From Plausible to Provable: Why We Say "Prove It With Posit"

When someone asks how
 

You're a scientist filing a regulatory submission, an underwriter assessing a loan, or an analyst briefing the board, and you used an AI chatbot to produce the analysis behind your number.

Now, a manager, a board member, or an auditor looks at that number and asks the following questions:

1. Walk me through how you got this number.

2. What exact data did it use?

3. Can you reproduce it?
 

If you used a standard AI chatbot, the answer to all three is silence. There's no code to inspect, no record of the specific data it used, and no way to run it again next quarter and get the same number.
 

Plausible is not provable
 

Large language models are built on statistics, not calculators. They're designed to guess the most likely next word, which makes them unpredictable. Ask the same question twice and you're likely to get two different answers, with no way to know which one is right.

That variability is a feature when you want creativity. It's a liability when you need a factual, mathematical deliverable. 64% of enterprise leaders now use AI to guide decisions and provide evidence*, yet 57% of analytical model failures trace back to committing too early to an incorrect interpretation☩, and fewer than 20% of organizations review AI-generated output before putting it to use‡.

The difference between "correct" and "verifiable"
 

A correct analysis is one where the method and the numbers are right. An AI chatbot can give you a correct answer some of the time.
 

A verifiable analysis is one that a manager, a board member, or an auditor can rely on before committing money or reputation to it: they can see what code ran, against what data and in which environment. They can also confirm who ran it and whether the whole artifact reproduces the same result later. A chatbot's answer is not a verifiable one. And in a regulated industry, or on a high-stakes call, "mostly right" fails the same way wrong does: a rejected filing, a mispriced loan, a board that stops trusting your numbers.
 

The solution: both “correct” AND “verifiable”
 

For an analysis to be verifiable, the AI cannot simply state a final answer. It has to generate the code that produces the result. When you execute that code within a governed environment using your ground truth data, you create an inspectable, repeatable artifact that someone else can audit and rerun.

Posit exists to make that possible. We give your data teams a secure environment that makes their results inspectable, verifiable, and repeatable.

To satisfy an audit, four things have to be true at once:

• Data. The specific inputs, as they were at the time.
• Code. The exact version that ran.
• Environment. A locked-down workspace where you can pin the package versions an analysis depends on.
• Repeat. The ability to get the exact same result today or years from now.
  
  

Real-world proof
 

This is the standard of evidence organizations rely on for their most consequential decisions, across industries and use cases:

• Novo Nordisk submitted the first fully R-based drug application accepted by the US FDA
• Pinterest eliminated local downloads of sensitive customer data
• Suffolk reduces recordable safety incidents by 72% using predictive analytics
• Gen Re saved 600 underwriting hours each day
• NASA accelerated delivery of inspectable what-if outputs to decision makers by 1200x
• Dow enabled more than 300 nontechnical people to build, check, and share their own work
• Biogen built, deployed, and governed custom AI applications enterprise-wide with Posit
   

In every case, the analysis ran as inspectable code, which made the decisions verifiable and defensible. When Novo Nordisk submitted the first fully R-based drug application accepted by the FDA, the reviewer could rerun every calculation in the exact environment that produced it, which is what provable looks like.

Show your work
 

Generative AI made analysis faster. It didn't make the standard for proving that analysis any lower. You still need to substantiate your claims.

"Show your work" is the first thing we're taught in grade school math, and the first thing we stopped asking for when AI arrived. If you have to put your name on a submission, a balance sheet, or a major decision, confidence isn't enough.

Prove it with Posit.

Ready to prove it?
 

See how Posit Workbench, Package Manager, and Connect build your AI-assisted analysis on open source instead of a black box, from exploratory work to regulated submissions.

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