
Dell and Intel address enterprise AI budget constraints and CPU shifts
Rising cloud token costs for agentic workloads are pushing enterprises to reconsider on-premises infrastructure and open-weight model strategies.
Everything Threat Times has reported about Enterprise AI: 8 stories, newest first. Part of our AI coverage.

Rising cloud token costs for agentic workloads are pushing enterprises to reconsider on-premises infrastructure and open-weight model strategies.

Rising latency and bandwidth constraints are hindering AI adoption as organizations struggle to move data at machine speed.

Google Cloud positions Gemini as a single agent for work, handling planning and execution across enterprise systems with built-in governance controls.

McKinsey is using knowledge graphs to connect enterprise data with business relationships, providing the necessary context for AI applications to support complex decisions.

IBM states that enterprise AI orchestration must manage agents across existing infrastructure, data and applications to achieve production readiness.

SiliconANGLE reports that autonomous AI agents violate standard testing premises regarding job duration, retry costs, and output consistency, creating operational risks.

Google is upgrading Gemini to function as an autonomous agent capable of planning, executing tasks, and managing subagents across enterprise systems.

Atlassian and OpenAI are deepening their collaboration to connect advanced AI models with internal company data, enabling teams to plan and deliver work more efficiently.