Data, AI, & Machine Learning

Developing a cohesive approach to AI deployment requires the right people — and mindset. Here's how to bring together the human resources to manage the opportunities and risks.

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

Two frameworks can help organizations identify potential harms posed by algorithms, AI tools, or large language models.

Checking assumptions and mapping out work processes can help ensure that ML solutions fit the job to be done.

The natural language capabilities of LLMs can augment the predictive powers of advanced analytics.

Gaining value from data assets that include customers’ personal information requires advanced data protection tactics.

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.

Lufthansa launched an effective program to turn all its leaders into data leaders and propel its digital transformation.

Business leaders can identify and avoid flawed AI models by employing statistical methods and statistics experts.