EkaLore explores the implications of AI in HR in our latest series of BAD (Big Data/AI/Data Science) posts. This is the second in the series. You can find the first post here.
Talent development has explored many elements to find “future seniors” among “new hires.” Some of these talent development steps are reliant on data sources (e.g., finding people overqualified for their positions who could be moved to different positions) or assumptions of talent development (e.g., “time in grade” to be ready for training for the next position or role). Enterprises have built more elaborate “succession planning” with manual techniques and formal processes.
Automating the talent development process uses different reasoning patterns (instead of just looking for an undergraduate math person to do SQC, the talent development process could look for someone with a quantitative social sciences background as well). It links different data for assessment and evaluation.
Domain knowledge can be difficult to assess for any process and even more difficult for enterprises across different geographies and vertical silos. The skills of a business analyst for a manufacturing operating division may translate poorly or fantastically to an analyst in a quantitative finance group. Domain knowledge in a vertical silo (retail marketing) may translate poorly to another silo (bond retailing). The ability to associate domain knowledge skills across large-scale enterprises is not assured, even as talent management applications bring larger “knowledge representations” and “knowledge bases” to the processing application.
Know-how (skills knowledge) has traditionally been difficult to quantify and integrate into job descriptions and individual assessments. Even in trade occupations, skills may have specific experiences not translate well across common roles. A “car mechanic” is very different in electric vehicles versus internal combustion engine vehicles. A “sales automation administrator” might be very effective across different packages (Siebold, Microsoft, SalesForce) or not. Matching know-how is a continual effort to define and quantify skill areas, competencies, and performance.
Matching interests during career paths are often an enterprise cultural specific process. Enterprises with separate technical, managerial, and sales/marketing ‘ladders’ often see conflicts when looking for talent interested in bridging across the ladders. Matching interests for ambitious staff can also be difficult as people who are highly motivated and interested can often make choices where an application (AI-style or not) will have difficulties evaluating presented data (and linked data) to identify ‘good matches.’
Integrating and synthesizing requirements, domain knowledge, know-how, and interests are clearly aided by AI-style applications capable of using additional knowledge bases and customized knowledge stores. A key element is incorporating additional recognition capabilities, matching/search algorithms, and capabilities to interact with a talent development application iteratively.
The use of talent development can be a key advantage for even small to medium businesses. It is a critical talent attraction tool to attract specific interested individuals and open to a different career pathing and talent development process. Services vendors may find advantages in using common cloud recruiting, job seeking, and social media sites. Services maintaining a deeper understanding (by natural language processing or associative identification) of individuals from their data may find revenue from the value they can provide to recruiters, staff augmentation, and other service vendors.
Prerequisites to the effective use of AI-style solutions for talent management have common ideas:
1) Leverage prior work building the domain knowledge representation, skills inventory, and individual profile data.
2) Very specific search/match criteria to produce better selection/recommendation sets.
3) Value from more productive and effective talent management
4) Continuous improvement through feedback loops and refining algorithms based on outcomes and performance.
In conclusion, applying AI in talent development can significantly enhance identifying, assessing, and nurturing organizational talent. By leveraging big data, AI, and data science, organizations can better understand the skills, interests, and potential of their employees and make more informed decisions about their career development.
AI-powered talent development can help organizations:
1. Identify potential future leaders among new hires.
2. Automate and refine the succession planning process.
3. Assess domain knowledge and know-how across different verticals and geographies.
4. Match employees' interests with suitable career paths.
5. Improve the overall efficiency of talent management processes.
However, to effectively implement AI-driven talent development, organizations must:
1. Invest in building robust domain knowledge representations, skills inventories, and individual profile data.
2. Develop specific search/match criteria to generate better selection/recommendation sets.
3. Focus on continuous improvement through feedback loops and refining algorithms based on performance and outcomes.
By embracing AI in talent development, organizations can gain a competitive advantage in the market, attracting top talent and maximizing the potential of their workforce. This, in turn, leads to improved productivity, innovation, and overall business success.
You can find more BAD project posts at www.ekalore.com/bad-project-blog
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