Too many generative AI rollouts fail, or fail to live up to expectations. Here’s what developers and tech leaders are learning about putting genAI first in enterprise development.
Despite big strides, generative AI is still in its infancy in the enterprise, and often when AI tools are deployed, they don’t live up to expectations. Success stories come from careful planning that creates a cohesive data and infrastructural foundation where generative AI tools and AI agents can thrive. This month’s top stories highlight some of the ways things can go wrong, and how to make them right.
Top picks for generative AI readers on InfoWorld
What ‘cloud first’ can teach us about ‘AI first’
Veterans of the dawn of cloud computing have a lot to say about ensuring AI rollouts go well, and what to watch out for.
Why enterprise investment in AI agents hasn’t yielded results
At many organizations, AI agent adoption is a prime example of a failed rollout. This article tells you why.
Agentic mesh: The future of enterprise agent ecosystems
We’ve seen the future of AI agents, and it is not siloed.
How to use genAI for requirements gathering and agile user stories
As AI takes on much of the scut work of writing code, requirements gathering is more crucial than ever. Fortunately, AI can help.
More good reads and generative AI updates elsewhere
An AI customer service chatbot made up a company policy, and made a mess
You’d think an AI company like Cursor would know the risks of hallucinations. But when a customer service chatbot insisted a bug was actually a new feature, it sparked a customer backlash.
AI hallucinations lead to a new cyber threat: Slopsquatting
GenAI writing code that depends on hallucinated packages is already bad enough. What happens when bad actors make those hallucinations real—and dangerous?
The hottest AI job of 2023 is already obsolete
It turns out that AI models are pretty good at intuiting what users desire. And just like that, writing perfect prompts isn’t really a career path anymore.
