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Machine learning is a subset of AI. ML uses mathematical models or algorithms to make classifications and predictions based on common patterns found in data.
Examples of ML used in recruitment include automated filtering and matching, talent pool generation, job description parsing, analyzing referral networks, multichannel talent sourcing, and more.
AI is a computing process that performs cognitive tasks by learning and problem solving. When people talk about AI today, they usually mean generative AI, machine learning or deep learning.
BI is technology used to gather, store, access, and analyze data to make business decisions. Findem is a BI-first platform and our 3D data is based on BI rather than AI.
Deep learning is a subset of AI that uses a neural network to classify large datasets and unstructured data. It is used to classify large datasets (billions of data points) and unstructured data (data that isn’t labeled such as this is a title, this is a name, this is a company).
Examples of deep learning used in recruitment include: generating profiles, verifying data, updating data, and predicting behaviors. Deep learning excels at providing context for candidate profiles, understanding intent for better matching, and bias mitigation.
Generative AI uses AI-based transformers to create a new work that is statistically probable by learning patterns from massive datasets. The quality of the output depends on the data used to generate the answer and the context or guidance used to make a request.
When people speak about AI in recruitment today, they usually mean using generative AI activities such as talent sourcing, candidate matching, creating job descriptions, candidate outreach and engagement, more inclusive communications, and employer marketing content.
A large language model (LLM) is a type of AI that uses massive data sets and deep learning to understand, generate, and manipulate human language..
Using recruitment and productivity software that leverages LLMs provides context and guardrails to the results generated by LLMs. Examples of LLMs: OpenAI’s ChatGPT, Google’s BERT, Microsoft’s Turing-NLG, Facebook’s RoBERTa, Hugging Face’s DistilBERT, Salesforce’s CTRL, IBM’s Watson Natural Language Understanding.
Hyperautomation is a framework that uses advanced technologies such as AI, ML, and robotic process automation (RPA) for scaling automation.
The goal of hyperautomation in the context of recruitment is to automate and optimize as much of the workflow as possible to improve efficiency, effectiveness, and the experience of finding, hiring, and onboarding employees.
Attribute search is the ability to find people by a combination of declared skills, verified skills, and implied skills as well as impact. Precision search of 3D data goes beyond what’s listed on a resume to capture what a person has done, who they are, and how they got there.
Attribute search examples:
Talent sourcing is the practice of identifying qualified candidates for recruitment, and attracting them to apply for a job at your organization. GenAI can be used to turn a job description into search criteria or for outreach in the sourcing process. ML might be used to filter, sort, and rank candidates with human supervision.
Multichannel sourcing is a talent acquisition strategy that seeks the best matched talent from any hiring channel – inbound job applicants, past applicants, referrals, alumni, talent pools, and external sourcing. Different channels signal different levels of interest and awareness of an open position and employer brand. The channel source and quality of the match can be used to prioritize candidates for outreach.
GenAI can be used to interpret a job description, search across data, and personalize outreach based on channel and match. ML might be used with human supervision to categorize and classify data for searching. Deep learning can be used to create connections between data sets.
Natural language sourcing enables sourcers, recruiters, or even hiring managers to start a search with a question or description of a candidate in their own words. They do not need to know keywords. The AI processes the question and understands the idea or intent of the request.
For example, a recruiter or hiring manager could ask: I'm looking for machine learning engineers with B2B SaaS experience at public companies in North America.
A copilot for sourcing is an AI companion for candidate sourcing. It assists with the talent sourcing process using AI techniques to automate complex data processing and analysis such as turning a job description into a search, searching across multiple channels at once, generating a list of prioritized, verified candidates, and assisting with outreach.
Candidate rediscovery is the strategy of searching for talent from the pool of past applicants within an ATS (Applicant Tracking System). ML and deep learning might be used to keep candidate profiles up to date with the most current information verified from public data sources. GenAI could be used to turn search requests into precision search terms.
Candidate relationship management is software used for the practice of nurturing and tracking qualified candidates. CRM software enables talent teams to strategically nurture talent qualified for future openings.
A talent ecosystem is a holistic, multichannel and multi-medium go-to-market strategy that empowers a company’s ability to identify, develop, nurture, and retain talent at scale.
Talent analytics are used to gain insight into and assess the health of recruitment, pipeline, and engagement processes with the aim of optimizing these processes throughout the entire lifecycle.
Talent insights are data-driven analyses and connections. Talent insights use ML and deep learning to draw conclusions and make predictions based on data and behaviors. This information can be used for making more informed decisions for workforce planning, succession planning, workforce forecasts, and attrition prediction.
Findem's attributes are the unique talent data that describe a person's skills, experiences, and characteristics. Attributes go beyond the limits of a resume and can be used to precisely match past experience and behaviors to potential for impact and value.
Examples of attributes include: company business models, company growth, role experience, and verified skills (Python, Java... etc), domain experience, team leader, mission-driven, career growth etc.
A person's professional digital footprint consists of much more than a resume. There are job titles, promotions, certifications and degrees. There are code commits, patents, publications, and social posts. A person has a location, a language, and a network of connections plus countless other hard and soft skills that can be discovered or inferred.
Person data includes include public resumes, public code profiles, social profiles, patents, publications, census data, podcast listings, open source projects, news articles, plus enrichment from a company's internal sources when integrated.
Companies have a digital footprint, documented in funding rounds, IPOs, and financial filings that show the growth and performance of the business. Data provides competitive context within an industry or a category of companies. Org charts, job descriptions, leadership profiles, and company reviews provide insight into organizational structure, growth, and perceptions. All of this data can be used to chart a company’s progress and organizational development.
Company data sources include indices of companies by product and product categories, public filings, public financials, funding rounds.
3D profiles (also called enriched profiles) are enhanced profiles for every candidate in Findem, generated from our 3D data using AI. They provide a detailed and factual view of a person's professional journey and impact, making 3D profiles a valuable tool for informed decision-making across talent acquisition and management.
Findem's 3D data combines people and company data over time into a format suitable for AI analysis. The continuously enhanced 3D dataset is exponentially larger and more factual than traditional sources of candidate data, making it a powerful tool for deep insights and automated workflows in talent acquisition and management.
BI is technology used to gather, store, access, and analyze data to make business decisions. Findem is a BI-first platform and our 3D data is based on BI rather than AI.
Insights are data-driven analyses and interpretations using Findem's 3D data.
AI is quickly becoming a part of the software we use every day. Talent and HR teams will be among the first business functions to see efficiency and productivity gains, because their work depends on analyzing massive amounts of data, and many TA processes can be automated.
Talent leaders will need to manage the changes up to the C-level and down to their teams, but the rewards will be great, providing better candidate experiences and better talent for companies.
Fearing the future won’t stop it. Those who embrace these technologies today have the opportunity to transform the talent function and unlock their own potential in the future.
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