Managing Artificial Intelligence

1- Artificial intelligence (AI) presents a new challenge for managers. Unlike past information technologies, AI is constantly evolving and presents a wider range of capabilities. Machine learning, a core component of AI, allows for a deeper level of autonomy and learning than ever before. This is evident in the variety of AI applications being used today, from facial recognition to self-driving cars. While AI offers vast potential to improve many aspects of our lives, there are also significant challenges to consider. These include ethical issues around data privacy and bias, potential job displacement, and national security concerns. Managers need to be aware of both the opportunities and challenges that AI presents.

2- While the definition of Artificial Intelligence (AI) remains elusive, with some emphasizing its ability to mimic human behavior (McCarthy et al., 1955) or learn (Castelvecchi, 2016), this very ambiguity has fueled progress. This open-endedness allows for diverse interpretations and exploration. Viewing AI as a process or a constantly evolving frontier of computing, rather than a single technology, better captures this essence. This perspective highlights that AI is not a device or program, but an ongoing quest to leverage computational advancements for ever-more intelligent problem solving.

3- The role of managers in the age of AI is crucial. They are the ones who make decisions about developing and implementing AI systems, and how to use them for tasks like decision-making and customer targeting. These decisions can range from routine to complex. Artificial intelligence (AI) is increasingly automating or informing these decisions, with a growing focus on AI’s role in non-routine decision-making. However, this doesn’t mean managers will become obsolete. Instead, their role needs to adapt alongside AI advancements. Managers will need to make decisions with and about AI, requiring a strong understanding of the technology.

4- One key facet of AI is its autonomy. AI can now process information and make decisions without direct human input. This presents both opportunities and challenges.  While AI can improve efficiency, managers need to address how this impacts human reliance and decision-making abilities.

5- Another facet of AI is its learning capability. AI can now learn from vast amounts of data, including data beyond an organization’s control. This raises concerns about privacy, trust, and data governance. As AI becomes more inscrutable, or difficult to understand, managers will need to ensure transparency and accountability in AI’s decision-making processes.

6- The vast potential and management needs of AI present a golden age for the information systems field to showcase the value of sociotechnical thinking. Considering both social and technical aspects, along with their interaction, is crucial for AI management. With this opportunity comes responsibility. We must address both the opportunities and challenges arising from our field’s position at the crossroads of evolving computing and its societal and business connections, constantly reflecting on how our norms, processes, outputs, and assumptions might be challenged by AI’s autonomy, learning, and complexity.

Adapted from the article by N Berente, B Gu, J Recker and R Santhanam

Glossary

Machine learning: A type of AI that allows computers to improve at a task without being explicitly programmed.

Bias: Prejudice in favor of or against one thing, which can be reflected in AI systems.

Job displacement: People losing their jobs because machines can do them.

Frontier: The most advanced area of knowledge or activity.

Leverage: To use something to one’s advantage.

Non-routine decision-making: Complex decisions requiring judgment and creativity.

Data governance: Rules for how data is collected, stored, and used.

Accountability: The responsibility to explain or justify actions.