Role of the large graph model (LGM) in transforming skill-based organisations of the future

By Saurabh Jain, Founder & CEO, Spire.AI

Every organisation strives to be a Skill-Based Organisation (SBO). In an ever-changing business landscape, SBOs are the envy of the talent management world. Why? Because skills, not job titles, are the driving force behind an organization’s success. An SBO understands its employees’ skills and empowers them to continuously learn, adapt, and excel in a world of constant change.

But here’s the rub: while the desire to become an SBO is widespread, the technological foundation to achieve it is lacking in current HR systems. A recent Gartner report also highlights that skills shortages are the top risk to organizational growth in 2024 and 2025. Understanding and preparing talent for the organization’s current and future needs is the top priority to remain agile and adaptable in the face of constant change.

The challenges of skill shortages

The current skill landscape is characterized by rapid change and growing complexity. The half-life of skills is estimated to be around five years, meaning the knowledge and abilities considered valuable today will quickly become obsolete. This creates a significant challenge for organizations as they strive to maintain a workforce with the right skills to compete. If companies struggle to find talent, innovation stalls due to a lack of expertise, and projects get delayed. Employees with inadequate skills become disengaged, further hindering growth. Organizations must take a proactive approach to bridge skill gaps to stay competitive. This empowers employees, fosters innovation, and unlocks business potential.

While generic AI technologies have made some advancements in talent management, they still fail to address the intricate relationships between skills, their adjacencies, and roles. Organizations require a more sophisticated solution to identify primary, complementary, and adjacent skills and map optimal role-skill relationships.

The Recipe:

Basic skill databases and dictionaries fall short, and are unable to fulfil the leaders’ vision. What is needed is an auto-evolving skill graph technology that comprehends skills, understands their associations, adjacencies, and interrelationships with roles. To successfully transform into Skill-Based Organizations (SBO), organisations must adopt a Skills-as-a-Fabric technology powered by a Large Graph Model (LGM) for Skills.

Core skill engines are the missing ingredient in the SBO recipe. Technologies with LGM empower organizations to automatically identify and generate their employee skills, recommend AI-powered personalized career paths, and provide skills-based growth opportunities.

By proactively comprehending skills, their relationships, and addressing skill gaps using LGMs, organizations can break this cycle of stagnation and unlock a wave of innovation, agility, and employee empowerment.

Large Graph Models (LGM) for skills: The key to unlocking skill-based potential

Large Graph Models (LGM) for Skills are a revolutionary AI technology that is transforming organizations into true SBOs. Here’s how technologies powered by LGM can empower this transformation:

Automated skill discovery and mapping: LGMs analyze vast amounts of data, including job descriptions, industry trends, and learning materials, to automatically identify and generate different roles and the complex mix of skills required for these roles, across any industry or business function. This auto-evolving skill framework eliminates manual mapping, which can be time-consuming and error-prone.

Predicting future skill gaps: One of the major hurdles for organisations is updated and complete employee skill profiles. LGMs help identify and auto-generate employee skill profiles by handling low data as well as no data scenarios and provides a unique solution for the traditional data problem for organisations. With employee skill profiles in place, LGMs help identify skills gaps within the existing workforce and future skill needs based on business context, industry trends, and technological advancements.

Crafting evolving skill maps: Unlike static skills inventories, LGMs enable organizations to build dynamic, auto-evolving skills maps. These maps continuously learn and adapt to changing business contexts, such as new technologies, market shifts, and evolving customer needs. This ensures the skills framework remains relevant and aligned with organizational goals, allowing the organization to stay ahead of the curve.

Personalised learning journeys: LGMs provide granular insights into an employee’s current skills, the specific proficiency levels required for their role, and potential AI-recommended career paths within the organization. This empowers them with highly personalized reskilling recommendations and learning paths for their employees. Employees receive targeted reskilling that addresses their skill gaps and helps them grow. This increases learning agility in organisation, benefits the employee, and allows them to take ownership of their growth in the organization.

Enhancing internal mobility: LGMs power internal skills-based talent marketplaces by continuously matching employees to open roles based on their skills and career aspirations. This is supported by proactive matching alerts to employees, fostering skills-based internal mobility and retention within the organization. Employees have the opportunity to grow and develop their careers internally, while organizations can maximize the potential of their existing talent pool, reducing the need for expensive external recruitment efforts.

Large Graph Model (LGM) for Skills is a transformative AI technology that automates skill discovery, predicts gaps, and personalizes learning and therefore is paving the way for a more agile, adaptable, and empowered workforce. It is the future of talent management and can help organisations transform into SBOs.