CIO

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.

Early adopters of gen AI typically used ChatGPT, Microsoft Copilot, and similar SaaS tools that cost money but didn’t create infrastructure challenges. As companies scale up, however, those challenges are beginning to emerge.

To maximize the business value of artificial intelligence, AI teams require a diverse range of skills and roles, from data scientists and domain experts, to strategic decision-makers.

Jumping on the gen AI bandwagon can backfire, but moving too slow isn’t wise either. Finding the optimum pace relies on a host of factors, starting with a healthy appetite for risk.