2025: Surprising AI Adoption Trends?

Dear Reader…

Data teams use an average of five distinct tools within their data stack to manage their workflows. While this figure encompasses a range of data platforms, a significant portion of these are increasingly AI-driven.

In the high-stakes world of data engineering, where the pressure to build scalable, reliable, and efficient data pipelines is relentless, a new class of co-worker has arrived. Artificial intelligence is no longer a futuristic concept discussed at conferences; it is embedded in the command line, the IDE, and the very fabric of data strategy. Our investigation into the 2025 landscape reveals a clear hierarchy of AI platforms being adopted by data engineers, with a triumvirate of tools—GitHub Copilot, ChatGPT, and Claude—leading the charge.

However, a closer look reveals a more complex story. Adoption is not uniform. It is shaped by deep-seated generational attitudes, the unforgiving pressures of career progression, and the specialised demands of the data engineering role itself. Whilst the tools may be new, the human patterns behind their adoption are familiar, creating a fascinating new digital divide within one of technology’s most critical professions.

This week we look into which platforms are truly gaining traction amongst data professionals, moving beyond the hype to analyse how age, experience, and professional focus dictate the new AI-powered workflow.

The Data Engineer's AI Arsenal in 2025

The role of a data engineer is evolving from a builder of pipelines to an architect of intelligent data systems. This fundamental shift is reflected in their choice of AI tooling, which is now augmenting—not replacing—their core capabilities. Our analysis, supplemented by the attached research on demographic patterns, shows that data engineers are prioritising platforms that deliver tangible gains in three key areas: code development, complex problem-solving, and high-quality documentation.

1. The Code Companion: GitHub Copilot

For the hands-on task of writing, debugging, and optimising code, GitHub Copilot has become the undisputed leader for technology workers, including data engineers. Its deep integration into the development environment makes it a frictionless assistant for everything from writing boilerplate PySpark jobs to translating complex SQL queries. Mid-career professionals, in particular, favour tools like Microsoft's Copilot for their seamless integration with existing enterprise workflows, prioritising proven productivity gains over more experimental platforms.

2. The Versatile Strategist: ChatGPT

When the task moves beyond pure code to system design, troubleshooting, and architectural planning, ChatGPT remains the go-to platform for its versatility. Data engineers leverage it as a sparring partner for complex problem-solving, exploring different approaches to data modelling, or generating initial drafts for project plans. Its broad knowledge base makes it invaluable for the kind of multi-faceted challenges that define senior data engineering roles.

3. The Quality Analyst: Claude

For tasks demanding nuance, precision, and high-quality written output, Claude has carved out a significant niche. Data engineers are turning to Claude for generating technical documentation, summarising complex white papers, and ensuring the quality and clarity of analytical reports. Its strength in handling large contexts and producing refined text makes it a preferred tool for tasks where accuracy and communication are paramount, particularly within the technology sector where 37.2% of its usage originates.

The Generational Divide: How Age is Impacting Adoption

The choice of AI tool is profoundly influenced by age group and their corresponding relationship with technology. The data reveals distinct patterns of adoption across generations, challenging the simplistic narrative that younger means more adept.

Millennials (26-42): The Strategic Adopters

Contrary to the "AI native" hype surrounding Gen Z, our investigation finds that Millennials have emerged as the most expert and strategic AI adopters in the professional world. A landmark 2025 McKinsey report found that 62% of millennials aged 35 to 44 reported strong expertise with AI, the highest of any age group.

As many Millennials now occupy mid-career and management roles, their adoption is ROI-focused. They champion tools like Microsoft Copilot that integrate into established systems and demonstrate clear productivity benefits. They are the organisational mentors, with two-thirds of managers fielding weekly AI questions, guiding their teams towards practical and efficient AI use.

Generation Z (18-25): The AI Natives and Experimentalists

Gen Z engineers, who have entered the workforce with AI as a given, display a different pattern. Whilst 70% use generative AI regularly, their engagement is characterised by experimentation and a multi-platform approach, using an average of 2.3 different AI platforms. They are comfortable with advanced prompting and use AI extensively for learning and creative exploration. For them, platforms like ChatGPT and Perplexity are not just for work; they are fundamental tools for research and skill acquisition. However, their reported expertise is lower than that of their Millennial colleagues, indicating a phase of broad exploration rather than deep, strategic implementation.

Generation X (43-58): The Cautious Professionals

Data engineers from Generation X approach AI with a healthy dose of professional scepticism. With a lower adoption rate of 34%, their usage is highly targeted towards workplace applications. They gravitate towards tools that are embedded within familiar environments, making Microsoft Copilot a strong favourite due to its Office integration. Their focus is on proven use cases and quality control; they need to see demonstrable value and reliability before integrating a tool into their workflow.

Baby Boomers (59+): The Selective Users

Amongst senior data engineers and leaders from the Baby Boomer generation, adoption is much lower, with around 80% having not used generative AI. When they do engage, it is often through simpler, voice-activated interfaces like Siri or Alexa for personal assistance tasks. For this demographic, trust and simplicity are paramount. Any AI tool must offer a clear, unambiguous benefit with a minimal learning curve to gain traction.

The Career Arc: Patterns of Adoption within the Profession

A data engineer's seniority directly shapes their AI engagement, shifting from task-based automation at the junior end to strategic governance at the senior level.

Entry-Level Engineers (0-3 years)

For those just starting their careers, AI is proving to be a critical tool for learning and survival. Facing a job market where executives openly consider replacing entry-level roles with AI, these engineers are using platforms for skill development, resume writing, and automating routine tasks. Their usage is learning-focused, relying on general-purpose AI to bridge knowledge gaps and accelerate their contribution.

Mid-Career Professionals (4-15 years)

This cohort represents the engine room of AI adoption. With a higher adoption rate than their senior colleagues (17% vs. 13% for those over 50), they are laser-focused on productivity. An overwhelming 85% report improved efficiency from using AI tools. They are the ones implementing AI solutions for their teams, using it for complex problem-solving, data analysis, and project management. Their tool choices are pragmatic, favouring platforms that enhance their ability to deliver results.

Senior Professionals (15+ years)

For principal engineers and engineering leaders, AI presents a different set of challenges and opportunities. Their engagement shifts from hands-on use to strategic oversight, focusing on AI governance, quality control, and compliance. They are grappling with the organisational tension AI can create and are concerned with maintaining authority and accuracy. For this group, AI is less a personal productivity tool and more a powerful, and potentially disruptive, force that needs to be managed strategically.

A Profession Apart: Data Engineering Adoption is Unique

Compared to other professions, data engineers have a distinct relationship with AI. Unlike legal professionals, whose adoption has been historically low (0.9% of Claude usage) due to accuracy and regulatory fears, or healthcare professionals who focus on specialised diagnostic tools, data engineers are embracing a broad suite of platforms that mirror their multifaceted role.

Their adoption rates are amongst the highest, driven by the nature of their work. The need for data processing, system design, and constant innovation makes them prime candidates for AI augmentation. This is reflected in the high concentration of AI usage within technology-focused roles. The pressure is immense, but so is the opportunity. As AI reshapes workflows, data engineers are moving from task execution to strategic enablement, playing a greater role in designing the intelligent infrastructure that will power the businesses of tomorrow. The effective adoption of the right AI platforms is no longer a competitive advantage—it is the baseline for survival and success in 2025.

That’s a wrap for this week

Happy Engineering Data Pro’s