The IT Objection Handler

The 48-Hour Rule

One of our clients established a simple standard: when an operator-engineer needs access to data for an AI prototype, IT has 48 hours to provide it. Not 48 hours to schedule a meeting about it. 48 hours to deliver access.

This sounds aggressive until you understand the math. Most AI prototypes should take one to two weeks to validate. If IT takes two weeks just to provision access, you've already doubled your cycle time — on every single experiment. And you need to run a lot of experiments, because most of them won't work. That's the point.

What does it take to hit 48 hours? IT needs APIs that are actually accessible. They need a secure authentication method — either SSO integration for a template project, or a fast process for generating anonymized sample data. The goal isn't to cut corners on security. The goal is to make security fast.

Your Job

As the executive, you have two responsibilities: set the priorities, and clear the red tape. Setting priorities means telling IT that data accessibility is now part of their core mandate. Clearing red tape means removing the procedural obstacles that turn every request into a committee decision.

The platonic ideal for an AI-ready IT department: they can provision secure data access in 48 hours, your operator-engineers can start building within a week, and you can make a kill-or-continue decision within two weeks. Rinse and repeat.

The Objections (And What's Actually True)

You will hear all of these. Here's how to think about each one.

| What They'll Say | What's Actually True | |---|---| | "We can't productionize this code." | You don't need to productionize most of it. You need to test ideas and see which ones work. 90% of this code gets thrown away. You only productionize the code that's been validated by actual users. | | "The code isn't maintainable." | Maintainability is a production concern. These are prototypes. The point is to validate ideas, not to build software that lasts ten years. If something works, you can rewrite it properly later. | | "It's not secure." | Use local development with anonymized data. No live customer data touches these prototypes until they're proven. IT can use tools like Claude Code to generate realistic sample datasets in hours. | | "It's a distraction from core work." | Your competitors are using these tools to move faster. This isn't a distraction — it's how modern software development works now. The distraction is falling behind. | | "Our data isn't ready. It's chaotic." | These tools can help clean chaotic data. You can interpolate missing fields, extract structured data from PDFs, normalize inconsistent formats. The messiness of your data is an argument for using AI, not against it. | | "AI can code, but it can't do strategic or creative work." | This magazine is produced largely with AI assistance. Strategy documents, analysis, creative direction — all of it benefits from AI collaboration. The limitation isn't the tool; it's how you're using it. | | "95% of AI projects fail." | That MIT study used old data and went viral beyond its validity. A more recent Wharton study shows roughly 50% success rates. And the failure rate drops when you run small, fast experiments instead of massive multi-year initiatives. | | "It's going to take jobs." | This is a real concern, and it deserves a real conversation — not a dismissal. But the world is moving this direction regardless. The question is whether your organization adapts or gets left behind. |

The Real Unlock

None of this requires live data or production hosting. Your operator-engineers can export data, build locally, and have end users test on their machines. If you need remote testing, tools like ngrok let users access locally-running code from anywhere. If security is paramount, use sample data.

The point is this: there's almost always a path to fast, safe experimentation. IT's job is to find that path, not to explain why it doesn't exist.