Frontiers

Partnering with motivated colleagues and community members can advance efforts to build more diverse teams.

Biometric technologies raise new issues of surveillance and privacy that can put customer trust at risk.

OSHA’s longest-serving administrator discusses effective safety approaches and their impact on operational excellence.

Learn how deep-tech startups in materials, biology, energy, and computing can help enterprise innovation efforts.

To get employees with not-invented-here syndrome to open up to new ideas, companies may have to incentivize or push them.

Image- and text-generation tools are helping innovation groups improve ideation and creativity and gain market insights.

Prompting users to spot errors when using generative AI to complete reports improves the accuracy of the final product.

Bolstered dignity eases the path to constructive problem-solving and collaboration.

Savvy companies recognize that, with their support, employee-creators could become their best social media assets.

When humans are in the loop with an AI system, they may not feel a sense of responsibility to intervene if things go wrong.

Digital nudges can encourage reactive thinking and limit employees’ ethical thinking.

Insecure managers who feel threatened by subordinates who have innovative ideas need incentives to change their behavior.

Companies serious about sustainability need to make a business case for investing in lobbying to advance climate policy.

Stock market reactions to the appointment of Black CEOs reflects both positive sentiment and negative biases.

Knowledge sharing platforms may not deliver full value if users are focused on self-promotion, not learning from others.

Many U.S. companies will soon have to follow EU sustainability reporting mandates — and contend with double materiality.

Innovators working on urgent problems need funders who understand deep-tech opportunities and take a long-term view.

Managers who seem to be delivering others’ messages rather than acting autonomously can lose credibility and authority.

Understanding large language models’ limitations can help users discern which tasks they are and are not well suited for.

Companies that want to experiment with generative AI might find open-source models a safer bet than commercial options.