CIO

Advanced chatbots, digital assistants, and coding helpers seem to be some of the sweet spots for gen AI use so far in business.

Synthetic data has long been used to create larger training sets for AI while preserving privacy and protecting sensitive information. But its use has historically been limited to companies able to afford data scientists. Generative AI is changing all that.

Organizations are deploying the ever-advancing technology to assist with software programming, create advanced business intelligence, and automate customer support and HR functions.

Digital twins have historically been used to better understand the workings of computer chips, factory floors, or complex machines like jet engines. But the turbo boost they get when combined with AI gives organizations exponentially more power.

Agentic AI is poised to transform business processes at a scale and pace never seen before. But are enterprises prepared to deal with the risks agentic systems pose?

Data engineers work for enterprises in various industries to help them manage and store the data they need to operate, and, increasingly, to support their AI initiatives.

AI agents are poised to transform the enterprise landscape, from automating mundane tasks to driving customer service and innovation. But having strong guardrails in place will be key to success.

With the pace of gen AI adoption accelerating across all business sectors and functions, required skills are increasingly in demand — but the people out there able to meet needs are in short supply.

Complexity and security are primary concerns but enterprises first have to ensure they have a modern stack.

The numbers are starting to come in, and many companies report that some gen AI investments are starting to pay off — but not all.

To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren’t a good fit to train large language models.

With the advent of generative AI, the pace of technological change has greatly accelerated. Is it better to take risks and experiment, or wait for proven use cases before jumping in?

This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.

Commercial generative AI platforms like OpenAI and Anthropic get all the attention, but open source alternatives can offer cost benefits, security, and flexibility.