5 Unspoken Truths About Why My Data Scientist Role Is Shifting to AI Builder

Read time: 2.5 minutes

5 Ignored Realities of the Shift to AI Builder

1. "I used to analyze data forever" → Now it's done in no time with AI's assistance.

Automating EDA and summary generation, as well as pattern detection, is a growing trend.

The shift is:

• From analyzing data

• To evaluating what's truly important.

2. "I wrote all of my own code" → Now AI does it as quickly as I breathe.

Generating SQL, Python and feature logic using AI at unprecedented speeds.

The shift is:

• From writing code

• To reviewing, validating, and integrating it.

3. "I was a value proposition for creating models" → Models are being commoditized.

With pre-trained models available to use, Cloud Services for AutoML, and APIs readily available to developers everywhere.

The shift is:

• From creating models

• To using models to make decisions.

4. "I was giving work to engineering" → But what a disaster that was.

Delayed delivery, poor alignment and no one using what was built – disastrous. Kill the ability to create value.

The shift is:

• From a silo-based approach to building solutions

• To owning a complete, end-to-end solution.

5. "I provided information people would look at" → but no one would do anything with it.

"In" dashboards are completely ignored. "In" reports are never read.

The shift is:

• From providing information

• To incorporating decision-making directly into workflow.

💡Key Takeaway: 

The next stage in our Data Science journey is for decision-makers to use Data / AI to support these decisions.