Getting artificial intelligence into production has become the real test for enterprises. As the proof-of-concept phase ends, harder questions about cost, data and control are taking its place. That shift is rewriting enterprise AI deployment strategy as the conversation moves beyond models and GPUs to the infrastructure, data and operating models underneath.

The next phase will be defined by who controls AI once it’s running. Token bills are pushing workloads back on-premises and autonomous agents are forcing governance to track runtime behavior. Startups such as Sycamore Labs Inc. are raising money to build governance layers for AI agents.

“No one is asking whether to adopt AI; that’s pretty much a done deal,” said John Furrier, executive analyst at theCUBE Research. “They’re asking who controls it, where it runs and whose law it operates.”

Furrier spoke with industry experts during the Dell Technologies AI Leadership Symposium in San Jose. The interviews covered moving AI into production, the partner ecosystem behind rack-scale AI factories, voice models at the edge, runtime governance for agents and the CPU’s return to the center of agentic AI. (* Disclosure below.)

Here are four standout insights from the event:

1. Rack-scale rollouts turn Dell’s ecosystem into a production advantage.

Dell Technologies Inc. is shifting its focus to customers taking AI proofs of concept into production, backed by an ecosystem that now reaches into venture capital. Long-standing chip partnerships let Dell roll out 350 racks within two weeks, according to Sean O’Connor, regional manager of ENT West, large enterprise and acquisition, at Dell.

Check out theCUBE’s complete interview.

2. Voice AI models travel from data centers to air-gapped laptops.

Deepgram Inc. trains its speech-to-text, text-to-speech and voice agent models on Dell infrastructure, and a large share of its customers host those models themselves. The next step is running them natively and air-gapped on AI-enabled laptops so AI meets people where they are, explained Nick Mann, staff technical program manager of Deepgram.

Watch the full interview from theCUBE.

3. Runtime governance becomes the gate for enterprise AI agents.

Enterprises want AI agents kept inside their own virtual private clouds, and governance is moving from fixed rules to watching agent behavior at runtime. Always-on autonomous agents make that oversight critical, noted Victor Jakubiuk, co-founder of MisaLabs Inc., Shiv Agarwal, co-founder and chief executive officer of Singulr AI Inc., and Sri Viswanath, founder and CEO of Sycamore Labs Inc.

See theCUBE’s full coverage.

4. Token budgets push AI workloads back on-premises and onto CPUs.

Customers burning through months of token budgets in weeks are testing workloads on-premises and rediscovering the CPU for agentic AI. Keeping data and AI close together, even on AI PCs, cuts those costs, emphasized Helen O’Sullivan (pictured, left), AI business development manager and solutions specialist at Dell, and Tim Wood (right), sales director for the Northwest region at Intel Corp.

Don’t miss the full interview on theCUBE.

Here’s SiliconANGLE’s and theCUBE’s full interview playlist from Dell Technologies AI Leadership Symposium 2026:

(* Disclosure: TheCUBE is a paid media partner for the Dell Technologies AI Leadership Symposium 2026 event. Neither Dell Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE