I recently happened to discover an interesting trend in the field of corporate strategy. Focused on improving efficiency, productivity, relationship management and even job satisfaction, this movement aims at achieving the ideal it calls the Skills-Based Organization (SBO). Convinced ChatGPT could provide me with a solid overview of this movement, I formulated this prompt:

 “What can you tell me about the trend known as skills-based organizations? Does it represent something new that can change the way enterprises work? What methods and techniques are associated with it?”

The chatbot began its response with this “Comprehensive Overview.”

**Skills-Based Organizations (SBOs)** are an emerging trend in organizational design that shifts focus from traditional job roles to individual skills and capabilities. This model aims to leverage employees’ specific skills to meet dynamic business needs, fostering greater agility, innovation, and efficiency.

It then treated me to “a detailed look at the concept, its potential impact, and the methods and techniques associated with it.” For brevity’s sake, I’ll simply list the “core concepts,” which I found particularly interesting.

Skills Mapping: Identifying and categorizing the specific skills possessed by employees.

Dynamic Team Formation: Creating project teams based on required skills rather than departmental affiliation.

Continuous Learning: Encouraging and facilitating ongoing skill development to meet future business demands.

Flexible Career Paths: Allowing employees to pursue opportunities that match their skills and interests, rather than following a linear career progression.

On the question of talent management, it had this to say:

“With the growing importance of specialized skills and the rise of the gig economy, retaining and effectively utilizing talent requires more flexible and skill-centric approaches.”

I was slightly surprised that it didn’t mention the growing role of AI that increasingly obliges organizations to rethink the challenge of human vs. machine-based skills.

Then there is the question of methods required for “Skills Mapping and Inventory.”

• Skills Taxonomy:** Developing a comprehensive framework that categorizes and defines the skills within the organization.
• Skills Assessment Tools:** Using assessments, surveys, and AI-driven platforms to identify and record employees’ skills.
• Skills Databases:** Maintaining an up-to-date inventory of skills that can be accessed and utilized across the organization.

Having worked for several decades in the field of talent development, I wondered about how today’s organizations identify and assess the skills they need to accomplish the critical goal mentioned earlier: “dynamic team formation.”  To find out, I asked a new question.

“When you analyze the skills that contribute to performance, most experts note that certain things –  such as required knowledge and native reasoning ability – are easily measured with traditional tools. But they give only a partial view of what an effective skill set looks like. Every job profile implies mobilizing a complex diversity of capacities that include perception of both the static and dynamic elements of a context, sensitivity to motivation and the interests at play, a capacity for empathy, emotional mastery as well as a host of other psychological factors that go well beyond the identification of ‘personality types.’ You mention ‘surveys and AI-driven platforms’ for evaluation, but are there any effective tools today or is that a question we can expect AI to solve in some distant future?”

ChatGPT’s response was thorough, listing the various tools that exist and speculating about future developments. One problematic category it mentioned caught my eye: “Behavioral and Situational Judgment Tests.” These promise to “assess how individuals react to different scenarios, revealing their problem-solving skills, decision-making processes, and emotional intelligence.” 

The nonlinear dimension of skill sets

I continued the conversation.

“This is fascinating in its scope, but the tools that exist, even the ones you mention, appear to me to be suffering from the traditional bias towards linear reasoning. Professional reality is fundamentally non-linear. Environments vary, personalities differ, actions and decisions taken at any given moment modify the existing situation. Reality is constantly being reconfigured. The non-linearity of real-life situations requires that assessment of skills take place in dynamic, changeable contexts rather than seeking to construct a skills profile on the basis of a combination of unrelated capacities, such as calculation, reasoning, judgment and abstract decision-making. Given what technology is now capable of doing, both in the representation of realistic contexts and the capacity to conceive of non-linear developments, shouldn’t  we be looking not only at changing the way we assess skills but also the way we define skill sets?”

ChatGPT predictably congratulated me on raising “a crucial point about the nonlinear nature of professional reality and the need for assessment tools to evolve beyond traditional, linear approaches. The idea of dynamically assessing and defining skill sets to better reflect the complex, changing contexts of the modern workplace is both timely and necessary.” It then reiterated the essential point before listing the “Key Concepts for Non-Linear Skill Evaluation”

“Traditional skill assessments often focus on static, siloed abilities such as cognitive reasoning, technical knowledge, and specific task-oriented competencies. However, real-world professional environments are far from static; they are complex, unpredictable, and constantly evolving. This necessitates a more fluid approach to understanding and evaluating skills.

Here are the six key concepts it identifies.

1. **Contextual Adaptability:**

2. **Integrated Skillsets:**

3. **Situational Awareness:**

4. **Emotional and Social Intelligence:**

5. **Non-Linear Decision-Making:**

6. **Creative Problem Solving:**

It went on, very helpfully, to contrast the old and new paradigms for assessment.

1. From Static Skills to Dynamic Capabilities:**

• Old Paradigm:** Static skills often defined in job descriptions and assessed in isolation.
• New Paradigm:** Dynamic capabilities that emphasize flexibility, integration, and contextual application. Focus on how skills interact and evolve in real-world contexts.

**2. From Individual Competencies to Collective Intelligence:**

• Old Paradigm:** Individual skills and competencies measured separately.
• New Paradigm:** Emphasizing the collective intelligence of teams and how diverse skills contribute to collaborative success in dynamic environments.

**3. From Predictable Roles to Agile Adaptability:**

•  Old Paradigm:** Roles and responsibilities defined predictably and rigidly.
• New Paradigm:** Agile adaptability where roles are fluid and individuals can pivot based on changing needs and contexts.

ChatGPT then provided its conclusion, which seemed to me very well observed.

“The future of skill assessment and definition lies in embracing the complexity and non-linearity of real-world environments. By leveraging advanced technologies and rethinking our approaches, we can develop more holistic, context-sensitive, and dynamic methods to evaluate and nurture the full spectrum of human capabilities. This shift not only aligns better with professional realities but also empowers individuals and organizations to thrive in an ever-changing world.”

In the modern market economy, humans and their organizations have been competing with one another for at least the past two centuries, a phenomenon aggravated in recent years by the gig economy. Now we humans find ourselves competing with machines, while organizations – whether they are “product-based” or “skills-based” –are left struggling to understand the true source of the productivity gains they need just to survive in the marketplace.

SBOs themselves need more than a new set of principles to guide their human resource management. They need emerging methods and tools that delve into and account for the complexity of variable contexts and the fundamentally non-linear nature of most professional and business operations.

I actively participated in the movement promoting the idea of Learning Organizations and have conducted my own research in the domain of competency evaluation. It is now being exploited by a startup engaged in producing a new generation of assessment tools based on the principles of context sensitivity and non-linear logic. Many of the skills today’s organizations require have evolved. The presence of AI means that today’s skill sets have become vastly different from those in the past.

 “Outside the Box” is an exercise that corresponds to points Wharton’s Ethan Mollick develops in his book, Co-Intelligence. I”ll cite his first three rules for interacting with AI:

1. “Always Invite AI to the Table: A good way to learn how to use AI is to constantly experiment with it, for various daily tasks.”
2. “Be the Human in the Loop: While using AI extensively for experimentation and learning purposes is good practice, best practice is to always maintain human oversight and judgment.”
3. Treat AI Like a Person (But Tell It What Kind of Person It Is).

This capacity to work dynamically with AI is just one example of a skill we all need to learn. It’s one that simply didn’t exist even two years ago.

Your thoughts

As always, please feel free to offer your commentaries on any of the questions raised in this discussion. Simply drop us an email at dialogue@fairobserver.com. We’ll build your reflections into our own ongoing research.

*[Artificial Intelligence is rapidly becoming a feature of everyone’s daily life. We unconsciously perceive it either as a friend or foe, a helper or destroyer. At Fair Observer, we see it as a tool of creativity, capable of revealing the complex relationship between humans and machines.]

The views expressed in this article are the author’s own and do not necessarily reflect Fair Observer’s editorial policy.