Category: Blogs

White Box vs. Black Box: What’s the Difference

Posted on by Isabelle Bichler

As AI becomes increasingly embedded in HR systems, enterprise leaders face growing accountability from regulators, their C-suite, applicants, and more to ensure their solutions use ethical, responsible systems to mitigate unintended bias. As a result, Responsible AI is becoming a business mandate, with increasing momentum around laws requiring audits to ensure all benchmarks of Responsible AI are in place.  

One key component of Responsible AI is explainability. Users of an AI-based system should understand how their AI gathers, organizes and interprets data, as well as how the platform produces outcomes.

White box = Explainability

The level of transparency needed to fully explain an AI solution can only be found in what is referred to as a white box solution. With this approach, a full end-to-end view of an AI system’s functionality enables system users to see the what of the system–its data output–while also being able to ask the why–the methodology behind the results.

Such interpretability also allows data scientists and analysts to test the design and internal structure of an AI system in order to authenticate the input and outflow, gauge for errors or inconsistencies, and optimize functionalities accordingly.

What White box Means for HR Leaders

A white box AI solution empowers users to question processes and challenge results, which is especially critical when using such technology within HR functions. Armed with a thorough understanding of their AI solution, an HR leader can be sure their system is performing critical functions, such as mitigating bias risk within its machine learning models. Assured of such mitigation, the organization can stand behind hiring practices that fully support their diversity and inclusion goals.

Black box = Blind Trust

Conversely, there are AI systems for which explanations are too difficult to understand–or aren’t available at all. These are often referred to as black box solutions. In certain settings, black box AI can be useful. The algorithmic complexities necessary in fraud prevention systems, for example, are not explainable in simple terms. 

But within HR functions, a black box system doesn’t allow users to understand how the AI arrives at its conclusions around hiring decision support. As such, there is no visibility to detect errors within the processes, including the presence of possible bias permeating the algorithms.

What Black box Means for HR Leaders

For these reasons, black box solutions represent a significant risk to HR innovators. In the larger sense, they demand a significant level of blind trust. More specifically, by masking information that can derail DEI hiring practices, they render an AI  solution non-compliant in the face of increasing Responsible AI regulation.

retrain.ai and Responsible AI

In providing end-to-end transparency for platform users, retrain.ai is a white box solution. In choosing this methodology, retrain.ai supports the rights of enterprises to know and understand how their HR platforms deliver critical information.

As part of our larger commitment to leading the forefront of Responsible AI innovation in the HR Tech space, retrain.ai works with the Responsible Artificial Intelligence Institute (RAII), a leading nonprofit organization building tangible governance tools for trustworthy, safe, and fair artificial intelligence. To see the retrain.ai difference book a demo. 

 

 

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. 

Disruption That’s Here to Stay: Skills Language & Talent Intelligence 

Posted on by Isabelle Bichler

Across industries, the state of workforce management has been rocked by perpetual change over the last three years. If one truth has arisen from trends like the war for talent and the great resignation, it’s this: People are an organization’s greatest asset. 

Without the right people in best-fit roles, businesses risk obsolescence in a competitive landscape driven by new and evolving in-demand skills. So real is the challenge, a majority of CEOs have reported that the ability to hire and retain skilled talent is their most critical barrier to achieving growth.

Unified Language: The Importance of Skills

For HR leaders, the new world of work demands that talent have the specific capabilities needed in order to succeed in their role. Gone are the days of impressive titles or degrees; in-demand skills are what make or break recruiting efforts. Internal mobility is forever changed as well, with the upward professional ladder climb giving way to a more agile rock wall where skills-based opportunities can come from any direction in a myriad of forms. Roles, projects, gigs, mentorships, learning pathways–all are integral parts of today’s professional development spectrum.

AI: The Rise of Talent Intelligence

To break down every open role and job description into skills needed, or to scan every CV into skills language, would take a traditional HR team more hours than are even close to possible. Yet having a clear understanding of what skills they already have in their workforce, where the skill gaps are located, and which internal or external candidates can bring those skills to the table is critical to future-proofing their organization.

To both expedite the process, and to do so with granular precision, HR innovators are increasingly implementing talent intelligence solutions. 

What is Talent Intelligence?

Talent intelligence is AI-driven technology that unifies, organizes and interprets a company’s internal data, and combines it with external data on market trends,  emerging skills and labor statistics  in a way that informs and empowers HR leaders to make better workforce planning business decisions. Similar to the groundbreaking capabilities demonstrated by ChatGPT, talent intelligence uses generative AI with similar language processing technology, but expands on the model to provide a fully explainable enterprise-level solution. Built on ethical, Responsible AI means such solutions actively mitigate the risk of unintended bias seeping into machine learning cycles, which can derail DEI hiring practices. 

AI-driven Talent Intelligence and Skills Matching

Using talent intelligence to synthesize the combination of AI capabilities and skills-focused workforce development empowers HR leaders to make faster, better business decisions.

Skills Architecture

Making the best decisions around hiring and internal mobility means HR leaders need to have a clear, granular view of what capabilities their employees have, where the skills gaps lie, and how to future-proof their workforce through developing talent.

Using AI-driven talent intelligence to skills-map an enterprise workforce, HRs can establish unified skills language and an agreed-upon skills framework. Matching it with data insights, they can then align talent decisions with organizational goals.

Talent Acquisition

It’s estimated that in the U.S., it takes more than a month to fill an open position–and that on average, an HR leader must review more than 150 CVs for a single role. Multiply that across a large hiring initiative and there’s a very real cost to an enterprise, including recruiting expenses, time invested by departmental leaders and managers in supporting the hiring process, and the productivity disruption of a prolonged vacancy.

AI-driven talent intelligence helps HRs zero in on best-fit candidates more quickly by analyzing applicants at an atomic level, breaking down their talents into individual skills. Matching applicants with open opportunities, roles or projects based solely on skills means HR leaders can link candidates to best-fit roles with room to grow; and they support DEI goals by eliminating demographic or other information that can introduce unintended bias into the equation.

Talent Management 

As millions of workers quit their jobs during the Great Resignation, one reason continually showed up in the research: Lack of opportunity for advancement. Put simply, at a time when there are more open roles than there are candidates going after them, HR leaders must strategize how to provide employees with a vision for future opportunities that will utilize, challenge and develop a worker’s skills.

AI-driven talent intelligence gives HRs a watchtower view of their workforce, including a granular understanding of employees’ strengths, skills gaps, potential capabilities and hidden talents. Fueled by these insights, HR leaders can provide their talent with personalized career pathing, internal mobility opportunities including roles, projects, gigs and mentorships–providing the kind of positive, proactive employee engagement that’s more likely to retain valuable talent. 

 

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. 

The 5 Pillars of Responsible AI

Posted on by Isabelle Bichler

Beginning in April 2023, NYC employers—and all organizations hiring and doing business in NYC—will be subject to one of the most stringent regulations governing AI to date. We’ve written extensively about the evolution of Local Law #144, which prohibits employers from using Automated Employment Decision Tools (AEDT) in hiring and promotion decisions unless they’ve taken affirmative measures. Specifically, employers using AEDTs in hiring must have them independently audited and must notify candidates in advance of their use. 

Why is this important? 

As AI becomes more embedded in HR systems, enterprise leaders face increased responsibility to ensure their solutions use Responsible AI to mitigate unintended bias risk. 

What exactly makes AI responsible? 

Responsible AI uses specific methodologies that continuously test for bias against personal characteristics and eliminate information that can introduce unintended bias. 

In all, there are 5 pillars of Responsible AI:

  • Explainability and Interpretability – AI machine learning outcomes, as well as the methodology which produces them, are explainable in easily understandable business-speak. Platform users have visibility into the external and internal data being utilized and the platform’s data structurization and outcomes delivery.
  • Fairness algorithms – AI machine learning models mitigate unwanted bias by focusing on role requirements, skills maps and dynamic employee profiles while masking demographic and other information that can potentially introduce bias.
  • Robustness – Data used to test bias is expansive enough to accurately represent a large data pool while being granular enough to provide accurate, detailed results.
  • Data Quality and Rights – AI system complies with data privacy regulations, offering transparency to the user around proper sourcing and usage of data, and avoiding using data beyond its intended and stated use.
  • Accountability – AI systems meet rigorous accountability standards for proper functioning, responsible methodology and outcomes, and regular compliance testing. 

In addition to building our Talent Intelligence Platform on Responsible AI from the ground up, retrain.ai exemplifies a larger overall commitment to innovation built on Responsible AI. As such, we work with the Responsible Artificial Intelligence Institute (RAII), a leading nonprofit organization building tangible governance tools for trustworthy, safe, and fair artificial intelligence. To learn more, visit our Responsible AI Hub.

 

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.

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. 

Update: Responsible AI and the NYC Audit Law Pushed to Q2

Posted on by Isabelle Bichler

On Monday, December 12, 2022, the New York City’s Department of Consumer & Worker Protection (“DCWP”) announced the Automated Employment Decision Tool (AEDT) Law (Local Law 144) slated to take effect in New York City, on January 1st will be delayed until April 15, 2023.

Created to ensure organizations using automated / AI-based hiring tools proactively protect against potential or unintended bias in the processing of candidate information or hiring decisions, the law requires organizations using such tools to comply with mandatory independent audits of AI systems and transparency about their use with candidates. With only months to go, this means the time for enterprises to evaluate their systems for ethical, Responsible AI is now. 

Learn how this law impacts HR Leaders everywhere, not just in NYC >>

Despite its designation as a local law, HR leaders everywhere must remain engaged in tracking its evolution. New York City is the epicenter of the business world, if an enterprise operates and has employees or is hiring employees in NYC this regulation applies to them.

So why the delay? 

The New York City Department of Consumer and Worker Protection (DCWP) is overseeing the rollout of the law. They say the delay is due to the high volume of public comments generated by a public hearing held in November. A quick review of the department’s website shows well over 100 pages of feedback and inquiries stemming from that hearing, including comments submitted by retrain.ai. The DCWP aims to review all input before planning a second hearing.

What sort of questions came up? 

Numerous points were raised, ranging from what specifically defines an AEDT to how regulation can remain effective without stifling innovation. A few specifics included:

  • What sort of qualifications and certifications will be required to select and authorize an independent auditor? 
  • How will data size be figured into the equation, given that some businesses won’t possess the robust data set necessary to accurately determine bias?
  • What options are available to candidates who opt out of the AI-based systems, as is their choice? How will they be assured equal consideration in the hiring process?

A second public hearing will be planned for the first quarter of 2023. In the meantime, we’ll keep you updated in our Responsible AI Hub, where you can also learn what constitutes unbiased, Responsible AI, what to look for in an HR Tech vendor to ensure compliance, and how retrain.ai uses the five pillars of Responsible AI to support the growth of a skilled, diverse workforce.  

To experience a personalized walkthrough of how retrain.ai can help you reach your HR goals, visit us here.

Additional resources: 

  • Responsible AI and the NYC Audit Law: What You Need to Know Before 2023 – On-demand webinar
  • Responsible AI: Why It Matters and What HR Leaders Need to Know – On-demand webinar

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. 

Not Headquartered in NYC? The New AI-based Hiring Regulations Will Likely Still Apply to You

Posted on by Isabelle Bichler

UPDATE: Local Law 144 will now go into effect on April 15, 2023. Learn more about the change here.

Beginning on January 1, 2023, companies using AI in their hiring practices in New York City must comply with Local Law #144, the Automated Employment Decision Tool Law (AEDT), which mandates independent audits of AI systems and transparency about their use with candidates, among other specifics. 

At its core, the NYC Law–and the larger EEOC statement that preceded it–aim to ensure that AI and other emerging tools used in hiring and employment decisions don’t introduce or augment bias that can create discriminatory barriers to jobs. You can read more about the details of the law in our earlier blog post. 

While some may believe the new regulation is just a niche city law that only applies to enterprises within the boundaries of New York City, impacting a relatively small pool of employers and job candidates, the reality is that its reach goes well beyond the NYC metro area and even the state as a whole.

Who needs to pay attention to the NYC Law?

Pretty much EVERYONE.

New York City is the epicenter of the business world, with many corporate roads running through it. If an enterprise operates any element of its business through NYC, and if they hire staff for that function, the law applies. 

Enterprises don’t need to be that expansive. Organizations using AI in hiring and promotions practices will need to ensure compliance with the new law if:

  • They have any sort of office or presence in NYC
  • They are based elsewhere but have open positions based in NYC
  • They have open remote positions that may attract candidates residing in NYC

But what if a company has only a single NYC employee, working remotely from their apartment in the City? Or if a global company has just one position to hire in Manhattan–which may be filled by a candidate living in New Jersey or Connecticut? 

It ALL counts. And reaches just about EVERYWHERE.

The geographic reach of the NYC law stretches far beyond the U.S. as well. New York City is a major hub for companies based all over the world and global companies who operate any part of their business–from a US Headquarters to a sales office, to a warehouse team and everything in between–fall under the requirements of the new legislation.

Strategize now for compliance next year.

Add up all the scenarios and you’ve got a massive number of companies that will be under the microscope come January. In today’s competitive landscape, stopping to retrofit HR systems for compliance presents a loss of momentum. Likewise accommodating multiple solutions across geographies or business functions. 

If you’re not sure whether your HR systems are using Responsible unbiased AI, now is the time to find a partner who can integrate with your HR tech stack, forming a unified system of intelligence that actively targets and eliminates unintended bias.

The retrain.ai Talent Intelligence Platform is built on the five pillars of Responsible AI to provide our customers with a transparent and bias-audited system. Our Talent Acquisition and Talent Management solutions help HR leaders hire faster and retain longer, while actively supporting a skilled and diverse workforce. 

To experience a personalized walkthrough of how retrain.ai can help you reach your HR goals, visit us here.

 

Additional resources: 

  • Responsible AI and the NYC Audit Law: What You Need to Know Before 2023 – On-demand webinar
  • Responsible AI: Why It Matters and What HR Leaders Need to Know – On-demand webinar

Using Data to Inform Talent Strategies and Address Global Workforce Challenges: IBM, Intel and retrain.ai Share Insights

Posted on by retrain.ai

When everyone has access to data and analytics, innovation can come from anywhere and go everywhere. In the current era of data abundance, what is the best way for enterprises to harness data innovations to take their business to the next level?

This was the question the Cloudera’s Evolve 2022 event in New York City set out to answer. 

An insightful mix of industry innovators, analytics experts, and data leaders spoke on topics ranging from managing data at scale, to modernizing architecture, to advancing analytics with machine learning (ML) and artificial intelligence (AI).

retrain.ai’s VP of Marketing, Amy DeCicco, presented alongside Madison West from the Global Corporate Responsibility Office at Intel and Hemanth Manda, Executive Director of Strategic Partnerships for IBM Data & AI. The session was moderated by Aman Kidwai, HR Strategy reporter for Morning Brew. 

The topic, “Using Data to Inform Talent Strategies and Address Global Workforce Challenges,” focused on how companies can use data and technology to guide long-term talent strategies, address the widespread skills gap nearly every industry is experiencing, ensure equitable access to stable career pathways and achieve business objectives.

Their lively conversation covered several key areas, including: 

  • Building a skills architecture as the first step to operationalizing a data-based approach to talent
  • Using AI to identify the right talent, their skills and skills gaps
  • Solving strategy challenges–pay gap, governance–through data
  • Investing in diverse talent and pipeline development with analytics

The conclusion?  People are your greatest asset, using technology, well-designed processes and holding an organization accountable for unbiased hiring and talent management is a key to the future success of any business.

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.