Maria Korolov
Contributing Writer
Maria Korolov has been covering emerging technology and emerging markets for the past twenty years. She has reported from Russia, India, and Afghanistan, and recently returned to the United States after running a news bureau in China for five years
How secure are your AI and machine learning projects?
Artificial intelligence and machine learning bring new vulnerabilities along with their benefits. Here's how experts minimized their risk.
AI center of excellence: A new engine for driving business transformation
AI centers of excellence can accelerate AI adoption — and organization-wide transformation — by consolidating talent, standardizing platforms, spreading successes across business lines, and uncovering new revenue models.
9 emerging job roles for the future of AI
Artificial intelligence is fast proving a business differentiator. Here are the key roles and skills you may soon need to fill for your AI A-team.
Remote agile development: Top tips and tools for managing dispersed teams
The coronavirus pandemic has agile teams adjusting to asynchronous remote collaboration. IT leaders who have spearheaded the agile development efforts of far-flung teams share their tips for success.
What is AIOps? Injecting intelligence into IT operations
Organizations seeking to better monitor IT assets are turning to artificial intelligence to get ahead of performance issues and to automate fixes before negative impacts are felt.
AI in retail: Survival depends on getting smart
Machine learning is fast becoming a must-have for retailers looking to stave off disruption, but the barriers to entry — upfront cost and data prep — remain an obstacle for most.
Explainable AI: Bringing trust to business AI adoption
For many organizations, AI remains a mystery not to be trusted in production, thanks to its lack of transparency. But demand, advances, and emerging standards may soon change all that.
Rethinking software development in the AI era
Data is fast replacing code as the foundation of software development. Here’s how leading organizations anticipate processes and tools transforming as developers navigate this paradigm shift.
6 reasons why AI projects fail
Data issues are among the chief reasons why AI projects fall short of expectations. But if you can learn from the mistakes and commit to the long term, your AI efforts are more likely to pay off.
AI’s top use cases today
Enterprises are undertaking AI pilots and putting artificial intelligence into production. Here’s where leading organizations are placing their bets — and seeing early results.
Anatomy of an enterprise-scale AI strategy
Looking to move beyond point solutions and proofs of concept? Here’s what it takes to develop to a holistic AI strategy honed for business results.
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