The Blueprint for Building Successful AI Products
Start with the Problem, Not the Data: A Guide to Successful AI Products
Building a Successful AI Product
Creating a successful AI product is challenging. It requires close collaboration between management and technical teams. Here are some key ideas to help you build a successful AI product.
Start with a Business Case
Instead of starting with cleaning up data, begin with a clear business case. Identify what you want to achieve and explore different ways to reach that goal.
Integrate AI for Measurable Outcomes
AI should be integrated into your business to achieve specific, measurable outcomes.
Identify the Business Problem
To identify the business problem, ask yourself:
- What problem are we trying to solve? Is it impactful?
- How does AI add value? Can we clearly quantify this value? Do we need AI, or can we solve it without it?
- How will we measure success?
If the problem is unclear, break it down into smaller, specific components.
Effective Success Metrics
Success metrics should be:
- Easily measurable
- Directly correlated to business performance
- Predictive of future outcomes
- Comparable to competitors’ metrics
Examples of success metrics include:
- Customer experience
- Revenue gain
- Customer engagement
- Business process automation
- Better and faster decision-making
Business Outcomes vs. AI Model Output
Focus on business outcomes like:
- Generating revenue
- Improving customer experience
- Increasing user satisfaction
- Automating processes to save costs
AI model outputs include:
- Accuracy
- Execution time
- Recall
- Precision
Monitor the accuracy, performance, and fairness of AI models but stay focused on business outcomes.
Data Quality
AI systems are only as good as the data used. Consider:
- What data do we need? Does it fit the problem?
- Do we have enough data? Is it representative of real-world scenarios?
- Is the dataset complete and correctly annotated?
Use production data, not academic data, and ensure the training data matches real-world scenarios. Continuously update and improve data quality.
Team Requirements
An AI product development team should cover:
- Business needs
- Infrastructure
- Algorithm development
- Performance
- Quality
- Data
- Usability
Suggested Roles
- Product Owner: Connects stakeholders and team, ensures the right product is built.
- Designer: Handles design, usability, and accessibility.
- Software Engineer: Develops product infrastructure and solves software problems.
- Data Engineer: Manages data infrastructure and model deployment.
- Data Scientist: Builds and selects models, structures problems, and uses data to answer business questions.
- DevOps: Ensures infrastructure reliability, manages scalability and performance, and mitigates security risks.
Top Challenges for AI Initiatives
- Implementation challenges
- Integrating AI into company roles and functions
- Data issues (privacy, access, integration)
- Cost of AI deployment
- Lack of skills
- Measuring and proving business value
Summary
- Start with the business problem
- Build an interdisciplinary team with the right skill set
- Ensure you have the right data
- Prototype and release a small version of your product
- Test, measure, update, learn, and iterate quickly
Reference
- “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett
- “Machine Learning Yearning: Technical Strategy for AI Engineers, In Production” by Andrew Ng
- “Applied Artificial Intelligence: A Handbook For Business Leaders” by Mariya Yao, Adelyn Zhou, and Marlene Jia
- “Building Machine Learning Powered Applications: Going from Idea to Product” by Emmanuel Ameisen
- Udacity AI Product Manager Nanodegree