Short executive briefs on the questions boards are asking in 2026 — grounded in twenty-plus years of delivery, not theory. Points of view, not product claims.
"In regulated industries, the AI bottleneck is rarely the model — it's governance. Boards are asking the same question: how do we adopt AI without failing the next audit? My answer: AI strategy and compliance have to be designed together, not bolted on after the fact. That means aligning every initiative to a governance framework — NIST AI RMF, SOX, SOC 2, PCI-DSS — from day one, with audit readiness, corrective-action planning, and root-cause analysis built into the operating model. The organizations that treat AI governance as a feature of adoption, not a cost of it, are the ones that ship AI value without the compliance cliff."
"The enterprise AI conversation over-indexes on models and under-indexes on data. The real leverage sits in retrieval: grounding a model in your own systems of record — ERP, CRM, analytics — so answers come from your data, not from the internet. That's why retrieval-augmented generation is the enterprise workhorse: it turns years of SAP, CRM, and BI investment into an AI moat instead of a liability. Across the ERP/CRM transformations I've led, the companies winning with AI are the ones that first got their data architecture right — clean systems of record, governed pipelines, and retrieval that knows where truth lives."
"Digital commerce looks different in luxury, CPG, and telecom — but the mechanics underneath are the same: customer data, trade promotion, retail execution, and a system of record that ties them together. I've run these playbooks across Estée Lauder's global brand strategy, Philips–Sonicare's trade promotion, and T-Mobile's national retail systems — and the transferable lessons are striking. The cross-industry leader isn't diluted by breadth; they're sharpened by it, because the patterns repeat and the failures are already mapped."