Glossary

Talent intelligence
and AI terms, explained

Talent
intelligence

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Talent management

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Talent
acquisition

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Resource management

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Diversity and
inclusion

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Talent intelligence

Talent intelligence is a new method that organizations use to collect and analyze data on internal and external talent, often with the help of AI. With talent intelligence, HR leaders can assess potential candidates, see skills in their organization and industry, and forecast future skill needs so they can align talent strategies to overall business success.
Employee data analytics is information about worker trends and patterns. Also referred to as HR, people, workforce, or talent analytics, it helps HR professionals measure and see employee data to inform decision-making and support business outcomes.
A talent analytics dashboard organizes and shows valuable information about a workforce that HR leaders can use to inform decision-making. Also known as an HR dashboard, these can include information on skills, employee performance and engagement, and other talent-related metrics.
Employee engagement analytics measure and evaluate the employee experience. These insights are helpful to HR professionals as they can show employee engagement, satisfaction, and well-being, key indicators of a healthy workforce.
Workforce planning is the process of aligning talent strategies to support the business. It’s imperative that talent leaders focus on supply and demand, skills gaps, fulfillment, measurement, and action to determine whether they need to buy, borrow, or build talent with the skills they need.
Algorithmic decision-making (ADM) uses analysis of large data sets to derive information considered helpful in making decisions. Once algorithms surface information, talent leaders can use it to inform their decisions.

Artificial intelligence (AI) and machine learning are often used interchangeably, but there are some key differences in how each performs. AI is broken down into subsets, including machine learning and deep-learning AI. AI is the broadest classification for programs that can sense, reason, act, and adapt. Machine learning is a subset of AI in which algorithms improve performance as they are exposed to more data. Deep-learning AI is a subset of machine learning in which multilayered neural networks learn from vast amounts of data.

AI and machine learning

Cloud delivery architecture refers to how different strategic components of cloud computing services are integrated to support an organization’s cloud computing. This includes the architecture necessary for cloud services, including front-end platforms, back-end platforms, a cloud-based delivery model, and a network.

Talent management

AI-powered talent management systems guide employer decisions and employee development. HR and business leaders often lose sight of their skills and capabilities after onboarding, so these systems ensure that leaders keep focused on developing employees’ potential.
Augmented talent management systems surface insights about employees’ skills and potential to help guide their careers. Talent management professionals often use these augmented technologies, powered by AI, to manage workforce development.

Hyper-personalization uses AI and real-time data to inform and create highly targeted products, services, content, or experiences. Talent management professionals using hyper-personalized systems powered by AI, generally receive more targeted recommendations that resonate with their audiences.

Talent experience (TX) is an employee-centric approach to managing talent. Using a talent management system that highlights employees’ skills and potential shows them learning and career growth opportunities that improve their experiences and instill purpose.

Talent retention, or employee retention, is the process of keeping top talent from leaving your organization. HR and business leaders need a strategy in place so they can easily see and engage high performers, as turnover can be expensive and disruptive to a workforce and delay business outcomes.

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Talent acquisition

AI-powered sourcing uses artificial intelligence to help source and select candidates. This is incredibly powerful in the talent acquisition process for recruiters, as they can surface best-fit candidates targeting their skills and capabilities from a larger talent pool.
Conversational AI recruitment uses artificial intelligence to facilitate conversations with potential candidates with natural language processing (NLP). With automated questions starting the conversation, recruiters can begin to qualify candidates for open positions.
Candidates experience (CX) is the entire end-to-end recruiting experience for the applicant. Providing a streamlined and personalized candidate experience ensures that applicants have a positive introduction to your organization and are more likely to accept an offer.
Diversity and inclusion (D&I) bias detection is identifying any areas of existing or potential bias in your talent processes. It is essential to ensure that any technology uses fair algorithms that promote unbiased and equitable decision-making.
Reference checks are essentially background checks with former employers, clients, colleagues, and others for candidates in the talent acquisition process. These typically occur at the end of the recruiting process before making an offer.

Talent resource management

Skills-based project matching uses data to identify and present new projects to available employees. By using skills-based matching, resource and engagement managers can ensure they’re finding the best-fit matches to take on projects.
Project-based workforce planning is the process of resource or engagement managers pairing the right worker with the right project at the right time. When aided by technologies like AI, they can get a clearer picture of best-fit candidates to align with project requirements, necessary skills, availability, and location.
Agile talent management is a strategy to increase workforce productivity and capabilities through highly adaptable talent management practices, tools, and resources that can be quickly adjusted to meet changing market demands.

A talent marketplace is an AI-powered digital platform that HR and talent management teams use to match best-fit talent to opportunities based on their skills, capabilities, and interests. An AI-powered talent marketplace can more efficiently and effectively match talent to roles, projects, gigs, mentorships, and more.

Diversity and inclusion (D&I)

Bias mitigation is the process of identifying and reducing bias from an organization’s systems and processes. While bias can be intentional or unintentional (unconscious bias), most people do have some form of bias. It’s important to have a system that can identify where bias might be coming into play, then work proactively to remove or mitigate that bias.
A diverse workforce includes and represents people from all backgrounds and experiences, including in terms of age, race, cultural background, physical abilities and disabilities, religion, gender, and sexual orientation. This inclusive environment should provide equal rights and opportunities for everyone.
Federal contractor compliance programs that originate from the Office of Federal Contract Compliance Programs (OFCCP) exist to protect workers, promote diversity, and enforce employment laws. This government agency conducts compliance evaluations and investigates complaints against federal contractors and subcontractors’ personnel policies and procedures.

A talent marketplace is an AI-powered digital platform that HR and talent management teams use to match best-fit talent to opportunities based on their skills, capabilities, and interests. An AI-powered talent marketplace can more efficiently and effectively match talent to roles, projects, gigs, mentorships, and more.

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