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What’s Next For You? How AI Is Transforming Talent Management

Bottom Line: Taking on the talent crisis with greater intelligence and insight, delivering a consistently excellent candidate experience, and making diversity and inclusion a part of their DNA differentiates growing businesses who are attracting and retaining employees. The book What’s Next For You? by Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, President of eightfold.ai provide valuable insights and a data-driven roadmap of how AI is helping to solve the talent crisis for any business.

The Talent Crisis Is Real

  • 78% of CEOs and Chief Human Resource Officers (CHROs) say talent programs are important, with 56% say their current programs are ineffective.
  • 83% of employees want a new job yet only 53% want to leave for a new company.
  • 57% of employees say diversity and inclusion initiatives aren’t working, and 40% say their companies lack qualified diverse talent.
  • Nearly 50% of an organizations’ top talent will leave their jobs in the first two years of being hired.
  • 28% of open positions today won’t be filled in the next 12 months.

The above findings are just a sample of the depth of data-driven content and roadmap the book What’s Next For You? delivers. Co-authors Ashutosh Garg’s and Kamal Ahluwalia’s expertise in applying AI and machine learning to talent management problems with a strong data-first mindset is evident throughout the book. What makes the book noteworthy is how the authors write from the heart first with empathy for applicants and hiring managers, supporting key points with data. The empathetic, data-driven tone of the book makes the talent crisis relatable while also illustrating how AI can help any business make better talent management decisions.

“Businesses are having to adapt to technology changes and changes in customer expectations roughly every 10 years – a timeframe that is continuing to shrink. As a result, business leaders need to really focus on rethinking their business strategy and the associated talent strategy, so they have the organizational capability to transform and capitalize on the inevitable technology shifts,” writes John Thompson, Venture Partner, Lightspeed Venture Partners and Chairman of the Board at Microsoft in the forward.

The book cites talent management researchers and experts who say “our current knowledge base has a half-life of about two years, and the speed of technology is outperforming us as humans because of what it can do quickly and effectively“ (p.64). John Thompson’s observations in the forward that the time available for adapting to change is shrinking is a unifying thread that ties this book together. One of the most convincing is the fact that using today’s Applicant Tracking Systems (ATS) and hiring processes prone to biases, there’s a 30% chance a new hire will not make it through their first year. If the new hire is a cloud computing professional, this equates to a median salary of $146,350 and taking best-case 46 days to find their replacement. The cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. Any manager who has lost a new hire within a year can relate to how real the talent crisis is and how urgent it is to solve it.

The Half-Life Of Skills Is Shrinking Fast

The most compelling chapter of the book illustrates how today’s talent crisis can be solved by taking an AI-enabled approach to every aspect of talent management. Chapter 4, The Half-Life Of Skills Is Shrinking Fast, delves into how AI can find candidates who can unlearn old concepts, and quickly master new ones. The book calls out this attribute of any potential new hire as being essential for them to adapt.  Using higher quality data than is available in traditional ATS systems, the authors illustrate how AI-based systems can be used for evaluating both the potential and experiences of applicants to match them with positions they will excel in. The authors make a convincing argument that AI can increase the probability of new candidate success. They cite a well-known Leadership IQ statistic of 46% of all new employee hires failing to adapt within 18 months, and the Harvard Business Review study finding between 40% to 60% of new upper management hires fail within 18 months. The authors contend that even Leonardo Da Vinci, one of the primary architects of the Renaissance, would have trouble finding work using a traditional resume entered into an ATS system today because his exceptional capabilities and potential would have never been discovered. When our existing process of recruiting is based on practices over 500 years old, as this copy of Leonardo Da Vinci’s resume illustrates, it’s time to put AI to work matching peoples’ potential with unique position requirements.

When Employees Achieve Their Potential, Companies Do Too   

Attracting the highest potential employees possible and retaining them is the cornerstone of any digital business’ growth strategy today and in the future. The book addresses the roadblocks companies face in attaining that goal, with bias being one of the strongest. “For example, McKinsey & Co., a top consulting agency, studied over 1,000 companies across 12 countries and found that firms in the top quartile of gender diversity were a fifth more likely to have above-average profits than those in the bottom quartile,” (p. 105). Further, “diverse executive boards generate better financial returns, and gender-diverse teams are more creative, more productive and more confident.” (p. 105).

In conclusion, consider this book a roadmap of how hiring and talent management can change for the better based on AI. The authors successfully illustrate how combining talent, personalization at scale, and machine learning can help employees achieve their potential, enabling companies to achieve theirs in the process.

Using Machine Learning To Find Employees Who Can Scale With Your Business

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A Data-Driven Approach To Finding Employees Who Can Scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

How AI & Machine Learning Are Redefining The War For Talent

These and many other fascinating insights are from Gartner’s recent research note, Cool Vendors in Human Capital Management for Talent Acquisition (PDF, 13 pp., client access reqd.) that illustrates how AI and machine learning are fundamentally redefining the war for talent. Gartner selected five companies that are setting a rapid pace of innovation in talent management, taking on Human Capital Management’s (HCM) most complex challenges. The five vendors Gartner mentions in the research note are AllyO, Eightfold, jobpal, Knack, and Vettd. Each has concentrated on creating and launching differentiated applications that address urgent needs enterprises have across the talent acquisition landscape. Gartner’s interpretation of the expanding Talent Acquisition Landscape is shown below (please click on the graphic to expand):

Source: Gartner, Cool Vendors in Human Capital Management for Talent Acquisition, Written by Jason Cerrato, Jeff Freyermuth, John Kostoulas, Helen Poitevin, Ron Hanscome. 7 September 2018

Company Growth Plans Are Accelerating The War For Talent

The average employee’s tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley, this trends to 14 months due to intense competition for talent according to C-level executives leading these companies. Fast-growing enterprise cloud computing companies and many other businesses like them need specific capabilities, skill sets, and associates who know how to unlearn old concepts and learn new ones. Today across tech and many other industries, every company’s growth strategy is predicated on how well they attract, engage, screen, interview, select and manage talent over associates’ lifecycles.

Of the five companies Gartner names as Cool Vendors in the field of Human Capital Management for Talent Acquisition, Eightfold is the only one achieving personalization at scale today. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position.

Gartner finds Eightfold noteworthy for its AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, HCM systems, ATS tools, and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app’s easily understood and navigated user interface. Gartner also finds that Eightfold.ai is one of the first examples of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The Eightfold.ai Talent Intelligence Platform is shown below:

Taking A Data-Driven Approach to Improve Diversity

AI and machine learning have the potential to remove conscious and unconscious biases from hiring decisions, leading to hiring decisions based on capabilities and innate skills. Many CEOs and senior management teams are enthusiastically endorsing diversity programs yet struggling to make progress. AI and machine learning-based approaches like Eightfold’s can help to accelerate them to their diversity goals and attain a more egalitarian workplace. Data is the great equalizer, with a proven ability to eradicate conscious and unconscious biases from hiring decisions and enable true diversity by equally evaluating candidates based on their experience, growth potential and strengths.

Conclusion

At the center of every growing business’ growth plans is the need to attract, engage, recruit, and retain the highest quality employees possible. As future research in the field of HCM will show, the field is in crisis because it’s relying more on biases than solid data. Breaking through the barrier of conscious and unconscious biases will provide contextual intelligence of an applicant’s unique skills, capabilities and growth trajectories that are far beyond the scope of any resume or what an ATS can provide. The war for talent is being won today with data and insights that strip away biases to provide prospects who are ready for the challenges of helping their hiring companies grow.

How To Close The Talent Gap With Machine Learning

  • 80% of the positions open in the U.S. alone were due to attrition. On an average, it costs $5,000 to fill an open position and takes on average of 2 months to find a new employee. Reducing attrition removes a major impediment to any company’s productivity.
  • The average employee’s tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley this trends to 14 months due to intense competition for talent according to C-level executives.
  • Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions.
  • Adroll Group launched nurture campaigns leveraging the insights gained using Eightfold.ai for a data scientist open position and attained a 48% open rate, nearly double what they observed from other channels.
  • A leading cloud services provider has seen response rates to recruiting campaigns soar from 20% to 50% using AI-based candidate targeting in the company’s community.

The essence of every company’s revenue growth plan is based on how well they attract, nurture, hire, grow and challenge the best employees they can find. Often relying on manual techniques and systems decades old, companies are struggling to find the right employees to help them grow. Anyone who has hired and managed people can appreciate the upside potential of talent management today.

How AI and Machine Learning Are Revolutionizing Talent Management

Strip away the hype swirling around AI in talent management and what’s left is the urgent, unmet needs companies have for greater contextual intelligence and knowledge about every phase of talent management. Many CEOs are also making greater diversity and inclusion their highest priority. Using advanced AI and machine learning techniques, a company founded by former Google and Facebook AI Scientists is showing potential in meeting these challenges. Founders Ashutosh Garg and Varun Kacholia have over 6000+ research citations and 80+ search and personalization patents. Together they founded Eightfold.ai as Varun says “to help companies find and match the right person to the right role at the right time and, for the first time, personalize the recommendations at scale.” Varun added that “historically, companies have not been able to recognize people’s core capabilities and have unnecessarily exacerbated the talent crisis,” said Varun Kacholia, CTO, and Co-Founder of Eightfold.ai.

What makes Eightfold.ai noteworthy is that it’s the first AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app’s easily understood and navigated user interface.

Based on conversations with customers, its clear integration is one of the company’s core strengths. Eightfold.ai relies on an API-based integration strategy to connect with legacy back-end systems. The company averages between 2 to 3 system integrations per customer and supports 20 unique system integrations today with more planned. The following diagram explains how the Eightfold Talent Intelligence Platform is constructed and how it works.

For all the sophisticated analysis, algorithms, system integration connections, and mathematics powering the Eightfold.ai platform, the company’s founders have done an amazing job creating a simple, easily understood user interface. The elegant simplicity of the Eightfold.ai interface reflects the same precision of the AI and machine learning code powering this platform.

I had a chance to speak with Adroll Group and DigitalOcean regarding their experiences using Eightfold.ai. Both said being able to connect the dots between their candidate communities, diversity and inclusion goals, and end-to-end talent management objectives were important goals that the streamlined user experience was helping enable. The following is a drill-down of a candidate profile, showing the depth of external and internal data integration that provides contextual intelligence throughout the Eightfold.ai platform.

Talent Management’s Inflection Point Has Arrived 

Every interaction with a candidate, current associate, and high-potential employee is a learning event for the system.

AI and machine learning make it possible to shift focus away from being transactional and more on building relationships. AdRoll Group and DigitalOcean both mentioned how Eightfold.ai’s advanced analytics and machine learning helps them create and fine-tune nurturing campaigns to keep candidates in high-demand fields aware of opportunities in their companies. AdRoll Group used this technique of concentrating on insights to build relationships with potential Data Scientists and ultimately made a hire assisted by the Eightold.ai platform. DigitalOcean is also active using nurturing campaigns to recruit for their most in-demand positions. “As DigitalOcean continues to experience rapid growth, it’s critical we move fast to secure top talent, while taking time to nurture the phenomenal candidates already in our community,” said Olivia Melman, Manager, Recruiting Operations at DigitalOcean. “Eightfold.ai’s platform helps us improve operational efficiencies so we can quickly engage with high quality candidates and match past applicants to new openings.”

In companies of all sizes, talent management reaches its full potential when accountability and collaboration are aligned to a common set of goals. Business strategies and new business models are created and the specific amount of hires by month and quarter are set. Accountability for results is shared between business and talent management organizations, as is the case at AdRoll Group and DigitalOcean, both of which are making solid contributions to the growth of their businesses. When accountability and collaboration are not aligned, there are unpredictable, less than optimal results.

AI makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. The company’s founders call this aspect of their platform personalization at scale. “Our platform takes a holistic approach to talent management by meaningfully connecting the dots between the individual and the business. At Eightfold.ai, we are going far beyond keyword and Boolean searches to help companies and employees alike make more fulfilling decisions about ‘what’s next, “ commented Ashutosh Garg, CEO, and Co-Founder of Eightfold.ai.

Every hiring manager knows what excellence looks like in the positions they’re hiring for. Recruiters gather hundreds of resumes and use their best judgment to find close matches to hiring manager needs. Using AI and machine learning, talent management teams save hundreds of hours screening resumes manually and calibrate job requirements to the available candidates in a company’s candidate community. This graphic below shows how the Talent Intelligence Platform (TIP) helps companies calibrate job descriptions. During my test drive, I found that it’s as straightforward as pointing to the profile of ideal candidate and asking TIP to find similar candidates.

Achieving Greater Equality With A Data-Driven Approach To Diversity

Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions. They’ve also analyzed hiring data and found that women are 11% less like to make it through application reviews, 19% less likely through recruiter screens, 12% through assessments and a shocking 30% from onsite interviews. Conscious and unconscious biases of recruiters and hiring managers often play a more dominant role than a woman’s qualifications in many hiring situations. For the organizations who are enthusiastically endorsing diversity programs yet struggling to make progress, AI and machine learning are helping to accelerate them to the goals they want to accomplish.

AI and machine learning can’t make an impact in this area quickly enough. Imagine the lost brainpower from not having a way to evaluate candidates based on their innate skills and potential to excel in the role and the need for far greater inclusion across the communities companies operate in. AdRoll Group’s CEO is addressing this directly and has made attaining greater diversity and inclusion a top company objective for the year. Daniel Doody, Global Head of Talent at AdRoll Group says “We’re very deliberate in our efforts to uncover and nurture more diverse talent while also identifying individuals who have engaged with our talent brand to include them” he said. Daniel Doody continued, “Eightfold.ai has helped us gain greater precision in our nurturing campaigns designed to bring more diverse talent to Adroll Group globally.”

Kelly O. Kay, Managing Partner, Global Managing Partner, Software & Internet Practice at Heidrick & Struggles agrees. “Eightfold.ai levels the playing field for diversity hiring by using pattern matching based on human behavior, which is fascinating,” Mr. Kay said. He added, “I’m 100% supportive of using AI and machine learning to provide everyone equal footing in pursuing and attaining their career goals.” He added that the Eightfold.ai’s greatest strength is how brilliantly it takes on the challenge of removing unconscious bias from hiring decisions, further ensuring greater diversity in hiring, retention and growth decisions.

Eightfold.ai has a unique approach to presenting potential candidates to recruiters and hiring managers. They can remove any gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills. And the platform also can create gender-neutral job descriptions in seconds too. With these advances in AI and machine learning, long-held biases of tech companies who only want to hire from Cal-Berkeley, Stanford or MIT are being challenged when they see the quality of candidates from just as prestigious Indian, Asian, and European universities as well. Daniel Doody of Adroll Group says the insights gained from the Eightfold.ai platform “are helping to make managers and recruiters more aware of their own hiring biases while at the same time assisting in nurturing potential candidates via less obvious channels.”

How To Close The Talent Gap

Based on conversations with customers, it’s apparent that Eightfold.ai’s Talent Intelligence Platform (TIP) provides enterprises the ability to accelerate time to hire, reduce the cost to hire and increase the quality of hire. Eightfold.ai customers are also seeing how TIP enables their companies to reduce employee attrition, saving on hiring and training costs and minimizing the impact of lost productivity. Today more CEOs and CFOs than ever are making diversity and talent initiatives their highest priority. Based on conversations with Eightfold.ai customers it’s clear their TIP provides the needed insights for C-level executives to reach their goals.

Another aspect of the TIP that customers are just beginning to explore is how to identify employees who are the most likely to leave, and take proactive steps to align their jobs with their aspirations, extending the most valuable employees’ tenure at their companies. At the same time, customers already see good results from using TIP to identify top talent that fits open positions who are likely to join them and put campaigns in place to recruit and hire them before they begin an active job search. Every Eightfold.ai customer spoken with attested to the platform’s ability to help them in their strategic imperatives around talent.

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