What Are Artificial Intelligence (AI) and Machine Learning?

Artificial Intelligence (AI) is a range of analytical techniques that allows a computer to detect relationships, predict outcomes, and often act based on the patterns in data without being explicitly programmed to do so. Machine Learning is a technology that uses algorithms that learn and improve based on experience and is a major subfield of AI. Together AI and Machine Learning can be powerful tools for companies, enabling them to automate manual processes, optimize customer recommendations, and develop innovative products.

Various subareas of Machine Learning and AI are unlocking innovation in creative fields, sciences, engineering, and others. For example, Deep Learning mimics human learning and is a major enabler of computer vision, natural language processing, Robotics, and others. While the adoption of AI and Machine Learning can unleash new opportunities and insights, reduce costs, and improve processes, it is not without challenges. A growing concern is the ethical implications of AI, such as the risk that data sets used to train AI might reflect real-world bias and discrimination. At the same time, enablers, like cloud machine learning platforms, compute accelerators, and managed AI services, are reducing the technological barrier for businesses to leverage AI products.

How Are Artificial Intelligence and Machine Learning Implemented?

To apply Artificial Intelligence and Machine Learning, companies should:

• Define the business opportunity. Identify the right applications, such as those with high repetition, strong data, regular patterns and, ideally, low mistake costs. AI does not have to replace a human to be effective; it can be very valuable in helping humans do their jobs faster and more accurately.
• Design with the goal in mind. AI exists to enhance human decision making. Companies get the most out of the technology when they know the applications and decisions they want to improve.
• Invest in the process. Modern AI or Machine Learning algorithms still require vast amounts of data and large manual efforts to train and tune them. Test-and-learn strategies can help companies figure out which approaches work best for their businesses.
• Determine the data sources. Combining data from customers, operations, and finance can generate valuable insights.
• Develop AI organizational capabilities. Train employees how to interact with AI-based applications, understand the output, and alter the approach as needed.
• Address AI risks. Establish roles and processes to identify and help mitigate risks related to bias, flawed decision making, and data security.

Related Topics

• Automation

• Data Mining

• Deep Learning

• Machine Learning Operations

• Predictive Analytics

• Robotics

 

What Are Common Uses of Artificial Intelligence and Machine Learning?

Companies typically use Artificial Intelligence and Machine Learning to:

• Improve internal processes, such as automating data entry, manufacturing, or customer support
• Improve existing products and services or develop new product and service offerings
• Provide more responsive and efficient service (e.g., chatbots, personalized content to enable self-service)
• Optimize marketing spend (e.g., better target their offerings to their customers)
• Detect fraud and security breaches

• Selected references (click to expand)

  Blackman, Reid. “Why You Need an AI Ethics Committee,” Harvard Business Review, July–August 2022.

  Brock, Jürgen Kai-Uwe, and Florian von Wangenheim. “Demystifying AI: What Digital Transformation Leaders Can Teach You About Realistic Artificial Intelligence,” California Management Review, Vol. 61(4) 110–134, 2019.

  Daugherty, Paul R., and H. James Wilson. Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press, 2018.

  Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. The MIT Press, 2016.

  Haenlein, Michael, and Andreas Kaplan. “A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence,” California Management Review, Vol. 61(4) 5–14, 2019.

  James, Gareth, et al. An Introduction to Statistical Learning: with Applications in R. Springer, 2021.

  Murphy, Kevin. Probabilistic Machine Learning: An Introduction. The MIT Press, 2022.

  Overgoor, Gijs, Manuel Chica, William Rand, and Anthony Weishampel. “Letting the Computers Take Over: Using AI to Solve Marketing Problems,” California Management Review, Vol. 61(4) 156–185, 2019.

  Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson Education Limited, 2022.

  Tambe, Prasanna, Peter Cappelli, and Valery Yakubovich. “Artificial Intelligence in Human Resources Management: Challenges and a Path Forward,” California Management Review, Vol. 61(4) 15–42, 2019.

  Yao, Mariya, Marlene Jia, and Adelyn Zhou. Applied Artificial Intelligence: A Handbook for Business Leaders. TOPBOTS Inc., 2018.

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