There’s a lot of marketing buzz and technical spin on artificial intelligence, machine learning, and deep learning. Most of what’s out there is either too fluffy or too mathy, either too general or too focused on specific applications, too disconnected from business outcomes and metrics, and too undirected.
This article provides an overview of these related technologies by:
- Defining AI, machine learning, and deep learning, explaining the differences from traditional approaches, describing when to use them, and noting their advantages and disadvantages.
- Explaining how they complement business frameworks and enable business outcomes and metrics.
- Describing common types of machine learning and deep learning model training, algorithms, architectures, performance assessments, and obstacles to good performance.
- Providing examples of machine learning models and algorithms at work.
- Presenting a potential framework for AI implementation for business outcomes.
Why AI: AI in the business context
All organizations work to specific outcomes, and they juggle several business metrics and processes to achieve this, such as revenue, costs, time to market, process accuracy, and efficiency. Yet they have limited resources (money, time, people, and other assets). So, the problem boils down to making good decisions about resource allocation (what kind of resources, how many/much of them, what should they do, what capabilities do they need, etc.), and making those good decisions faster than competitors and faster than the market is changing.
Making these decisions is hard, but clearly, they become much, much easier when data, information, and knowledge are available. Assuming these inputs are available, they need to be aggregated and mined for nuggets. Analysts need time to pull tribal knowledge out of subject matter experts’ heads, to adjust to fluctuating business rules, to calibrate for personal biases where possible, and to spot patterns and to generate insights. Ideally, analysts and managers should (time permitting) assess multiple scenarios and run several experiments to increase confidence in their recommendations and decisions. Finally, the decisions need to be operationalized.