Table of Contents
- From answers to reasoning
- Teaching AI to think well
- Peeking into the AI’s mind: the Claude experiment
- What does this mean for business?
- A new kind of AI — and a new kind of trust
For years, artificial intelligence has been surrounded by an aura of mystery. The phrase “AI is a black box” became almost a cliché — we know these systems work, but we don’t understand how. And for many businesses, that lack of transparency has been a serious barrier. In fields like healthcare, finance, or public policy, simply trusting a system that “magically” produces answers without explanations isn’t enough.
But things are starting to change. Advances in research and technology are helping us gradually open that black box, offering insights into how AI makes decisions, solves problems, and — in a way — thinks. Two emerging techniques are driving this shift: Chain-of-Thought prompting and process supervision for reasoning. These strategies are revolutionizing how we interact with and understand intelligent systems.
Let’s take a closer look.
From answers to reasoning
Traditionally, a language model — the technology behind chatbots and virtual assistants — tries to guess the most likely next word based on huge amounts of text. It gives you the most statistically probable answer when you ask it a question.
Chain-of-Thought prompting flips that logic. Instead of asking the model to just answer, users can encourage it to think out loud — to walk through the logical steps that lead to a solution. Like a student solving a math problem, the model might say: “27 times 40 is 1080, 27 times 2 is 54, so the total is 1080 + 54 = 1134.”
This has two major benefits. First, it improves accuracy because the model slows down and reasons instead of guessing. Second, it improves trust — humans can now follow the AI’s logic, catch potential mistakes, and better understand how the system reaches conclusions.
It’s not just about technical performance; it’s about creating a collaborative relationship between humans and machines. If I know why you’re suggesting a certain strategy or recommendation, I’m much more likely to act on it.
Teaching AI to think well
While Chain-of-Thought prompting improves AI’s output, process supervision works during training. In the past, models were judged like students who only get graded on their final answers. Now, we’re also evaluating the how: how did you get there? Where did your logic shine? Where did it falter?
With process supervision, every step in the AI’s reasoning path is evaluated and, when necessary, corrected. It’s like having a team of teachers watching over the model, rewarding good reasoning and correcting flawed logic. This drastically reduces mistakes and helps prevent the infamous “hallucinations” — the bizarre, made-up answers that AI sometimes generates.OpenAI recently published a study showing that models trained with this technique are significantly better at solving complex problems, especially in math, logic, and programming. It’s not just about finding the right answer — it’s about thinking through it the right way.
Peeking into the AI’s mind: the Claude experiment
But how far can we go in understanding what an AI is “thinking”? Anthropic, one of the leading AI research companies, asked just that with their model, Claude.
Researchers managed to observe the model’s internal processes while it was formulating an answer. And what they found was fascinating: Claude not only thinks, but it thinks strategically. It organizes ideas, sets priorities, and even plans its answers. In some cases, it appeared to be mapping out entire paragraphs before typing a single word.
What’s more, Claude demonstrated the ability to reason across languages simultaneously, suggesting there may be a kind of “universal language of thought” at play. These findings are bringing us closer to understanding the “mind” of a machine, not just its outputs, but its inner logic.
What does this mean for business?
Transparency. Control. Explainability. These are more than buzzwords — they’re business imperatives. For companies that want to leverage AI across operations, marketing, or decision-making, it’s no longer enough to get “the right answer.” You need to know how the AI arrived at that answer.
Imagine using an AI system to support your marketing team. It recommends targeting a specific customer segment with a specific message. Wouldn’t you want to know why? With Chain-of-Thought reasoning and process supervision, you can see what data it used, which assumptions it made, and what alternatives it considered. That’s not just powerful — it’s transformative.
Similarly, in legal, financial, or medical contexts, an AI that can explain its reasoning becomes a true collaborator, not just a fancy calculator.
A new kind of AI — and a new kind of trust
All of this leads to a powerful realization: AI is no longer just a black box. It’s becoming a transparent window into a new kind of intelligence — one we can observe, understand, and work alongside.
Investing in explainable, transparent AI isn’t just a smart tech move. It’s a smart business strategy. It means building systems that are more reliable, more ethical, and more useful — systems that don’t just give answers, but build understanding.
AI Evangelist and Marketing specialist for Neodata
