NTT Builds AI That Maps Expert Decisions So Interns Can Pretend They Know What They’re Doing

In a move sure to delight overworked managers and terrify knowledge workers everywhere, NTT has unveiled the world’s first AI that turns expert-level decision-making into a glorified flowchart—because apparently “learning on the job” now means asking ChatGPT what your boss would do.

The Tokyo-based telecom giant announced today that it has successfully trained a large language model to watch seasoned professionals do their jobs and then reconstruct their thought process with “approximately 90% accuracy.” This is either an incredible feat of engineering… or the world’s most expensive attempt to automate common sense.

NTT says the goal is to help underqualified staff mimic expert responses in fields like call centers and security incident response—two jobs famously known for being chill and not at all high-stakes. By analyzing massive dialogue datasets, the system reconstructs the Q&A logic used by pros and turns it into structured trees of “if-this-then-that” decision paths. Basically: a choose-your-own-adventure for not screwing up customer support.

🎓 The AI That Knows What Karen Will Ask Before She Does

Here's how it works, and we swear we’re not making this up:

• Step 1: The AI reads thousands of conversations and flags all the questions and suggestions, kind of like your one coworker who copies you on every email “for context.”
• Step 2: It creates master lists and backtracks who said what, when, and why, like a nosy office gossip with access to logs.
• Step 3: It turns these into branching decision trees, where the most common transitions bubble to the top—giving your new hire the illusion of being an expert, as long as the conversation sticks to the script.

NTT tested the tech on a public dataset (FloDial) and claims it recreated 90% of the expert decision pathways. The remaining 10% likely involve irrational executive decisions, IT tickets that “resolve themselves,” or the eternal mystery of why rebooting works.

💼 From Human Expertise to “Good Enough” Automation

What’s actually impressive (and maybe terrifying) is the long-term vision. NTT wants to feed these decision trees into AI systems so that the machines can eventually skip the humans altogether. That means your favorite support agent “Jason” might soon be a bot following a flowchart made from Jason’s old Slack logs.

It’s business succession planning meets Blade Runner: when your best employees leave, their ghost lives on in a vaguely helpful AI assistant.

🧾 In Conclusion, Please Refer to the Flowchart

NTT’s innovation is a dazzling display of what LLMs can do—and a reminder that we are just a few short iterations away from everyone in middle management being replaced by an autocomplete function.

The next time someone asks, “Can we document institutional knowledge before Karen retires?”—you can smile and say:
“Already done. We fed Karen into the machine.”