Using Artificial Intelligence to Rethink Talent Management

Artificial intelligence in the workplace allows employees to focus on more important tasks by automating many mindless administrative tasks.

FREMONT, CA: Artificial intelligence (AI) in the workplace allows employees to complete more important tasks by automating numerous mindless administrative tasks. For instance, HR professionals can focus on the most promising candidates for a position by automating the filtering method through hundreds to thousands of job applications.

AI refers to advanced data analysis techniques that enable us to study the clean, organized, numerical data that conventional regressions can handle and messy, unstructured, non-numerical data. This innovation is revolutionizing the tech industry, allowing automobiles to process their surroundings, intelligent speakers to comprehend human speech, etc. However, implementing AI requires effort and may not be appropriate for all organizations. How should leaders approach the application of AI to HR legacy functions?

Today, people inhabit a world characterized by big data and AI. Leaders must realize that while AI and algorithms can be beneficial, they require data—and lots of it. In many instances, the required data is either not collected or inaccessible.

A Presentation of Talent Analytics

There are a few essential factors to understand before implementing AI in talent management, even though it may not be for every business.

Level 1: At this level, operational reporting, such as efficiency and compliance metrics and EEO (equal employment opportunity) reporting, is emphasized. There are few automated processes and a heavy reliance on manual, ad hoc reports. Instead of integrated, interconnected datasets, units rely on spreadsheets or siloed databases to coordinate information. Consequently, insights are limited.

Level 2: Companies must consider which data sources are helpful at this stage and how to gain access to them to advance to this level. Examples include organization-wide candidate and hiring process data. Organizations may need to construct a business case for securing the necessary data before collaborating with IT to determine how to access it.

Level 3: This level assesses the data organizations access and investigates additional data sources. A central warehouse allows companies to access and integrate as much as possible. As they collect more and more data, they can calculate average levels for various organizational areas. Businesses may also be able to integrate external data sources, such as the demographic composition of the local labor market, to ensure that their hiring demographics are representative. At this level, the power to compare and contrast facilitates the identification of strengths and weaknesses and the formation of well-informed judgments.

Level 4: Typically, achieving Level 4 requires additional and richer data sources, particularly regarding job performance or outcome. A company must be able to link hiring data to relevant outcomes to understand its applicability and predictive value truly. In Level 4, analytics can be quite advanced, but they are manually done or ad hoc rather than automatically and at scale.

Level 5: Automating analytics is the key to achieving this final level. Instead of relying on manual, analyst-driven processes, this level maximizes the value of AI and big data by expanding analytical capabilities to mine and comprehend data.