AI Talent Management

Break free of keyword constraints and supercharge your recruiting team. Get in-depth and insightful candidate data to instantly source, score, and rank top talent.

Censia Talent Intelligence Benefits

Supercharge Your Talent Acquisition Strategy

Automate redundant and unproductive recruiting tasks. Free up recruiter time to connect with and secure top talent instantly.

Time to Hire

Cut time to hire by 50%

Be the recruiting hero hiring managers dream of

Team ROI

Reduce TA costs up to 75%

Boost your recruiting ROI

Diversity, Equity & Inclusion

Triple the diversity of your talent pipeline

Eliminate unconscious bias in sourcing

Sourcing

Reduce candidate sourcing time by over 90%

Instantly discover high-quality passive candidates

Screening

Accelerate time to interview 
by 85%

Spend time connecting, not screening

Expert & Executive Search

Pinpoint talent with specialized hard to find skills

Uncover Hidden Gems

Time to Hire

Cut time to hire by 50%

Be the recruiting hero hiring managers dream of

Sourcing

Reduce candidate sourcing time by over 90%

Instantly discover high-quality passive candidates

Screening

Accelerate time to interview 
by 85%

Spend time connecting, not screening

Expert & Executive Search

Pinpoint talent with specialized hard to find skills

Uncover Hidden Gems

Diversity, Equity & Inclusion

Triple the diversity of your talent pipeline

Eliminate unconscious bias in sourcing

Team ROI

Reduce TA costs up to 75%

Boost your recruiting ROI

Unleash The Power of Talent Intelligence

Eliminate unconscious bias and power efficient and effective recruiting decisions with high-quality data and deep system intelligence.

Discover the best talent

Passive Candidate Sourcing

Get instant access and deep insight into more than 500 million highly-skilled professionals from around the world to consolidate passive sourcing efforts.

Clone top performers

Ideal Candidate Modeling

Choose top employees and model the skills you seek based on their profiles. Censia then maps out related skills and analyzes dozens of career factors to develop a candidate model creating a ranked slate of similar talent profiles.

Woman asking herself "what is talent intelligence?"

Automate candidate comparisons

Predictive Analysis

Save hundreds of hours screening resumes and instantly develop a shortlist of best-fit candidates. Machine learning and predictive analytics process candidate information, compare it to millions of other profiles and ranks suitability for open roles.

Unlock existing employee potential

Internal Mobility Matching

Limited resources often cause internal mobility to take a back seat to new hires. Our Talent Intelligence Platform instantly sources internal candidates and compares them to external talent, allowing you to promote from within and extend the average employee lifespan by 25%.

Reignite previous candidate interest

Candidate Rediscovery

Instantly find qualified previous applicants inside your applicant tracking software (ATS), match them to new openings, and compare them to your current employees and new candidate pipeline.

Put blinders on bias

Anonymous Mode

Mitigate unconscious bias in candidate sourcing by masking bias identifiers like gender, ethnicity, age, race through anonymous mode to ensure recruiter focus on talent, skill, and fit first and foremost to uncover qualified diverse talent previously overlooked.

The Censia Advantage

What makes our Talent Intelligence Platform Stand Out?

Up-to-date data sources

We continuously map billions of data points on both people and companies across the entire talent landscape to provide the most relevant and up-to-date information available.

Single source of truth

Censia centralizes, standardizes, deduplicates, enriches, and contextualizes candidate profiles from across multiple sources to provide what we call a Golden Record. The Golden Record acts as a single source of truth providing a 360-degree professional view of an individual for high-quality data you can count on.

In-depth analysis

Take a talent-centric approach to hiring. The Talent Genome leverages a proprietary taxonomy to cluster skills and experience revealing hidden insights and capabilities to predict if an individual will thrive in a given role and determine who could be your best candidate fit.

Seamless ATS integration

Censia integrates natively with many leading ATS solutions and as a completely headless solution. Insights and intelligence are delivered through our robust and highly secure API to power your data-driven hiring decisions.

“We had been searching for a tool to help our Talent Acquisition Team find quality candidates in less time and that’s why we chose to work with Censia. The ideal candidate model is what sold us. Finding quality people within minutes versus sorting through resumes for hours is a game-changer. Censia’s Talent Intelligence Platform was a great addition to our Talent Acquisition strategy!”

Lorie Bryce
Talent Acquisition and Management Leader, Gerdau North America

“Censia helped us find hidden talent for hard-to-fill roles. We weren’t having much luck finding candidates and Talent Intelligence surfaced high-quality people for us.”

Ed Sayson
Talent Acquisition Leader, Human Resources, ARC Document Solutions

“We’ve had a very strong response with building models and reaching out. We have an extensive agreement with another platform, but more and more of my recruiters are using their Censia solution first to get in there and get some results, so there’s a bit of an irony there that one is taking over the other. These talent networks have a lot of data but Censia understands the data with greater accuracy.”

Dennis Wilson
National – Director, Talent Attraction & Acquisition, American Heart Association

AI Talent Management

AI has a wide range of possible applications in talent management. These include recruitment, onboarding, and performance management. It would help managers make a perfect fit with a candidate and a good match between a team and a specific business unit or process. The tool may recommend candidates who would fit best into the role as per their skills and interests and pick out ones that should be rejected without human intervention. To conclude, we can say that AI will save recruiters’ time and give them a better chance of getting a successful hire by learning about a company’s needs and matching them with job seekers’ preferences and personality types.

Furthermore, AI could prove helpful in creating structured data for performance reviews which could then be used for a more comprehensive feedback process at the end of a project or a year.

In talent management, AI can boost talent acquisition by identifying candidates with a higher probability of success in their roles. It would also help in reducing bias and inefficiencies during recruitment.

HR managers should use AI in talent management to streamline the process and free up resources that could be better used for other aspects of talent management.

Some important questions need to be asked before adopting AI talent management practices:

  1. How will AI technology change the way human resource professionals carry out tasks?
  2. What are the business’s short-term goals for talent analytics tools?
  3. How will HR stay ahead of competitors who adopt this technology faster than others?
  4. How will talent management processes change in the future?
  5. Which talent management process must be automated first for HR professionals?
  6. What are the challenges and limitations before adopting this technology?
  7. How can business leaders collaborate with their HR counterparts to determine a proper AI talent management strategy?

HR managers must answer the above questions to reap fruitful talent management results via AI.

A new way of talent management is emerging, so HR professionals should prepare themselves for a paradigm shift. In this case, it means predicting talent demand with the help of analytics tools. It would provide them with visibility into where talent areas are most needed, which would then help match them up with workers who have relevant skills. The result is that both business leaders and employees stand to gain from this approach.

AI talent management technology and services could prove beneficial by automating the candidate selection and shortlisting, making hiring processes faster and more accurate. Another way it would benefit human resources is by improving the quality of feedback given during performance reviews, so employees know where to focus their development efforts.

Businesses need a diverse workforce, but they also require skilled workers who are highly talented and fit in with their talent management process and talent acquisition strategy. AI talent management software can help businesses find talent gaps and fill them quickly by automatically screening resumes, generating job postings according to pre-specified criteria, and detecting turnover risk early on before it becomes a problem.

Using AI talent management technology can be a win-win for both talent managers and human resource departments as they save time, money, and energy. HR recruiters who implement AI talent analytics tools will also gain better insight into their source of hires while improving the quality of talent data over time.

In conclusion, AI talent management tools can change HR practices by automating parts of their work while improving the quality of decisions made during hiring processes. Another benefit for human resources departments is that they can stop relying on outdated talent management practices and instead use data and technology to focus on talent-related problems that cannot be solved without human intervention.

AI Recruiting Software

AI recruiting software is recruiting software that works similarly to human recruiters. It can understand a recruiter’s workflows and perform the same tasks much more quickly, allowing a recruiter to focus on higher-value activities such as executive search or networking with a company’s employees.

The software uses a form of artificial intelligence called machine learning, which is a type of AI that provides computer systems with the ability to learn without being explicitly programmed. For example, suppose a human trainer teaches a machine-learning algorithm how to identify a cat by feeding it a large number of labeled images of cats and a large number of images not containing cats over time. In that case, the algorithm will be able to identify new cats on its own based on previous data it has learned from and the differences and similarities between a cat and other furry four-legged creatures.

Once a machine learning algorithm is trained to a desired level of accuracy, it can be put to work identifying cats in some different ways without human supervision. For example, a recruiting software company may train an algorithm to identify relevant candidates for a job opening based on a specific set of keywords. After completing its training phase, the algorithm can automatically identify qualified candidates using real-time data inputs about applicants’ resumes or social media profiles. The recruiter can then screen through what the system has identified as relevant applications before manually identifying which applicants are worth contacting.

Benefits of AI in Recruitment

Machine learning algorithms provide several benefits that make them more efficient and effective than a human recruiter in many different scenarios. Because a machine learning algorithm can process a high volume of data much more quickly. It is ideally suited for quickly searching through a large pool of applicants to identify the most relevant candidates.

For example, a recruiting software company may use a machine-learning algorithm to scan a set of applicants’ resumes for a desired set of keywords. The algorithm receives a list of new resumes daily and filters them according to the information it has learned from previous data inputs. Then, after checking that the applicant’s resume contains the necessary keywords, the recruiting software assigns a score to each candidate based on how closely their resume matches the selection criteria.

The recruiter can then review top-scoring candidates before initiating a dialogue with any of them by clicking the “Screen & Contact” button displayed on their screen. If no new applications are submitted 120 days after an opening is posted, or if more applicants than a recruiter can contact, a machine learning algorithm can select a random selection of resumes to contact on the recruiter’s behalf.

Machine learning models are also a more precise alternative to a human recruiter in scenarios where a candidate with a high score may not be a good fit for an organization or position. A recruiting software company may even provide recruiters with keywords specific to each role in their database that a candidate may not have included in their resume or social media profile.

Even in scenarios where a human recruiter’s intuition is still a valuable asset, a machine learning algorithm can complement a recruiter’s work by freeing up some of their time for more critical tasks. For example, after an algorithm has ranked the most relevant candidates in a data set based on a specific set of keywords or traits, a human recruiter can review top-scoring candidates’ profiles before identifying which ones are worth contacting manually. This frees up the recruiter’s time to communicate with leads that have been identified as especially promising by a machine learning model, allowing them to contact more applicants overall while engaging only with the most relevant.

Moreover, a machine learning algorithm can continually learn from new data points without dropping accuracy or productivity. In contrast, a human recruiter must undergo a significant amount of training before working efficiently. If a company has a recruiting team consisting solely of new graduates straight out of college, their skills will not be sharpened over time as they may only have access to limited volumes and types of data. Thus, hiring an automated system such as recruiting software will improve a company’s recruiting team’s overall ability level and efficacy.

Access to Higher Quality Candidates

Using a recruiting software platform is a good way for a company to access a higher quality pool of applicants than it otherwise would by hand-screening resumes submitted via a job board. A machine learning algorithm using keywords specific to a job opening can automatically identify relevant applications based on their resume content rather than simply scanning keywords in a candidate’s resume or profile. Some companies even allow human recruiters to upload lists of preselected keywords into the system to get an idea of which candidates will be identified during each scan and how long it takes the system to process these applications. This means that keyword filtering can be done ahead of time so that a human recruiter can focus on a smaller volume of applications that are more likely to be a good fit.

Accessing a candidate’s social media presence can also offer a better way to screen applicants than reviewing their resumes alone. A talented software engineer may not have included the term “software engineer” in their resume. Still, by looking at their GitHub or Stack Overflow accounts, a machine learning model can identify relevant programming languages and projects that they worked on early in their career. This could be especially helpful for companies advertising junior-level roles who cannot afford to wait until a candidate has gained sufficient experience before allowing them to apply. Social media data may contain signals about a candidate’s personality or values and their technical ability or domain expertise, which a human recruiter may not have the capacity to explore.

A recruitment software platform can also ease a company’s workload by labeling candidate data in a central location. Because a machine learning algorithm analyzes resume content based on a specific set of keywords or traits, it is helpful that this data is all kept together in one place so that human recruiters know where to find it when they need it. For example, a recruiting team can use a recruiting software tool to label candidates who passed their initial keyword screening process with a “passed ATS” tag under their contact information or profile link. Other employees whose job roles are related to hiring (for instance, an HR team member responsible for a compensation guide) could search for this tag and quickly gather many resumes that a machine learning algorithm has determined to be a good match.

Although recruiting software cannot yet replace all human tasks in a recruitment department, it can provide a faster, more accurate way to prequalify many job seekers at once. With a global pool of applicants to submit their resumes via an online portal, human recruiters would no longer need to sift through countless applications on popular job boards or wait for a candidate’s submission to come through the mail. Recruitment software is a rising star that will supplement existing recruiting teams by handling time-consuming administrative functions that allow its human counterparts to focus on what they do best: building a long-lasting and mutually beneficial connection with a candidate.

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