This week’s email is a long email, but I think there is a powerful concept here that may have some good utility value here.  New models like Grok 3 make it easier than ever for workers to learn and use hard skills, like tech or data skills, faster. Anyone interested can now generate code and build an application at a rapid pace.

But now, the focus is moving to soft skills because they matter more in a world with AI. Industry leaders call them “power skills” since things like talking well, working together, being creative, and solving problems help people succeed with smart machines.

The tricky part is figuring out what these skills mean—like how problem-solving differs from thinking hard—and how to get better at them. One idea is using Goleman’s Emotional Intelligence (EI), which says knowing your feelings, understanding others, and being good with people is key. EI helps because AI can’t feel or connect as humans do—it’s great for leading teams or fixing issues AI can’t handle alone, like cheering up a frustrated coworker.

Advancements in AI are shifting the focus to the importance of soft skills. While soft skills have traditionally been assigned at the job role level, we’ve developed a model that breaks them down to the task level and further explores the interrelationships among the top soft skills. This analysis can be applied across job families, roles, and occupations.

We did something interesting this week.  We built a model to assign Soft Skills at each responsibility level and see what soft skills boils to the top.  We can make this model available for you if you are interested

Step 1:  We took the AI Product Manager job description and mapped the required Soft Skills by Responsibility.  Here is the summary of the same

Table1:  Soft Skills assigned to each Responsibility

Step2:  We then ran a simple scoring model using these rules

Scoring Steps:

1. Frequency Count: Tally the number of times the skill occurs to reflect its prevalence across responsibilities.
2. Weight Assignment: Assign a weight (1-3) to each skill based on its perceived importance to the Product Manager role, informed by the JD’s emphasis on innovation, collaboration, and compliance.
   • Weight 3: Critical skills tied to core outcomes (e.g., innovation, stakeholder alignment).
   • Weight 2: Important skills supporting execution.
   • Weight 1: Supplementary skills enhancing performance.
3. Score Calculation: Multiply frequency by weight to get a total score per skill, indicating its overall significance.

The result of the table is as follows:

This table tells that the top 5 skills for AI Product Manager are Communication, Teamwork, Adaptability, Analytical Thinking and Leadership

Step3:  We developed a correlation matrix across the skills

• The correlation matrix measures how strongly soft skills relate to each other using a 0-to-1 scale based on their overlap in tasks and taxonomies.
• It draws from research, like factor analysis, and logical connections (e.g., Communication aids Teamwork), assigning values like 0.8 for very strong links.
• The result shows skill interrelationships, helping identify which skills improve together, such as Communication and Teamwork, for the Product Manager role.

Summary:  Advancements in AI are shifting the focus to the importance of soft skills. While soft skills have traditionally been assigned at the job role level, we’ve developed a model that breaks them down to the task level and further explores the interrelationships among the top soft skills. This analysis can be applied across job families, job roles, occupations, and more.