AI Execution

When I ran Microsoft ventures we would give the same advice to every non-technical founder: find a technical co-founder, or raise enough money to hire one. That advice made sense in 2015. In 2026, it is just gatekeeping dressed up as wisdom. The tools to ship a real, profitable product without a developer exist. The […]

The guide to using Lovable, Replit and Peridot For twenty years, building an app meant you were either a “gatekeeper” (a developer) or you had to pay one. You needed five figures in the bank or three years of your life to learn a language like Swift or Java. That gate just collapsed. In 2026,

TL;DR Tracking AI tool usage across employees and departments requires continuous monitoring of tools, users, and data flows. Most enterprises rely on incomplete snapshots, missing the majority of actual usage. Effective tracking combines SaaS visibility, network data, and user-level insights to maintain control over AI adoption. You can grab the Shadow AI Benchmark Report to

TL;DR Auditing AI usage across teams requires combining tool discovery, employee reporting, SaaS analysis, and policy review. Most organizations underestimate usage by 2–5x. A structured audit helps identify risks, enforce governance, and gain visibility into how AI is actually used across departments. Introduction Most enterprises don’t have an AI problem. They have a visibility problem.

TL;DR Most enterprises underestimate how many AI tools employees are using. Discovering them requires a combination of network monitoring, SaaS audits, employee surveys, and policy enforcement. Without visibility, organizations risk data leakage, compliance violations, and uncontrolled AI sprawl. Introduction If your CEO asked today, “How many AI tools are we using across the company?”—most teams