by Ryan Stevens
As AI adoption accelerates, organizations are shifting their focus from experimentation to large-scale deployment. The challenge now is building secure and scalable systems that can support AI agents, modern developer workflows and the growing demands of enterprise AI architecture.
Yet not all of these considerations are created equal, with the urgency behind each one depending entirely on who is bearing the cost of getting it wrong. The challenges AI clearly differ across the enterprise, ranging from developers choosing the right tools, chief information officers focusing on security and DevOps teams figuring out how AI agents will integrate with existing applications, according to Ignacio Riesgo (pictured, left), senior director for developer advocacy, IBM and Red Hat application development, at IBM Corp. The one shared thread running through all of these conversations is the rapid evolution reshaping enterprise priorities.
“If you think about one year ago, we were talking about modernization as one of the critical areas. This year we are completely changing the pace,” Riesgo said. “We are talking about agents, we are talking about LLMs. The conversation has evolved and now the level of complexity is in another level.”
