Data, AI, & Machine Learning

Recording expectations improves decision quality, risk management, and leadership development.

Digital tools are making the hiring process easier and more precise — despite their limitations.

Both consumers and businesses should be cognizant of potential decision-making biases online recommendations introduce.

The automotive sector believes that machine learning can help them achieve their marketing goals, but that doesn’t necessarily mean it invests in that ambition.

MIT SMR’s 2018 Strategic Measurement study reveals how organizations using ML to enhance KPI-driven decision-making are pulling ahead of their competitors.

Retailers have new challenges in getting customers to accept different prices on different channels.

Viewing technology as a set of solutions misses opportunities to innovate in bigger, bolder ways.

The future of AI looks much like the present, with machines helping humans to do their jobs better, not replacing them.

MIT professor Munther Dahleh proposes a marketplace for data that bases the cost of data on the financial value it generates.

Less than 5% of companies are using AI to reinvent how they do business, but the competitive intensity surrounding the technology suggests that a wait-and-see strategy could be a costly mistake. To get a share of the global profit pool of US$1 trillion that AI will produce by 2030, McKinsey’s Global Institute says companies should begin adopting it at scale within the next 3 years.