Introduction:

In today’s dynamic job market, leveraging data-driven insights is not just an advantage; it’s a necessity. Oracle Recruiting Cloud (ORC) is at the forefront of this revolution, integrating Adaptive Intelligence (AI) to transform how organizations find, engage, and hire talent. This post delves into the groundbreaking AI features that Oracle has introduced, promising to redefine talent acquisition strategies.

AI-Driven Features in Oracle Recruiting Cloud:

Similar Jobs – Enhancing candidate engagement by suggesting roles similar to those they have shown interest in, encouraging a deeper exploration of opportunities.
Time to Hire – Using AI to predict the recruitment duration for roles, offering valuable insights for better planning.
Recommended Jobs – Personalizing the candidate experience with AI-suggested positions that align with their profiles.
Best Candidates – Prioritizing recruiter decision-making by leveraging AI to suggest the most suitable candidates for a job.
Similar Candidates – Aiding recruiters by recommending potential hires that match a selected candidate’s profile, ensuring a quality candidate pipeline.

Time to Hire

• In the dynamic job market of today, gauging the time needed to recruit and onboard a new employee is crucial for hiring professionals. Leveraging historical data, Oracle’s AI and machine learning tools predict and refine the hiring duration for specific roles.
  
• This empowers Recruiters and Hiring Managers with precise insights, streamlining recruitment planning and expectation-setting.

• Recruiters can use the Time to Hire feature to get estimates about the time it will take to make a first hire for a job requisition. The Time to Hire feature uses artificial intelligence (AI) and machine-learning algorithms to estimate the time for a first hire, based on previous similar job requisitions.
  
• When you start creating a job requisition, only the estimated time to hire is displayed. When the requisition reaches the Open phase, another number is displayed to indicate the current number of days for which the requisition has been open, allowing you to compare the current time with the estimated time to hire. Once a first hire is made on the requisition, the current number of open days is no longer displayed; it’s replaced by the number of days it took for a first hire to be made.

We can modify the value of these 3 fields displayed in the Estimated Time to Hire section so we can see the impact it has on the estimated time to hire. 
– Requisition Title
– Education Level
– Locations
When you modify these values, it has no impact on the actual requisition values. You need to modify the corresponding values in the requisition details if you want to keep them.

• The Time to Hire feature offers an estimate, drawn from historical data on similar past requisitions, on how long it’ll take for a candidate in the Open phase to progress to the HR phase.
  
• For initial requisitions lacking ample data, no estimate will be displayed, suggesting users disable the feature until enough historical data accumulates to generate reliable predictions.

Best Candidates

• Oracle’s ‘Best Candidates’ feature harnesses the power of AI and machine learning to streamline the recruitment process, swiftly identifying and ranking prospective hires for any job requisition. Drawing insights from anonymized resume data, job details, and candidate profiles—without tapping into personal identifiable information (PII)—this tool offers curated top recommendations that enhance a recruiter’s efficiency, reduce hiring timelines, and ensure the selection of high-caliber candidates.

• As recruiters navigate the ORC interface, they are met with these AI-driven suggestions, clearly sorted for ease of review, each recommendation being an outcome of Adaptive Intelligence algorithms that match job requisition specifics with candidate data, spanning from their work history and qualifications to their skills and certifications.

The ‘Best Candidates’ feature not only elevates a recruiter’s efficiency but also curtails the selection process duration, ensuring optimal candidate quality. Harnessing Adaptive Intelligence algorithms, this tool generates recommendations by analyzing job requisition & candidate Job applications

Job Requisition Data:

Title, Description, Education Level, Job Function, Job Family, Qualifications, Responsibilities 

Candidate Profile Data:

• Work History (Previous Employment) 
  • Title, Achievements, Responsibilities 
• Degrees (Education) 
  • Degree, Major, Education Level 
• Licenses or Certification  
  • Title, Comments 

• When a recruiter or hiring manager loses a promising candidate, it can be frustrating. Especially when it comes to continuously filling evergreen roles, finding candidates with matching skills and experience is paramount. Our ‘Similar Candidates’ feature, equipped with intelligent machine learning engines, addresses these challenges.
• It identifies candidates akin to a selected profile, presenting a ranked list of recommendations for hiring managers and recruiters to consider—effectively bridging the gap when a top candidate slips away.

The Similar Candidates feature uses Adaptive Intelligence matching algorithms to recommend candidates based on the below candidate profile data.

• Work History (Previous Employment) 
  • Title, Achievements, Responsibilities 
• Degrees (Education) 
  • Degree, Major, Education Level 
• Licenses or Certification  
  • Title, Comments

Recruiters can seamlessly employ the ‘Similar Candidates’ feature to discover individuals who share traits with an existing candidate, whether they’re active candidates, prospects, or part of the candidate pool. It offers a maximum of 30 similar candidates based on a specific candidate profile, all driven by the robust AI and machine-learning algorithms at its core.

Recommended Jobs

• Creating a standout experience for candidates is key in today’s job market. Our platform enhances this journey. When candidates browse your career site or update their profiles, our system uses AI to suggest jobs that suit them best. This means they see roles most relevant to their experience, making their search more efficient and personal.
  
• The “Recommended Jobs” feature does the work. It uses advanced algorithms to match job details with candidate information. Whether it’s based on their past roles, education, skills, or certifications, we ensure that the jobs they see are the ones that align best with their background.

The Recommended Jobs feature uses Adaptive Intelligence matching algorithms to recommend job requisitions based on the job requisition data and candidate profile data.

Job Requisition Data:
– Title, Description, Education Level, Job Function, Job Family, Qualifications, Responsibilities 

Candidate Profile Data:
– Work History (Previous Employment), Degrees (Education), Licenses or Certification, Work History (Previous Employment), Degrees (Education), Skill, Licenses or Certification  

• Engage candidates more effectively within your career portal. With our AI-driven “Similar Jobs” feature, as candidates browse or apply for roles, they’re presented with tailored job suggestions that align with their interests. This not only increases their engagement but also broadens their understanding of opportunities within your organization.

• Imagine a candidate discovering a parallel role in another department they hadn’t considered or being presented with a series of roles related to what they viewed, deepening their interest in your organization.

• This feature uses artificial intelligence (AI) and machine-learning algorithms.

• The Similar Jobs feature recommends job requisitions based on the below job requisition data.
  
  • Title 
  • Description 
  • Education Level 
  • Job Function 
  • Job Family 
  • Qualifications 
  • Responsibilities