AI

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.

End users at the Dana-Farber Cancer Institute saw the possibilities for generative AI use cases soon after the technology emerged. But adopting it required careful thought about safety, security, and especially governance.

Open source generative AI models can be downloaded for free, used at scale without racking up API call costs, and run securely behind corporate firewalls. But don’t let your guard down. Risks still exist and some aren’t only magnified, but new ones specific to gen AI are emerging.