Data, AI, & Machine Learning

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.

When pundits talk about the impact that artificial intelligence will have on the labor market, the outlook is usually bleak, with the loss of many jobs to machines as the dominant theme. But that’s just part of the story — a probable outcome for companies that use AI only to increase efficiency. As it turns out, companies using AI to also drive innovation are more likely to increase headcount than reduce it.

Virtually all human achievements have been made by groups of people, not lone individuals. As we incorporate smart technologies further into traditionally human processes, an even more powerful form of collaboration is emerging.