Author: Dr. Shay David

AI’s Role in Revolutionizing Talent Acquisition and Retention

Posted on by Dr. Shay David

This article originally appeared in Forbes.

As corporations grapple with the challenging task of attracting and retaining highly skilled talent in an intensely competitive market, AI-assisted HR tools are creating a new paradigm. Of course, these innovations can create potential ethical issues during the recruitment and internal mobility processes. Here are things that HR leaders must consider when weighing the pros and cons of implementing these technologies.

AI In Talent Acquisition

Traditional recruitment methods, often laborious and time-consuming, require HR leaders to sift through hundreds of résumés for every open position. These processes can potentially cost thousands of dollars if a position remains unfilled, even reaching six figures when considering senior or technical roles. Furthermore, hastily rushing the recruitment process can lead to improper fitting, resulting in higher turnover rates. With AI-powered platforms, HR leaders can streamline their processes by ensuring a more accurate selection of candidates and accelerating the hiring timeline.

Algorithms can process vast amounts of data swiftly, eliminating the painstaking manual review of résumés. By leveraging natural language processing and machine learning, AI-powered tools analyze and use skills extraction to identify the most relevant skills for a given role. These systems go beyond simple keyword matching; they can apply context to infer skills that aren’t explicitly mentioned in résumés. Semantic skills extraction reduces missed opportunities that occur using only keyword search, creating a selection process that’s more comprehensive for recruiters and more fair to candidates.

At a time when enterprises are rapidly transitioning to skills-based models, introducing an AI-powered platform can help HR leaders quickly assess and rank internal and external candidates based on their skills and capabilities. This not only saves time by revealing best-fit candidates faster but also goes even further by enabling role matching.

Finally, recruitment professionals can use AI to enhance the candidate experience with personalized interactions. Tools like chatbots and virtual assistants provide real-time updates on application status and offer tailored job recommendations, reducing candidate effort and time.

AI In Employee Retention

High employee turnover can significantly impact a company’s bottom line. A survey showed that 63% of employees changing jobs cited lack of advancement opportunities as a main factor. In this context, AI can help HR leaders understand their employees’ needs and aspirations better, then use that knowledge to enhance their journey within the organization.

With AI platforms, talent management teams can analyze large volumes of data to gain insights into factors contributing to employee attrition, such as job satisfaction, work-life balance and career growth opportunities. This personalized insight, regardless of workforce size, allows HR professionals to identify and address at-risk employees’ concerns proactively. For example, an employee who’s remained in one position for a long time may have unrealized potential to succeed on another team in the company. A proactive HR leader will capitalize on AI-driven insights to spot that opportunity and present it to the employee, offering a new challenge and possibly keeping them from looking elsewhere.

Performance management and feedback systems receive support from AI technologies that provide objective evaluations of employee performance. This can help HR leaders and people managers provide personalized coaching and development plans, enhancing overall job satisfaction for employees.

Ethical Concerns And Potential Biases

While AI technology offers numerous advantages, it does raise ethical concerns that HR leaders should stay aware of. These systems can unintentionally perpetuate biases and stereotypes present in historical data. In 2018, Amazon came under fire when it was revealed that an AI-based recruitment system discriminated against women when hiring for technical roles. The platform sought top candidates by positions on their résumés, and considering women had held only about 24% of STEM jobs in the U.S., the majority of résumés fed into the system were from men. As a result, the algorithm developed male preference and gradually deprioritized résumés from women.

Clearly, unintended bias like this can have devastating consequences for an enterprise on several fronts beyond skewed workforce growth. The ripples can be felt throughout brand reputation, customer backlash, candidate trust and more.

Such dangers have prompted an increase in regulation around responsible AI, including Local Law 144 in New York City. The new law requires independent audits of what it categorizes as automated employment decision tools (AEDT) used in hiring within, or from within, New York City—an expansive reach, given the city is a global business hub. While it can be argued that AI-driven platforms don’t automate decisions but rather inform humans’ decision-making, the systems present within an organization’s HR tech stack are included in the regulations.

To remain compliant, HR leaders must ensure diverse and representative training data for any AI systems they implement. Additionally, systems should comply with the five pillars of responsible AI: explainability and interpretability; bias mitigation and fairness algorithms; data robustness and granularity; data quality and rights; and accountability through regular audits and monitoring of the AI’s decision-making process.

HR innovators looking to employ responsible AI-based systems will benefit from first researching available platforms and asking potential vendors the important questions: Is your solution transparent? Can you easily explain how its algorithms work? What bias mitigation is in place? What client onboarding experience can we expect and what training is included?

AI Is Here To Stay

Artificial intelligence is undeniably transforming the world of HR, especially in talent acquisition and retention. The benefits of AI, like streamlined recruitment processes and improved employee engagement and satisfaction, are significant for organizations. By employing ethical, responsible AI-driven systems, enterprises can future-proof their workforce and reap immense benefits.

Navigating The Promise And Peril Of Generative AI In HR

Posted on by Dr. Shay David

This article originally appeared in Forbes.

Language has long been the bedrock of our human world; it’s the collective operating system that powers the way we think, feel, interact and make sense of our surroundings. But with the rapid advancements in artificial intelligence (AI), language has also become a crucial interface bridging the gap between humans and machines. Particularly in the HR sector, this evolution comes with both significant opportunities and challenges.

Generative AI, designed to create content that mirrors humanlike patterns of speech and writing, is already beginning to transform HR operations. Leveraged responsibly, it has the power to augment the employee and candidate experience significantly, specifically enabling organizations to identify, attract and retain the best talent effectively while also supporting diverse workforce growth. Conversely, misuse or misunderstanding of these tools can lead to significant pitfalls, from spreading misinformation to challenging trust, authenticity and identity altogether.

The Promise Of Generative AI In HR

Firstly, let’s consider the potential benefits. Generative AI offers unprecedented efficiency and accuracy and can enable the automation of routine HR tasks like screening résumés, answering frequently asked questions and scheduling interviews. This automation not only saves HR professionals’ valuable time but also minimizes the risk of human error, enhancing the fairness and accuracy of these processes.

Organizations are increasingly taking advantage of generative AI for these specific action items in the pre-employment phase. In a recent Littler study, among respondents whose organizations said they are deploying AI and data analytics in workforce management, nearly 70% reported using AI and analytics tools in the recruiting and hiring process.

Secondly, generative AI is a potent tool for improved decision-making. By analyzing patterns and predicting trends, it can generate actionable insights to empower more informed HR decisions. For instance, a generative AI solution could help identify which candidates are most likely to excel in specific roles or flag employees who might be on the verge of seeking new opportunities.

Lastly, generative AI can personalize the HR experience. By understanding individual preferences and needs, it can tailor communications and recommendations, offering a more customized, engaging experience for employees and candidates alike.

The Perils Of Generative AI In HR

However, the advent of generative AI in HR is not without its hazards, most notably the significant risk of misinformation. So concerning is this risk that according to Gartner, by 2027, 80% of enterprise marketers will establish a dedicated content authenticity function to combat misinformation and fake material.

In HR, for example, this could mean an AI system inadvertently disseminates incorrect or outdated information about a company’s policies or job roles, leading to a ripple effect of confusion and potentially serious legal complications.

In addition, bias remains a thorny issue. AI models learn from existing data, which may unintentionally reflect historical biases. Without careful management, these AI systems have the potential to perpetuate these biases, leading to skewed hiring or promotional decisions.

Moreover, privacy and trust are critical concerns. The use of AI in HR often involves collecting and analyzing personal data, which raises privacy questions. As has been emphasized by increasing AI regulations, organizations need to be transparent about their AI usage and take robust measures to protect employee and candidate data.

Lastly, the issue of authenticity and identity cannot be ignored. The line between human and machine interactions becomes blurry with AI. If a candidate interacts with a generative AI system during the recruitment process, they may question whether their responses are genuinely understood or valued. Again, the onus is on the organization to quell these concerns as part of transparent candidate communications.

Navigating The AI Landscape In HR

As we traverse this new landscape, it’s essential to use generative AI tools in HR responsibly. Transparency, fairness and privacy should be the cornerstones of any AI implementation strategy. It’s also crucial to recognize that AI does not make for a “set it and forget it” scenario. Organizations must continually monitor and adjust AI systems to prevent the potential spread of misinformation and the unintended perpetuation of bias.

The future of HR is undeniably intertwined with generative AI. Despite its many benefits, there is still no substitute for the human touch, especially in a field as people-centric as HR.

As we integrate these powerful tools into our HR practices, we must do so with our eyes wide open, keeping in mind that AI should augment human capabilities, not replace them. Maintaining this balance is key to harnessing the promise of AI while avoiding its perils.

 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills taxonomy, enterprises unlock talent insights and optimize their workforce effectively to hire the right people, keep them longer and cultivate a successful skills-based organization. retrain.ai fuels Talent Acquisition, Talent Management and Skills Architecture all in one, data-driven solution. To see retrain.ai in action, book a demo.

Ready Or Not: 3 Points To Consider As Generative AI Tools Rush To Market

Posted on by Dr. Shay David

This article first appeared in Forbes.

About halfway between the day you first heard about ChatGPT and the day you started wishing you never had, the news became all about a new era of thinking machines. Faster than you can say “Generative AI,” new models are moving into the spotlight, each claiming to be better than the last.

ChatGPT is drawing big names into the generative AI race.

ChatGPT, the groundbreaking chatbot developed by OpenAI, became the talk of the tech world almost overnight and is the most advanced chatbot to date. Predominantly a consumer-focused tool, it was designed to interact conversationally with a user, providing answers and responding to follow-up questions. Demonstrating the extraordinary ability of artificial intelligence to use machine learning to index retrievable content and mimic writing styles, ChatGPT can even adjust tone and voice when provided with direction.

OpenAI technology is also used to power Bing, Microsoft’s less popular search engine launched in 2009 that’s now making a phoenix-rising-from-the-ashes comeback. Claiming capabilities more powerful and accurate than ChatGPT, the company says they’ve applied the AI model to the Bing search ranking engine to increase the relevance of even basic search queries. While this might be true, I think they still have a long way to go. The technology has more than a few kinks—for one thing, it recently told one researcher it was in love with him.

And Microsoft is not alone in its conundrum of determining when these technologies might be ready for market. Despite having arguably the strongest alignment with AI-charged search capabilities, Google fast-tracked its own chatbot, Bard, in order to compete directly with ChatGPT. However, a factual error churned out during a marketing demo derailed its momentum and even caused the stock of its parent company, Alphabet, to drop 9% within a day. Regardless, it’s possible that Bard may ultimately gain an edge over ChatGPT given its access to a wealth of data when integrated into Google’s search engine.

As a specialist in the AI space, my company sees the rapid uptick in generative AI products as a positive. But the promise comes with peril. As of now, these technologies lack the hallmarks of fully enterprise-level solutions. As we observe a burgeoning new tech space, here are a few points to consider:

1. AI is a tool, not a threat, but we must assign it to the right tasks.
Consumer-level chatbot technology showcases what we in the AI space already know: that machine learning and intelligent technology can greatly enhance the human experience. One could argue that when AI takes on more repetitive, mundane business tasks—and does so with a near-zero error rate—people will be freed up to generate more creative contributions. In the HR arena, AI-driven tools can map the skill sets of entire organizations, revealing hidden talent and new opportunities that may have otherwise been missed.

2. Responsible AI means more than content filtering.
The companies producing these new publicly available chatbots talk about responsibility as the importance of mitigating harmful content. Microsoft, for example, says the new Bing implements safeguards to defend against issues such as misinformation and disinformation. But for an AI product to be truly responsible, the design itself must be responsible. We are seeing this in the HR tech world, as increasing regulations are being introduced to stave off unintended bias in hiring processes. Chatbots and similar technologies must include responsible AI components even before the first piece of content is generated.

3. Better is subjective.
In the scramble to eclipse ChatGPT’s entry into the market, its competitors were launched amid bold superlatives. Microsoft introduced Bing as the tool that would “reinvent search,” providing a faster and more powerful, accurate and capable option than ChatGPT. Meanwhile, Google Bard’s access to more recent data seemed beneficial in the race with ChatGPT, as the OpenAI chatbot model was initially restricted to data collected only through 2021.

When AI is tailored to enterprise-level functionality, however, what’s considered superior in one scenario may not translate to an advantage in another. Whereas industry-specific AI tools are designed to organize, analyze and structure data precisely enough to inform critical business decisions, vertical-specific leaders must build AI models that are based on industry know-how and language to perform specific tasks. Businesses utilizing such technologies also depend upon contractual assurances like Service Level Agreements (SLAs) to outline vendor expectations and set performance metrics, something open chatbots can’t provide.

Conclusion
No doubt the consumer-facing generative AI race is just beginning. Advances and missteps are an inevitable part of growth, but I look forward to seeing how it all plays out, with the hope that it helps people view AI anew, through the lens of curiosity and potential.

HR Evolution in the Age of Talent Intelligence

Posted on by Dr. Shay David

This article originally appeared in Forbes.

In a year defined by the stark contrast of looming layoffs and a continued skilled worker shortage, it’s likely that 2023 will highlight the importance of HR’s role in leveraging data-driven insights.

At the end of last year, recruiting was a top priority for 46% of HR leaders, with enterprises revamping sourcing strategies to meet the demand. At the same time, 61% of business leaders were predicting layoffs at their companies. None of that slowed the nation’s quit rate, which landed at 4.1 million workers for the month of December 2022, the same time Robert Half’s “Job Optimism Survey” reported that 46% of professionals were looking, or planning to look for, a new job in 2023.

Such contradictory data can make an HR leader’s head spin. Figuring out how to manage it all on budgets tightened by economic instability is the icing on a very bitter cake. The answer?

In 2023, organizations need to be creative and efficient in their approach to achieving HR goals when using data-driven insights.

HR leaders need to synchronize talent acquisition, talent management and organizational skills mapping to create an agile and continuously growing workforce. By acting on AI-fueled, data-driven insights, enterprises can gain a better understanding of their organization’s job and skills architecture and plan for future skills demand—that is, if HR approaches talent intelligence properly.

HR’s Role In Talent Intelligence

Here are some tips for HR leaders on how to approach talent intelligence:

  1. Avoid the DRIP Problem. 

To compete within the volume, speed and disruption of today’s talent landscape, data is king—but actionable information and knowledge are the castle. Amassing data for data’s sake holds little value and can often lead to a DRIP problem: being data rich, information poor. Conversely, organizing, analyzing and interpreting data can provide rich knowledge and actionable insights needed to fuel better business decisions.

Given the continued talent shortage, expedited skills identification can be a great differentiator for high-performing recruiting teams. To source, screen, hire and retain the right employees with the in-demand skills needed for business success, enterprises should focus on harnessing the true value of data. By actively converting insights into strategies, HR leaders can proactively address skills gaps, plan for future skill needs and develop a more engaged workforce.

  1. Consider starting with job architecture and skills mapping.

To remain competitive and future-proofed, HR innovators should evaluate the skills their employees have, those they need and those that will likely become critical in the future.

More HR leaders are starting with job architecture, building a skills framework using unified skills language to better understand their people and spot hidden talent, diverse capabilities and skills gaps. They can then deploy talent efficiently during times of rapid change, scaling teams up or down as needed. Given the economic uncertainty, knowing where the risks lie can empower HR innovators to upskill, reskill and redeploy—rather than lose—good people.

  1. Don’t fall victim to talent scarcity.

Today’s talent shortage has negatively impacted businesses for well over a year, with more than 77% of CEOs reporting the ability to hire and retain skilled talent as an important factor in achieving growth. Traditional sourcing strategies aren’t enough; HR leaders must be more targeted and efficient than ever to navigate inevitable shifts in the market.

HR leaders can aim to expedite time and labor-intensive tasks—like scanning CVs for skills—so talent acquisition leaders can focus on more complex work. The goal is to find the right people quickly and accurately. In working alongside talent intelligence, HR leaders should focus on skills-based candidate profiling to revive past candidates as additional potential hires.

  1. Focus on internal mobility for talent retention.

Pursuit of the right talent doesn’t end once HR leaders hire people into open roles. Enterprises must proactively “recruit” their employees throughout their tenure via professional growth opportunities. Employees without a clear vision of their future within their organization are statistically more likely to leave.

Career development for today’s workers means learning and evolving within a multidirectional framework, with space to explore open roles, projects, gigs or mentorships, and with access to learning opportunities to reach them. As such, HR innovators can identify hidden talent within their workforce and visualize internal mobility pathways through skills development, increasing the likelihood that their best people will look for their next opportunity within the organization rather than leave to seek out other options.

Learning Curves And Challenges With Talent Intelligence

As HR leaders continue to define their role in the age of talent intelligence, it’s important to note the current limitations, challenges and learning curves associated with the technology.

Regulations And Responsible AI

As AI becomes more embedded in HR processes, so does the responsibility placed on enterprise leaders to manage the potential implications of AI adoption and to implement responsible AI principles. Responsible AI sources and screens applicants based on capabilities, masking demographics or other factors that can introduce unintended bias.

Regulations mandating bias audits, fairness algorithms and explainability to ensure responsible AI compliance are already on the rise. Regulations take shape from municipalities to states and globally, with compliance guidelines being defined in real time. Enterprises must continue tracking these developments to ensure their responsible AI parameters are in place.

HR Tech: What’s Working, What’s Not

Finally, the explosion of new technologies and AI-driven solutions in the HR space has put more data and automation in the hands of enterprises than ever before. HR leaders under pressure to make business decisions that fuel success, and to do so under tightening budget constraints, are taking a closer look at their HR tech stack to determine what’s adding value and what isn’t.

Overlap between HR tech solutions, or inefficiencies within them, represent an unnecessary expense that’s no longer considered the cost of business. Evaluate your tech stack to ensure there are no redundancies.

As 2023 continues to unfold, HR leaders can solidify their role in the age of talent intelligence.

 

retrain.ai is a talent intelligence platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills taxonomy, enterprises unlock talent insights and optimize their workforce effectively to lower attrition, win the war for talent and the great resignation in one, data-driven solution.

To see it in action, request a demo.

 

Sources:

What Will HR Focus on in 2023? | Gartner | October 2022

When Does Your Salary Become a Threat to Your Job Security? | NASDAQ News | 2023

80% of workers who quit in the ‘great resignation’ have regrets, according to a new survey | CNBC make it | February 2023

Nearly Half of U.S. Workers Plan to Look for a New Position in the New Year | Robert Half | December 2022

 

ChatGPT Is Changing the AI Game, But Enterprises Need More

Posted on by Dr. Shay David

Chances are you’re one of the millions of people who have played with ChatGPT, the game-changing generative AI assistive technology released by OpenAI. Designed to interact conversationally, the advanced chatbot can engage in dialogue with a user to provide answers, respond to follow-up questions, correct mistakes, and adjust tone and voice when provided with direction. 

A consumer-focused tool, ChatGPT aptly showcases the groundbreaking ability of generative AI to use machine learning to index retrievable content and mimic writing styles. As such, it has prompted a conversation around its possible business uses, garnering opinions from those who see great potential–and those who fear for their jobs. Some even suggest that we are nearing the singularity, or at least seeing for the first time machines that can pass the (in)famous Turing test.

>> Book a demo to see retrain.ai’s generative AI in action

As leaders in the AI space, we see ChatGPT as an example of a set of tools with the potential to transform business processes. Yet it has notable limitations when viewed through the lens of an enterprise-level solution. There are four main areas in which this differentiation is most apparent:

  1. AI-driven technology designed for business incorporates features optimized for a particular industry. retrain.ai, for example, was built from the ground up as a specialized solution for the HR space. As such, our technology expands beyond a ChatGPT-level machine learning model to one which can organize, analyze and structure data precisely enough to inform critical business decisions. We anticipate that in each industry, vertical-specific leaders will emerge who build AI models that are based on industry know-how and language, and are tailored toward specific tasks.
  2. Explainability is another critical feature of specialized AI technologies that you won’t find in a general-purpose chatbot platform. Explainable solutions are referred to as white-box technology, meaning machine learning outcomes, and the methodology which produces them, can be explained using general business-speak. For enterprises trusting generative AI systems with critical decision assistance, this means they have a clear enough understanding to question or challenge the platform’s output. 
  3. Without white-box explainability, an AI system is lacking a key component of Responsible AI, a non-negotiable design element, when it comes to bias prevention in hiring processes. Only by using Responsible AI can an enterprise ensure candidates are being screened solely on skills, eliminating information that can introduce unintended bias. Increasing regulations will also hold enterprises accountable for making sure they are using Responsible AI in hiring practices.
  4. Enterprise-level solutions are implemented to directly impact business performance. They come with contractual assurances like Service Level Agreements (SLAs) to outline vendor expectations and set metrics by which the technology’s effectiveness will be measured. Open platforms like ChatGPT don’t offer performance metrics or customized services, leaving adopters with no recourse should something go wrong. The same is true about data sovereignty, and compliance with privacy standards like GDPR. We anticipate that the big vendors like Microsoft and Google will soon offer enterprise grade service assurances around consumer tools like ChatGPT (or Google’s Lambda), but until that time, the use of consumer tools cannot be relied upon.

The retrain.ai Talent Intelligence Platform uses generative AI with similar language processing technology to ChatGPT’s, but expands on the model to provide a fully explainable enterprise-level solution designed specifically for talent intelligence, while complying with SOC2, GDRP, and offering enterprise grade SLA. We’re excited to see how the market continues to develop and how enterprises transform years old practices with new tools. 

>> Book a demo to see retrain.ai’s generative AI in action

See how the retrain.ai Talent Intelligence Platform fuels your talent acquisition, talent management, job architecture and DEI goals, contact us today. 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and the industry’s largest skills architecture, enterprises unlock talent insights and optimize their workforce effectively to lower attrition, win the war for talent and the great resignation in one, data-driven solution.

Learn more: book a demo. 

VIDCAST: Keep Your Best People Longer with Opportunities to Thrive

Posted on by Dr. Shay David

Of the millions of workers who quit their jobs over the last two years during the Great Resignation, many cited lack of opportunity for advancement as a major factor. Employees saw investment in their professional development as validation that their contributions were valued and rewarded; its absence sent the opposite message.

Today’s HR leaders must strategize how to hang on to their best-fit hires so they become long-term employees. Much of this comes down to providing a vision for future opportunities in the form of roles, projects and gigs that will utilize, challenge and develop a worker’s skills.

In this session, retrain.ai Co-founder and CEO Dr. Shay David and Chief Research Officer Ben Eubanks of Lighthouse Research discuss how organizations can build a mutually beneficial path forward for valued talent. Their conversation covers:

  • How HR tech can counter today’s quit rates 
  • The connection between internal opportunities and worker retention
  • What we can learn from Great Resignation data
  • The DRIP Problem: Data Rich, Information Poor
  • Implications of the new employer-employee dynamic
  • How AI enhances the human experience at work
  • The importance of Responsible AI and explainability
  • Tips for HR leaders new to using AI-driven tech
  • Talent scarcity as a business problem, not just an HR problem
  • How HRs and hiring managers can align to optimize Responsible AI solutions

 

 

 

Digging For Sand Or Finding Oil: Could Talent Intelligence Make A Difference?

Posted on by Dr. Shay David

This article originally appeared in Forbes.

In our last post, we studied the idea of upskilling and flow theory: Employees who are continually challenged, and offered professional development opportunities to meet those challenges, are given the chance to reach a state of flow. In other words, employees can gain a sense of working “in the zone” where intellect and ability are tested, and thus stay engaged and rewarded on a consistent basis.

In this post, we look at the “how” behind the “what,” explaining how HR leaders can chart these optimum paths for a plethora of individuals, each with their unique level of challenge and flow. To address this most human of characteristics at scale, data is critical but only effective if analyzed and interpreted with a goal in mind.

In 2006, British mathematician Clive Humby coined the phrase, “Data is the new oil.” His original intent was to describe data as something that, like oil, needed to be refined and transformed to be useful. Humby’s study of data came as he created the first-ever data-based consumer loyalty program for British supermarket chain Tesco. Through its implementation, he recognized that in gathering a continuous stream of transactional data, Tesco could refine lucrative business opportunities by understanding consumer buying habits. His observations convinced him that social media would only further solidify data as the key to predictive capabilities.

In 2020, as digital transformation accelerated at unprecedented rates due to Covid-19, technologist Tim O’Reilly challenged Humby’s metaphor in an opinion piece for The Information. He suggested that rather than being the new oil, “Data is the new sand”—one of the earth’s most abundant materials but one that only becomes something of value once it has been processed, researched and developed correctly. Using Google as an example, he pointed out that the search engine crawls the entire web, collecting and indexing trillions of data sources, using complex algorithms and AI to answer user questions 3.8 million times per minute. The value doesn’t come gushing from the ground as the oil metaphor suggests; rather, data mining requires constant and expensive ongoing efforts.

So it is in the realm of workforce management, where the endless dunes of data reveal the shapes of industries transformed by the winds of digitization, current workforce trends, skills of the future and projected business impact. To use O’Reilly’s terminology, enterprises that focus on amassing data for data’s sake will indeed gather plenty of sand, but what they do with it next determines its value.

In its Future of Work Trends report, Gartner points out that one such focus area is redefining skills criticality, in that skills needed to meet strategic organizational goals will no longer equate with individual roles. Talent mobility and career development depend on identifying and developing critical skills. Workforce data can inform best practices to provide this support but only if interpreted correctly. The focus on criticality is a good example of focusing on leading indicators within the vastness of data.

Artificial intelligence enables the processing, analysis and interpretation of billions of data points at speeds that were inconceivable even a decade ago. By collecting and harmonizing input to produce actionable insights, an enterprise can avoid the “DRIP” problem: data rich but information poor. This is arguably the biggest pitfall of the big data era. In other words, simply collecting the data is not enough. The data needs to be made actionable and inform HR processes from sourcing through screening and all the way to onboarding, development and career pathing.

For some enterprises, a crew of skilled data professionals—scientists, analysts and engineers—can direct collaborative efforts toward workforce and labor market skills taxonomies and job architectures to inform HR efforts. For others, as Gartner points out, enlisting external talent intelligence platforms can utilize analytics to identify trends in skill evolution and talent profiles. But what of enterprises without either option?

In these cases, HR leaders can benefit from a well-structured, empathetic introduction to AI-fueled data analytics. The change can be daunting for HR leaders who are not up to speed or are flat-out uncomfortable with adopting AI-driven capabilities. Without team buy-in, the risk of DRIP is high; proactive education and engagement are key to bridging that divide.

A logical first step in introducing AI to the skeptical is to point out the many ways AI is already playing a role in their day-to-day life. The playlist Spotify built for them, the book recommended by Goodreads or the morning weather forecast from Alexa all have become commonplace yet represent the power of AI to enhance (not replace) the human experience. Through education and engagement, HR leaders can upskill their own way to embracing AI as a tool that can free them up from repetitive tasks (setting up benefits enrollment systems and internal communication) to expediting critical talent acquisition processes (e.g., scanning hundreds of CVs in minutes versus screening them manually).

Structuring internal and external data, whether through an in-house data team, an upskilled HR staff or a third-party vendor can enable enterprises to keep pace with future workplace trends. HR needs to know the difference between proactively planning ahead or reactively responding in real-time.

Despite some early hesitancy around AI, the age of intelligence upon us now is a powerful milestone for both humans and machines. In the larger picture, by identifying future trends aligned with professional potential and upskilling opportunities, an empathetic approach to talent intelligence could help close the skills gap and secure future-proof worker employability.

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and real-time labor market data, enterprises unlock talent insights and optimize their workforce effectively to lower attrition, win the war for talent and the great resignation in one, data-driven solution. To learn more book a demo. 

Upskilling and Reskilling in Uncertain Times: Wix.com, Second Nature and retrain.ai On Why It’s Time to Double Down (Part 2 of 2)

Posted on by Dr. Shay David

In a recent panel discussion, Dr. Eli Bendet-Taicher, Head of Learning and Talent Development at WIX.com, Ariel Hitron, CEO of Second Nature, and retrain.ai CEO Dr. Shay David shared insights into current upskilling and reskilling trends and challenges, the transformative nature of AI, and what it all means for the future of learning and development in HR. 

In part one of this blog series, we shared their thoughts on the importance of investing in talent development, mapping skills and unifying skills language across disparate HR tech systems within organizations. Here are more highlights:  

To see the full session on-demand, click HERE.

 

Ariel Hitron: How do you consolidate between the macro and micro, especially for a large enterprise that has thousands of employees? On one hand we’re thinking of skills in terms of capabilities, tasks, roles, etc. in the macro environment, then there’s the day-to-day. Where do you spend most of your energy, time and effort? What are the strategies and tactics? 

Shay David: That’s a great question because it’s kind of global versus local. In our system, we have a process we call calibration. We’ve trained our system to basically help automate the building of that job infrastructure, of that skills taxonomy, and we allow organizations that use that intelligence layer to begin to build their job architecture. 

Our system has learned through natural language processing and has analyzed tens of millions of job descriptions and hundreds of millions of CVs to learn, for example, what are those jobs in practice? From that layer, our system can be calibrated for a specific company–different equipment, different locations, different values, etc. We allow customers to start with a labor market data-fed template and then go through a process of validation. Further input to the system then provides more for it to learn and the process can replicate at every level. We want to get tools to the people that are actually in the field–that need to hire people and train people–so that they can use sophisticated AI not to replace themselves, but rather as decision support.

AH: What do you see when you think about the skills gap in broad strokes like corporate level, and then the people who are actually being hired or reskilled into new roles? How do you connect the two?

Eli Bendet-Taicher: Companies really need to first understand what kind of roles make the most impact and what kind of roles they see changing the most. They need to focus on the problematic roles, the revenue-generating roles—all the roles that make a big impact. We started there because it pains more to lose people there than in other departments. The end goal is to cover everything, but when you have a huge monster like Wix or other big companies, it’s a bit difficult to do all the mapping of roles very, very quickly.

You have to understand what the heat map is–where you really need to focus–and start there. Once you do that, and it’s an exercise that works well, then you can implement it for other roles using a similar methodology. Tools really help you do that. AI is a great tool, but you need to do the fine-tuning through continuous calibration. Once you do that, you’re on a roll.

AH: So after you’ve done the mapping, and know where those skill gaps are, how do you actually deliver in a way that drives change? Making a change in behavior within how people do their day-to-day job is really really hard because people generally don’t love change.

SD: The overall digital transformation and disruptive landscape mean that the environment is changing. And when the environment is changing, the question is, how do we respond to that? The customer-facing teams are probably the first to change, so sales and customer service, which use a lot of soft and hard skills. Second is that there are big gaps, generally speaking, in the market around digital skills, particularly for the older generations. If you were a shift manager at a manufacturing facility and your line of business is changing–maybe because it’s now automated or because some manufacturing was shifted abroad or something like that–what do you do next? We think about skills as a ladder and for a lot of people displaced by automation, digital transformation, or now recessionary pressures, without help they’re at risk of falling too many steps down the ladder.

But what if you could learn some of those new digital skills? It doesn’t mean you become a Python programmer and start building robots yourself, but it could mean you learn how to operate drones, which is an emerging job of the future. There are jobs in moving from old energy to new energy, or from old banking to new banking. Those are all a combination of soft and hard skills but mostly focused on digital. And the good news for learners is that many of those skills can actually be learned online using free content from public sources like Coursera, Udemy, or corporate learning programs, all of which could be made to fit those specific roles and those specific skills.

AH: The acceleration of Covid does put a lot of pressure on salespeople, for example, who have these amazing soft skills they’ve honed over many years like empathy and relationship building. You have very tenured employees having to reskill into this new environment. What do you see in your organization? 

EBT: We always listen to our people in action. So if we see issues with active listening or asking powerful questions, for example, we say okay, we need to create training that is specific for that. We also need to understand whether these behaviors are changing post-training. Then we need to really measure that behavior change to understand, will we be able to move the needle there? How does that translate to more revenue? 

We’re trying to correlate our learning data to performance data to revenue data to show ROI. It’s challenging for every L&D professional to correlate their work to business success, but if they’re able to do it, and they have the tools to offer enough insights and data to show it, they’ll get the budget, they’ll get the headcount. We’re not usually viewed as a revenue-generating department but if my KPIs are derivatives of the business KPIs, I can connect myself to the success and show ROI.

 

 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and real-time labor market data, enterprises unlock talent insights and optimize their workforce effectively to lower attrition, win the war for talent and the great resignation in one, data-driven solution. To learn more book a demo. 

Flow Theory And Talent Development: Are You Keeping Your Employees In The Zone?

Posted on by Dr. Shay David

This article originally appeared in Forbes.

In our last post, we examined the changing dynamic of today’s enterprise landscape, one in which employers are scrambling for talent while skilled candidates are in the driver’s seat. In outlining the needs of today’s workers, we highlighted recognition, purpose, career-path visibility and personalized development opportunities as core elements of employee retention. In this post, we’ll explore another critical element of professional satisfaction: the chance to grow through challenges and how employers can provide that opportunity.

To understand this, we must first understand flow theory.

Long considered one of the founders of positive psychology, Mihaly Csikszentmihalyi was the first to research, recognize and identify the concept of “flow,” describing it as the positive mental state of being completely absorbed, focused and involved in an activity for its own sake. It’s when the ego falls away, time flies and every action follows intuitively from the one before it. Akin to being “in the zone,” a person experiencing flow is immersed, using their skills to the utmost. Moreover, Csikszentmihalyi claimed, “The best moments in our lives are not the passive, receptive, relaxing times… The best moments usually occur if a person’s body or mind is stretched to its limits in a voluntary effort to accomplish something difficult and worthwhile.”

What if we can keep our employees in constant flow like elite athletes or top musicians?

 

Flow Theory And Talent Development

 

Given the human need to be both productive and challenged, HR innovators need to focus on constantly developing the skills of their employees through both formal training and repeated on-the-job learning. This doesn’t just boost employee engagement—it’s imperative for business. According to a recent Gartner TalentNeuron™ data analysis, the number of skills required for a single job is increasing by 10% year over year, while many skills become irrelevant after just three years. To stay ahead of the curve, skills development must be relevant, fast, effective and ever-present.

Today’s workforce agrees. As we outlined in our previous post, those in the new employee-centric workforce emphasize the importance of recognition, purpose and career-pathing in best-fit positions. Individualized talent mapping reinforces an employer’s commitment to keeping employees “in the zone.” This means employees are less prone to the anxiety of being in over their heads—like in a project for which they lack the skills—or, in contrast, the complacency of being overqualified for a role, which could lead to apathy.

Achieving and maintaining a state of flow benefits all: The individual is provided an atmosphere in which they can thrive, while the enterprise benefits from an engaged, fulfilled employee who keeps learning.

 

Talent Development And Upskilling

 

The traditional upward, linear career path of yesteryear is outdated. Today’s skilled workers are less inclined to dutifully follow a career course set before them and are more driven to blaze their own trail based on professional and personal priorities. In fact, according to a recent Gartner survey referenced earlier, 69% of HR executives report increased pressure from employees to provide development opportunities that will prepare them for future roles. For employees looking for a healthy dose of challenge through which to grow professionally, a two-day professional development event offsite once per year or a coupon to some online course just doesn’t cut it.

A traditional corporate learning marketplace used by many employers is a good start, but it can be overwhelming for anyone who isn’t quite sure what training best fits their goals; as such, it is likely to be underutilized. To keep ambitious employees in a state of flow, HR leaders must be proactive. Maintaining a clear job architecture and tracking organizational and individual skills enables HR innovators to proactively identify best-fit options as well as potential roles achievable through both formal and informal learning and development.

Whether focusing on internal mobility, succession planning, gig opportunities or other workforce strategies, providing an end-to-end workforce upskilling experience is an effective way to provide a career road map for employees. More than opening doors for employees, such initiatives walk them through the door and point the way, empowering employees to remain in the zone by finding the right balance. Enterprises that create such an environment are best positioned for future success.

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and real-time labor market data, enterprises unlock talent insights and optimize their workforce effectively to lower attrition, win the war for talent and the great resignation in one, data-driven solution. To learn more book a demo. 

Upskilling and Reskilling in Uncertain Times: Wix.com, Second Nature and retrain.ai on Why It’s Time to Double Down

Posted on by Dr. Shay David

Economic shifts are causing some companies to slow down, lay off or cut back on employee services. Does that mean it’s time to hit the brakes on talent development? 

Absolutely not.

In fact, it’s the perfect time for enterprises to invest more in the reskilling and upskilling of their people. Doing so only helps to better address the new challenges of today’s financial crisis and prepare to fuel productivity when the economy recovers. 

In a recent panel discussion, Dr. Eli Bendet-Taicher, Head of Learning and Talent Development at WIX.com, Ariel Hitron, CEO of Second Nature, and retrain.ai CEO Dr. Shay David offered insights into current trends and challenges, and what it all means for the future of learning and development in HR. 

To see the full session on-demand, click HERE.

 

Here are some highlights:

 

Ariel Hitron: To quote the World Economic Forum, “One in three global organizations is accelerating upskilling or reskilling programs in response to COVID-19. In doing so, they recognize the value of their people — the vast potential of each individual to leverage his or her existing skills to add value beyond their current role and learn new skills in response to changing needs.” 

Backing up a bit, why do you think some enterprises are accelerating upskilling efforts at a time when others are cutting back? 

Shay David: When we talk about acceleration, it’s acceleration of several secular trends that already started years ago–namely the capability to be flexible and work remotely, the capability of doing more knowledge work, the capability of having flexible teams. If we look at the larger trends in the market, a lot of it has been about the move into digital services, digital economy, digital transformation in general.  

COVID didn’t invent any of these trends, it was just the accelerator that forced a lot of people to rethink: What are we doing? What skills does our team need? Are we giving our teams those skills? All of the sudden, businesses found themselves needing to reinvent. And when you reinvent a business, you have to add new skills. At retrain.ai, we focus on understanding what that skills landscape looks like, and with a lot of our customers, we’re definitely seeing that trend. 

AH: Now we’re heading into a slowdown in the market. Hiring is definitely changing. Do you think this will also have an impact on the upskilling, reskilling and learning programs? What are your thoughts on that?

Eli Bendet-Taicher: A lot of companies have been downsizing in the past few weeks and months–but they don’t want to downsize their business. So they’re finding ways to be more effective and productive with fewer people. They may need employees to take on more responsibilities or change roles, which in turn means they need to be reskilled or upskilled through programs that are ready to go.

So I actually think this recession will make companies and organizations actually invest more in L&D, more in reskilling and upskilling programs, because they just have to. They still need to thrive, they still need to bring money to the table, and there may be other changes coming. They may even need to pivot the business at some point, and they’ll need to train their people with everything they have in order to do that.

AH: Okay, so as a business leader you have to do more with less, or more with what you have in terms of human resources. Picking up on that, what do you think learning and development leaders need to do to support that?

EBT: At Wix, we needed to really map the skill set and competencies for each role at the company. You have to be able to see what kinds of roles you have, what kinds of roles you need, and what it will take to deliver the expertise in each skill set for every role. It’s a full understanding of: This is what I want, this is what I have, and what is the gap. From there you can create programs specific to bridging that gap. 

We found we also needed to have a great interoperability policy. If I’m moving a person from one role to another, our organization needs to support that person with upskilling and reskilling so they’re able to do the job the best way they can. At the end of the day, you need to invest in people’s learning and development so they know they have that support. 

AH: Wow, a lot, a lot unpack there. So why don’t we start with the mapping of the skills for each role? Shay, you’ve spent quite a bit of time thinking about this challenge. Maybe you can share some of your insights? 

SD: In our view, skills are the atoms that can help define what tasks are; tasks join into roles and roles join into occupations. You can also talk about an interesting hierarchy like capabilities and competencies–there are many different ways to skin the skills cat, if you will–but the bottom line is that you need to have a unified language that consolidates different systems within an organization that are otherwise siloed. 

Think about most of the organizations you’ve met to date. They probably have some sort of human resource information system or some human capital management system, most have learning management systems, probably some sort of onboarding system, employee performance systems, comp and benefit, and so on. Organizations, particularly global enterprises, have six or seven different systems to view their employees. The challenge that we have is that most of those systems don’t speak the same language. So the first order of business is to get the proper language in order to create a cohesive job architecture and skills taxonomy.

In our second post of this series, hear from our panelists about calibrating the macro and micro elements of skills mapping, the demand for hard and soft skills, and how AI can both transform data into actionable insights and enhance–not replace–the human experience of work. 

 

retrain.ai is a Talent Intelligence Platform designed to help enterprises hire, retain, and develop their workforce, intelligently. Leveraging Responsible AI and real-time labor market data, enterprises unlock talent insights and optimize their workforce effectively to lower attrition, win the war for talent and the great resignation in one, data-driven solution. To learn more book a demo.