AI-based Talent Management Approaches

Artificial Intelligence is an advanced data analysis technique that allows us to examine both clean, organized, numerical data that traditional regressions can handle, as well as messy, unstructured, non-numerical data.

Fremont, CA: As artificial intelligence emerges in the workplace, many mindless administrative tasks can be automated, allowing employees to focus on more critical aspects of their work. HR professionals, for instance, can automate the process of sifting through thousands of job applications in order to focus on the most promising candidates.

Artificial Intelligence is an advanced data analysis technique that allows us to examine both clean, organized, numerical data that traditional regressions can handle, as well as messy, unstructured, non-numerical data. In today's technological world, this revolutionizing advancement enables automobiles to process their surroundings, and intelligent speakers to understand human speech.

How can leaders approach using technology like AI to impact legacy functions in HR?

Today, in an era of big data and artificial intelligence it is important for leaders to understand that AI and algorithms can be useful, but they require a lot of data. Most of the time, the data needed is either not captured or not accessible.

Introducing Talent Analytics

While AI might not be a fit for every company, organizations need to understand a few crucial elements if a business is leveraging AI in talent management. We have created a new maturity model of talent analytics to guide organizations in their use of data and analytics. The intent is to show how a company can progress from analyzing limited data to continuously capturing quality data, automatically analyzing that data, and taking action based on the findings.

Level 1: The lowest level focuses mainly on operational reporting, such as efficiency and compliance metrics and EEO (equal employment opportunity) reporting. There are few automated processes and a reliance on ad hoc, manual reports.

Level 2: To reach this next level, organizations need to consider what data sources are helpful at this stage and how to access them. For instance, candidate and hiring process data across the organization.

Level 3: This level takes stock of the data access and considers additional data sources. The more access and integration into a central warehouse, the better.

It is also necessary to integrate external data sources, such as local labor market demographics, to ensure hiring demographics are representative. At this level, the ability to compare and contrast helps to identify strengths and deficiencies and to make informed decisions.

Level 4: This level often requires additional and richer data sources, especially on job performance or outcome. While analytics at this level can be quite sophisticated, they still happen manually or in an ad-hoc fashion rather than automatically and at scale.

Level 5: The key to this final level is analytics automation. Rather than manual, analyst-driven processes, this level relies on maximizing the value of AI and big data by scaling analytical capabilities to mine and understand data.

Human resources leaders should take the time to understand what integrating AI into their talent management might entail rather than jumping on the AI bandwagon without realizing what it involves.