AI at Work: How Data Science Is Being Rewritten

AI is no longer a future concept.
It’s already changing your day-to-day.

Not with hype—
With real tools.
Real use cases.
And real impact.

Let’s look at how.

1. AI Is Killing Low-Value Tasks

You didn’t become a data scientist to copy-paste SQL.
But here we are.

Until now.

What AI replaces:

• Slack requests like “What’s last month’s GMV?”

• Deck-filler queries

• Repetitive dashboard edits

Why BI tools fail:
They’re either too simple or too complex.
Most teams still end up asking you.

How AI helps:

• Text-to-SQL: Just ask, get the query. Tools like Snowflake, Uber’s QueryGPT, and others are leading here.

• BI Chat Assistants: Looker with Gemini, Tableau AI—chat your way to charts.

• Automated Docs: Tools like Cursor and Atlan auto-document your tables.

Bottom line?
Fewer one-off requests.
More time back.

2. AI Is Speeding Up High-Impact Work

Here’s where it gets exciting.
AI isn’t just saving time.
It’s multiplying output.

What used to slow us down:

• Manual EDA

• Googling syntax

• Untapped text data from surveys, tickets, reviews

Now AI can:

• Auto-generate notebooks (Google’s Data Science Agent)

• Write + debug code (GitHub Copilot, Cursor)

• Analyze raw text instantly (OpenAI API, internal LLM pipelines)

It’s not perfect.
But it’s fast.
And improving daily.

3. What to Do Next

Forget the hype.
Focus on tools that solve real problems.

Start with what saves time today.
Experiment with what adds value tomorrow.

AI won’t take your job.
But someone using AI might.

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