Artificial Intelligence and Machine Learning
Management Tools
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.
Brief
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.
To apply Artificial Intelligence and Machine Learning, companies should:
Automation
Data Mining
Deep Learning
Machine Learning Operations
Predictive Analytics
Robotics
Companies typically use Artificial Intelligence and Machine Learning to:
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.
On the 30th anniversary of our survey, managers seem surprisingly upbeat.
Digital Transformation is a way to integrate digital technologies into an organization's strategy and operations.
Scenario Analysis and Contingency Planning is a process that allows executives to explore and prepare for several alternative futures.
An Engine 2 business can create options for growth, and seven neobanks owned by traditional banks show what it takes to succeed.
Dynamic Strategic Planning and Budgeting is a method to help companies plan and invest during times of great uncertainty.
A Customer Experience Management (CXM) Program is how a business oversees its interactions with customers.
Define the business opportunity. Design with the goal in mind. Invest in the process. Determine the data sources. Develop AI organizational capabilities. Address AI risks.