Main image of article What AI is Changing About Data Careers — and What it Isn’t

By Iris Adae, VP of Data & Analytics (DatA), KNIME 

I’ve spent the past several years leading and scaling data and analytics teams, and I’ve seen firsthand how rapidly AI is transforming the way we work.  

When I started, we were focused on traditional workflows: cleaning datasets, building dashboards, and optimizing pipelines. Today, we can integrate AI models directly into those workflows, and what used to take days often takes hours, or even minutes. This is not only a major speed-up, but it also opens up a new set of possibilities.  

AI isn’t just transforming the tools we use; it’s redefining data careers entirely — reshaping expectations for tech professionals and forcing organizations to rethink the skills they prioritize and develop. 

AI has lowered the technical barriers to entry for working with data, empowering more people across the business to explore insights and act on them. But at the same time, it has elevated the need for human judgment, context awareness, and critical thinking.  

In my role, I’ve seen this duality play out daily. Tasks are getting easier to automate while decision-making is becoming more complex. How do we adapt our data analytics skills, structures, and strategies to meet these new demands and thrive alongside AI?  

Summary

Historically, data science was a specialized discipline reserved for those fluent in programming, statistics, and machine learning. But generative AI and no-code platforms are rapidly changing who can work with data and how, enabling non-technical users to query data, test hypotheses, and even build workflows in seconds. 

Today, nearly 8 in 10 organizations report using AI in at least one business function, a sharp increase from just over half two years ago. That rapid adoption is reshaping roles across the board. 

For example, junior data analysts are no longer spending most of their time formatting reports or building dashboards. Instead, they’re expected to develop domain expertise and gain the skills to validate, interpret, and refine AI-generated outputs. For senior professionals, AI is automating repetitive tasks, freeing them to focus on strategy, governance, and higher-value problem-solving. 

We’ve seen this evolution firsthand on our team. We’ve integrated AI nodes into our visual workflows. Instead of manually training models or writing complex scripts, we can combine AI-powered prompts with pre-built components to generate insights, classify data, or automate summaries within minutes. This has changed not only what we do, but how roles are defined and where expertise adds the most value. 

Democratization has made analytics more accessible, but with greater accessibility comes greater responsibility. Data teams now need stronger processes to ensure outputs are accurate, secure, and actionable. 

While AI can produce impressive and sophisticated results, it can also make confident mistakes. That’s why human oversight is critical: Every insight still needs to be tested, validated, and placed in context.  

In healthcare, for example, AI-assisted imaging tools can flag anomalies faster than ever, but physicians must still determine clinical significance. The same is true in data science: AI can help you uncover patterns, but only experts can decide what those patterns actually mean. 

To succeed in this new landscape, organizations must blend automation with accountability, empowering teams to experiment with AI while also ensuring quality and trust. That requires building a foundation of support for tech talent, investing in upskilling, and creating organizational structures that make AI adoption safe, intentional, and sustainable. 

Without clear guidance and support, the pace of change driven by AI can feel overwhelming for employees. But with the right structures in place, you can empower people to work smarter, grow their skills, and thrive in this evolving landscape. 

Here are five actions that can make a significant difference in how you prepare your workforce for the AI transition. 

1. Build AI literacy across the organization 

According to research from Deloitte, 79% of early-career professionals are excited that AI can open new opportunities in their work. But that will only happen if they feel prepared and confident about what AI can and can’t do. Help your teams develop practical fluency so they can apply AI thoughtfully in their workflows. Invest in training programs that demystify AI, explaining its capabilities, limitations, and responsible practices for using AI effectively. 

At KNIME, we’ve started hosting internal “AI Lunch & Learns” where teams walk through real-world use cases, successes, and lessons learned. These sessions encourage open discussion, build confidence, and give employees the foundation they need to experiment with AI safely and strategically. 

2. Cultivate critical oversight, always 

Speed without verification creates the potential for risk. That’s why it’s crucial to build processes where humans remain firmly in the loop and validate insights before decisions are made. On my team, when we use AI to summarize pipeline failures we always assign a data analyst to review and confirm what the model surfaces. 

Encourage teams to adopt a “trust but verify” mindset. AI can highlight trends, propose solutions, and flag anomalies, but experts must test assumptions, question results, and add business context. As we’ve learned, the best AI outputs still require human judgment to make them actionable. 

3. Nurture curiosity with pilots and experiments 

Creating space for curiosity — among individuals and across teams — is vital to unlocking AI’s full potential. Encourage short, focused pilot projects where employees can try new tools or workflows without fear of failure. Curiosity paired with regular reflection can turn experimentation into strategy. 

This trial-and-error mindset helps teams understand what works and why.  I’ve seen how small, daily experiments with AI-assisted insight generation can lead to unexpected value. We’re not only gaining better results, but improving what teams learn about data, assumptions, and user needs. 

Cultivate soft skills that AI can’t replace 

As AI takes on more of the heavy lifting in analysis, the real differentiators are shifting toward human strengths. Research shows that mastering soft skills — like communication, storytelling, and critical thinking — can be just as crucial, if not more, than technical know-how in today’s AI-driven workplace.  

Cultivating these strengths alongside technical skills is key. Consider what my colleagues call the “Five C’s” of analytics success: communication, collaboration, critical thinking, curiosity, and creativity. These skills enable teams to translate AI-generated outputs into strategy, present findings persuasively, and anticipate how insights ripple through a business context. 

5. Define guardrails early 

Without governance, AI experimentation can create unnecessary risks. Establish clear policies that define where and how your employees should use AI, along with approved tools, security protocols, and escalation paths. 

Here at KNIME, we developed internal guidelines to help teams safely incorporate AI into workflows and tools without compromising data quality or privacy. When employees understand the boundaries, they’re more confident experimenting, knowing they’re working within safe parameters.  

AI can surface patterns and possibilities, but it’s ultimately people who interpret insights, influence decisions, and drive real value. 

The future of data careers will be shaped by collaboration between human expertise and AI capabilities. When experts and intelligent systems come together thoughtfully, they unlock real innovation and new ways of working. 

Now is the moment to reassess, reinvest, and rethink your organization’s approach to tech talent. Equip employees with the tools, skills, and frameworks they need to collaborate with AI rather than compete with it.