Ai Transformation

NEW YORK – October 29, 2026 -- Sol, a vendor-neutral AI-enabled business and IT transformation firm, has appointed Dan Silberman, who spent twenty years at A+E Global Media, as Head of Marketing & Communications. He reports to Managing Partner Kaan Aslansan and is based in New York.

Silberman takes end-to-end ownership of Sol's brand and pipeline-linked marketing

Silberman's remit covers positioning and narrative, earned media, content and thought leadership, digital presence, internal communications and reputation management. Sol ties the work directly to pipeline and growth.

Aslansan framed the hire as a shift from delivery to visibility: the firm has built its business on delivery and is now investing in making sure the market knows it. He said Silberman will make sure the market sees "what our clients already do."

Boston – October 14, 2026 -- Nearly 90% of CEOs believe their employees trust their leadership, but only 46% of employees agree, according to the second edition of Boston Consulting Group's CEO Insomnia Index.

The gap is widest around AI transformation. Four in ten employees say their CEO has not been transparent enough about how AI will affect their roles and career opportunities. Only 15% of CEOs worry that their AI approach could erode employee trust.

The study draws on global surveys of 645 CEOs of companies with at least $100 million in revenue and 500 nonexecutive employees, plus five years of CEO turnover data from the S&P 1200. This edition adds employee perspectives to the Index for the first time.

Shanghai – September 23, 2026 -- Huawei used a dedicated session at HUAWEI CONNECT 2026 to lay out a new architecture for wide area networks (WAN) built for AI-driven traffic, unveiling its Stellar AI Integrated Data WAN Solution and a jointly published white paper with GlobalData on intelligent WAN design for the agentic AI era.

Legacy WAN architecture cannot absorb AI's unpredictable traffic spikes, GlobalData warns

Dustin Kehoe, Head of AMEA Tech Research at GlobalData, told attendees that AI workloads generate massive, real-time data flows that conventional networks were never designed to handle. He said AI requires distributed, edge-enabled frameworks that process data closer to its source to cut latency and optimize bandwidth, alongside deterministic performance guarantees at a granular level.