TechWolfs' Workforce Intelligence Index - A technical breakdown

Jeroen Van Hautte
September 11, 2025
3 min read

New to TechWolf? Get the big picture first via our vision blogpost: The why, what and how of our Workforce Intelligence Index : Read the vision

How it works

TechWolf’s Workforce Intelligence Index shows the work that is actually performed in your organization, the skills required to do that work, and how these are changing in the AI era.
This gives you the confidence to make people decisions - upskilling, reskilling, or redeploying - at the right time and pace.


In four steps, our AI turns raw information into trusted task and skill insights that support your workforce transformation. Below is a detailed breakdown of those steps:

1. Data Sources

It begins with high-quality inputs.
We start from a global labor market dataset of over 2 billion vacancies.
From there, we select vacancies published by your company to capture the most precise signal. You can enrich this with internal data, job architecture, HR systems, and even workflow tools like project management and documentation, so the picture reflects the work happening in your context.

For this demo, only aggregated public data was used.

2. Task extraction & normalization

Next, we move from job titles to the work itself.
TechWolf’s AI breaks roles into specific, measurable tasks and aggregates them across all data points. We then normalize those tasks using TechWolf’s proprietary work-embedding models so that different descriptions map to the same underlying work.

This harmonization makes it easier to benchmark roles across teams and industries, compare how work is distributed, and spot patterns in skill demand or automation potential. It also helps link market insights to your internal workforce data for more actionable intelligence.

Example — Customer Success Manager

  • Account Management: client relationships, account health, expansion opportunities.
  • Technical Support: troubleshooting, implementation coordination, product guidance.
  • Strategic Planning: success plans, usage analysis, adoption strategies.

Example — Software Engineer

  • Software Development: design, implementation, code reviews.
  • Bug Resolution: reproduce, fix, validate.
  • Collaboration & Coordination: refine requirements, sprint planning, cross-functional alignment.
  • System Maintenance & Optimization: performance monitoring, refactoring, CI/CD upkeep.

3. Workforce Intelligence Index

With tasks mapped, we can gauge where people and AI each add value.
For every task, our AI computes a Workforce Intelligence Index using the Human Agency Scale published by researchers at Stanford University (Shao et al., 2025). We summarize it into three practical buckets:

  • Human: tasks that rely on judgment, creativity, or empathy - AI supports.
  • Augmentable: strong outcomes when people and AI work together.
  • Automatable: structured, repeatable tasks that AI can handle with limited input.

To place tasks into these buckets, we consider six dimensions - kept simple and people-first:

  • Cognitive complexity: how much problem-solving or abstract thinking is needed.
  • Uncertainty or risk: the impact of getting it wrong and the predictability of outcomes.
  • Domain expertise: the depth of specialized knowledge required.
  • Empathy: the need to understand and respond to people’s perspectives.
  • Physical labour: the manual effort or handling of materials involved.
  • Ethical / sensitive topics: confidentiality, cultural sensitivity, or moral judgment.

These lenses clarify where AI can responsibly help and where human expertise remains essential.

4. From insight to action

With the Index, you can move at a pace that fits your organization.
By combining task-level and job-level analysis, TechWolf provides recommendations that support thoughtful change:

  • Visibility into real work: look beyond job titles to day-to-day activities across roles, job families, and departments.
  • Clarity on AI’s role: see where augmentation or automation is realistic - and where human strengths lead.
  • Focus for change: understand which roles are most affected so efforts land where they matter.
  • Targeted skill development: build capabilities that prepare people for the future of work.
  • Supportive mobility: match people to roles where their strengths create the most value.

Discover the Workforce Intelligence Index

To explore the Workforce Intelligence Index in detail, visit the index here.

Get your index score
Get your index score

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Using AI while interviewing at Techwolf

At TechWolf, we see generative AI as part of the modern toolkit — and we expect candidates to treat it that way too. We love it when people use AI to take their thinking to the next level, rather than to replace it.You are welcome to use tools like ChatGPT, Claude, or others during our interview process, especially in take-home assignments or technical exercises. We encourage you to bring your full toolkit — and that includes AI — as long as it reflects your own thinking, decisions and creativity.We don’t see AI as replacing your skills. Instead, we’re interested in how you use it: to brainstorm ideas, speed up iteration, validate your thinking, or unlock new ways of approaching a challenge. Great candidates show judgment in when to rely on AI, how to adapt its output, and where to go beyond it.

What we’re looking for:

Our interviews are designed to understand how you think, solve problems, and express ideas. Using AI in a way that amplifies those things — not masks them — is encouraged.

What to avoid:

We ask that you don’t submit AI-generated work without review, or present answers that you can’t fully explain. We’re not testing the model — we’re getting to know you, your skills, and your potential. If there are cases where we don’t want you to use AI for something, we’ll tell you ahead of the interview being booked.In short: use AI as you would on the job — as a smart assistant, not a stand-in.

Example: Programming with AI

In a coding challenge, you’re welcome to use generative AI to support your workflow — just like you might in a real development environment. For instance, you might use AI to quickly generate boilerplate code, look up syntax, or get a first-pass solution that you then adapt and debug collaboratively. What we’re interested in is your ability to reason through trade-offs, communicate clearly, think about complexity and iterate effectively — not whether you memorized the syntax perfectly. If using AI helps you stay in flow and focus on higher-level problem-solving, we consider that a strength. There could be some challenges where we won’t allow you to use AI - in that case we’ll tell you in advance, and will tell you why.

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