Skills Architecture is the key to SBO – and AI is the tech enabler

Talent acquisition specialists are continuously challenged to match candidates with the right skills to positions that feed evolving business needs. Technology and data are the one-two punch that empowers HRs to do so effectively.

Skills Architecture: Building the SBO Foundation

A comprehensive skills architecture helps HR leaders start the process with a clear understanding of what skills are already within their talent pool and where the skills gaps lie so they can target the right candidates more quickly. 

As such, skills architecture acts as the blueprint that defines and structures the skills within an organization, unifying skills language and moving beyond traditional job descriptions to establish an agreed-upon nomenclature. It’s the foundation on which a centralized Skills-Based Organization is built, pulling together skills data across teams to form a larger, more strategically malleable view.

“We’ve always historically said that this system doesn’t talk to this system, and this system doesn’t talk to this system, and the spaghetti plate of systems is what disables us from getting data or from making informed decisions that are embedded and rooted in true data and analytics,” says Sadia Ayaz, VP of Talent Management for Veolia. “Here we are today, capable of doing that. AI can take all of that unstructured data and make sense of it.” 

AI: The SBO Tech Enabler

AI’s machine learning algorithms and expedited data analytics are the technological backbone of a sophisticated skills architecture. This is due to its ability to: 

  • Ingest vast datasets to identify both existing and emerging skills within an organization 
  • Detect patterns and trends, allowing HRs to stay ahead of critical skill gaps or areas that require upskilling
  • Reveal hidden talent within an existing workforce, uncovering internal job candidates for consideration

Beyond talent acquisition, AI-driven technologies also fortify talent management strategies in a Skills Based Organization. Through personalized learning paths and recommendations, AI enables employees to acquire new skills in alignment with organizational goals, while also benefiting from individualized professional development. 

By harnessing the power of AI to construct a detailed and dynamic skills architecture, and engaging AI as the tech enabler for a successful Skills-Based Organization, HRs can hire and retain the right talent to fuel an SBO workforce model. Synergy between skills architecture and AI is the cornerstone of shaping the future of talent acquisition and organizational success.

Learn more about the power of retrain.ai’s Talent Intelligence platform here.

Mastering Hiring Challenges: How AI Levels the Field for Companies of All Sizes

Large enterprises and smaller organizations have similar challenges when it comes to hiring: Finding the right people for best-fit positions quickly, accurately and efficiently.

This at a time when 53% of in-house recruiting pros predict their recruiting budget will decrease or stay flat this year.

Fortunately, the emergence of AI-driven talent intelligence platforms has leveled the playing field, providing companies of every size with powerful tools to attract, hire, and retain top talent with the skills needed for success. 

The AI Advantage

A December 2023 report from Gartner names technology as the No. 1 investment area for HR leaders–for the third year in a row. The reason? HR increasingly relies on technology to meet a growing list of business demands, including fulfilling employee experience needs, enabling talent agility and continuing HR tech transformation.2

AI-driven talent intelligence platforms act as force multipliers, by automating time-consuming tasks, enabling the agility needed for companies to achieve more with fewer talent resources. 

Data insights generated by AI can also empower companies to target their efforts more precisely, ensuring that every investment in talent pays off in the quest for sustainable growth and competitiveness in the marketplace.

AI-driven platforms provide an advantage by: 

  • Analyzing vast amounts of data at lightning speed, providing actionable insights to help HRs make informed hiring decisions
  • Streamlining the sourcing and screening process, reducing time-to-hire and ensuring HRs can compete effectively for top talent
  • Personalizing internal mobility opportunities, using skills data to predict potential career pathing options for employees.

AI-driven automation frees up HR professionals to focus on larger strategic initiatives; and just as importantly, to spend more time on the face-to-face, human-centric aspects of the job. 

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 it in action, request a demo.

Gartner report: Mitigate Bias From AI in Technology

The following is a summary of “Mitigate Bias From AI in Technology,” a report from Gartner, available here for Gartner partners. The full report is available for download here (For Gartner Subscribers)

Summary

Organizations are rapidly adopting AI in HR, while regulations are struggling to keep up. As part of their HR strategy, HR leaders must promote responsible AI in their applications by mitigating bias that poses risks to talent management, the employee experience, DEI and more.

Key Findings from the Gartner report

  • Fifty-three percent of HR leaders are concerned about potential bias and discrimination from AI. Bias in AI is unavoidable; however, HR leaders can establish best practices that mitigate this bias.
  • Thoroughly vetting HR technologies for bias requires an understanding of business processes. Broad overarching assessments can lead to missed sources of bias, or to inaction due to fear of getting it wrong. The organization should assess each use case individually.
  • AI regulations, including HR-specific measures relevant to bias, are gradually taking effect. Since many HR functions plan to buy AI capabilities built by vendors, HR leaders face the need to monitor technology providers for their regulatory compliance and ethical considerations.
  • HR leaders are positioned to take a leading role in advancing practices that bolster openness about the potential impacts of bias from AI applications, and 35% of HR leaders recently reported they expect to lead their organization’s enterprise wide AI ethics approach.

Gartner Recommendations

HR leaders responsible for technology strategy must:

  • Map possible sources and outputs of bias for each AI use case in HR to assist in flagging areas of risk and monitoring vendors for their commitment to responsible AI practices.
  • Require and evaluate bias mitigation from HR technology providers offering AI functionality by assessing criteria related to their data, algorithms, organizational context, regulation compliance and ethical considerations.
  • Promote transparency into the potential impacts of AI’s bias by collaborating with external and internal stakeholders to take decisive steps in protecting the organization, the future of work and society at large.

Gartner, Mitigate Bias From AI in HR Technology, By Helen Poitevin, 16 October 2023

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Webinar Recording: Unlocking the Power of AI in HR

A recent webinar sponsored by Crain’s New York Business and NYU School of Professional Studies explored the advancements in AI in HR, including a discussion on why it’s critical for organizations to venture into this technological realm responsibly.

Moderated by Dr. Anna Tavis, Clinical Professor & Chair of the Human Capital Management Department of NYU’s School of Professional Studies, the conversation featured retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf along with with Sadia Ayaz, VP of Talent Management for Veolia, and Heidi Ramirez Perloff, SVP of Global HR Strategic Initiatives and Delivery Solutions for the Estée Lauder Companies. The full recording is below.

 

 

 

HR Tech 2023 Highlight: Generative AI Isn’t a Trend, It’s a Problem Solving Tool

At HR Tech 2023, retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf sat down with Dan Riley, co-founder of RADICL, to talk about the surge in Generative AI solutions in the HR space and the importance of its ethical, responsible use. Below are excerpts from their conversation; the full recording can be viewed here.

DR: You brought up responsible and ethical AI. How are we doing? Not just retrain.ai, but the industry in general. Are we getting there?

IBE: This year, everybody is talking about AI. Specifically, everybody’s talking about generative AI, and ChatGPT was a great demonstration to show the amazing abilities of AI. But it also showed the pitfalls. It showed it to be erroneous, biased and very generic, not stable enough. So Responsible AI is all about that; putting safeguards on the AI. It’s just a tool, right? So you need to use it wisely with the right safeguards. 

Responsible AI principles span from explainability and bias reduction to consent and embedded privacy rights and so forth, and that’s what we’ve been doing at retrain.ai from the get-go. This is something that’s very important to me, it’s something that I’ve done as part of my research as well around the risks of AI. So now I’m happy to see that a lot of people are thinking about it and starting to do something about it. 

Too often, we either blindly trust AI or we blindly distrust it. But it can’t be a binary conversation. So how do we find that middle ground? How do we challenge it and use it for good? What are some of the things retrain.ai is doing to make sure that happens?

It’s about design, development and deployment. It’s about the safety that we put into the technology, first of all to understand the data that we need to be distributed. And, you have to have representation for different protected classes, for example, to prevent biases. You also have to constantly measure the output and understand if it’s having an adverse impact on certain protected classes – gender, age, ethnicity, and so forth. Those safety guards must be in place all the time.

There’s also a lot of regulation emerging now to enforce that. Local law 144 in New York City is actually mandating that companies show and prove that their output isn’t biased. Beyond biases and discrimination, it’s also about explainability. With our product, we explain why a person is a good fit for a position based on their skills. It’s not a black box; the tool has to be transparent and explainable. 

So we’re here at HR Tech, where for the most part if you talk to any vendor, they’re going to talk about what they’re doing with AI. What’s your advice for the industry in general?

I think you first need to really understand the problem that you’re solving. AI is a tool; so it’s not just about saying hey I want to bring in AI, I want to bring efficiency. What is the pain point? What is the problem you’re solving? What’s the use case? And then, do you have the right tool for it? Once you have that, you’re okay. But just adopting AI across the board because it’s something trendy or because the notion of efficiency is there, or productivity, it’s not enough. You need to know the problem you’re solving. And depending on the use case, you need to have technology with deep AI; not just augmentation and chatbots. It’s really the data that’s used, and the algorithms, and the generation of AI. You need to use really advanced models based on LLMs, with safety guards, in place to give you the results you want. 

 

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 it in action, request a demo. 

AI’s Role in Revolutionizing Talent Acquisition and Retention

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.

Update: NYC AI Audit Law (Local Law #144)

As of July 2023, Local Law #144 in New York City is in full effect for enterprises using Automated Employment Decision Tools (AEDT) to assist in hiring decisions. The complete rollout of the new regulation includes punitive enforcement for those found in disagreement with the law.

Given the prospect of a civil penalty for non-compliance, HR leaders are well-advised to keep up to speed on specific requirements of the law, starting with AEDT audits. The law mandates that:

  • AEDT system audits be conducted by an independent entity
  • Summarized audit results be posted on the employer’s website
  • Alternative selection processes or accommodations be provided to job candidates who opt out of AEDT-assisted selection

Despite being known as a NYC-based law, Local Law 144 has generated questions about its actual domain. The geographical guidelines now clarify: 

  • If the role is located in New York City: A bias audit and notice must be sent to NYC residents
  • If the role is located outside of New York City: A bias audit and notice are not required
  • If the role is fully remote but the company has only a New York City office: A bias audit and notice must be sent to NYC residents
  • If the hiring company does not have an office in New York City: A bias audit and notice are not required
  • If the hiring company has offices both in and outside of New York City: Specifics of the role will dictate the need for a bias audit and notice

To break the law down further, let’s start with a few, tangible moves HR leaders and Talent Acquisition specialists can make now to plan for Local Law 144 compliance:

  1. Conduct internal assessments to find out what tools are currently being used that may qualify as an AEDT
  2. Build an inventory and tracking plan for identified AEDTs, including where and how they’re used
  3. Monitor and track AEDT performance, including data input and outcomes, to test accuracy
  4. Develop policies and procedures for storing demographic and selection data needed for AEDT audits
  5. Create a compliance team of HR leaders, legal advisors and tech representatives to prepare or adapt compliance disclosures 

As stewards of Responsible AI, we at retrain.ai are keenly aware that the issues raised by Local Law #144 directly impact the talent intelligence space. Automated, accelerated, bias-free hiring processes powered by innovative technology act as the key to growing and supporting a diverse and inclusive workforce; Responsible AI is the key to reaching DEI goals and remaining compliant.

If your organization is considering integrating an AI-driven solution into your HR tech stack, be sure to check out our Buyer’s Guide to Talent Intelligence to learn more about what to look for, what questions to ask, and how to engage your teams in the process. 

 

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 it in action, request a demo.

Navigating The Promise And Peril Of Generative AI In HR

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

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

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