HOW DO WE GET
OUR DATA?

The "Will AI Replace Me" application is designed with multiple objectives in mind:

Raising Awareness

Enlighten professionals about
the impending risk of
automation within their
respective roles.

Detailed Insights

Provide professionals with an
in-depth elucidation of these
risks, emphasizing the core
tasks associated with their
roles.

Strategic orientation for HR

Equip Human Resources
professionals with a precise
understanding of the roles
that are affected by the
proliferating influence of AI
within their organizations.

 Skill sets Evolution

Construct a roadmap for
nurturing resilient skills within
their teams, ensuring long-
term adaptability and
productivity in the face of AI
-driven changes.

Method for assessing the
degree of

Data Sources

The application offers a curated list of 100 key jobs sourced from two databases:

1. The Operational Directory of Professions and Jobs database (ROME 4.0).
2. The "Occupations" from the O*Net®28.0 Database

From these, we identified three primary activities for each profession. These activities, or 'tasks,' have undergone assessment based on various criteria to determine their potential for automation.

Breakdown of the 100 jobs studied according
to the ROME nomenclature:

  • Business Support: 23 roles
  • Personal Services: 6 roles
  • Banking, Finance, Insurance, Real Estate: 11 roles
  • Transport, Logistics, Purchasing: 7 roles
  • Environment & Sustainable Development : 5 roles
  • Healthcare: 8 roles
  • Industrial Production: 7 roles
  • Media and Digital Professions: 11 roles
  • Commerce, Sales, and Retail: 3 roles
  • Construction & Public Works: 5 roles
  • IT, Telecommunications & Security: 14 roles

AI's Impact on Tasks :

To gauge the degree to which AI could influence task automation, we've formulated eight pivotal criteria, with ratings spanning from 1 to 5:

1.Task
Complexity

Assesses if a job's tasks are monotonous and straightforward or if they necessitate complex cognition, discernment and situational awareness

3.Professional
Judgment
Level

Jobs demanding profound knowledge, innate insight, and accumulated experience may resist complete automation.

5.Regulation
Level

Jobs subject to strict regulations may be slower to automate due to the high level of risk control that also relies on human judgment.

7.Creativity and
Strategy

Jobs requiring strategic foresight and creative prowess may be more resistant to automation.

2.Human
Interactions

Some tasks require constant dialogue and collaboration with various departments or external entities as well as creating cohesion within teams.

4.Volume of Data
Handled

Tasks that imply sifting through large data sets could benefit from AI, particularly for predictive analysis and pattern identification.

6.Risk Factor

The repercussions arising from mistakes in particular occupations, such as credit evaluation, can be severe, carrying potentially financial and reputational implications. Consequently, the imperative to minimize these risks may limit the thorough integration of AI technologies.

8.Physical
Nature of the
Task

Occupations centered around physical tasks may be less influenced by AI-driven automation and more by robotics, a domain outside the scope of this research.

Differentiation between robotics and automation

Among the studied professions, some involve physical activities of varying intensities. We consider that, to enhance the productivity of these professions, the approach is that of robotics rather than automation. Therefore, the outcome for these professions will be 'not influenced by AI'.

Correlation between Chosen Criteria and Degree of Automation

Take, for instance, the criterion "Intensity of Task Complexity."

Our evaluation framework is informed by the Goldman Sachs study entitled "The Potentially Large Effects of Artificial Intelligence on Economic Growth (Briggs/Kodnani)" that was published on March 26, 2023. This research inspects professional activities susceptible to AI impacts, referencing the task components from over 900 U.S. professions and 2,000 European roles found in the ESCO database.
Tasks are categorized on a spectrum from 1 to 5, with the ensuing definitions:

Level 1 : Basic Complexity

The task is linear, recurrent, and adheres to explicit protocols.

Level 2 : Low Complexity

The task involves a degree of discernment or choices based on predetermined parameters.

Level 3 : Substantial Complexity

The task calls for intricate decision-making, analysis, or evaluation.

Level 4 = High Complexity

The task demands profound expertise, critical thinking, or advanced skills.

Level 5 = Very High Complexity

The task is of the utmost complexity, necessitating top-tier expertise, human creativity, or intuition.

From this, the relationship between task complexity and the degree of automation is derived as:

  • Basic Complexity = Maximum Susceptibility to Automation
  • Low Complexity = High Susceptibility to Automation
  • Substantial Complexity = Considerable Susceptibility to Automation
  • High Complexity = Moderate Susceptibility to Automation
  • Very High Complexity = Minimal Susceptibility to Automation

This association provides both clarity and a structured approach to comprehend how varying levels of task intricacy directly influence their susceptibility to automation in the backdrop of AI advancements.

Method for Identifying

Overview of the Neobrain AI Utilized for the Research

Founded in 2018, Neobrain has been pioneering an AI platform that aggregates data from a staggering 2 million job listings across France, the UK, Germany, and the US. This extensive data reservoir resides in a non-structured database, optimized for retrieval and investigation based on dynamic variables:
- Salary scales
- Skill Sets
- Tools and technologies required to perform job-related tasks.

In the context of job automation potential, the ROME 4.0 and O*Net databases offer an exhaustive compilation of the top 20 skills imperative for each profession under scrutiny. Every cardinal task associated with a profession is intricately linked to the vital skills essential for its accomplishment. This detailed association is facilitated by Neobrain's advanced Machine Learning mechanism, which has already been seamlessly integrated by over 700,000 users spanning France, the US, and Germany.

How do we define "a skill at risk of obsolescence"?

Skills at risk of obsolescence are capabilities, proficiencies, or areas of expertise that, due to technological evolution, organizational transformations, or, pertinent to our research, the influence of AI, are experiencing diminishing importance or demand in the occupational sphere.

How do we define "a future-proof skill"?

Future-proof skills are characterized as abilities that maintain their significance and application in the work environment despite the winds of technological, organizational, or AI-driven change. These skills often bridge foundational and cross-industrial realms, and they're typically resistant to automation. As certain skills wane, these future-proof skills are poised to endure and might even amplify in prominence.

Application in Our Research:

At-Risk Skills: Skills associated with tasks showcasing a pronounced potential for automation as being at risk of becoming obsolete.

Future-Proof Skills: Skills tied to activities with minimal automation prospects, or those essential for tasks inherently difficult to automate, are delineated as future-proof.

A Progressive Stance on Skill Assessment: 

We've made a deliberate choice to spotlight at-risk skills even within professions that currently exhibit minimal susceptibility to automation. Such foresight allows us to anticipate future trajectories and equip professionals accordingly.

Similarly, professions on the cusp of substantial automation disruption were dissected to pinpoint skills that may still carry weight for an enterprise. This methodology empowers businesses to reflect on their workforce's adaptability and to possibly sculpt training initiatives tailored for roles that might be waning. In doing so, we ensure a forward-facing strategy to human capital evolution in a dynamic work milieu.

Summary of

Our findings segmented professions into four
distinct brackets based on AI's influence: 

Category 1 - Profoundly Transformed Professions: 
These are roles undergoing significant transformations due to AI, with between 40% and 66.6% of their tasks impacted.
Total : 32 roles

Category 2 - Moderately Affected Professions:
These roles experience a moderate AI impact, with certain tasks benefiting from AI enhancements. Our study reveals that AI influences 20% to 40% of their tasks.
Total: 36 roles

Category 3 - Marginally Affected Professions
In these professions, AI makes only a marginal difference, affecting between 10% and 20% of their tasks.
Total: 18 roles

Category 4 - Shielded Professions
These are roles deemed "protected", where the inherent nature of their tasks remains untouched by AI advancements.
Total: 13 roles.

Do you have future-proof skills
in your company ?

In no time, map out all the skills currently at your disposal and identify the ones that
will be vital tomorrow, thanks to Neobrain AI SKills
Management