The role of large graph models and AI engines in shaping future talent tech ecosystem
By Saurabh Jain, CEO & Founder, Spire.AI
Picture this: A world where skills evolve faster than software updates, and the fabric of talent technology is rewoven rapidly. Welcome to the era of talent transformation, where traditional approaches are being revolutionised by ground-breaking innovation. As the custodians of the vision for a world where talent is resilient, talent transformation platforms are not just driving the change, but revolutionising the essence of talent technology through transformative new talent operating models.
With roles rapidly evolving, the challenges for organisations become multifaceted. This includes addressing issues in skill gaps, adapting to new technologies and optimising the cost of the workforce and delivery. While organisations have implemented technology for talent management, they’ve only touched the surface, leaving a vital gap that must be addressed to fully realise their transformation goals and tackle today’s talent challenges. It is like relying on floppy disks to store files, in an era of cloud computing.
Therefore, organisations need to adopt and evolve their existing talent technology stack in line with the growing complexity of skills and their talent transformation vision.
What organisations need is a robust technology powered by a core skills engine, capable of aggregating and generating skills data, that can seamlessly run multiple talent operating models and varied business rules, and integrate effortlessly with existing HR systems, thereby realising the organisation’s talent transformation vision.
Understanding large graph model (LGM) for Skills and domain-intelligent AI
In the pursuit of identifying pathways to resolution, Domain-Intelligent AI, which leverages the Large Graph Model (LGM) for Skills, comes to the rescue. This technology dynamically maps and analyses the intricate relationships between skills, competencies, experiences and qualifications, empowering organisations to optimize talent management and drive innovation. Domain-Intelligent AI engines bring these LGM for Skills to life, by offering deeper understanding and skills intelligence capabilities to match the right people to the right roles and transform how talent is sourced, deployed, developed, and matched with organization needs. They process vast data—from job descriptions to current and future industry trends and generate actionable insights, identifying emerging skill clusters and potential talent gaps before they become critical for the organisation.
Application of large graph model (LGM) for skills in talent mapping
LGM for Skills offer auto-evolving role-skill frameworks that adapt to real-time business changes. These frameworks update based on trends, new technologies, and evolving needs, ensuring organisations have an accurate understanding of required roles, skills and their intricate relationships. This empowers organisations to gain a comprehensive understanding of their employees’ skills and capabilities while identifying critical gaps between current competencies and future requirements to keep their talent relevant.
It also helps in recommending top talent for promotions and growth—enabling effective succession planning, fostering internal mobility, enhancing talent retention strategies, and building talent resilience to adapt to future challenges.
Challenges and ethical considerations
While concerns about bias in AI systems are valid, the Domain-Intelligent AI presents a paradigm shift in addressing these issues ensuring they comply with changing societal norms and industry practices. By leveraging only skills intelligence, these advanced systems do not need historical data like other traditional AI models, ensuring evaluations are based purely on skills and experience and not skewed by past experiences, personally identifiable information (PII) data and any other personal or sensitive information etc, ensuring a fairer assessment of talent based on skills only.
This dynamic approach not only mitigates bias but also enhances the accuracy and relevance of talent matching, creating a more equitable and effective talent ecosystem. The result is an auto-evolving, ever-vigilant Domain Intelligent systems that aligns seamlessly with the principles of fairness and inclusivity.
In short, the conversation centres on two pivotal issues: maintaining relevant talent and optimising the costs for talent, service and delivery. The answers lie in the power of LGM for Skills and Domain-Intelligent AI engines. These tools provide the insights needed to maximise internal mobility, enhance workforce productivity, and strategically develop talent to meet present and future demands.
The real challenge—and opportunity, for today’s industry leaders, is to embrace these technologies at the pace of their demand. CHROs who understand the strategic importance of LGM for Skills, will not only improve operational efficiency but will also elevate their role in driving business outcomes. The stakes are high, but the rewards for decisive action have never been greater.
