How to Use AI and ML to Your Advantage as a Product Manager

Artificial intelligence (AI) and machine learning (ML) are transforming industries and redefining what’s possible with technology. As these fields continue to evolve rapidly, AI and ML are increasingly being incorporated into products and services across many sectors.?

For product managers, this represents a huge opportunity as well as a significant challenge. To build successful products powered by AI and ML, product managers need to understand these technologies and how to apply them effectively.

In this article, we’ll explore what product managers need to know to work on AI and ML products, including the following key topics:

? What AI and ML are and why are they important

? Key applications and use cases for AI and ML

? How to identify opportunities to apply AI and ML

? Understanding the AI and ML model development process

? Designing effective human-AI interaction

? Key challenges and risks to consider

? Best practices for managing AI/ML products

Grasping these concepts will prepare any product manager to navigate the world of AI/ML products successfully.?

What are AI and ML and Why are They Important?

To start, it’s important to level set on what artificial intelligence and machine learning are and why they matter.?

Artificial intelligence refers broadly to computer systems that are designed to perform tasks that would otherwise require human intelligence. This includes capabilities like visual perception, speech recognition, and language translation. AI systems are powered by machine learning.

Machine learning is a subset of AI that enables computers to learn and improve at tasks without being explicitly programmed. Machine learning algorithms use historical data as input to uncover patterns and insights that can then be used to make predictions or decisions. Through hands-off training, ML algorithms “learn” iteratively to improve their performance.

AI and ML represent a massive leap forward in computer capabilities. They enable computers to tackle complex tasks across many industries that have historically required human cognition and judgment.?

Key applications include:

? Computer vision - Image recognition, object detection, facial recognition

? Natural language processing - Sentiment analysis, language translation?

? Predictive analytics - Forecasting, recommendations

? Anomaly detection - Fraud prevention

? Personalization - Customized user experiences

? Automation - Intelligent process automation

The business value of these applications is enormous. AI/ML allows companies to automate processes, gain predictive insights, save costs, and deliver more value to customers. As AI/ML continues to progress, its applications will become only more advanced and valuable.

That’s why product managers need to understand these technologies and identify where they can be applied successfully. Those who harness AI/ML will gain a real competitive advantage.

Identifying AI/ML Opportunities

Product managers are uniquely positioned to identify where AI and ML can add value. Here are some tips for recognizing promising AI/ML opportunities:

- Look for repetitive human tasks that can be automated - Anywhere that repetitive rules-based tasks or data processing is being done manually is ripe for automation.

- Consider where predictive insights would be valuable - If being able to forecast outcomes or detect anomalies earlier would be useful, AI/ML models can probably help.

- Identify areas where personalization would improve customer experience - AI can power individualized recommendations and custom experiences.

- Find processes where human subjectivity or bias are issues - AI models apply rules consistently without human bias or subjectivity.

- Determine where achieving scale is constrained by headcount - AI/ML doesn’t require hiring more people, so it can easily scale.

- Look for tasks requiring complex perceptual or judgment capabilities with a large amount of data - Problems requiring human-level cognition are great AI application opportunities.

No industry is off limits. Manufacturing, healthcare, financial services, e-commerce, transportation - AI and ML are already being applied across all sectors. Product managers just need to be constantly on the lookout for ways to take advantage of these technologies.

Understanding the AI/ML Model Development Process

Once promising AI/ML applications have been identified, turning those opportunities into actual product capabilities requires carefully developing AI/ML models. Product managers need to understand at a high level what’s involved in developing production-ready AI/ML models.

The model development process generally involves:

1. Data collection and preparation

2. Training and evaluating candidate models?

3. Hyperparameter tuning

4. Deploying the model to production?

5. Monitoring and maintaining the model

Data Collection and Preparation

The first step is assembling a sufficiently large dataset to train the model. For supervised learning, the dataset needs to include the input data and the desired outputs. Structured data like tables or XML work best.

The data then needs to be cleaned and preprocessed. Outliers and errors must be handled. The data should be formatted consistently. Features need to be selected, extracted, and transformed as needed. Preprocessing is crucial for ensuring quality model development.

Training and Evaluating Models

Once data is ready, different machine learning algorithms can be tested to determine the best approach. Algorithms like linear regression, random forest, and neural networks have different strengths and weaknesses depending on the problem.

Choosing the best model often takes experimentation and evaluation. Models are trained on a subset of the data and then tested against a holdout validation set. Metrics like accuracy and AUC are used to evaluate model performance.

Hyperparameter Tuning?

The adjustable parameters that control model training are called hyperparameters. Values for learning rate, regularization rate, number of layers in a neural network, etc. must be tuned to optimize model performance.

Tuning is done iteratively by running training with different hyperparameter sets and assessing which produce the best results. Grid search and random search can methodically scan for optimal settings.

Deployment to Production

Once a final model is selected and tuned, it needs to be deployed into the product and integrated with application code. The model inputs and outputs need to be connected and formatted properly.

For web or mobile applications, the model is typically hosted on a server and accessed via API calls. Monitoring and health checks need to be implemented to ensure reliable service.

Maintenance and Monitoring

Even after models are put into production, product managers must continue monitoring their performance and validating that they continue to serve the business need.

Data drift, concept drift, and new scenarios not represented in the training data can degrade model performance over time. Models need to be retrained and updated periodically.

By overseeing this full development and deployment process, product managers can ensure that AI/ML capabilities are engineered effectively.

Designing Effective Human-AI Interactions

Incorporating AI and ML-powered functionality into products inevitably impacts the user experience and how customers interact with the product. Product managers should carefully consider these human-AI interactions.

Some guiding principles for designing effective human-AI product experiences include:

- Establish transparency and build trust - Be clear about when and where AI is involved so users understand what to expect.

- Augment human capabilities - Position AI as enhancing humans rather than replacing them.

- Allow user control - Provide options for users to customize or override model outputs if needed.

- Engineer for edge cases - Ensure AI handles out-of-distribution data gracefully rather than failing badly.?

- Make AI accessible - Complex ML models should be encapsulated behind a simple, intuitive UI.

- Validate usability with testing - Iteratively gather user feedback to improve interactions.

By keeping the user experience with AI front and center, product managers can maximize adoption and satisfaction.

Key Challenges and Risks to Consider

While the benefits of AI and ML are undeniable, product managers must also be aware of key challenges and risks that come with these technologies. Here are some top issues to consider:

- Models can be brittle. Small input changes can cause drastically different results. Rigorous testing is essential.

- Biased data leads to biased models. Historical training data often reflects societal biases which get ingrained in models.

- Models can fail on new data. AI/ML models may not perform as expected on data that is very different from their training data. Re-training and monitoring are necessary.

- Interpretability is hard. It can be extremely difficult to explain why an AI model produces certain outputs. Lack of transparency is an inherent challenge.

- There can be high compute costs. Training and running advanced ML models requires access to significant cloud computing resources which can get expensive.

By anticipating these issues and developing mitigation strategies, product managers can avoid major pitfalls in bringing AI/ML products to market. Adopting best practices around transparency, testing, monitoring, and diverse training data is essential.

AI and ML Best Practices for Product Managers

Here are some overarching best practices that can help product managers successfully oversee the development and launch of AI/ML products:

- Start with a clear business objective - Tie ML projects to concrete business KPIs and metrics for success from the start. Don’t use ML just for its own sake.

- Collaborate closely with data scientists - Partner continuously with data experts who can guide proper ML methodology.

- Plan regular model monitoring - Don’t “set and forget” models. Update models regularly as new data arrives.

- Isolate models behind APIs - Encapsulate ML models behind APIs to allow easier updates and integration.

- Extensively test models - Rigorously test models under different scenarios to surface potential weaknesses.

- Collect feedback from users - Gather user feedback during development to improve model usability and performance.?

- Develop processes for ongoing annotation - Ensure there is budget and process for getting human annotations to retrain models.

- Build trust through transparency - Be clear when and where models are used and allow visibility into model logic.

- Define measures of success - Identify metrics like reduced fraud or increased click-throughs that indicate models are working.

By applying these product management best practices, businesses can maximize their chances of success with AI and ML products.

The Future with AI and ML?

There is no doubt that artificial intelligence and machine learning are going to continue revolutionizing products and services across every industry. To stay competitive, leveraging these technologies strategically will only become more important.

Product managers have a major opportunity and responsibility to shape how AI/ML capabilities get incorporated into customer offerings. By deeply understanding the core concepts of AI/ML, recognizing where they can add value, and applying best practices in managing AI/ML products, any product manager can successfully lead their company into the future powered by AI.

The time to embrace AI and ML is now. With the right knowledge and preparation, product managers can steer their products and businesses to new heights with AI and ML. The future is full of potential to improve people’s lives with the power of these transformative technologies.

(In my next article, I’ll go into a little more detail on actual systems and frameworks that your company can tap into to make the process easier.)