[OC] Which jobs will AI automate — and which ones will it actually help? Visualization

What This Visualization Shows

This data visualization displays "[OC] Which jobs will AI automate — and which ones will it actually help?" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on *Source:* [*https://www.ebrd.com/home/news-and-events/publications/economics/transition-reports/transition-report-2025-26.html*](https://www.ebrd.com/home/news-and-events/publications/economics/transition-reports/transition-report-2025-26.html)

*Visualisation tool: Flourish*

**TL:DR:**

TOP RIGHT QUADRANT - PROFIT

BOTTOM RIGHT - YOU'RE SCREWED

LEFT - FINE

***Explanation:***

AI doesn’t affect all jobs in the same way.

In some roles, new AI tools help people work faster and more effectively — for example, many IT managers already use AI to support decision-making and coordination. In other jobs, AI can replace parts of the work altogether, as is increasingly the case in some accounting and administrative roles.

To understand what AI is most likely to do in each job, it helps to look at two simple ideas:

1. **How much of the job’s day-to-day work can be done by AI**, and 2. **How well people and AI can work together in that job to improve productivity**.

These measures are based on the kinds of tasks people actually do in each occupation.

Using this approach, jobs tend to fall into three broad groups. Jobs that are highly exposed to AI *and* allow strong collaboration between people and machines — such as managerial or medical roles — are most likely to see productivity gains. In these jobs, AI acts more like a tool than a replacement.

By contrast, jobs that are highly exposed to AI but leave little room for human–AI collaboration — such as some secretarial or accounting roles — face greater disruption. Workers in these roles are more likely to need retraining as tasks are automated and job requirements change. There is already evidence that generative AI is reducing opportunities in some entry-level positions, especially where tasks are routine and easy to automate.

Finally, jobs with low exposure to AI may see only small changes in the near term — or remain largely unaffected for now., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

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The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.