Generative AI is changing the way data scientists work — and the way businesses think.
“Generative AI has impacted data preparation, model building, and analysis by taking on the tasks that are more manual and laborious,” says Dmitri Adler, Co-Founder of Data Society. “It gives highly skilled data scientists the ability to work with good product faster.”
From writing code and cleaning datasets to generating documents and summaries, generative AI is rapidly becoming a core part of modern analytics workflows.
LEARN MORE: How Can Data Literacy Be Improved? A Targeted Learning Approach
A Shift in Expectations
Where AI once required specialized technical expertise, modern generative tools now assist with many of the more repetitive or technical steps.
“When I was writing code, getting the right syntax used to take forever,” Adler says. “Now, I can just ask generative AI. That’s meaningfully moved the needle in how long it takes to do something.”
This shift means:
Faster data prep and validation
More efficient prototyping of models
Increased accessibility to advanced analytics for non-coders
And while AI can streamline workflows, it also raises the bar. “The quality of your insight is much more readily obvious,” Adler notes. “You have to be a deeper expert in your field. You have to really understand the area you’re operating in and have great conceptual thinking.”
Real Business Applications
Generative AI is already proving valuable across industries:
Energy: Sorting drone footage to identify infrastructure maintenance priorities
Healthcare: Assisting in molecule design for biomedical research
Marketing: Generating content, testing messaging, and personalizing communications
Public Sector: Enhancing city planning with computer vision and structured analysis
Adler explains, “Tailoring generative AI to create contextualized search is critical. Generic models have generic limitations. If you want something that works for your industry or your use case, you need to adapt it to your data.”
This requires not only collecting the right data, but also having the literacy to evaluate outcomes critically.
MUST READ: City of Dallas: Guiding a City’s Workforce Towards Data Maturity
The Risks of Misuse
Despite its advantages, Adler cautions that AI use must be rooted in understanding. “The risk of using anything you don’t understand is that you’ll get results you don’t expect — or worse, misinterpret them.”
Even with generative support, the fundamentals still apply:
Validate inputs and outputs
Understand model assumptions
Know your data’s limitations and biases
“You should not use generative AI in data science unless you have a crystal understanding of the inputs, the outputs, and how it got there,” Adler says.
This mirrors emerging best practices from AI research institutions and responsible AI governance frameworks.
Training the Next Generation
The rapid rise of generative AI is also reshaping data science education. While it simplifies syntax and execution, it doesn’t replace collaboration, creativity, or critical thinking.
“Generative AI can’t teach humans how to cooperate,” Adler says. “But in order to be a data-driven organization, people have to work together to accomplish something.”
The future of data education, he argues, lies in:
Project-based collaboration
Hands-on model development
Emphasis on context, not just computation
Looking Ahead
Where is generative AI headed next? Adler’s response is measured.
“I don’t know if I’m smart enough to know where generative AI is heading,” he says. “But I hope that, like other technologies, it helps raise our standard of living — gives us more, with less.”
AI may never solve finite-resource issues like land or housing. Still, it can dramatically improve how organizations deliver goods, services, and insights — especially when embedded in a strong data culture.
Are you looking to integrate generative AI into your analytics workflows responsibly? Data Society helps organizations balance innovation with understanding to maximize the value of AI. Contact us to learn more.
Q&A How Generative AI Is Reshaping the Role of the Data Scientist
Generative AI is accelerating core tasks like data preparation, coding, and model prototyping, allowing data scientists to work faster and focus on deeper insights. It streamlines manual processes and democratizes access to advanced analytics.
