Technology Implementation

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.

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.

Will Grannis of Google Cloud explains the organization’s collaborative approach to AI and machine learning innovation.

This Strategy Guide covers identifying AI use cases — and how to know when AI isn’t the best answer.

TCS and Halliburton Landmark executives discuss digital transformation in the oil and gas industry.

Lyft’s Craig Martell talks education, eliminating bias, and cross-functional collaboration on machine learning projects.

Use these metrics and red-flag indicators to boost your chances of business ecosystem success.

This webinar outlines how human-AI collaboration leads to measurable results for organizations.

View this webinar to hear how IT teams are meeting the challenges of the ongoing COVID-19 pandemic.

Revolutionary recommendation engines, data access as a leadership priority, and the essentials of successful corporate social justice efforts.

MIT SMR’s winter issue looks at why teams work (or don’t), plus innovation, supply chains, and data for AI.

Data accessibility must be managed from the start of AI projects in order to be implemented in production.

The fashion retailer’s chief data and analytics officer uses agile pilots to assess and scale technology initiatives.

In this webinar, AI experts from Pure Storage and Nvidiadiscuss a unified plan for implementation.

Companies that change processes to facilitate organizational learning with AI realize the biggest business value.

Companies can deliver services more efficiently as modular tasks that can be shifted among global teams and locations.

Leaders’ implicit biases toward new technology can cause them to make poor investment decisions.

Returning “boomerang” CEOs, job crafting, and working with robots.