Become a SponsorEvent coverage
Reporting from The AI Enterprise Conference, COLLIDE, the ALIGN AI Executive Summits and our live webinar series.
Coverage of keynotes and sessions from The AI Enterprise Conference in New York City. See the 2027 event
Enterprise AI has moved from an era of speed to an era of control, says Fulcrum Digital founder Rajesh Sinha. He lays out a seven-layer sovereign AI architecture, why many projects fail at the inference layer and agent use cases in insurance, finance and manufacturing.
By The AI Enterprise Magazine Team ·
The best-funded AI programs will pay for themselves by reclaiming wasted data and AI spend, argues Revefi's Girish Bhat. Verisk Global Chief Data Officer Louis Dimodugno describes how Snowflake savings covered the cost of the tooling, and why token prices may soon rise.
By The AI Enterprise Magazine Team ·
Fragmented systems, stale data and reactive governance hold back most AI projects, says IBM's Tim Richer. He identifies the use cases scaling fastest, how IBM runs agents on its own HR, IT and procurement systems, and client results from Ferrari and EY.
By The AI Enterprise Magazine Team ·
AI pilots stall because companies can't prove they control what agents reach, which models touch their data and what it all costs, argues Hemakumar Velugula of Xnode. He describes a control plane layer built to close that gap.
By The AI Enterprise Magazine Team ·
Coverage of sessions from COLLIDE, the largest AI conference in the Southern U.S. See the 2027 event
Stalled AI initiatives usually suffer from unclear goals and weak alignment, not weak technology, says Analytic Vizion founder Nelson Davis. He lays out an impact-first planning sequence and five questions leaders can use to get projects moving again.
By The AI Enterprise Magazine Team ·
AI agents can only act as fast as their data allows, says IBM's Ann Leach. She makes the case for the streamhouse, a real-time layer that complements the lakehouse, and points to Ferrari, which used live telemetry and AI to double daily app users.
By The AI Enterprise Magazine Team ·
Model choice isn't what sinks enterprise AI projects, says Kindo's Shan Zhou; systems, governance and cost are. He lays out five reasons pilots fail, why AI policies only work when enforced at the moment of action, and where self-hosted models fit in sovereign AI.
By The AI Enterprise Magazine Team ·
Most AI agent prototypes never become trusted production systems. LangChain Deployed Engineer Christine Manegan says the teams that succeed own their intelligence, from models and harness to evals and governance, and describes how an agent harness manages context, sub-agents and human-in-the-loop controls.
By The AI Enterprise Magazine Team ·
Coverage of panels from the invitation-only ALIGN AI Executive Summits. See the 2027 event
Enterprise AI scales when it is tied to a real problem, validated, observed and cost-controlled, not when the model is impressive. Leaders explain how.
By The AI Enterprise Magazine Team ·
Leaders from BlackRock, Dollar General and ManTech explain what it takes to run AI agents in production: bounded use cases, engineering discipline, governance and cost visibility.
By The AI Enterprise Magazine Team ·
Recaps of AI Connect webinars, from expert panels to live demos. See the webinar schedule
AtScale's Mark Palmer argues that enterprises get more AI value from better context than from bigger models. He points to benchmark results on semantic layers, the four most common sources of AI errors and the case for testing AI accuracy every day.
By The AI Enterprise Magazine Team ·
What does a single source of truth mean when AI agents consume and create data? Leaders from SAP, Turgon, Skyflow and AtScale point to semantic layers, permissions that travel with data, agent-generated data and evals against a company's own KPIs.
By The AI Enterprise Magazine Team ·
Precisely's agent checks data quality scores before it acts and repairs failing data through governed pipelines. Coder runs coding agents in self-hosted, sandboxed workspaces with budgets, session logs and human-owned pull requests.
By The AI Enterprise Magazine Team ·
An AI system can look perfectly healthy and still be wrong. Leaders from SAP, Dynatrace and RaceTrac lay out what to measure beyond accuracy, how to catch drift and how observability creates the audit trail AI governance depends on.
By The AI Enterprise Magazine Team ·
