Exploring the Task-Skill Connection

January 6, 2025
3 min read

In our first post, Task Intelligence: TechWolf's Exploration on the Bridge Between Skills and Work, we introduced the concept of task intelligence and its transformative potential in connecting skills to work.

Today, we take this conversation further, focusing on how tasks provide a clearer context for skills, create new opportunities for automation, and drive measurable value in the workplace. By sharing real-world insights and results, we aim to inspire organizations to rethink how they approach skill-based systems.

Tasks & Skills: the theory

Skills represent what people can do, while tasks represent what they actually do. Together, these elements create the foundation for translating workforce planning into actionable, measurable outcomes. Yet, understanding this connection is key to unlocking value.
In this picture, skills are the unit of ability to do something, while tasks are the unit of work itself.

Blog Exploring the Task Skill Connection Picture1
Visual Tasks & Skills: the theory

The added value here is twofold:

  1. By defining and measuring the work, we narrow the gap between the skills and the outcomes they drive down the line.
  2. By adding an understanding of work, we understand the why behind our skills much more deeply.

For instance, workforce strategies often outline required skills, but without linking them to actual tasks, the gap between planning and execution remains. Tasks create that bridge, providing tangible evidence of how skills drive value.

How Task Intelligence Works in Practice

Task intelligence uses AI to analyze the interplay between skills and tasks, offering actionable insights. To test its potential, we applied our technology to TechWolf’s Professional Services team, integrating task intelligence with project management tools like JIRA and Asana.

This integration allowed us to:

  • Analyze tasks being performed by team members.
  • Identify the skills associated with each task.
  • Pinpoint tasks with high potential for automation.

Real-world findings:

Our analysis uncovered several recurring, high-frequency tasks, such as:

  • Creating dashboards and visualizations for customer reports.
  • Generating reports, charts, and graphs to present findings.
  • Providing technical support for existing tools and dashboards.

These tasks relied heavily on technical skills such as PowerBI, data engineering, API management, and scripting. By automating these tasks through a combination of our own product and external tools, we achieved $76,000 in productivity gains over six months.

But the impact didn’t stop there:

  • Strategic shift: Our team redirected their focus to higher-value, strategic work such as project management, data governance, and API integration.
  • Changing skill requirements: Skills like data engineering became less critical, while others like project management gained importance.
  • Influence hiring practices: This evolution not only transformed how our team worked but also influenced hiring practices. By redefining the ideal profile for implementation consultants, we found it easier to attract candidates with the right mix of skills.

Deepening our understanding of skills

Tasks don’t just connect skills to outcomes—they also enhance the way we define and measure skills.

1. Identifying critical skills

In our latest prototype, task intelligence helps organizations identify the most critical skills for each role. We calculate this by determining the proportion of tasks requiring a particular skill. For example, if 40% of a role’s tasks require proficiency in Python, it’s clear that Python is a critical skill for that role.

Blog Exploring the Task Skill Connection Picture2
Visual Identifying critical skills

2. Defining proficiency levels

Proficiency levels often feel abstract, but task intelligence provides concrete definitions by linking skills to specific tasks. For example:

  • Beginner: Able to perform basic data analysis tasks.
  • Intermediate: Capable of creating automated dashboards.
  • Advanced: Skilled in developing complex data pipelines and integrations.

By grounding proficiency levels in real tasks, organizations can set clearer expectations and foster more effective skill development.

Blog Exploring the Task Skill Connection Picture3 V1
Visual defining proficiency levels

The Future of task Intelligence

At TechWolf, we believe task intelligence has the potential to make skill-based systems smarter and more context-aware. By embedding an understanding of work into skills platforms, we can help organizations:

  • Optimize workforce planning.
  • Enhance learning and development initiatives.
  • Drive better hiring decisions.

The results we’ve achieved internally are just the beginning. As we continue exploring this space, we aim to bring these innovations to our customers, empowering them to unlock new levels of efficiency and impact.

Closing thoughts

Tasks and skills aren’t opposing forces—they’re complementary elements of a more integrated approach to workforce strategy. By connecting the two, organizations can build systems that not only measure skills but also apply them more effectively to real-world work.

Are you ready to take your skills strategy to the next level? Let’s start the conversation. Get in touch with our team today.

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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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