Why Most AI Fails at Scale

January 10, 2026 | data²

In this episode of National Energy Talk, data² CEO Jon Brewton breaks down why enterprise AI continues to overpromise and underdeliver, despite massive investment in models, agents, and data centers.

Jon explains that AI doesn't fail because of compute or ambition. It fails because enterprises are fragmented. Disconnected systems. Opaque workflows. Data that can't be trusted or scaled. Adding agents or more infrastructure only amplifies the problem.

The conversation reframes the AI narrative for 2026, arguing that real value comes from connecting data at the foundation, not reengineering entire organizations around black-box platforms. data²'s graph-native, explainable approach focuses on outcomes first, delivering measurable ROI without vendor lock-in or massive deployment overhead.

Inside the episode:

• Why AI adoption stalls in complex, fragmented enterprises

• The hidden cost of agentic AI and overbuilt data centers

• How data-layer connectivity enables scale without reengineering

• Real-world results delivering consistent 10x ROI

If your AI strategy is long on promise but short on real value, this episode explains what's broken, and what works.

Listen to the podcast: New Year 2026 with Jon Brewton