Standard image sensors, like the billion or so already installed in practically every smartphone in use today, capture light intensity and color.

The future of work—of hybrid and fully remote workers—will require upskilling of employees for organizations that wish to succeed in the post-Covid world.

At some point, every medical device company will encounter an issue that requires an internal investigation.

Negotiating a salary increase or a job promotion ranks high on the list of hard conversations to have at work, and it doesn’t get any easier without a plan.

Top management often struggles to approve large sums required for annual maintenance because the expense is seen as a necessary evil. As a result, if a business encounters short-term financial constraints, the first place it looks for savings is maintenance.

From design to prototyping to manufacturing and scaling up, manufacturing is fraught with risk. Machining of critical parts may not be on the front burner until well into a product’s development. This isn’t in stakeholders’ best interests.

Not every CEO can be the next Steve Jobs, constantly conjuring up game-changing new ideas and revolutionary products. But what all CEOs and senior leaders can be are champions for innovation within their own organizations.

Seems everybody has a horror story about health insurance: Kafkaesque debates with robotic agents about what is and isn’t covered. Huge bills from a doctor you didn’t know was “out of network.” Reimbursements that take months to process.

Which companies deploy machine intelligence (MI) and data analytics successfully for manufacturing and operations? Why are those leading adopters so far ahead—and what can others learn from them?

Even though it is notoriously resistant to change, the maintenance industry is not immune to the advances in maintenance automation.