How AI is Transforming Data Skills and Team Dynamics in 2026

Organizations worldwide are increasingly recognizing the transformative impact of artificial intelligence (AI) on data skills, team structures, and upskilling strategies. In an insightful discussion, Iris Adae, Vice President of Data & Analytics at KNIME, shared her expertise on this evolving landscape, offering a glimpse into how businesses can adapt to these changes.

As AI tools become more accessible, companies are rethinking their approach to upskilling. According to Adae, the last few years have marked one of the fastest technological transformations in recent history. Just a few decades ago, mobile phones were a rarity, primarily utilized by technical enthusiasts. Today, AI is revolutionizing work processes by automating repetitive tasks and streamlining information retrieval. Adae points out that while we may be experiencing peak hype surrounding AI now, it is expected to settle into regular, everyday usage.

Upskilling Non-Data Professionals

A key focus for organizations should be on upskilling non-data professionals to leverage AI effectively. Adae outlines three essential pillars for creating a robust upskilling strategy.

First, foundational AI learning is crucial. Organizations should invest in short online courses that cover data and AI literacy, providing employees with the necessary knowledge to engage with AI and automation. Following this, courses on AI usage and best practices, particularly those addressing prompt engineering, can significantly enhance understanding. Finally, hands-on courses that allow employees to build and implement an AI solution in their daily work can reinforce learning.

Collaboration between data teams and business units is the second pillar. Adae emphasizes the effectiveness of partnerships, noting that her team recently assisted the finance department in fully automating cash reporting. While the data team leads the project, ownership ultimately resides with the finance team, empowering them to make independent adjustments and improvements.

The third pillar involves selecting tools that enable transparency and maintain human oversight. Many organizations are investing in advanced AI solutions for tasks ranging from customer support to financial processes. Adae advises opting for transparent systems that allow users to understand AI operations. Low-code and no-code platforms provide visibility into AI functions, particularly when integrated with automation features.

Adae highlights how AI can serve as a filter for data teams. For instance, instead of manually analyzing extensive KPI reports, her team uses AI-generated, pre-filtered insights to identify actionable recommendations. As AI integration deepens, awareness of regulatory frameworks, such as the upcoming AI Act slated for rollout in 2026, is essential for leaders.

Valuable Skills for the Future

As automation becomes more prevalent, Adae identifies three high-value skills that data teams will require by 2026. The first area of focus is Data Engineering. Clean data is vital for AI models, and poor data quality can lead to flawed outcomes. Organizations must invest in enhancing their Data Engineering teams, both in terms of tools and staffing.

The second area involves Data Operations, which is responsible for maintaining AI systems. In some cases, these teams may merge with Data Engineering functions. Lastly, organizations should rethink how they utilize Business Analysts. Instead of simply increasing headcount, companies can leverage AI to filter and preprocess information, allowing analysts to concentrate on applying their domain expertise and exercising human oversight.

Adae urges teams to evaluate their existing projects and workflows. Many tasks may no longer be necessary, and streamlining processes can free up resources for developing new AI workflows. For example, a team discovered that they were sending quarterly reports that neither party found valuable, and a brief discussion led to the elimination of this redundant task.

Strategies for Successful AI Integration

Determining effective ownership of AI functions is crucial for organizations preparing for deeper integration. Adae notes that some companies have established dedicated AI teams within their Data & Analytics departments, while others have distributed responsibilities across existing business units. Both approaches can be effective, but she highlights the necessity of having a dedicated AI strategist. This individual should oversee AI strategy and implementation throughout the organization, facilitating cross-team projects and guiding change management efforts essential for successful AI adoption.

To summarize, organizations must prioritize several key areas as they prepare for AI in 2026. Ensuring data quality is paramount, as is appointing a dedicated AI strategist to spearhead AI initiatives. Additionally, companies should eliminate outdated workflows to allow teams to transition to new, AI-driven routines.

The insights shared by Iris Adae underscore the importance of proactive adaptation to the evolving landscape of AI and data analytics, highlighting the potential for organizations to thrive in the coming years.