thinking Like a Data Scientist” methodology blog series:
- Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part I https://www.datasciencecentral.com/integrating-genai-into-thinking-like-a-data-scientist-methodology-part-i/
- Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part II https://www.datasciencecentral.com/integrating-genai-into-thinking-like-a-data-scientist-methodology-part-ii/
- Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part III https://www.datasciencecentral.com/integrating-genai-into-thinking-like-a-data-scientist-methodology-part-iii/