Artificial intelligence is being touted as a way to boost lagging productivity growth, but research suggests that gains don't come cheap or easy. While AI can increase productivity on low-skill tasks such as taking meeting notes and customer service, or work done by junior workers, it requires difficult, complex, and expensive organizational groundwork.
Introducing AI can be slow and expensive, and organizations often rely on feedback from skilled workers or vendor claims to measure productivity changes. Moreover, AI use may create new risks and problems for workers, such as worker alienation and decreased job satisfaction when jobs become primarily about overseeing AI systems.
Measuring productivity changes due to AI is tricky, and organizations struggle to quantify the benefits. Furthermore, AI tools require high-quality, well-structured data to perform effectively, which can be a challenge for organizations with poor data management. Additionally, there are concerns about data security, privacy, and the potential for AI to magnify existing human biases.¹
Some experts recommend starting small, testing AI tools in specific areas, and building habits around their use. Others emphasize the importance of balancing AI-driven insights with human judgment and expertise to make well-rounded decisions.
