Striking a balance between these four forms of conjoined agency is essential, as an overemphasis on either automation or augmentation can have adverse consequences for both organizations and society. Below, we outline examples of how to thoughtfully integrate technologies across all facets of talent management, ensuring a balanced approach that leverages both automation and augmentation strategically.
In (1) talent acquisition, historical data can be analyzed by HR analytics team to identify patterns that suggest high-potential candidates (guided augmentation). Preconditioned augmentation can then automatically flag candidates who match these patterns, marking them as priority profiles. HR managers use technologies for foundational support to efficiently sort and review these flagged applications. Finally, pure automation provides interviewing managers with a tailored list of interview questions in different languages to probe deeper into these flagged patterns, ensuring consistency and focus in evaluating candidates during interviews.
For (2) onboarding, the hiring manager could recommend personalized onboarding pathways based on algorithms that connect data about the new hire’s role, location, department, and notes from their interview (guided augmentation). Automated compliance modules ensure that new hires complete mandatory training (e.g., safety protocols) before advancing, thereby securing compliance with the local institutional environment and enforcing global MNE standards (pure automation). Preconditioned automation can further support new hires in navigating paperwork and completing essential system-related tasks (foundational support and preconditioned augmentation).
In (3) learning and development, combining different forms of conjoined agency offers the greatest potential for unlocking talent potential by creating a structured yet adaptable process that supports individual growth and organizational capability - no matter where the talent is located. Assisting technologies, such as Learning Management Systems, lay the foundation by organizing training content, allowing employees to access training courses and learning modules. Building on this, preconditioned augmentation can enforce essential learning prerequisites by requiring individuals to complete relevant modules (e.g., training for a new work context) before they transition into that context, establishing a consistent baseline of knowledge across the whole organization. Guided augmentation would enhance this experience by analyzing data from LMS platforms and pairing it with performance management information to recommend courses, skill paths, or resources tailored to each employee’s role, past performance, and career goals. This analysis can then inform conversations with a line manager or group coach to further personalize the learning journey. Finally, automating technologies may adjust content difficulty as the employee progresses or adapt quiz questions to match their knowledge level, creating a dynamic learning experience.
In (4) performance management and succession planning, integrating different forms of conjoined agency has the greatest potential. It fosters a cohesive, data-driven approach that links insights to action, balances human judgment with technology, and minimizes biases - particularly those unique to dispersed teams, such as recency bias, and proximity effects. Ultimately, decisions should remain evidence-based, with a "human- in-the-loop" to ensure thoughtful and contextually aware outcomes. For example, assisting technologies, like performance tracking tools, provide managers with a structured way to record and monitor employee goals and progress (foundational support). By standardizing documentation across locations, these tools ensure consistent records regardless of an employee's location, helping to reduce recency bias and proximity effects often experienced by remote working employees. Building on this foundation, preconditioned augmentation - such as automated performance review forms - enforce standardized feedback and ratings, ensuring that critical evaluation criteria are consistently applied before advancing in the review process.
This structured approach reduces the risk of geographical and cultural biases by applying a uniform set of criteria to all employees, regardless of where they are located. Guided augmentation would further enhance fairness by using AI-driven analytics platforms to analyze performance data across all regions, identifying high performers and flagging potential candidates for leadership based on objective patterns rather than subjective judgment. By drawing insights from comprehensive datasets and sophisticated analytics projects, guided augmentation helps reduce recency and proximity biases, highlighting talent that might otherwise be overlooked due to geographic distance. Additionally, this form of conjoined agency is employed in calculating the adjusted gender pay gap and supporting evidence-based decisions across other equity, diversity, and inclusion (EDI) areas. Finally, automating technologies, like AI-powered succession planning systems, bring the process full circle by autonomously identifying, ranking, and recommending development paths for potential successors based on role requirements, performance data, and skills assessments. These systems seamlessly link back to learning and development by assigning tailored training modules to address any identified skill gaps, setting up regular check-ins, or creating personalized growth plans, regardless of location. This interconnected approach not only establishes a solid link between performance management and succession planning system but also reduces the risk of discrimination and regional biases, thereby aligning performance metrics and development opportunities with both organizational goals and global talent.
Our main overarching message is that strategic management of talent should never be entirely outsourced to either humans or technology alone; instead, it requires a thoughtful collaboration that leverages the strengths of both. We know that technologies, including AI, are limited in their judgment, often acting as ‘black boxes’ that provide literal responses - doing exactly what they’re programmed to do without explaining why they offer particular recommendations. At the same time, we recognize that humans are limited by myopic search and bounded rationality. This ‘short-sightedness’ can result in overlooking valuable talent, failing to identify skill gaps accurately, or ignoring potential successors simply because they do not fit immediate criteria. In a global talent context, myopic search can lead to favoring candidates from certain regions, backgrounds, or social groups simply due to their similarity, familiarity or proximity, while highly qualified individuals in other locations or with less ‘mainstream’ qualifications may go unnoticed. In sum, by approaching talent and technology interactions with intentionality and purpose, we can address the deep-rooted disparities that persist in the representation of underrepresented groups - especially in leadership - and ensure fairer allocation of resources and opportunities for talent development and advancement. This commitment to thoughtful, inclusive technology use has the power to unlock talent potential that often goes unseen, creating a more equitable and dynamic future for talent.
