AI Questions Company Executives Are Asking

What enterprise leaders actually ask when the hype ends and the work begins — evals, security, spend, adoption, agents, and operating models

← Part of the AI knowledge base · Productivity · CEO OS · Company Brain

Source & Credit

Compiled from field conversations shared by Alex Lieberman (@businessbarista), founder of Morning Brew and Tenex Labs. His team spends its days talking AI with enterprise executives; he asked them for the most common questions they hear.

Original post: x.com/i/status/2077143133049995619

Questions below are organized by theme for scanning. Wording is lightly cleaned for readability; meaning is preserved from the source list. Thread replies noted a through-line: security, governance, accessibility, and real-world effectiveness often matter more than “which model.”

How to Use This List

Exec offsites / board prep: pick 5–10 questions you cannot answer cleanly yet — those are the strategy gaps.
AI steering committee agenda: one category per meeting (e.g. spend this month, skills next month).
Partner / vendor RFPs: require written answers before demos.
Agent prompts: paste a category into Claude/Codex as a diagnostic brief for your company context.

Related systems thinking on this site: Company Brain, AI Operating System, CEO OS, agentic setup checklist, 5 roles in AI-first companies.

1. Quality, Evals & “Is This Agent Good?”

How do we properly build a UAT suite that tests not only the new software we are building, but also accounts for AI features in some benchmark eval suite?
How do we know an agent we create is actually “good”? How do we measure that? How can we improve it?
When an agent does eight hours of work, how does a human check it in eight minutes?
How do I know which models are actually good?
What does the cutting edge of AI SDLC look like?
What’s the bleeding edge of applied AI look like today?

2. Security, Data & Guardrails

How do I ensure we roll out Claude Code and Cowork securely without putting company data at risk?
How do I protect my data while still having the harness of Cowork and Code?
How do we protect our proprietary data when using AI?
How do we ensure our AI usage is safe (infra & security controls)?
What data is safe to put in (especially sensitive functions)?
How do we let people access internal data safely?
How can I give employees full agency to create without impacting mission-critical systems and workflows?
How can AI apply when I’m in a highly regulated industry?
How do we govern AI?

3. Data Architecture & Company Knowledge

How do you deal with fragmented data? What is the process to unify without a massive overhaul of our org’s data architecture?
What are the big investments I need to make in my data to make AI effective?
How do we develop a central company brain to capture embedded organizational tacit knowledge?

4. Adoption, Change & Culture

How do I stand up the internal motion to drive AI tools and workflows when everyone also has their day job?
How do I uniformly transform a multi-thousand person org to adopt AI? How do we not leave anyone behind?
What’s the path from AI Literate → AI Enabled → AI First?
How do we get people excited vs. scared to lose their jobs?
How do I “sell” AI internally within my company?
Are we behind?

5. Operating Model, Ownership & Build vs Buy

What does the operating model have to look like with my direct reports — and the org — with AI?
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?
Who should own AI internally?
Who owns a build after it’s deployed?
Who should I give access to Claude Code or Codex?
How do we prioritize use-cases?
When processes are the problem, where do I start?
What are other companies doing?

6. Spend, Tokens & ROI

How do I control spend of AI usage across my organization without limiting employees’ productivity?
How do I start controlling token spend, and how do I think about attributing value to a token?
How do we manage costs and prevent runaway sessions?
Should I be implementing spending limits?
How do I capture the full scope of ROI?

7. Skills, Tools & Front-Line Enablement

How can I provide guardrails and direction so the front line can develop useful tools and applications for the business?
How can non-technical people access, change, and iterate on apps they did not build?
How do employees in different business units edit and manage their own skills?
Should I allow skill creation? Artifact creation?
How do we distribute skills?
What is an agent harness?

8. Multi-Model Strategy & Lock-In

What are the best ways to be multi-model and have a multi-threaded approach to partnerships?
How do we adopt AI so that we aren’t vendor-locked with one frontier lab?
How do I make big bets and also avoid lock-in?
How do we build model-agnostic capabilities?

Full List (Quick Scan)

All questions from the source thread in one place:

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 org’s 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)?
What’s 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?