---
title: "The Paradox of AI and Data Roles: How Automation Will Increase Demand for Data Professionals"
description: "Despite AI automating 25-50% of data work, demand for data professionals will surge as companies need experts to manage AI systems, validate models \u0026 ensure compliance."
categories: ["AI","trends"]
keywords: ["AI automation","data professionals","data analysis","AI-infused software","data compliance","data validation","data teams","business systems","Tomasz Tunguz","Theory Ventures"]
ai_summary: "AI will automate many data tasks, yet increase the demand for skilled data professionals to manage and validate AI systems."
date: 2023-07-28
lastmod: 2026-07-23
canonical_url: https://www.tomtunguz.com/why-data-teams-will-grow-ai/
author: "Tomasz Tunguz"
---

AI will automate 25-50% of white collar work including data analysis. Does that mean will data teams shrink in size?

On the contrary, while AI can automate some work, it will also demand much more from data teams. 

Typical tasks - writing SQL & charting data - will become mostly automated. This ease of use will enable data teams to focus elsewhere : on the sea of demand for data coming from the edges of the company.

AI will infuse marketing, sales, customer support, product, & engineering in new ways - nearly all of it requiring data to deliver cost savings & productivity boosts.

The data team will bear the responsibility of providing the data, validating it, & ensuring that it meets both internal & external standards/compliance. 

Data teams will become important contributors in software procurement. As departments evaluate new AI tooling, data teams will validate the approaches & the models to ensure they work as promised.

After deployment, data teams may be responsible for tuning & optimizing different AI features enabled by AI-infused software applications. Data teams will debug applications with models, unearthing challenges with them such as which training data is required to supplement a model & providing that data.

AI will exert greater demands on data teams. They will operate at a greater layer of abstraction. But more people will be needed to staff the pipelines & monitor the models that will power business systems.