My team spends all day talking AI with enterprise execs. I asked them to share the most common questions they get. Here's what we're hearing from the field: • How to properly build a UAT suite that can test not only the new software we are building but also account for the AI features in some benchmark eval suite? • How can I provide guardrails and direction to enable the front line to develop useful tools and applications for the business? • How do you deal with fragmented data? What is your process to unify without a massive overhaul of our orgs data architecture? • How do I stand up the internal motion to drive AI tools and workflows when everyone also has their day job? • How do we know an agent that we create is actually "good"? How do we measure that? How can we improve it? • How can I give employees full agency to create without impacting mission-critical systems/workflows/etc.? • How do I uniformly transform a multi-thousand person org to adopt ai? How do we not leave anyone behind? • How do I ensure that I roll out claude code and cowork securely without putting my company data at risk? • How do I control spend of ai usage across my organization without limiting my employees' productivity? • What does the operating model have to look like with my direct reports as well as the org with AI? • How do I start controlling token spend and how do I think about attributing value to a token? • How do we develop a central company brain to capture embedded organizational tacit knowledge? • What are the best ways to be multi-model and have a multi threaded approach to partnerships? • How can non technical people access, change, and iterate on apps they did not build? • When an agent does eight hours of work, how does a human check it in eight minutes? • What are the big investments I need to make in my data to make AI effective? • How do I protect my data while still having the harness of cowork and code? • How do employees in different business units edit, manage their own skills? • How do we adopt AI so that we aren't vendor locked with one frontier lab? • How do we ensure our AI usage is safe (infra & security controls)? • What data is safe to put in (especially sensitive functions)? • Whats the path from AI Literate, to AI Enabled, to AI First? • How do we get people excited vs scared to lose their jobs? • How can AI apply when I'm in a highly regulated industry? • As a CEO, what do I need to know about AI to run my org? • Should I hire a team vs. work with an external partner? • What's the bleeding edge of applied AI look like today? • How do we protect our proprietary data when using ai? • How do we manage costs, and prevent runaway sessions? • When processes are the problem, where do I start? • How do we let people access internal data safely? • Should I allow Skill creation? Artifact creation? • Who should I give access to Claude Code or Codex? • What does the cutting edge of AI SDLC look like? • How do I "sell" AI internally within my company? • How do I make big bets and also avoid lock in? • How do I know which models are actually good? • How to build model agnostic capabilities? • Should I be implementing spending limits? • Who owns a build after it's deployed? • How to capture the full scope of ROI? • How do we prioritize use-cases? • What are other companies doing? • Who should own AI internally? • How do we distribute skills? • What is an agent harness? • How do we govern AI? • Are we behind?