CIO

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.

From agentic systems to zero-shot prompting, generative AI can feel like a new language. Here are the terms CIOs need to know.

Lower-level IT jobs are expected to be the most impacted, or replaced altogether, by AI, but even senior level jobs aren’t immune. CIOs should be worried.

Chatbots sit and wait to be asked questions. Agents, however, are proactive, can act autonomously, and adapt to their environments. And when multiple agents develop into agentic frameworks, the potential power increases exponentially. But with strength in numbers and added complexity comes amplified risks, and the need for more fortified checks.

Enterprises continue to pour money into gen AI projects as the pace of change increases. But in the rush to keep up, some companies are seeing little ROI. To remedy the situation, a combination of tools, strategy, and perspective can help.

Regulators, experts, and AI vendors talk a lot about alignment: the degree to which an AI model conforms to values. But enterprises are still coming to terms with what or whose values should be prioritized.

AI guardrails are the technical tools companies use to ensure their systems conform to evolving policies and responsible practices. But with increasing options now available from big providers, startups, and the open-source community, finding the right solution isn’t always straightforward.

Generative AI can solve a litany of data challenges — filling gaps, extracting information from documents, and improving data quality — but experts say to exercise caution and use it in conjunction with traditional approaches.