Let us draw a comparison table like the one below, where I randomly chose three occupations (you can do the same exercise with others):

Occupation	In the 1970s–1990s	Current
Logisticians	– Manual tracking of inventory and shipments – Paper-based planning – Linear routing models – Use of Excel or early mainframe software for basic operations	– Data-driven route optimization (e.g., using GPS, real-time data) – Simulation modeling (e.g., supply chain resilience scenarios) – Predictive analytics for demand forecasting – Use of tools like Tableau, SQL, Python, Power BI – Integration with IoT sensors and AI-based warehouse systems
Marketing Specialists	– Focus on creative messaging – Use of surveys, focus groups (offline) – Basic segmentation and demographics – Print, TV, radio analytics (limited)	– Web and social media analytics (Google Analytics, Meta Ads Manager) – A/B testing and conversion rate optimization – Marketing automation platforms (e.g., HubSpot, Marketo) – SEO/SEM performance metrics – Customer journey mapping using behavioral data – Competence with Python/R for campaign analytics (in advanced roles)
Lawyers	– Case research via books and legal journals – Manual case comparison and precedent lookup – Paper-based filings and evidence handling	– Use of legal analytics platforms (e.g., Lex Machina) – Contract analysis using AI (e.g., Kira) – e-Discovery tools to process vast digital evidence – Data privacy & compliance analysis (GDPR, etc.) – Familiarity with quantitative risk models (especially in compliance/legal ops) – Increasing use of dashboarding and KPI tracking in in-house legal teams

What were your first thoughts?

Exactly—compared to around 50 years ago, we can see that each of these roles has more data-driven analytical components. This reflects the reality of our time: overall, occupations are becoming more analytical. In the U.S. alone, according to the Burning Glass Institute, nearly a quarter of job postings in 2023 required some form of data science-related skills. This trend has been intensifying and is expected to continue.

Technological progress and the ever-growing amount of information are some of the factors behind our world becoming more complex. And a more complex world demands greater cognitive and analytical preparation.

I asked ChatGPT the following:

“Let’s talk about how occupations are becoming more analytical and data-intense. For instance, if we had to order or rank occupations in terms of their intensity of analytics and create a five-layer pyramid with representative occupations at each level, how would it look like?”

This was ChatGPT’s output:
(I tried hard to get a proper pyramid image, but the result wasn’t quite what I expected. I suppose that’s the price of not using the premium version!)

 “Here’s a 5-layer pyramid that ranks occupations by the intensity of analytical or data-related content. The pyramid goes from Level 1 (low data intensity) at the base to Level 5 (highly analytical/data science-heavy) at the top.

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🔺 Level 5: Pure Data & Algorithmic Professions

Pinnacle — Occupations where data is the core product or input; require deep statistical, machine learning, or algorithmic expertise.

Representative Occupations:

1. Data Scientist
2. Machine Learning Engineer
3. Quantitative Analyst (Quant)
4. AI Research Scientist
5. Statistician
6. Operations Research Analyst
7. Data Engineer
8. Computational Biologist
9. NLP (Natural Language Processing) Specialist
10. Computer Vision Engineer

🔷 Level 4: Data-Driven Strategic & Technical Roles

Heavily analytical roles where decisions and outputs are deeply informed by data, but less focused on algorithm-building.

Representative Occupations:

1. Business Intelligence Analyst
2. Financial Analyst
3. Marketing/Data Analyst
4. UX Researcher
5. Bioinformatics Specialist
6. Risk Analyst
7. Pricing Analyst
8. Policy Analyst (with quantitative focus)
9. Growth/Product Analyst
10. Customer Insights Analyst

🟩 Level 3: Hybrid Roles (Core + Analytics)

Hybrid roles that blend domain expertise with a strong need for data analysis. These roles often require storytelling with data, dashboarding, or basic modeling.

Representative Occupations:

1. Product Manager
2. Digital Marketing Specialist
3. Supply Chain Analyst
4. Human Resources Analyst (People Analytics)
5. Education Researcher
6. Urban Planner
7. Sustainability Consultant (with data modeling)
8. Operations Manager (in data-enabled environments)
9. Sales Operations Specialist
10. Public Health Analyst

🟨 Level 2: Emerging Analytical Roles

Traditionally non-analytical roles now incorporating dashboards, KPIs, and data-informed decision-making.

Representative Occupations:

1. Journalist (Data Journalism, Investigative Reporting)
2. Teacher (using learning analytics and adaptive tools)
3. Nurse (with EMR data interpretation)
4. Social Worker (evidence-based case management)
5. Recruiter (data-driven hiring pipelines)
6. Legal Analyst (contract analysis using tools)
7. Project Manager (with KPI tracking)
8. Customer Success Manager
9. Policy Advocate (using impact data)
10. Environmental Educator (using climate data)

🟫 Level 1: Low-Data/Intuitive Roles

Roles primarily driven by direct human interaction, craft, or physical tasks. Data use is limited or superficial.

Representative Occupations:

1. Hairdresser
2. Construction Worker
3. Chef
4. Massage Therapist
5. Actor/Performer
6. Fitness Instructor
7. Taxi Driver
8. Gardener
9. Waitstaff
10. Cleaner/Housekeeper”

Still, I’d say the output was a fair representation, to which we can add the following. Overall, the higher an occupation appears in any data intensity-related pyramid, the higher the salary premium tends to be—for example, roles involving data strategy and machine learning skills command around a 14% premium, according to Burning Glass. In other words, the more sophisticated the analytical skills required, the higher the earning potential.

This does not necessarily mean the occupation itself guarantees that wage level. Rather, it’s the possession of such skills that boosts compensation. For instance, lawyers with AI/ML skills can command higher salaries, thanks to their rare combination of domain expertise and high-level analytical capabilities.

On the other hand, being at the bottom of the pyramid doesn’t mean a job has no analytical demands. Today, someone working in a “simple” job such as a weeder might end up operating an AI-powered machine—which requires acquiring some digital skills.

Does this all boil down to simply mastering a digital tool?

In other words, is the trend of occupations becoming more analytical equivalent to just mastering digital skills?

Not exactly. While digital literacy—broadly defined, from commonly used IT tools to advanced AI systems—plays a big part, other skills also matter. Skills like data strategy, and especially critical thinking (a soft skill), are deeply analytical in nature and increasingly vital.

Do I really need to become more analytical, given AI’s progress (especially GenAI)?

I believe so.

Thomas Davenport (Babson College) and Steven Miller (Singapore Management University), in their book Working with AI: Real Stories of Human-Machine Collaboration, conducted a series of case-based studies that point in this direction. The short version? Collaborating with AI greatly benefits experienced professionals from a wide range of occupations—whether at the top or bottom of the pyramid—but it also “raises the bar” for analytical skills.

You’ve probably heard the term “human in the loop.” It refers to the role of humans in overseeing AI-generated outcomes. But here’s the catch: there’s no meaningful human in the loop without the analytical skills and experience needed to collaborate with machines effectively.

What does this mean for career choice and development?

There’s still no large-scale research showing the full impact of AI on entry-level jobs—or any experience level, for that matter. We shouldn’t expect comprehensive statistics too soon; institutions typically take time to produce those.

In the meantime, uncertainty is real, and concerns are understandable. For instance, it’s been reported that an experienced programmer can achieve the same outcome using an AI-powered coding tool as working with a number of junior programmers. No wonder many college students—and those preparing to enter college—are feeling lost.

According to the OECD’s latest report, The State of Global Teenage Career Preparation (2025), which covers 80 countries, a growing number of high school students are unsure about their career plans.

There’s no stopping of the increase in analytical components across a growing number of occupations. This means that—regardless of the major you choose or the career path you follow—you’ll benefit if your mindset leans toward developing analytical skills.