December 18, 2018

How AI Can Improve Your Return on Recruiting Investment

Bottom Line: Improving how you recruit using AI directly drives revenue and makes future business models and plans achievable, increasing the returns on recruiting investment.

Today every organization’s new business initiatives and business models are setting the challenging goal of digital transformation. Chief Human Resource Officers (CHROs) are among the primary architects of these new digital business models as they must find the talent to make them succeed.

Recruiting For Long-Term Results With AI

During a recent conversation with a CHRO who ran one of the world’s leading enterprise cloud computing companies’ global HR organizations, the challenges became clear. The average tenure for a new hire is just 19 months, and in high-demand fields including data science and programming, it’s a short 14 months. She and her team are using AI and machine learning to improve recruiting and bring greater contextual insight into their hiring programs. The following survey findings quantify how existing recruiting methods aren’t delivering the results needed:

  • Businesses lose an average 23% of new hires before their one-year anniversaries, and13% lose 50% or more in the first year according to Allied’s Workforce Mobility Survey: Onboarding and Retention.
  • 28% of open roles will not be filled, making companies less able to deliver shareholder value in the years ahead, stalling growth and hurting competitive position according to a recent Harris Interactive poll.

Reinventing Recruiting With AI & Machine Learning

The Talent Intelligence and Management Report, 2018 by Harris Interactive in collaboration with Eightfold found that 22% of companies are using AI to solve their talent challenges, yet 44% of all C-level execs interviewed believe that AI can improve talent acquisition and talent retention.

Key Areas Where AI Is Improving the Return on Recruiting Investment

The greater the level of competitive intensity an organization is facing, the greater the return they achieve by adopting AI-driven talent management strategies.

PARC brings together leading scientists, engineers, and designers to form teams across a series of focus areas that they believe are the future of technology, science, and innovation. Russell Williams, the former Vice President of Human Resources at PARC and an industry thought leader says: “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions, including data scientist, than are evident from just reviewing a resume. With AI, I want to do it at scale.” Williams, and other forward-thinking leaders like him, believe that the ideal talent management platform would give their organizations the flexibility to define ideal candidates and search for matches on all aspects of performance.

To identify ideal candidates, AI-based platforms need to combine analysis of publicly available data, internal data repositories, HCM systems, ATS tools, and spreadsheets, then create ontologies based on organization-specific success criteria. An AI-based platform also needs to have a self-updating corporate candidate database so profiles can be continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The design goals and structure of the Eightfold platform reflect these needs and is proving effective in improving recruiting ROI by delivering the following benefits:

  • Gain greater precision finding the best-fit hires that have the innate capabilities need to excel in each position. Just 30% of new hires excel in their new roles and make long-term contributions to their teams. While at PARC, Williams initiated the approach of finding the top performers in each position and then defining those attributes to guide recruiting strategies. Using an AI-based platform, Russell significantly improved best-fit hire performance.
  • Reducing time-to-hire by breaking free of reliance on resumes and using AI-based platforms to find the best possible pipeline of candidates. Hiring an engineer for a new app or product development team on average takes 58 days. Multiply this by the number of engineers needed to create an entirely new app, product or cloud service that is the foundation of a new business model, and one can see the challenge CHROs and line-of-business leaders face. Using an AI-powered platform to find candidates who have the best possible combination of capabilities, experiences, and skills drastically reduces lost recruiting and time.
  • Reducing the cost-to-hire by having more new hires flourish instead of fail. The cost to hire an individual contributor is $4,000. Imagine you’re hiring 25 new engineers, and eight will be at your company a year from now. Using traditional recruiting techniques, you’ll waste $68,000 in the first year as on average of 17 of them will be gone within 19 months or less. Using an AI-based platform to precisely define the engineer candidates who have the capabilities, experiences, skills, and strengths your company needs reduces the risk of making bad hires and reduces overall cost-to-hire immediately.
  • Achieve true diversity by evaluating candidates based on their experience, growth potential and strengths first. An AI-based platform can provide a pipeline of qualified candidates based on a true assessment of their accomplishments, capabilities, experiences, and strengths. Data is the best equalizer of all, reducing conscious and unconscious biases from hiring decisions.


It’s easy to see how competitive a company is going to be in the future by evaluating their recruiting and talent management strategies today. Relying on resumes alone is a sure path to churn, and will see time-to-hire go beyond 60 days or more and cost-to-hire soar.

Recruiting needs to be reinvented on an AI-based platform that provides a continual stream of rich, real-time data on applicants. Only then will employers be able to put their exciting new business initiatives and models into motion.