Leaders must deliver rapid AI innovation while handling many related people management challenges, ethical dilemmas, and security risks. One key is developing strong AI leadership skills while recognizing that human judgment remains essential. Get expert advice to help your team succeed with AI.
The resilient, knowledge-based economy; a COVID-19 data disaster; smart buildings; and democratized AI.
By better integrating human and device intelligence, we can foster collective intelligence.
The U.S. needs professional management and leadership of its health data supply chain.
Developing AI-enabled business models, managing corporate social responsibility, and growing digital ecosystems.
Leaders must focus on managing the gaps in AI skills and processes within the organization.
Companies and leaders must strive to build business models using three key components for growth.
CFOs need to lead AI technology decision-making — and they should start now.
Your AI strategy needs to be approached differently than regular technology strategy.
The emerging frugal economy, ethical employee surveillance, and building organizational AI capabilities.
A successful AI-enabled workforce requires key hiring, training, and risk management considerations.
Employers could use surveillance tools — with constraints — to keep workers safe and healthy.
Preparing for AI’s next phase means prioritizing your talent pipeline and technology infrastructure.
A Q&A with AWS’s Michelle K. Lee on the challenges and advantages of adopting machine learning.
Insights for developing and executing AI strategy at the leadership, organization, and talent levels.
Artificial intelligence can give businesses a competitive advantage during (and despite) the COVID-19 pandemic.
Digitalization can’t deliver agility or reliability unless you first determine data access, quality, and lineage.
This week’s must-reads for managers: harnessing disruption for a better future, developing innovation capital, and aligning company culture with corporate values.
Company practices often conflict with corporate values. Closing the gap starts with communication.
Shareholders and stakeholders, data science’s pandemic shift, and combating workplace discrimination.
Companies are beginning to reboot their machine learning and analytics, disrupted by the global pandemic.
