Managing Technology

Executive Conversation: TCS and Travelport executives discuss a new era for the travel industry.

The data science management process, job moves for pay equity, and political concerns in M&As.

This Strategy Guide explores implementing AI as part of a larger digital transformation strategy.

Organizations should manage data science with an appropriate structure and enterprisewide process.

Addressing social capital in return-to-office plans, deploying AI to manage wealth, and reducing coordination complexity with microservices.

Many organizations have challenges with deploying AI. Wealth management is a clear exception.

Executives from TCS and Honeywell share their insights on digital transformation in manufacturing.

Monolithic, highly interdependent organizations can become modular ones by embracing microservices.

Elizabeth Renieris of the Notre Dame-IBM Technology Ethics Lab discusses how businesses can responsibly govern AI projects.

For competitive advantage, embrace digital marketing, a Google-sponsored Forrester survey advises.

This Strategy Guide offers expert advice on gauging your organization’s technical readiness for AI.

A new interview series will offer lessons to help leaders manage their teams, navigate complexity, and adapt to change.

Tassilo Festetics shares how in-house technology innovation is helping AB InBev enhance its products and service.

Identifying the potential public harm of new AI tools should be part of prelaunch due diligence.

Dramatic changes within the life sciences industry present unique opportunities to use AI.

Assessing effective frameworks, managing the risks of digital personas, and benefiting at work from volunteerism.

The technology for creating deepfakes offers the potential for companies to build positive customer experiences.

JoAnn Stonier, chief data officer at Mastercard, discusses how design thinking enables better AI implementation.

This Strategy Guide offers expert advice on assessing your organization’s business readiness for AI.

To make data migration more effective, start with a minimum set of viable data, leave out nice-to-have data, and weigh speed vs. quality.