Over the past year, I have worked with clients from various industries and use cases who have consistently asked about AI (Artificial Intelligence) in their product strategy and product maps. In a recent blog post, I discussed the current hype cycle surrounding AI and compared it to previous hype cycles, such as the dot-com era. As we approach the end of 2023, I wanted to briefly preview my upcoming blog series.
My clients and readers have appreciated my approach to Applied AI, the practical implementation of artificial intelligence technologies to solve real-world problems and enhance various aspects of business and daily life. Applied AI involves applying AI techniques such as machine learning, natural language processing, computer vision, and more to specific tasks and domains to achieve particular objectives. It represents the practical implementation of AI technologies to create tangible benefits, such as improving efficiency, enhancing decision-making, or delivering better customer experiences.
I have been spending much time diving deep into this topic. I have also spent a lot of time with leaders at some of the world's largest technology companies and some of the smallest. Some of the products I am using and learning about have genuinely changed how I work and how their customers work, and others have not. Companies should understand the difference between Hyped AI and Applied AI most from this exciting new technology, as this will be the defining success factor for all of them.
Therefore, as a product leader, I use the following key aspects to frame the term “Applied AI” with teams and business leaders less versed in technology, but exploring how to separate hype from actual use cases (applied AI):
- Problem-Solving: Applied AI addresses specific challenges and problems in various industries and domains. It leverages AI technologies to find innovative solutions to these problems.
- Use Cases: Applied AI encompasses various use cases across different sectors, including healthcare, finance, e-commerce, manufacturing, and more. These use cases can vary from improving customer service to automating complex tasks.
- Data-Driven: Applied AI heavily relies on data for training AI models. High-quality, labeled data teaches AI algorithms to make predictions, recognize patterns, and provide insights.
- Machine Learning: Machine learning, a subset of AI, plays a central role in Applied AI. Machine learning algorithms allow systems to learn from data and make predictions or decisions without being explicitly programmed.
- Automation: Applied AI often involves automating tasks previously performed by humans. This can lead to increased efficiency, cost savings, and the ability to handle repetitive tasks at scale.
- Personalization: AI personalizes user experiences by recommending products, content, or services based on individual preferences and behavior.
- Natural Language Processing (NLP): Applied AI leverages NLP to understand and generate human language. This technology is used in chatbots, virtual assistants, sentiment analysis, and more.
- Computer Vision: In domains like healthcare and autonomous vehicles, computer vision is applied to analyze and interpret visual data, such as images and videos.
- Predictive Analytics: AI models are used for predictive purposes, such as forecasting sales, predicting equipment failures, and identifying potential security threats.
- Continuous Learning: Applied AI systems incorporate mechanisms for constant learning and adaptation. They can improve their performance over time by encountering more data and feedback.
In the upcoming weeks, I will highlight some fantastic companies leading in building tangible AI experiences that aren’t just marketing hype but are real and available today. So watch this space or my website for my blog updates.