Data-driven decision making

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.

When deciding whether to deploy a machine learning model, focus on business metrics, not technical ones.

What’s this year’s average marketing budget? Bad question: That figure misleads marketers who want to benchmark spending.

Executing strategy requires understanding your critical roles and putting your best people in them.

Researchers are seeing stronger business benefits when KPIs are adjusted with or created by AI tools.

A research-based framework can help companies select philanthropic projects that align with their business strategies.

Hype around generative AI tools like ChatGPT impede business leaders’ ability to make well-informed technology choices.

Many finance offices aren’t benefiting from advanced analytics. A new framework can help CFOs assess their data skills.

Understanding how AI algorithms are trained and validated can help decision makers pick the right tools and avoid risk.

It’s impossible to abolish AI bias in the data behind artificial intelligence models, but companies can remediate it.

Before investing in products touting business insights from brain research, managers must understand how they fall short.

Managers need more thoughtful and effective data collection practices to advance DEI in their organizations.

Most companies that are using AI are deploying it for augmentation, not large-scale automation.

Leaders must answer eight questions to successfully tackle innovation’s toughest trade-offs.

Successful digital initiatives require metrics that track business results, not technology use.

When we rely on machines to make decisions, we substitute data-driven calculations for human judgment.

AI techniques can generate training data that retains the predictive power of real-world data.

New research shows that an individual’s decision-making style informs the choices they make when using AI-based inputs.

To address algorithms’ potential harm, companies must be willing to focus on users and rethink their business models.