Talent Classification Guidefor the AI Era of HR

Contents

Introduction

1. A brief history of HR tech

2. The “big data” problem in talent

3. The case for talent classification

4. Use cases for talent classification

5. Buying AI: 101

6. Getting started with an AI project

Closing

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Introduction

Hi, I’m Andrew, the founder and CEO of Vettd. I’m happy you’re here.

For the last 5 years, Vettd has been working to bring together AI and HR in a meaningful way. We’ve built numerous solutions and iterated through more technologies than we can count. This effort lead us to create an entirely new AI practice. Talent classification has become a complete game-changer for the organizations brave enough to adopt it. We’ve seen companies finally harness the power of their talent data and create new ways for humans and AI to work together to maximize the impact of HR. We put together this ebook to help you learn about talent classification and navigate the current HR tech landscape. After reading, you’ll become better equipped to take on an AI project and learn:

• The nuances of HR’s big data problems.• Why talent classification is the foundational missing piece of your

HR tech stack.• What questions to ask when shopping for AI.• How to get started with talent classification.

We hope you find this ebook helpful. Please reach out to me (andrew@vettd.ai) if you’d like to keep the conversation going. Happy reading!

Andrew Buhrmannlinkedin.com/in/andrewbuhrmann

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1. A brief history of HR tech

HR and technology began their tumultuous relationship in the 1970s with the introduction of mainframe computing. With these monstrous machines, companies were able to capture records and digitize their reporting for payroll as well as some workforce management activities. Data entry clerks got to set their mechanical punch cards aside to use a keyboard and video display for their manual data input work. In 1979, SAP introduced an enterprise resource planning (ERP) system for these mainframe computers capable of combining HR and corporate data in real-time to regulate processes across the business. 10 years later, PeopleSoft introduced a dedicated human resource management system (HRMS) built with a new client-server architecture that was a step up from the mainframe model. The late 1990s marks the beginning of HR technology as we know it today. HR solutions accessed through the web became the new norm and online job boards were launched. The focus of these new tools remained largely on capturing and storing talent data to support the administrative nature of HR. Throughout the 2000s we’ve seen the HR industry embrace numerous tech trends such as social networking, gamification, mobile, and more.

Most recently, we’ve all watched as countless companies try to fit an AI-powered square peg into a round hole. AI has been promised as

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the answer to just about every HR-related problem companies face, but the actual impact of the latest tech trend is yet to be determined. Companies have felt the pressure, been oversold, and began to apply AI in HR. There have been very few clear wins and multiple notable blunders. As AI finds its footing, HR tech as a whole is evolving faster than ever before. 12 million employers spend over $5 trillion on payroll, benefits, and other employee programs in the US alone. Today, the average large company uses 9 core talent applications and spends $310 per employee per year on HR software (a 29% increase over last year). Spending is higher than ever and there are 731 different HR software solutions available at the time of this writing. This flurry of spending and activity is happening alongside a major shift in the expectations of the HR department. Over the last 30 years, HR has gone from solely being responsible for the administrative personnel function to owning full talent management processes that tie together data from staffing, training, compensation, and more. Many analysts believe that we’ve now approached a new era in which boardrooms expect HR to directly support business execution. Company leadership now expects much more than simply keeping track of who employees are. They now expect HR to be able to ensure that employees are being used effectively to support the company’s strategic initiatives. The reality, however, is that most HR departments are not equipped to answer the call. One-off AI tools have been ineffective in moving the needle. Every company has their ERP system or ATS in place. Their data is stored safely, easily accessed, and mostly organized thanks to modern

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technology. The data entry efforts of the ‘70s and ‘80s have become automated and modern systems are capable of storing nearly infinite amounts of data. We now face a new set of problems as we attempt to leverage AI to extract meaningful insights from these oceans of data.

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2. The “big data” problem in talent

With HR expected to contribute to the strategic alignment of a company’s workforce, it’s time to get value out of every last byte of data that companies store on their talent. This information typically lives in a few different systems and is used in a number of different talent workflows. One example is that candidate resumes will get stored in an ATS to be reviewed by recruiters and later passed into another HRIS.

Reviewing Resumes

For every 1 candidate that is hired, 249 additional resumes didn’t make the cut. Many recruiters admit to not being able to review every resume. Additionally, they spend less than 10 seconds on average reviewing each resume they do make it to. This is not nearly enough time to thoroughly read a resume and understand the nuances of the experience it represents. The volume of data and pure tedium of the task forces resume reviewers to take massive shortcuts when reading resumes:

• Skim each resume in 7.4 seconds rather than read it completely.• Use biases to make quick judgments on which resumes to

progress through the hiring process.• Review the resumes that come in earliest and stop when there are

enough solid ones to progress.• Skip reviewing the bulk of the resumes because reviewing 250 per

job would take too long.

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At times, the task of reviewing inbound applicants is skipped altogether in favor of:

• Pursuing passive candidates via LinkedIn (at a high cost).• Considering referrals only.

These shortcuts and alternatives undermine the hiring process. How is the hiring manager supposed to be confident that they’ve selected the best possible candidate if the majority weren’t even reviewed? Focusing on pursuing passive candidates or only considering referrals will hurt the employer brand, increase cost-to-hire, and still not assure that the best candidates were hired.

Navigating talent data

A secondary set of problems deals with the navigability of talent data. Often, organizations sit atop a gold mine of candidates that have expressed interest in working with them. But, surfacing these candidates when new opportunities arise is not an easy task. Resume databases are typically very limited, being organized only by the job that the resume is associated with. This provides only a single dimension for recruiters to search within. Some systems enable keyword search across all the text in every resume, but this has its own set of issues.

The sheer size, growth rate, and lack of depth render most talent databases close to unusable. It’s been found that people spend 30% of their working hours searching for information or duplicating existing information. For recruiters this means:

• Searching for talent profiles in their HRIS that aren’t there.• Failing to find talent profiles that are stored in their HRIS.

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• Sourcing the same candidates over and over again.

Tagging resumes manually

In an effort to increase the usability of talent databases, companies attempt to generate metadata manually. This means that recruiters will manually tag resumes with skills, capabilities, industries, roles, and any other attribute they value. In theory, this would generate a database with enough depth to become navigable and thusly incredibly valuable to the organization. Unfortunately, in practice, manual tagging just doesn’t work. Here’s why:

Time commitment & scalabilityWe’ve touched on the time it takes for most recruiters to review a resume (7.4 seconds). If tasks like tagging are added to the equation, recruiters now need to do more than just skim. They need to read, interpret, and assign tags. This will take more time and the only way to scale is to increase the number of people doing the manual review work.

Willingness & completenessBehavior change is hard. If recruiters have been doing something a certain way for years and a new process is introduced, it’s likely going to be an uphill battle. When some records inevitably fall through the cracks, the practice becomes far less valuable because dark data remains.

Accuracy & consistencyAs with any repetitious human task, accuracy will be an issue.

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People have different interpretations of words and ideas which will indefinitely lead to inconsistent metadata. Biases are a tool that can help humans get the job done quicker, but accuracy and consistency across a group of reviewers will suffer greatly. Additionally, biases and HR don’t mix well together.

Measurement & traceabilityA human-driven process also limits the capability to systematically trace the justification for certain decisions. The human decision-making process is complicated and often riddled with subconscious biases that simply cannot be explained. This prohibits the ability to systematically improve the metadata tagging.

These issues all stem from the fact that natural language data is both prevalent in HR and incredibly hard to deal with at scale. Talent acquisition is centered around resumes and greater strategic workforce planning initiatives often involve some form of unstructured language such as employee reviews, descriptions of project work, or written communication.

Whether hiring today or anticipating future organizational needs, it’s imperative to have a deep understanding of your talent. Mission-critical knowledge and insight is hidden in the natural language documents that companies have been storing for decades. The best way to achieve a deeper understanding of the workforce is to unlock the value of this data. With a powerful combination of technologies, this is now possible.

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3. The case for talent classification

Talent classification is simply the process of organizing human capital according to shared qualities or characteristics. Often, the characteristics that HR leaders value are skills, job roles, and experience levels. The combination of these attributes creates a picture of a person’s work identity which you’re then able to use in strategic decision-making.

Thanks to AI, these attributes can be pulled out of the natural language talent documents that we’ve been referring to.

How it works

Natural language processing (NLP) is a form of AI capable of analyzing large amounts of written (or spoken) words. This is the technology that allows computers to essentially “read” text. Some tasks commonly associated with NLP include:

• Parsing in HR typically refers to the extraction of fields into a structured set of information suitable for storage.

• Named Entity Recognition (NER) is the practice of mapping words in a text to a given set of proper names such as places or institutions.

• Natural Language Understanding (NLU) uses a combination of NLP capabilities to comprehend text in context. This is considered “an AI-hard problem”, which basically translates to - it’s complex and cannot be solved with one specific algorithm.

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A deep neural network (DNN) is a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. Below you’ll find a few tasks that DNNs are good at:

• Classification. Through a training process known as supervised learning, where humans transfer their knowledge through labeled datasets, a DNN is capable of categorizing items as accurately as a human might. Examples beyond talent include face detection, voice recognition, and spam email filtering.

• Clustering. Through a training process without labeled data, known as unsupervised learning, a DNN is capable of detecting similarities in items within a dataset. Examples beyond talent include similar images in Google search or anomaly detection for fraud in banking.

• Predictive Analytics. Similar to classification but dealing with time series data, DNNs are capable of making correlations between past and future events. Basically, a DNN would be capable of predicting the next number in a string if fed the right training data. Examples beyond talent include predicting customer churn based on activity or health predictions based on information from wearable devices.

A taxonomy is a classification scheme that typically follows a tree structure. We’ll call each leaf in the structure a “class”. There is typically one high-level class that every other class falls within. Underneath the main class, there are multiple sub-classes and each of these can have their own sub-classes. This forms a structure with multiple levels. For the purpose of talent classification, we can think of this like a folder structure on a computer.

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Imagine the highest level simply being candidates. Underneath this you could have candidates for marketing, sales, support, etc. Each of these forms the first layer. Inside marketing, you could have different roles, skills, disciplines, or any other distinction. These could be the classes or labels that get assigned to the candidates. Or you could drill down even further as long as there are meaningful correlations to make between groups of candidates.

Each circle represents a deep neural network (DNN) that

determines which sub-class to assign. These taxonomy structures

are unique to every organization and reflect how they understand

and organize talent.

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{ "skills": { { "Skill": "Enterprise Sales", "Score": 0.928365482 },{ "Skill": "Sales Management", "Score": 0.845383773 },{ "Skill": "SaaS Sales", "Score": 0.816390768

},{ "Skill": "Consumer Sales", "Score": 0.491903834

},{ "Skill": "SMB Sales", "Score": 0.380825829

} }}

In talent classification, the system leverages NLP to process and contextualize text, then uses a series of DNNs to appropriately categorize the document according to a given taxonomy. Let’s take a look at an example.

A lot is going on in the background, but at the core of talent

classification you have the resume as the input and confidence

scores as the output.

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Where it fits

Most applications of AI in HR are focused on screening, scheduling, or engagement activities delivered through chatbots. These experiences can lead to efficiency gains in HR workflows experiencing a specific set of pain-points. However, this is a narrow application of AI which we know to be capable of much more.

Talent classification is a foundational practice that aims to improve the quality and depth of data you rely on for talent acquisition and strategic workforce planning. With better data, you create far-reaching organizational impact that can improve every talent decision your organization makes. With more intelligence, you’re able to improve existing workflows and even create new ones.

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The HR Tech Stack

Talent classification is a foundational layer of your HR tech stack.

Any metadata that’s generated can be used by the rest of your HR

applications to create new workflows or provide deeper insight.

HR

Information Systems

Payroll & Bene ts

Comp & Equity

Talent Assessment

Plan Management

Recruiting, Sourcing

&

Applicant Tracking

& Selection

References, Onboarding&

Background Checks

Performance&

Development

EmployeeEngagement

Talent Classi cation

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Why you need it

Without talent classification in place, you’re essentially deciding to ignore key data. You’re paying (a lot) for core HRIS’s, but leaving value on the table by not doing enough with the data they’re designed to store.

Without an automated system in place to extract meaningful metadata, you rely on manual processes to pick up the slack. In reality, this practice just causes more inefficiency as you’re asking humans to do a task that most are simply not suited for. This translates to wasted time and effort as well as the increased risk of having frustrated or overwhelmed employees.

The IDC, a global provider of market intelligence, projects that the amount of global data will grow from 33 Zettabytes (ZB) to be 175 ZB by 2025, resulting in over a 400% increase. HR data is being generated quickly and growing alongside this global trend. For most organizations, this means their databases are becoming more and more disorganized every day. The effort to get a talent database into a more usable state becomes more costly as the amount of data exponentially rises.

Eventually, talent classification will be commonplace. Augmenting the manual review of HR documents is the perfect application for AI and it’s only a matter of time until it’s widely adopted. Although AI is still finding its footing in HR, experts agree that “automating parts of a job will often increase the productivity and quality of workers ... as well as enable them to focus on those aspects of the job that most need their

Fun Fact

1 Zettabyte is equal to

1 Trillion Gigabytes

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attention.” This potent combination of cutting-edge technology will process every piece of data that enters any HRIS so that HR practitioners can spend more time on impactful tasks. The end result will be realized by HR leaders who are able to make more informed decisions and position HR as the strategic business partner it’s expected to be.

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4. Use cases for talent classification

Let’s take a look at some examples of how talent classification is used in practice.

Skill Analysis

Skill analysis lets you identify relevant skills even if they aren’t explicitly mentioned. This means that you get a full understanding of the capabilities represented. Furthermore, advanced NLP techniques decipher context. For example, if a salesperson mentions that they sell a specific software, this will not be mistaken as direct frontline experience with that specific software. Rather, it will be interpreted within the sales context.

With skills identified in your current workforce and inbound applicants, you can begin to monitor supply and demand across the entire organization. You’re able to repurpose talent based on the skills fingerprint of each employee. Additionally, you can monitor and track trends. Are you seeing a shortage of machine learning talent? Maybe it’s time to start advertising to these candidates more heavily on job boards.

At the risk of getting too detailed for some, let’s zoom in further to see what skill analysis looks like when it’s operationalized. We like to think of this as being comprised of 3 elements: event, decision, and outcome. Here’s what those elements look like when the skill analysis AI model is being used as part of a talent acquisition

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workflow:• Event: New candidate applies to a job through a company

career page.• Decision: AI determines which skills and capabilities are

represented in the candidate resume.• Outcome: Tags are automatically applied in the ATS.

Recruiters can utilize these tags to better navigate their candidate list and make decisions.

This is one of the most straightforward applications of talent classification, but it alone generates data that’s much more useful than any manual process.

This illustrates a simple workflow where AI analyzes inbound

resumes for skills and applies labels within the ATS.

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A more complex example might include 2 AI models and look something like this:

• Event: New job description is added to ATS and manually tagged with required skills as well as a corresponding role type. Each of these tags correlates to an AI model.

• Decision: Each AI model examines incoming candidates for skills and role type (covered in next section). Then, added logic determines if the candidate that comes in matches the specified skill and role tags.

• Outcome: If the candidate tags match the ones that were assigned to the job description, the candidate is automatically tagged as a ‘Priority’ within the ATS. Using additional logic, recruiters could even be alerted anytime a priority candidate enters the system.

You can start to see how it’s possible to combine AI models and business logic with your existing workflows to automate complex tasks. If we lost you there, we’re happy to elaborate in a conversation.

Role Matching

Role matching lets you align talent with appropriate roles or role groups based on experience. Job titles are an insufficient way to organize data because there are simply too many variations to comprehend. (In fact, we found 159,014 unique job titles when building an early version of our backend). Everyone utilizes different terminology and it can be difficult to map these titles back to your organization’s perspective.

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With job labels applied to your workforce, you’re able to chart career paths for employees and recommend specific training to help them achieve their goals and stay engaged. With role labels assigned to incoming talent, you’re able to label candidates with the positions they fit best regardless of the job they applied for. From here, you can automate a workflow that recommends specific jobs to candidates based on their experience.

Project Matching

Similar to role matching, project matching lets you align talent with given projects. This is particularly useful for consultancies with rotating projects. You’re able to take a project outline and understand who would be the best fit to take on that project. This allows you to begin building project teams that are better suited to handle the job.

Position Leveling

Position leveling helps you understand the nuances between different experience levels for given positions. This goes beyond any simple years of experience calculation and drives at the type of work the person has done. Use this intelligence to make more accurate hiring decisions if seniority level is tricky to determine in a given domain. This can also be used as a means to educate recruiters and hiring managers on the differences between position levels.

These use cases only cover some of what is possible with talent classification. Take a minute to reflect on your own organization. Can you think of a way to leverage this technology? If so, read on.

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5. Buying AI: 101

The intersection of HR and AI is still fairly confusing to most (hence this ebook). Numerous vendors promising a wide range of benefits make it difficult to navigate the market. On top of that, most organizations are under extreme pressure to adopt the latest technology.

Here are a few things to consider and questions to ask when shopping for your next AI solution.

Beware of “the perfect hire”

When shopping for AI, be wary that the solution you’re evaluating may not live up to the hype. Many solutions tout “better hires, faster” or “the perfect hire” but HR’s many nuanced workflows and the complexity of hiring make these near impossibilities for AI to deliver. Ask the right questions. Are PoC/trials available? How will this affect my current workflows? How does this integrate with my current solutions? What is the learning curve for the end user? What data is needed to get it set up?

Privacy & Bias

Make sure your AI software vendor has a story around transparency and privacy. This is a topic that everyone needs to be thinking about since GDPR came into effect. The other big one is bias. We’ve covered how both AI and people can be biased when making determinations. What’s

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important is that your AI vendor has thought about this and has the capability to measure for and minimize bias.

Further reading: https://www.vettd.ai/blog/the-transparency-problem-with-ai

Customization

Many AI vendors sell the same AI models to everyone. This translates to no real competitive advantage for you. If there is no customization - what do you do if the model seems to be incorrect? Every organization has a unique perspective on the way they view talent. Your AI should reflect that.

Hosting & Ownership

You’ll want to understand where the AI will live and who has access to it. Think about the current systems you have in place and how AI should interact with them. What will happen to your AI if you switch systems in the future? Who actually owns and ultimately controls the models being used? Will your vendor give you the model files?

Pricing

Make sure to pay attention to how the AI is priced. Think about how it will be used in practice and the impact it will have across your workflows. How many end-users will it affect? Is the pricing structure conducive to how you intend to use it? Is it a flat rate or priced per usage?

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Goals

This goes without saying, but make sure to have a goal in mind when shopping for a solution. You may be under pressure to adopt any AI solution, but make sure to understand the AI addressable problems you’re facing. This way, you won’t be in trouble when it comes time to communicate the success of your project.

Further reading: https://www.datasciencecentral.com/profiles/blogs/twelve-types-of-artificial-intelligence-ai-problems

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6. Getting started with an AI project

Still with us? There’s a lot to wrap your head around here, but we promise you can handle it. Getting started with an AI project like talent classification doesn’t have to be difficult. In fact, most teams that we engage with have never implemented AI before. Here are a few things to think about as you get started with AI for talent classification:

Data

Typically, our customers are sitting on top of large databases filled with talent data that has been manually tagged, sorted, or categorized in some way. This is what is used to train the AI models that power talent classification. It’s still possible to take on an AI project even if you don’t have organized data. In this case, there is a bit more work upfront to acquire data and prepare it for training. Here are a few questions to consider when getting started:

• What data do you have access to?• Where does the data live and how can it be exported?• Is the data you have access to comprised of natural language?

Workflows

Another idea we’ve referenced throughout is workflows. We use this term to describe any sequence of processes that is performed to achieve some business objective. Typically, a workflow involves an end-user who uses the system, consumes information, and makes decisions.

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Workflows also encompass data. Data advances through a workflow and is modified or augmented at various stages. Thinking about the various workflows that make up your HR practices will help you understand where talent classification can fit. Consider the steps that make up some of these workflows:

• Inbound applicant review by recruiters• Application experience from the applicant’s point-of-view• Finding and selecting internal candidates• Recommending learning and development opportunities.

Team

There are a few key personas that make up an effective, AI-ready team. Covering these areas will help ensure success.

Business AnalystThe business analyst is a catchall persona representing the person from your team that will do the necessary to push the project through. This means working with a technology partner and the internal team. This person should be able to analyze data and make decisions that reflect the overall project and business goals.

Data TechnicianYou will need to have someone that can work to get data out of your current systems. This could be someone in IT or anyone responsible for maintaining the software that your organization uses. The process of getting the bulk of your data out of an HRIS varies greatly. It can be as simple as pressing an export button or require getting in touch with the software provider. Your data

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technician will handle that.

SponsorAs you know, it’s tough to get any project off the ground without a budget. Getting buy-in from leadership on AI initiatives is critical. This means bringing together everything we’ve covered in this ebook to make a solid argument for your talent classification use case.

Technology PartnerThe last element to think about when getting started with an AI project is who your technology partner will be. We think you should consider us and here’s why:

• Vettd has been focused on AI for talent classification for over 5 years

• We have 4 patents pending on NLP technologies that tackle talent data issues

• We’ve been recognized as leaders by industry experts such as Gartner and 451 Research

• We literally wrote the book on AI for talent classification. :)

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Closing

If you stuck with us, you should have a good understanding of what talent classification is, why it’s important, and how to get started. We hope this puts you in a position to start meaningful conversations about AI with your team. With the realities of AI for HR in check, we hope that you become part of the new wave of organizations generating true impact with AI.

Talent classification is the most foundational application of AI in HR to date with the power to improve decisions and workflows across every aspect of talent. In the near future, this new AI practice will be a default layer of any complete HR technology stack. HR leaders will use a newfound depth of knowledge to become the strategic partner that their organizations require. HR is poised to not simply just take advantage of the latest tech trend, but to set a new standard for data management that will transform how organizations work.

Thanks for reading!

For more information, email us at info@vettd.ai or visit us on the web at www.vettd.ai.

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“Humaaans” in cover art by Pablo Stanley is licensed under CC BY 4.0

Written by: The Vettd Team

Illustrations by: Kurtis Dane

The author of this ebook and the accompanying materials have used their best efforts in

preparing this ebook. The author makes no representation or warranties with respect to the

accuracy, applicability, or completeness of the contents of this ebook. The information contained

in this ebook is strictly for educational purposes.

Copyright © 2019 Vettd, Inc.