Webinar: retrain.ai and Citi Global Insights discuss how AI empowers HRs (Part 2 of 2)

retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf was recently invited to speak with Rob Garlick and Wenyan Fei from Citi Global Insights for a webinar entitled “Are Talent Intelligent Platforms the Solution to Reskilling and Upskilling?” 

Below is the second of a two-part blog series highlighting excerpts from their conversation. You can also view a recording of the full session here.

Isabelle Bichler Eliasaf, Rob Garlick, Wenyan Fei

You mentioned that retrain.ai represents fifth-generation AI. Can you share with us some unique features enabled by this latest generation of AI and what differentiates your platform versus other providers?

Yes, for sure. It’s really honing in on the skills taxonomies we’re creating; retrain.ai built one of the biggest data assets that exists today. We’re bringing a holistic solution from hire to retire, but the first thing you need to do is start with the skills architecture model. It uses the robustness of skills taxonomies to see details and skills, quickly analyzing all the roles of an organization. We’re providing a complete, dynamic catalog to our customer so they can have all their roles analyzed by skills–technical skills, soft skills, hard skills, and so forth. They have an understanding of the skills that are in demand, emerging skills, those that are declining, etc. This is the first step; really building the foundation in order to add on the next floor which is going to be talent acquisition or talent management and so forth. 

What we found is that a lot of organizations don’t do all that; the problem is that they operate in silos, without a unified skills language. They don’t share the same philosophy and the same strategy across teams. But cross-functional strategy should be shared because they’re going to use the same foundation of skills, the same framework. So what we’re doing at this stage is consolidating and synchronizing all teams in the same language. We’re taking the LMS skills framework and adapting it to the HCM and all the others used in an organization. That’s the first step, and now with that accurate, granular, robust skills data, I can inform talent acquisition; I can quickly create a job description based on the skills that I’ve analyzed for that role. Then I’m able to analyze the skills of your people; I can generate a heat map to understand what skills and organizational capabilities you have, and the skills gaps that represent weaknesses in specific departments. The skills data is also going to serve for learning and development to understand what the top skills are, and what the emerging skills are to fill skills gaps.

You say you’re a technology company built on data. Can you walk us through the kind of data that you ingest and how you train your model? It must be a huge amount to get to the point where you need to be in order to add value to people. Does this just get better and better as you are able to train it?

Exactly. It’s learning constantly. And the more data you collect, the more accurate you get so you can generate those predictions. What we’ve done throughout our first two years of inception is build a database by collecting from job boards, Indeed, all those different job boards, job descriptions, and so forth. Just on LinkedIn, you have 400 million active users that you can learn from; what career pathways they’ve pursued, and what skills they have for their position. We ingest all of this, plus pre-built taxonomies from Europe and Canada, and we continue to increase with market data. You can add even more from sites like Udemy; what are the learning trends, and what topics are in demand? All of this together feeds into one data asset. That is really integrated intelligence because we integrate with HCMs and older systems of record, not replacing them but just making them smarter with data. 

People are fearful about data privacy, algorithmic bias, bias in hiring, etc. How do we tackle this as a society? Is AI part of the solution or part of the problem? How are you guys doing that at retrain.ai in terms of responsible AI?

This is why I’m very passionate about responsible AI; because this is a problem and solution. AI is amazing. It’s like saying you’re not going to use electricity because there are risks. Of course you will, but you need to take some precautions. For us at retrain.ai, GDPR is what we’re embedding so that we’re compliant with the most stringent requirements. We’re also making sure all data we have is used with the consent of candidates; we know this because they chose to put their data online with all the information and informative disclosures involved. 

There’s also new legislation around telling you that you’re going to be assessed by a machine and allowing you to opt out: Local Law 144 here in New York City. I actually submitted public comments on the law along with others and one thing they hadn’t figured out is, if I’m a candidate opting out, am I able to be fully assessed? Am I actually discriminated against because I’m choosing that AI not be used? We had an amazing conference with Commissioner Keith Sonderling of the Equal Employment Opportunity and we were talking about that; his take on it is that the law didn’t change anything that wasn’t already in place. It didn’t do anything new. It’s still saying you shouldn’t discriminate based on gender, ethnicity, age, and all those different parameters–there’s another entity performing that assessment. I agree with him, but I think that the fear of AI makes us more aware and want to limit it. So now we need to balance between innovation and regulation.


Webinar: retrain.ai and Citi Global Insights discuss how AI empowers HRs (Part 1 of 2)

retrain.ai Co-founder and COO Isabelle Bichler-Eliasaf was recently invited to speak with Rob Garlick and Wenyan Fei from Citi Global Insights for a webinar entitled “Are Talent Intelligent Platforms the Solution to Reskilling and Upskilling?” 

Below is the first of a two-part blog series highlighting excerpts from their conversation. You can also view a recording of the full session here.

Isabelle Bichler-Eliasaf, Rob Garlick, Wenyan Fei

What is a Talent Intelligence Platform? What is the technology that’s been transformed by AI to make upskilling possible?

Talent intelligence is really one of the biggest successes when it comes to AI for HR use cases. Talent Intelligence Platforms bring together all the relevant internal and external data sources into one useful tool to create a holistic view of a company that empowers decisions about talent or workforce planning. It’s really a merger between people analytics, market insights, market trends and workforce planning. 

If you’re looking at the system of records–HCM or ATS–AI makes them smarter. Why? Because it answers the most critical questions of recruiters: Who should they hire? What are the criteria based on skills, but not just basic skills? What is the experience required? This informs the recruiter as to who they’re looking for. With AI, they can take an omnichannel approach to sourcing candidates from two different pipelines: External sources, plus internal talent that may warrant a fresh look based on skills that are in demand now or that are emerging. HRs are able to hire much more efficiently by broadening the talent pipeline and looking at potential, not just unrelated qualifications and job titles. This is how talent intelligence is informing and empowering those HCM and ATS HR flows.

It’s my understanding that this is the same transformation technology that lies behind the large language models. If you don’t mind, maybe just contrast today’s tech with some of the legacy HR systems that create challenges for companies in terms of being able to extract data. 

What really has enabled us to bring increased efficiencies for retention and faster hiring and so forth are those models, of course, plus there’s been exponential growth–a leapfrog really–in AI This is because there’s much more data to train our models upon. In just the last two years, 90% of the current data was generated; that’s how big the computing power is now. It’s also been cheaper so we’re able to train better and more efficiently.

At the most recent HR tech conference in Las Vegas, analyst Josh Bersin recognized our work and categorized companies in AI into three types of companies. There are companies that are now adding AI into their flows, like adding a chatbot. Then there are companies such as SAP Oracle that have a lot of transactional data about candidates and employees and are now running some models to predict what would be a good fit, or what would be the next course an employee should take. Then there are the new types of companies such as ours that are built from the start on AI. We are the companies using neural networks. retrain.ai is using 1.8 billion data points and counting from all different sources to support HR flows; so it’s not just going to give an understanding of what you have in your company but also the ability to benchmark that against the market.

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.

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.



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


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

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.