List of Top AI Software for Recruiting and Staffing

Apr 18th, 2018
List of Top AI Software for Recruiting and Staffing

With all the buzz (and hype) around Artificail Intelligence (AI), it is evident that anyone in HR or Talent Acquisition is thinking seriously and willing to spend big bucks to see if new AI techniques may help them.

Many companies and software vendors hype the benefits of AI for anything that is even a bit automated (AI washing). Unfortunately there are no industry standards to say what qualifies to be called AI/machine learning powered, so anyone can claim to be using AI. The only way to actually test it is to see if it solves your problem significantly better than your current non AI systems.

The reality is that while AI technologies- especially deep learning techniques have grown tremendously in the last couple of years, applying it to practical applications and handling edge cases is still really hard. As an example, even the most advanced companies in AI- Google, Apple, Microsoft, Amazon still struggle when it comes to understanding basic human context as evidenced in ‘spelling autocorrect’ in text messages, answering search queries, etc. They may work right 80% of the time, but it’s the 20% failures that causes all the frustration, lack of trust and problems.

Additionally it is very difficult to trust that an AI system could reliably do a very subjective task of evaluating a human being on the basis of what the person said on his resume, because obviously everyone would only say the good things. For example- A software engineer may have graduated at the bottom of his class from a top school in CS but would be highly rated always because of his school. AI tools can’t be expected to see through such anomalies- especially in the beginning.

That said, AI also presents lots of opportunities for increasing efficiency and recruiting in general if used properly. Think about a strong software engineer that chose to go to lower ranked State School because they got a full ride scholarship. This person may have worked on great projects at lesser known companies and be missed by a large company recruiter that doesn’t understand this kind of background. However, an AI system could scan though millions of profiles and more easily pick such a candidate out based on its ability to process the educational background and company experience, from watching other similar candidates that were successful or just by understanding this background. How many times have you wondered how you are a perfect fit for a job only to get a ‘Dear John’ rejection within 60 seconds of applying for the job? Wouldn’t you like to make a case to the recruiter? If AI systems work as they are designed to- you won’t have to. The AI system would have the intelligence to see it.

What we layout here is the entire recruiting lifecycle and the primary AI software vendors playing in that space for the staffing industry, the main value add activities they provide, and our guess around how these might change with mass adoption of AI. As you will see, each part of the lifecycle is a very complicated process and could have a specialized tool that solves that problem. Many times a generic HR software is used by companies which then offers modules for different activities- but they are rarely good enough as specialized solutions. Although this creates a fragmented market of software requiring integrations- many companies opt for such best of breed software. Over time we think the successful ones would probably be bought and integrated into the big HR software but till then recruiting and talent teams should feel free to check out what works best for them.

1. Sourcing- Searching and identifying the right candidate prospect list

Sourcing is how recruiters create search queries and decide on sources on where to look for candidates. For example- if a recruiter is tasked with searching for a UX designer for healthcare software company. The sourcer will look for people with UX experience on job boards, Linkedin, research what companies are in similar fields in similar cities, or develop a list of designers that might be open to working in health IT.

Current Options- Today the main sources of candidate pipelines are Job boards, Linkedin, using contract or fulltime or offshore sourcers, RPO. These recruiters can probably talk to 5-10 candidates a day and generate about 5-10 leads a week.

Potential Impact from AI/Machine Learning- High

This is one of the key areas in recruiting that can benefit from the ability of computers to process, understand patterns, create lists or buckets created by humans, look for opportunities and trump human recruiters with the ability to process immense amounts of data to create a larger and better list of prospects. Reach out and get initial interest automatically and decide on a far better prospect list.

For example-  A machine learning system could create a list of the top healthcare it companies, determine a ranking for them based on where talent seems to drift towards as they get more experience and become more valuable, create a list of UX designers from companies that are lower in ranking and ping them. It could also try and see patterns on UX designers that came from other fields into healthcare IT design jobs and look for similar trends in people’s resumes. For example- something in their resumes indicating their interest in improving the health of others, a bachelors degree in biology or chemistry, or extracurricular activities related to helping sick or disabled people.

Current AI Software Vendors that help you automate/leverage AI for sourcing

Hiretual: A Chrome Extension that can read the profile and tell you more about a profile page you are on Linkedin- or give you the contact details of the person. Although there are plenty of extensions, Hiretual is the most recommended by recruiters. How much of it is AI vs keyword matching is hard to say- but it is one of the more advanced extensions.

Linkedin: The 800 lb. gorilla of professional profiles has more knowledge about who is looking for what, and who is looking to recruit and is best positioned to make matches- but will its business model let it?

Entelo: Actually more of a profile aggregator that pulls a person’s footprints from all over the Internet to save recruiters time can make judgements about how good or bad a fit a candidate may be for a position. No AI, but consolidated intelligence.

 

2. Candidate Reachout/Communication/Engagement

Current Options- Today the main sources of candidate reachouts are through Job boards, Linkedin inmails, using contract or fulltime or offshore sourcers, RPO, phone calls. These recruiters can probably email 50-100 candidates a day, hear back from 10-20, talk to 5-10 candidates a day and generate about 5-10 leads a week.

Potential Impact from AI/Machine Learning- High

This area can see major disruption through automation with chatbots saving recruiters or sourcers lots of time in initiating communication, answering basic questions, processing responses and filing notes into an ATS.

Current AI Software Vendors that help you automate/leverage AI:

Allyo

Paradox- Olivia

Seekout

Mya Systems

 

3. Recruiter Resume Screening: Matching Candidates to Jobs

Current Options-.

Potential Impact from AI/Machine Learning- High

Current AI Software Vendors that help you automate/leverage AI

Most ATS Software- although not sure if they use manual, common or artificial intelligence

Eightfold.ai

 

4. Interviews- Screening Candidates with Video Interview, Coding Tests

Current Options-. Hackerrank, Top Coder, CodeFights, TripleByte

Potential Impact from AI/Machine Learning- Medium

Current AI Software Vendors that help you automate/leverage AI

 

5. Scheduling and AI Assistants- Chatbots, AutoSchedulers, Assistants

Current Options-. Mya, Olivia/Paradox, X.ai

Potential Impact from AI/Machine Learning- High

Current AI Software Vendors that help you automate/leverage AI

 

6. Candidate Relationship Management- Engagement, Nurture, Moveability

Current Options-.

Potential Impact from AI/Machine Learning- High:

Current AI Software Vendors that help you automate/leverage AI

 

7. Offer Evaluation and Negotiation

Current Options-.

Potential Impact from AI/Machine Learning- High:

Current AI Software Vendors that help you automate/leverage AI

 

8. Onboarding: Background/Reference Checks, Joining, Knowledge Transfer

Current Options-.

Potential Impact from AI/Machine Learning- High:

Current AI Software Vendors that help you automate/leverage AI

 

9. Performance Management, Career Development, Internal Job Transfers

Current Options-.

Potential Impact from AI/Machine Learning- High:

Current AI Software Vendors that help you automate/leverage AI

 

10. Termination, Layoff and Alumni

Current Options-.

Potential Impact from AI/Machine Learning- High:

Current AI Software Vendors that help you automate/leverage AI

10. Staffing Agencies

11. MSP Model in Staffing

12. Self Sourcing

13. VMS Systems

14. Freelancer Management Systems

15. Freelancer Markets

16. Contingency Recruiting

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