choose a skill cluster
skills & motivations.
This section provides a breakdown of the inherent skills, learned skills and motivations/aspirations for each skill cluster. Inherent skills and motivations/aspirations combined are commonly defined as “soft skills” in the market.
inherent skills
- 1. complex problem solving
- 2. logical and analytical thinking
- 3. mathematical aptitude
- 4. curiosity and inquisitiveness
- 5. creativity and innovation
- 6. attention to detail
- 7. interest in technology
- 8. persistence and determination
- 9. spatial reasoning
- 10. collaboration
- 11. ethical awareness
learned skills
- 1. machine learning
- 2. robotics
- 3. AI frameworks and libraries
- 4. natural language processing
- 5. computer vision
- 6. AI infrastructure and tools
- 7. automation tools and platforms
- 8. ethics and compliance
motivations/aspirations
- 1. technological innovation
- 2. problem-solving and intellectual stimulation
- 3. societal/industrial impact
- 4. financial incentives
- 5. career advancement
- 6. research/academic engagement
- 7. entrepreneurial prospects
- 8. ethical considerations
- 9. global collaboration
- 10. continuous learning and skill development
- 11. creative expression
- 12. contribution to sustainability
- 13. work-life balance
what it shows
The chart here illustrates the sub-level of learned skills required for AI and automation in each of the 24 markets researched. The findings presented here are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks and job boards, as well as career sites.
need to know
- All tech skill clusters share a significant portion of sub-skills, although there are a few outstanding skills that create most of the candidate profiles matching the AI and automation skill cluster.
- Some of the sub-skills that are most recurring include: applied machine learning, Python, deep learning, neural and deep learning.
- Python, PyTorch and Tensorflow are some of the most common skill combinations for modern AI profiles.
skills supply.
what it shows
Skills supply data indicates the total number of individuals who have the skills required for AI and automation in each of the 24 markets researched. These figures are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks and job boards, as well as career sites.
Use the chart to understand the availability of skills (“supply map”), availability of sub-skills (“skill type”), talent with recent job search activity (“active talent”), as well as the share of talent who prefer permanent or contract work (“preferred employment type”).
need to know
- The AI and automation skill cluster has gained a significant number of candidates over the last year, with approximately 20% being new entrants, and 80% being people who obtained relevant skills.
- The general supply of AI and automation talent has grown quite significantly; growth is approximately 24% per market, driven mainly by India and the United States.
- The AI and automation skill cluster has relatively higher mobility than other clusters (almost 21% of people changed jobs over last year). Other talent groups with AI skills also tend to be more mobile than their counterparts without.
skills demand.
what it shows
Skills demand data indicates job postings that require AI and automation skills in each of the 24 markets we researched. These figures are based on a combination of verified, normalized labor market data by market and granular, skill-based data sourced from professional social media networks and job boards, and career sites.
See demand for each skill cluster by market, explore demand for sub-skills within each cluster or view the job vacancy ratio (JVR) — defined as hiring complexity — to understand market competitiveness for these skills. The higher the JVR, the more competitive it is to recruit.
need to know
- Global hiring complexity has taken a hit across the globe, considering a high influx of talent and relatively lower demand. JVRs, however, increase (more than double) when it comes to sourcing experienced talent.
- On an individual sub-skill level, skills that are part of the AI and automation cluster are among those with the greatest hiring complexity (i.e., NLP).
- Countries with the most significant struggles, in terms of the AI and automation cluster, are Romania (7.5%), Singapore (4.8%) and the U.K. (4.7%). Markets like the U.S. are seeing inflated demand around specific skill categories, such as NLP (14.3%).
compensation.
what it shows
The data included in this graph shows the average salary brackets in U.S. dollars for AI and automation skills in the 24 markets examined by level. Compensation data is mapped and analyzed from combined sources providing current pay data.
Select the markets of interest to understand which salary ranges are considered competitive and in which markets you should recruit to stay within budget.
need to know
- Compensation for the AI and automation skill cluster has stabilized due to the general growing popularity of these skills. The average compensation, however, is still relatively high, with no particular change of composition in terms of the most/least expensive markets.
- AI is frequently being brought to various job advertisements that do not squarely focus on AI. This further lowers the average associated with the general high perception of those jobs.
- There are some markets where the average compensation has shifted up, including Malaysia and Singapore.
remote & hybrid working.
what it shows
Remote working data shows the percentage of job postings that offer candidates remote or hybrid work for AI and automation roles (noted as “demand”), as well as talent working preferences (noted as “supply”) in each of the 24 markets researched.
It is estimated that the actual share of remote/hybrid working opportunities is higher than advertised online. You can view the data by both skill cluster and individual skills.
need to know
- There is a significant share of AI and automation talent who prefer remote work; although this is one of the few clusters that is experiencing a drop. This may be attributed to an increase of new talent in the skill cluster.
- The supply of remote job opportunities is trending downward quite significantly (over 37%), while hybrid demand has increased by approximately 9%. This reflects a global general tendency, however, this skill cluster is shifting away from flexible arrangements at the slowest pace.
- Brazil, Argentina and Mexico (all 30-40%) are the markets whose number of remote job advertisements has shifted most significantly. The markets with the most hybrid opportunities are Poland, Portugal and the Netherlands (all 30-40%).
gender diversity.
what it shows
Gender diversity data shows the current balance of male to female employees currently working in roles that require AI and automation skills in each of the 24 markets researched. Findings are based on self-identified, normalized data from talent supply sources.
Use the chart to understand in which markets you are more likely to engage female talent with AI and automation skills. You can view the data by both skill cluster and individual skills.
need to know
- Gender diversity is among one of the major challenges for this skill cluster, with a relative 10% drop in female-to-male talent composition year-over-year.
- Over the last 2 years, there was a great share of early careers talent (0-1 year experience) who self-identify as female across APAC markets; at the same time there is a fluctuation of talent from different tech specializations that is causing a generally positive trend for gender diversity.
- Complexity of hiring for those skill clusters increases quite significantly when analyzing only female talent (i.e., in Belgium the JVR increases to almost 25%). Hiring complexity increases even more when considering individual skills (i.e., female NLP for U.S. is more than 38%).
take a deep dive into the in-demand skills research and find your competitive talent advantage.