Events

Key Findings

1. Skills and competencies are two sides of the same coin. Skills and competencies both help answer 2 critical questions:
   • “What can our workforce do now?”
   • “What will our workforce need to be able to do in the future?”

Although there are differences between the 2—both material and perceived—forward-thinking orgs are finding ways to reconcile skills, competencies, and the data they both offer to solve their people challenges.

2. Leaders don’t need to choose between skills and competencies. HR functions sometimes debate about which framework—skills or competencies—to use. Instead of trying to choose 1 of the 2, however, all of HR should embrace both frameworks to ensure as much information as possible about employees’ abilities and expertise is surfaced. This approach can both afford employees opportunities they wouldn’t otherwise have and also enable the org to make better-informed talent decisions.
3. Skills and competencies can help solve business challenges. Skills and competencies can only be effectively reconciled within the context of the people-related business challenges an org is facing. The 4 people challenges that skills and competencies most often help with are:
   • Employee development
   • Career mobility
   • Diversity, equity, inclusion, and belonging (DEIB)
   • Performance management

4. Leaders should consider all the skills and competencies data available. Having more data (both broader and better) usually equates to having a more complete picture of the skills and competencies within an org—and what’s still needed to meet org goals. As companies begin to consider skills and competencies together, accounting for the data provided by both can help to more fully inform the org’s strategies and decisions.
5. Employees need clear messaging about skills and competencies. Employees and business leaders are often confused by skills and competencies. Many employees don’t care about the differences between the two: They just want to know what’s expected of them. Leaders should create clarity by using consistent terminology and messaging that highlight not only how employees should use skills and competencies systems, but also the benefits of skills and competencies for employees.

Skills & Competencies: Why We Struggle with These Terms

The ongoing skills conversation has ramped up since the beginning of 2020. COVID-19, a social justice movement, and multiple natural disasters have created a business imperative for orgs to pivot quickly and continually. These events have also spotlighted the longstanding need for orgs to support all employees, not just a select few.

Skills, which used to be a conversation about robots taking human jobs, has become one about org agility and inclusivity. Central to this conversation are 2 questions:

• What can our workforce do now?
• What will our employees need to be able to do in the future?

Leaders must help their businesses by figuring out fast, user-friendly ways to answer these questions. Orgs can’t effectively pivot in rapidly evolving environments without clearly understanding what’s currently possible and what’ll be needed in the future.

Orgs can’t effectively pivot in rapidly evolving environments without clearly understanding what’s possible now and what’ll be needed in the future.

Two of the most common frameworks used in this situation are skills and competencies. During our research on this topic, leaders reported having spent hours talking in circles about the definitions of and differences between the 2 frameworks. Most leaders agree that it often feels like a futile and unhelpful conversation, yet they still find themselves engaging in seemingly endless debates about the differences.

Why? From our research, we’ve discovered 3 reasons.

1. Different perceived purposes. Skills and competencies, as defined by HR, are seen to have different purposes, which may affect how they are used to fill business needs. For example, competencies are still important to efforts like performance management and leadership development. And skills have gained in popularity. But orgs haven’t necessarily found a way to reconcile all the perceived differences between skills and competencies.
2. Inconsistent language. The terms “skills” and “competencies” don’t have consistent definitions in the literature—and even within orgs they’re often not clearly defined. People in various parts of the org, therefore, often try to answer the same questions using an assortment of terms (or the same few terms with different meanings), which only creates miscommunication and confusion. Leaders with cross-functional perspectives should see this as an opportunity to align everyone and speak the same language.
3. Technology. Tech platforms often account for skills and competencies in different ways. In some orgs, information about skills and competencies may even reside in multiple systems. This causes confusion and sometimes incomplete data when, for example, one system uses competencies and another skills.

Orgs must reconcile how skills and competencies are defined and used across the business in a helpful and clear way.

Bottom line: Orgs must reconcile how skills and competencies are defined and used across the business in a helpful and clear way. Let’s start by first exploring these 2 terms.

How ARE they different & does it matter?

Confusion with these terms clearly exists. From our literature review, we found lots of contradictory information about what skills and competencies really are. Some sources used these 2 terms interchangeably, while others drew clear distinctions. Leaders we spoke with corroborated this confusion, reporting that most people in their orgs don’t know if the 2 terms are synonymous or different—and whether it even mattered.

The differences do matter—but only to those of us trying to make them work together in our orgs.

As it turns out, the differences do matter—but only to those of us trying to make them work together in our orgs. As we discuss later in this report, messaging to the broader org may need to be very different from what’s going on “under the hood” with skills and competencies.

That said, understanding the key characteristics of skills and competencies can help orgs both leverage each more fully and combine them in helpful ways. When we dug into the characteristics, we found that most leaders agree on the characteristics for each as shown in Figure 1.

Characteristics of skills

In general, skills tend to be more granular, descriptive, and applicable across jobs or orgs. They describe what an employee can do, but rarely do they prescribe how a task or job should be done. Skills data is often dynamic and real-time, since it’s gathered through a number of continually updated sources such as employee skills profiles, social media pages, and operational systems like email. In many cases, the employee is responsible for their own skills profile and development.

Skills tend to be more granular, descriptive, and applicable across jobs or orgs—describing what an employee can do, but rarely prescribing how a task or job should be done.

In more traditionally structured orgs, skills are used to understand (by accessing far better information than a resume can provide) which employees can fulfill open roles. More orgs are paying attention to current employees’ skills as they’ve realized the benefits of hiring internally versus acquiring skills from the outside.

Understanding the skills a workforce has allows leaders to identify individuals who can form teams to accomplish pieces of work.

While the conversation on skills has been around for some years now, the skills movement continues to gather steam as “future of work” conversations continue to happen and orgs continue to move toward project- and team-focused ways of managing work. Understanding the skills a workforce has allows leaders to identify individuals who can form teams to accomplish pieces of work.

Characteristics of competencies

Competencies tend to be broader than skills, prescriptive, and specific to (or at least in the context of) a job or org. They tend to describe the behaviors expected from employees by explaining how a job should be done, or how an employee should perform to succeed in that particular org or job.

Competencies tend to describe the behaviors expected from employees by explaining how a job should be done, or how an employee should perform to succeed in that particular org or job.

While competencies can come across as slightly archaic and cumbersome (particularly as the world becomes more skills-focused), they can provide tremendous value to orgs. Competencies are often seen as the “how” in comparison to the “what” of skills. Because of their focus on behaviors, competencies are essential in some orgs to maintain a healthy culture and, quite frankly, public image. They also provide robust and defensible structures for many performance management systems.

Competencies are often seen as the “how” in comparison to the “what” of skills.

Competencies are typically identified from the top down, involving leaders to identify and outline key expectations—aligning competency data closely with broad org needs, goals, and often culture. But it also means the data is typically static and from a point in time. As such, HR often owns competencies, and is responsible for periodically reviewing and updating them as needed.

“And” not “or”: Making the most of skills & competencies

Perhaps due to the differences in these terms, HR functions may disagree about which one—skills or competencies—to use. Instead of trying to choose 1 of the 2, however, all of HR should embrace both frameworks to ensure as much information as possible about employees’ abilities and expertise is surfaced. This impacts business in 2 ways, as this info:

• May well afford employees opportunities they wouldn’t otherwise have access to or know about
• Enables the org to make better-informed talent decisions

All of HR should embrace both frameworks to ensure as much information as possible about employees’ abilities and expertise is surfaced.

This is no simple feat. There’s no clear “right” way to implement skills and competencies together in an org. That said, our review of almost 100 articles, discussions with 50 leaders in our roundtables, and in-depth interviews with 6 orgs all led us to 3 key strategies that enable orgs to find what works best for them:

1. Using skills and competencies to solve business challenges
2. Identifying and using the data they both provide
3. Crafting a clear message about skills and competencies

In the following sections, we take a closer look at each of these strategies and how they can impact different business challenges.

Using Skills & Competencies to Solve Business Challenges

One of the resounding themes of this research is the need to take business goals into account when considering skills and competency strategies. Leaders emphasized that skills and competencies can only be effectively reconciled within the context of the business challenges the org is facing. One leader put it quite well:

“What’s the business problem you’re looking to solve? A new way to hire people? Learning objectives? Career paths? You might want skills in one situation and competencies in another.”

Christina Norris-Watts, Head of Selection Assessment & Competencies, Johnson & Johnson

We find this idea enlightening. Like most people decisions, use of skills and competencies should depend on what orgs are trying to do. In our discussions with leaders, it was refreshing to see how they view skills and competencies helping with some of their larger people challenges. From those discussions, leaders identified 4 primary business challenges for which skills and competencies could play a role:

• Employee development. How and what kinds of resources are used to build the skills and competencies of the workforce
• Career mobility. How employees move around, up, down, and out of an org, based on both their preferences and the needs of the business
• Diversity, equity, inclusion, and belonging (DEIB). How well an org provides transparent opportunities to all of its employees
• Performance management. How orgs determine an employee’s progress and pay

Interestingly, while for some business challenges either competencies or skills takes the lead, both were identified as being useful to each of these challenges. Let’s dive into them.

Like most people decisions, use of skills and competencies should depend on what orgs are trying to do.

Employee development

We listed employee development as the first business challenge that skills and competencies can help address because it’s likely the most obvious. A 2019 McKinsey study emphasized that the future of L&D depends on the ability to identify and develop the employee skills and competencies which will support the execution of the company’s business strategy.¹

L&D can no longer succeed using the “shotgun” approaches of yesterday—strategies that provide all employees with the same training, regardless of whether or not they need it. Instead, orgs should know, in very targeted ways, what their employees can do and what they need to be able to do—then fill in the gaps.

In very targeted ways, orgs should know what their employees can do and what they need to be able to do—then fill in the gaps.

Understanding skills and competencies can help L&D functions fill those gaps by enabling many types of development opportunities, not just providing content. As development becomes more widely defined, for example, orgs are helping employees find assignments that enable them to grow.

“Let’s say a project requires 10 skills. An employee has 8 of them and wants to develop the other 2. That’s a perfect match because we know the employee can do the project without failing, but will also get some development out of the experience.”

Caroline Theaker, Senior Manager for Learning Strategy, Lloyds Banking Group

In Figure 2, we offer examples of how skills and competencies are each being applied to development issues in orgs today.

Using both skills and competencies improves employee development by helping an employee move toward their career goals (illuminated by skills) and helping an employee improve against performance expectations for their current role (associated with competencies).

Career mobility

The goal of career mobility is to move employees around the org in ways that benefit both the employee and the business. To do this effectively, leaders should know what an employee can do, what they want to do, and where the org has a need.

Skills and competencies can contribute to career mobility efforts by identifying what the employee can do, what they want to do, and where there’s a need.

Thinking in terms of the skills and competencies employees have can contribute to career mobility efforts by identifying what the employee can do (the employee’s skills and competencies) and where there’s a need (the org’s skills and competencies gaps).

Most orgs view skills as being more granular and transferable, so knowing what skills employees have can be valuable for making informed decisions about roles anywhere in the org for which employees may be best-suited.

For example, during the early days of the pandemic, one org had to jettison many of its retail stores. At the same time, it realized an increased need in its customer support function. Because the company understood the skills required to succeed and the skills its retail employees already had, the company reskilled its existing retail employees for a stint in customer service—allowing the org to respond quickly to the crisis. One leader views this as a major benefit of having more granular skills data.

“Skills data is usually transferable across jobs or even industries. That allows companies to be more nimble, agile, and quicker to react to the VUCA² world.”

Learning Lead, global food corporation

Interestingly, while most of the literature and discussion about mobility has focused mainly on skills, competency data can also play a key role in career mobility. Most orgs, thankfully, don’t make decisions regarding how people move about the company based solely on the skills they may have. Particularly in cases of leadership positions, competencies play a big role in mobility decisions, as they define the “how” in how work gets done.

Competencies can also play a role in team formation as well as in the fit and potential performance of employees in new positions.

In our roundtables and interviews, several ideas surfaced about how skills and competencies may be used to address robust career mobility challenges (see Figure 3).

We talked with an enterprise that’s successfully leveraging skills for both employee development and career mobility through a recently launched talent marketplace. Its Talent and Diversity team experimented with the skills data in the system—and specifically, what companywide insights could be extracted from the data.

Diversity, equity, inclusion & belonging

The events of 2020 have compelled orgs to pay much more attention to issues of diversity, equity, inclusion, and belonging (DEIB). Leaders continue to look for ways to level the playing field, give employees the widest possible access to opportunities through mobility and development, and ensure that all employees feel they belong and are supported.

Skills and competencies can help leaders level the playing field, give employees the widest possible access to opportunities through mobility and development, and ensure that all employees feel they belong and are supported.

While it may not seem immediately obvious, using skills and competencies can be useful in DEIB efforts. Skills and competencies data can be used, for example, to identify biases that may exist in a company, inequities in salary and performance management structures, and outdated talent processes, among others.

For example, InterVarsity Christian Fellowship (IVCF) is a highly decentralized org. Its leaders noticed that employees were being evaluated differently across its varying locations, despite having the same roles and responsibilities. As a result, IVCF is introducing a competency framework to reduce this evaluation bias and ensure all employees are rated fairly.

From our research, we offer specific ways in which skills and competencies can play a role in DEIB (see Figure 4).

Performance management

Performance management (performance) was originally put into place to help orgs differentiate pay.³ While that original purpose hasn’t necessarily changed, it’s been supplemented. Orgs now see the performance process as continuous—as a way to mentor and coach, to facilitate ongoing feedback conversations, and to develop employees.

Skills and competencies support innovations in performance management—helping make it a continuous effort to mentor and coach, facilitate ongoing feedback conversations, and develop employees.

With this new mindset around performance come innovations—many of which involve competencies and skills. For example, some newer approaches (such as paying for skills) can help make the criteria used in performance decisions more transparent and objective. Skills, by describing what an employee can do, help with this objectivity.

Competencies, on the other hand, have long provided robust and defensible criteria for managers in many orgs to reference in their performance, promotion, and compensation decisions.

Figure 5 identifies ways in which skills and competencies can support performance.

Summing up

These 4 business challenges—employee development, career mobility, DEIB, and performance management—became persistent underlying themes in our conversations with leaders about skills and competencies. Leaders should continually ask how skills and competencies can both be used—in ways that make sense for the org and employees alike—to solve business challenges.

Orgs should use skills and competencies—not skills or competencies—to address business challenges.

From our research and participating leaders, the resounding agreement is that orgs should use skills and competencies—not skills or competencies—to address these challenges.

Use All of the Data

Within the skills conversation, a core theme is how to use skills data to solve business challenges, including the ones we previously listed, among others. Having more data (both broader and better) usually equates to having a more complete picture of the skills and knowledge an org has—and what’s still needed—to meet org goals.

As companies begin to consider skills and competencies, accounting for the data provided by both can help to inform the org’s strategies and decisions.

While there’s been near-frenzied discussions about skills data, orgs often overlook the information that competencies can provide. As companies begin to consider skills and competencies (basically, as 2 sides of the same coin), accounting for the data provided by both can help to inform the org’s strategies and decisions.

When, in the course of our research, we identified that skills and competencies can and should exist peacefully within the same org, we naturally assumed that the data from both would also peacefully meld together. That turned out not to be true in all cases, as illustrated by 2 stories from our roundtables:

• One org identified and partnered with a platform that allowed the consolidation of data from many sources. Having many sources of skills and competency data in one place, paired with AI and machine learning, allowed the company to both understand the skills and competencies it had—and infer abilities based on that information.
• Another org realized it could not, at least in the short term, effectively merge all its skills and competency data into one system due to technical limitations on its existing systems. So the company worked with different data sets to accomplish different things. For example: Skills data was mined by asking individuals to self-identify skills, and was used primarily to support individual employee development and career mobility. Competency data was gleaned from performance management systems and used primarily to support analysis at the functional level. While the data wasn’t combined together, identifying and understanding the data from the 2 sources still allowed the org to accomplish its goals.

Regardless of whether orgs consolidate all information into 1 system or use separate systems to service different aspects of skills / competency building, leaders in our study indicated they’re using several types of data. So, let’s talk about the sources of that data.

Data sources

Most orgs starting their skills journey often make the mistake of thinking of skills data in terms of what can be extracted from a skills platform (or a platform with skills functionality). While such platforms are a good place to start, orgs that focus solely on the data in 1 system tend to have a one-note view of their workforce’s abilities. Why? Most often, 1 system doesn’t account for more qualitative data or data provided in conjunction with competencies.

Orgs that focus solely on the data in one system tend to have a one-note view of their workforce’s abilities because one system doesn’t account for more qualitative data or data provided in conjunction with competencies.

Our research identified several sources that orgs currently plumb for skills and competencies data. Figure 7 shows these sources and their reliance on humans to gather the data. We explore this reliance on humans—from “active data sources” like talent profiles, which employees must complete specifically as part of a skills / competencies effort, to “passive data sources” like email and chat, through which employees generate skills / competencies data in the course of their normal work.

Aside from providing several sources that offer skills and competency data, Figure 7 also indicates whether the data can be collected and stored using technology, or whether it relies on humans. Several sources exist in the middle of the spectrum that, depending on the org’s tech savviness and willingness to invest, can use either tech or more manual methods.

Passive data

Increasingly, orgs use sources which yield data that doesn’t require a separate initiative: This data is created in the course of the org doing its business and the employees doing their jobs. We call this “passive data.”

While we think of HRIS / HCM systems as providing passive data, increasingly learning systems, productivity systems, and social media profiles and communication tools are being used as such data sources.

While we mostly think of HRIS / HCM systems as providing this type of data, increasingly learning systems (like LXPs), productivity systems (like Asana and Jira), and social media profiles and communication tools are being used as data sources for skills and competencies.

It’s relatively easy to extract and consolidate information from these systems: Vendors are getting much better at enabling them to talk to each other to provide real-time data. Having quantitative and qualitative data in 1 place typically allows orgs to do deeper analysis, including inferring skills or channeling data to other systems to help personalize learning or mobility for employees.

Active data sources

While not nearly as sexy, for decades, orgs have used other, more manual sources of data as well. This includes sources such as 360 evaluations, word-of-mouth referrals, surveys, and skills inventories. These, like their passive counterparts, offer all kinds of interesting data—but rely on humans to do the heavy lifting. This type of data almost always involves an initiative separate from work to gather it.

Active data sources offer all kinds of interesting data—but rely on humans to do the heavy lifting.

More and more, orgs are utilizing tech to minimize the human lift: For example, surveys are now online, which means they’re digitized, and easier to mix with other skills and competency data. Still, the lift is real. We look forward to seeing more solutions for passive data in the future, as some of the tech (e.g., natural language processing, listening, etc.) gets better.

One of the most commonly used methods of gathering skills and competencies data is to have employees fill out a talent profile. But getting lots of employees to fill out their profiles is no trivial matter. Collecting data in this manner works if employees are motivated to actually fill in their information—otherwise the system remains unused and unhelpful.

Some orgs launch a skills or competencies system with a massive push for all employees to complete their profiles. Others launch with little fanfare, like Cornell University in the following real-world story.

In Cornell’s case, employees immediately saw the value of the skills platform for their own development and engaged accordingly. The skills platform presented employees with a list of recommended skills to add to their profiles, from which they could choose the ones they found most applicable. The platform then made recommendations about networking and development opportunities that might help employees close the skills gaps they’d identified in their profile.

Other orgs have found that prepopulating the skills profile, and then asking employees to review and edit their profiles—rather than fill out a blank slate or choose from a list of recommendations—is easier for employees and gives better response rates. In all cases, leaders emphasized that it’s critical for employees to see how they’ll benefit personally from completing their profiles.

The challenges

Skills and competency data don’t necessarily have to be used together—and often technology prevents them from being used together. Some of the challenges leaders mentioned when dealing with skills and competencies data include:

• Inconsistent formats. Data formats from different technologies (dates in the U.S. vs dates in Europe, for example) make it difficult to align data.
• Many, many sources. Skills and competency data live in many places, making it difficult to consolidate and analyze all of it.
• Silos. We’ve talked with some orgs whose structures are so siloed that they can’t (or won’t) easily share information across boundaries, or they utilize incompatible systems that prevent an easy exchange of information.
• Residing in people’s heads only. Much of the information about activity that happens outside the walls of the org, including skills developed at home, on volunteer or services assignments, or in past roles, isn’t accounted for in any system.
• Paper. Some information about skills and competencies lives on paper in file cabinets in deep, dark parts of the org—and are hard to both find and use in that form.

Leaders emphasized the importance of thoughtfully setting up data collection systems to prevent issues associated with clunky, irrelevant, or unusable data.

Luckily, tech is increasingly able to help with these challenges. The middle of the data collection spectrum contains sources like job descriptions, performance evaluations, and job histories (see Figure 7). Many orgs rely on spreadsheets and manual entry to track skills and competencies based on these sources—leading to issues with data sharing, version control, and data siloing. But tech exists that can parse these documents to extract skills and competencies: This tech is getting better and more deeply integrated into many skills and competencies platforms.

Craft Clear Messaging

The final area we want to address in using both skills and competencies is crafting a clear message. As we mentioned at the beginning, the differences between skills and competencies may matter to those of us on the HR backend—but they often confuse leaders and employees on the frontend. In fact, one leader told us:

“What does your average consumer want? Whether it’s a people leader, an employee, or a prospective candidate, they just want to know what’s expected. They’re asking, ‘What do you need from me?’ Just give them the answer to that question in really plain language.”

VP Talent & Diversity, multinational media conglomerate

Unfortunately, in many orgs, the answer to the question, “What do you need from me?” is about as clear as mud. This lack of clarity creates fuzziness about expectations and messes with unity of purpose. As both skills and competencies are supposed to create clarity and provide a unified sense of purpose and direction, unclear messaging foils our efforts.

Unfortunately, in many orgs, the answer to the question, “What do you need from me?” is about as clear as mud—creating fuzziness about expectations and messing with unity of purpose.

To create clarity, leaders focused on 2 things:

• Consistent terminology. As we’ve discussed, the terms “skills” and “competencies” are inconsistently used in the literature and within orgs. Leaders can create clarity for employees by intentionally choosing, defining, and using consistent terminology to discuss questions of “What can employees do, and what do they need to be able to do?”
• Clear communication of expectations and benefits. Employees want to know how they should interact with skills and competencies systems—and, critically, how they’ll benefit from those interactions.

Regardless of the specific messaging content or terms chosen, leaders emphasized the importance of being clear and consistent: Make a decision and stick to the chosen message.

Leaders emphasized the importance of being clear and consistent—make a decision and stick to the chosen message.

Leaders also noted that clear communication about skills and competencies can create the buzz needed to help others in the org get onboard with a skills and competencies effort.

Choosing terminology to boost clarity

While we in HR know the purposes of skills and competencies, the distinctions between the 2 are of little consequence to most employees and managers. In most orgs, employees just want to know what they need to do. In these orgs, choosing terminology that makes it easy to talk about expectations becomes paramount, regardless of whether the terms “skills” or “competencies” are actually used—see the first and second examples in Figure 9 below.

In other orgs—for example, in an org in which competencies have been used for some time and skills are just being introduced—clearly distinguishing the terms generates more success. The third example inFigure 9 represents such an org.

The messaging strategy your org uses should be chosen with your workforce in mind.

In the examples listed in Figure 9, orgs reported that their chosen strategy reduced employee confusion—and increased employee acceptance of their skills and competencies systems. The strategy your org uses should be chosen with your workforce in mind.

Messaging expectations & benefits to employees

As we mentioned earlier, inaccurate or untimely data can sometimes do more harm than good. Given that much of the timely and accurate data is provided by employees themselves, orgs should also think about their messaging with respect to getting employees to input their data. To do this, leaders in our study strongly recommend that orgs get serious about consistently and clearly communicating 2 things:

• What is expected of employees. Employees want to know what systems are available for helping them track and develop skills and competencies, and how they should engage with those systems. The question they’re asking is: “What do you need me to do?”
• Benefits to employees. Equally important, employees need to know why and how they’ll benefit from engaging with these systems. The question they’re asking is: “What’s in it for me?”

Leaders emphasized the importance of communicating how employees should interact with systems that help track skills and competencies—and, critically, the benefits to employees of those interactions. Employees need to understand these systems—both how to use them and how those systems can potentially affect their lives.

Leaders emphasized the importance of communicating how employees should interact with systems that help track skills and competencies—and, critically, the benefits to employees.

Inputting data is the part of the process where leaders particularly emphasized clarity of expectations. What information should employees input? Why? Where? How? How often? Answering these questions clearly and consistently can go a long way toward getting the information that’s needed in the systems.

Answering the “Why?” question at both the org and employee levels turned out to be particularly important. Many times, employees fail to update their data in employee profiles, keep track of skills developed, or record recent projects completed—because they don’t understand the benefits of keeping those systems updated.

Employees need to understand that this data is being used by the org to make real decisions that can impact them.

Employees need to understand that this data is being used by the org to make real decisions that can impact them. These systems help the org create a more complete picture of their employees, what experiences they have, and what they can do. On a macro scale, this data is used to understand what skills should be developed and to make opportunities more transparent for everyone. On a micro scale, this data is used to make decisions about future roles and projects, opportunities for development, and even performance.

Often we see orgs invest a lot of money to implement systems that focus on skills and competencies—but fail to adequately or effectively market them. Orgs should make employees aware of these systems, set expectations for their use, and very clearly help them understand the benefits of using them.

How to best communicate the message

We started this research with the assumption that all orgs should stop trying to distinguish between skills and competencies. It’s a confusing and unhelpful effort, we thought, and orgs should create a unified message using language like, “What employees can do.”

The most important factor is clear and consistent terminology and messaging that highlights the benefits of skills and competencies for employees.

We discovered, however, that the picture is more nuanced: The most important factor is clear and consistent terminology and messaging that highlights the benefits of skills and competencies for employees. Specific messaging strategies differ based on org culture and employee familiarity with skills and competencies.

Wrap-Up

When we started this research, we, like many leaders out there, didn’t understand the differences between skills and competencies. We thought they were the same—or could be blended into one and the same thing. After careful research, we see a need for both skills and competencies, and the data they each provide. Each has a unique place in an org’s ecosystem.

We see a need for both skills and competencies, and the data they each provide—each has a unique place in an org’s ecosystem.

That said, we see a great need for orgs to do 3 things:

• Consider the strengths of skills and competencies, and use those strengths to solve business challenges.
• Consider the data skills and competencies offer. Use them appropriately, and find ways for technology and systems to use them together more.
• Craft simple, clear, consistent messaging that tells employees what’s expected of them and how they’ll benefit from skills and competencies.

We hope this discussion illuminates for you some of the ways orgs are defining and using skills and competencies—sometimes together, sometimes in parallel—to address their most pressing people challenges.

As always, we welcome dialogue! Please feel free to reach out to RedThread Research and tell us your experiences. Reach us at [email protected] and visit our website at redthreadresearch.com.

Appendix 1: Methodology

As the skills conversation transformed from a one-note debate about robots taking human jobs to a multifaceted exploration of “What can our workforce do now, and what do they need to be able to do in the future,” we noticed HR leaders grappling with skills and competencies, and were compelled to take a deeper look at how the 2 can work together toward org goals.

We launched our study in fall 2020. This report gathers and synthesizes findings from our research efforts, which included a lit review of 93 articles from business, trade, and popular literature sources; 2 roundtables with a total of 53 participants; and 6 in-depth interviews with learning leaders on their experience with skills and competencies.

For those looking for specific information from those efforts, you’re in luck: We have a policy of sharing as much information as possible throughout the research process. Please see these articles on our website:

• Skilling: 5 Themes in the Conversation
• Competencies vs. Skills: What’s the Difference?
• Skills & Competencies: Differences, Utility, & Messaging
• Skills & Competencies: Finding & Using Skills Data

Why this report, and why now?

We started paying attention to coaching technology well over 2 years ago. At first, it was out of curiosity: we were seeing vendors developing new ways to address a very old learning technique. Earlier this year, we took a look at our vendor database and realized that we had over 45 vendors claiming to provide some sort of coaching functionality.

Coaching technology has exploded. As orgs have developed a larger appetite for coaching, vendors have begun to think through what coaching actually entails and how some or all of those steps can be simplified.

Interestingly, as vendors have thought through what coaching entails, the definition of coaching has also morphed: it is no longer defined only as a 1-to-1 relationship where the coach utilizes tools and a methodology to help the coachee gain actionable insights that can help them perform better. It has expanded in ways that we didn’t imagine when we started this research.

To that end, it’s important to clarify that, in this report at least, we are making no judgments on what coaching actually means. Vendors sell what they believe the market needs; in our conversations with orgs, we also found varied definitions of coaching, as well as varied needs for coaching. So, when a vendor tells us they have coaching functionality, we take them at their word.¹

This report was written to help us, and hopefully you, get a clearer understanding of the vast range of coaching technologies. As such, it answers the following questions:

1. What are the major trends in coaching tech?
2. What does the landscape look like, and what sense can be made of it?
3. Who is playing in this space, and what are common and unique functionalities?
4. How can orgs choose? What criteria should they use to find something that will work for them?

Let’s get started.

Major trends in coaching tech

Indeed, things have changed when it comes to the coaching world. In the last section, we mentioned that the definition of coaching has morphed, and that it is no longer defined solely as a 1-to-1 relationship between 2 humans. Technology is playing a bigger role.

And, as in other industries where tech begins to play a role (e.g., transportation: taxis, Uber and Lyft, self-driving cars, teleporting), things accelerate quickly. In the case of coaching, several things helped with that acceleration, including the pandemic, a greater focus on the employee experience, fear of the Great Resignation, and the promise of the ability to scale something that used to be just for top leaders, so it’s available to more people in the org.

What, exactly has changed when it comes to coaching tech? Our data and briefings with vendors pointed to a few attributes.

More coaching tech

First, there’s just more coaching tech, both in terms of players in the space and revenue being earned. We mentioned that our own vendor database has grown to over 45 vendors saying they have coaching functionality. We are seeing that growth in stand-alone tools as well as in add-on functionality in learning and performance tools.

Additionally, when we asked vendors about their revenue growth in the past 3 years, the majority of them, or 63%, said that their revenue has grown steadily (see Figure 1).

A broader definition of coaching

More organizations are offering more coaching to more employees. Whereas coaching was once reserved for top leadership (or those with severe behavioral challenges), orgs now understand that there are benefits to coaching more broadly.

To accommodate the uptick in demand, coaching tech vendors are thinking about coaching differently. Our briefings and surveys surfaced several flavors of coaching, including:

• Coaching on demand: Employees leverage expertise of an external or internal coach to work on a specific challenge at a specific time
• Managers as coaches: Managers are trained and supported with platforms that can create accountability and discipline around coaching conversations
• Peers as coaches: Peers utilize tech to pair up and participate in guided coaching sessions designed to bring clarity to challenges they may be facing
• Reverse mentoring and coaching: Younger employees are matched with more experienced employees to share viewpoints and insights that can help them lead better
• Coaching circles: Tech helps employees with similar challenges to meet to talk through issues and receive feedback and advice from the group
• AI or machine-delivered coaching: Technology takes the place of a coach, helping employees become aware of certain behaviors and providing insights and data to help correct those behaviors

The coaching tech vendors we examined support a variety of these different coaching configurations. The tech they offer can help orgs to scale coaching, making it more cost-effective and helping leaders make a business case for increased coaching.

Coaching in more areas

Another trend we’re seeing in the coaching space is a broader range of topics offered by coaches and coaching tech. Whereas coaching used to only include performance or business coaching, new subjects are becoming more common. Figure XX illustrates the topics that coaching tech vendors are currently focusing on.

 

Not surprisingly, coaching follows some of the trends we’re seeing in the people space in general. Business and leadership coaching remain predominant, but wellbeing has crept in, as has Diversity, Equity, Inclusion, and Belonging (DEIB), both spurred by the pandemic and by the social justice movement.

Coaching on DEIB and wellbeing have both grown, spurred by the pandemic and by the social justice movement.

These new topics also speak to the fact that coaching is being provided as a benefit to individual employees. Health & fitness and financial in particular do not necessarily improve org performance; rather they are being offered to engage employees and help them eliminate stress and avoid burnout.

New things tech can do

Finally, there is more functionality. While earlier coaching tech focused on streamlining tasks associated with coaching (finding and paying coaches, facilitating conversations between coaches and coachees, and matching coaches to coachees), newer tech goes beyond those traditional functions to include aspects of AI, coaching without humans (machine delivery), integrations with work tech, nudges, and absorption of work tech data to make coaching algorithms better. We’ll talk a lot more about functionality in subsequent sections of this report.

AI, machine deliver, integrations with work tech, nudges, and more are changing the way orgs are offering “coaching” to their employees.

A model for thinking about coaching tech

Classifying coaching tech has been one of the most difficult HR tech projects we have ever taken on. Because coaching now has a much broader definition and because we are seeing a lot of new functionality, coming up with a picture that adequately describes what is going on has been a challenge.

That said, in looking at the tech and characterizing how orgs are making decisions about coaching tech, there appear to be 2 major factors driving decisions about coaching.

1. Resources: Are resources available internally to support a coaching effort?
2. Philosophy: Does the org believe that humans are necessary to deliver coaching?

There are many, many other questions that define what type of coaching tech orgs are looking for, which we’ll address in the final section of this report, but the above 2 factors seem to be the primary drivers. If orgs develop clarity about those 2 things, they can narrow down their coaching tech options significantly.

When we plot those 2 factors against each other, we get the 4-square shown in Figure 3 below, which gives us a helpful way of classifying coaching tech solutions.

 

This model divides the coaching tech landscape into 4 distinct quadrants:

1. Quadrant 1: Human coaches, external resources. Coaching tech vendors in this quadrant match external (usually professional) coaches with internal coachees and provide tools and a platform to support the coaching relationship.
2. Quadrant 2: Human coaches, internal resources. Coaching tech vendors in this quadrant match internal coaches with internal coachees and provide tools to support the coaching relationship.
3. Quadrant 3: Machine coaches, internal resources. Coaching tech vendors in this quadrant leverage org-supplied data (often from other work systems) or provide tools to help coachees self-guide to receive insights, nudges, and other aids, often without the involvement of a human coach.
4. Quadrant 4: Machine coaches, external resources. Vendors in this quadrant provide machine-delivered coaching based on their own frameworks or programs and offer up insights, nudges, and other aids as needed.

Obviously, not all vendors fit neatly within one quadrant. Most fall somewhere along each of the axes in the model. Figure3 places them roughly where we think they fall on both axes.

So, for example, CoachHub provides external coaches, and focuses exclusively on delivering coaching through humans. As a result, this vendor falls squarely into the upper right-hand quadrant of the model. Cultivate, on the other hand, uses internal data to drive its algorithms and includes no humans in its coaching, so the company is placed to the far left and bottom of the model.

The next sections are organized around each of these 4 quadrants. For each quadrant, we’ll share some of its characteristics and some of the common functionality. We’ll also share some of the more interesting functionality some of the coaching tech vendors provide. Finally, we’ll look at some of the best use cases for coaching tech in each quadrant.

A caveat: while we classified and identified many, many vendors, we’re sure that we missed some. The next sections highlight what some particular vendors are doing, but are written broadly enough to give you enough information to make good choices about solutions that are not included in our assessment.

Quadrant 1: Human coaches, external resources

The first quadrant (upper right) includes coaching tech that matches external (usually professional) coaches with internal employees and provides a platform for continued interaction between coach and coachee.

 

This, undoubtedly, is the most traditional way to think about coaching—and consequently the tech that supports it. When most people think about tech to help with coaching, they think in terms of external coaches being matched with internal employees. It’s the way coaching has been done for centuries. And it’s probably the simplest and most straightforward of the 4 quadrants.

Common functionality

Coaching tech orgs in this quadrant share a lot of the same functionality. Of course, there are differences, and those differences could make a big difference in how the tech may function in a given company. But understanding what is common to vendors can help you make better purchasing decisions. We discuss some of these points of commonality below.

Platform for communication

One of the main functionalities in almost all coaching tech solutions in this quadrant is provision of a platform—that is, a place for coaching to happen. Platforms offer a place for the coach and employee to meet and a way to document agenda, goals, and topics to be worked on.

Coach matching

The majority of coaching tech solutions in this quadrant also offer a way to match coaches and employees. Solutions falling in this quadrant generally believe that the coach/employee relationship is critical to success; thus, matching is an important functionality. In the solutions we looked at, matching primarily happens in 2 ways:

• Artificial Intelligence (AI). Many coaching tech solutions tout AI matching—utilizing algorithms to find the best coaches based on the employee’s needs and the coach’s expertise. Note: While we didn’t dive too deeply into the AI algorithms of individual coaching tech solutions, we do know that AI is a very buzzy word and apt to be used in many ways. Those looking for solutions should ask some pointed questions about the algorithms and how exactly they function.
• Matching Assessments. Many solution vendors take a more tried-and-true approach to assigning coaches by asking employees exactly what they’re looking for through short assessments, and then matching coaches to them accordingly. As with AI, we encourage additional questions about matching, as well as ability to personalize the questions on which employees and coaches will be mapped.

Competency assessments

One of the beauties of coaching tech is that it can standardize coaching practices across the organization. Many of the solution vendors in this quadrant accomplish that by way of assessments. Assessments run the gamut—from asking a few questions about what you want to focus on to asking for a complete 360° assessment by the coachee, to get a sense of what their development areas are. This information is available to the coach and the coachee so that they can work together to strengthen those areas.

From the org’s perspective, standard assessments and a common development model can unify coaching initiatives and provide a consistent vocabulary, particularly if coaching is being used to get a group of employees (leaders, say) to have similar behaviors. It also provides the possibility of analyzing rolled-up coaching data for all participants.

Coaching tech vendors in this space tended to utilize their own models and data to create these competency assessments.

Data

One of the big promises of coaching tech, which is not unique to this quadrant, is the availability of valuable data that can be used to evaluate the success of the coaching and the progress of the coachee. Until coaching tech was born, most orgs were reliant on self-assessments, satisfaction surveys, or their faith that the coach was doing a bang-up job. Now, almost all coaching vendors offer some sort of metrics for this purpose (although they can be sparse in some areas).

At the very least, orgs are now able to understand coachees’ level of engagement with the coaching initiative and a general sense of how coaches are performing. For many orgs, this is enough, both because it’s likely more than they have had in the past, and because of their concerns about privacy and confidentiality in the coach / coachee relationship. Orgs choosing this kind of tech tend to be less worried about the granularity of the data than in some of the other quadrants we explored.

That said, with some coaching tech solutions, particularly those utilizing standardized models across all orgs, data about topics being worked on could be rolled up to the manager, department, and org levels to give orgs more information about the types of things that were being coached.

Coaching quality

Many of the vendors in this space are also concerned about the quality of their coaches, and have therefore put interventions in place. This usually takes the form of a consistent coach onboarding process, vetting (many mentioned ICF-certified coaches), ongoing training, and quality checks. Because coaching tech keeps tabs on satisfaction data with respect to coaches, it is easier to intervene earlier when things are not going well.

Common coaching tech functionality for Quadrant 1 includes platforms for communication, coach matching, competency assessments, data, and ensured coaching quality.

Things we saw and liked

We found it interesting that coaching tech solutions in this quadrant understand the market and some of the threats to more traditional coaching. Coaching tech solutions that leverage external coaches understand that it is an expensive solution that most orgs are going to reserve for high-potential employees (HiPos) and present and future leaders.

Even so, one of the strengths of coaching tech is its ability to offer coaching to more people in the org. Within this particular quadrant, coaching tech solutions are facing constraints to their ability to attract and retain well-qualified, external coaches; in response, many are creating functionality that helps orgs to scale or show value beyond simplifying the administrative aspects of running a coaching initiative. Some of the features we liked are outlined below.

Alignment with existing leadership initiatives

Pluma (recently acquired by Skillsoft), among others, offers the flexibility to upload an org’s leadership model and customized 360, and provide coaching to leaders with respect to that model. This could be particularly useful in situations where orgs have well-established leadership development programs and frameworks, and want to ensure that any coaching initiative is aligned with them.

Human coaching for specific topics

Many of the coaching tech vendors in this space appear to have recognized new needs in the marketplace and created particular “programs” to address those needs. In an earlier section, we identified several coaching subjects. While the majority of orgs still seek coaching to help with the classic topics of leadership and business growth, many are interested in several new coaching areas.

These new areas, including wellbeing and DEIB, can be slightly more programmatic than the more traditional areas, and can therefore be more easily standardized. This has allowed coaching tech solutions to provide consistent coaching topics and additional resources to help orgs struggling with how to address some of these topics.

For example, CoachHub saw a need for leaders with better Diversity, Equity, Inclusion, and Belonging (DEIB) skills; it created a framework that can be used to build those skills throughout the org.

Another example is Betterup’s recent foray into wellness and mental health (in fact, if you check out their website, coaching is now de-emphasized as they move into these new topics). Their coaches now include therapists and peak performance experts, with the goal of ensuring that employees receive the individual care they need to be at their best.

Just-in-time or drop-in coaching

Torch.io has worked with clients to create drop-in coaching—opportunities for employees beyond traditional HiPos and leaders to schedule time with professional coaches on an as-needed basis. This time can be used to role play an upcoming situation, discuss career moves, or talk through business challenges.

 

 

Early career coaching

One of the newer applications for coaching is serving those early in their careers. From an individual perspective, an external perspective can be valuable as younger employees consider their first big career decisions. From an org point of view, coaching is increasingly being used as an engagement tool.

One coaching tech solution focusing specifically on early career is The Lighthouse. Lighthouse is a small company that leverages grads from top MBA programs to provide one-off coaching sessions with early-career coachees.

Best used for

In our initial conversations with org leaders, we found that many are looking at several different technologies for different types of initiatives. Based on those conversations, our assessment of the best uses for technology in Quadrant 1, which uses human, external coaches, are:

Ensuring higher-quality coaches with credentials

Many of the tech vendors in this category tout the quality of their coaches; many refer to ICF (International Coaching Federation) certifications and/or years of service. These vendors place a very high value, not only on tech and scaling, but also on ensuring a quality experience.

A human touch

With the pandemic has come a push by many organizations to provide more, not fewer, points of human contact. Vendors within this quadrant also highly prize human interaction (some going so far as to claim that unless coaching involves humans, it’s not actually coaching). For orgs looking for more human touch, vendors in this quadrant may offer the best solution.

Offloading administrative tasks

Before this type of coaching tech existed, many orgs relied either on a binder full of bios of trusted coaches that they then assigned manually, or on programs run by outside consultancies or training firms. This type of coaching tech takes some of the administrative burden out of providing coaching by automating tasks such as matching coach to coachee, or ensuring continued interaction.

Quadrant 2: Human coaches, internal resources

Given that coaching is becoming an increasingly hot topic, it comes as no surprise that organizations are trying to provide the opportunity to as many people as possible. One of the obvious challenges is cost and scalability: offering coaching to everyone can put a fairly hefty dent in development budgets.

Coaching tech in Quadrant 2 addresses this issue by leveraging internal resources. In most cases, this takes the form of leveraging human coaches from among the employee population. Coaching tech solutions in this quadrant also tend to leverage internal or proprietary data, models, and content as well, rather than utilizing standard models provided by coaching tech.

In this quadrant, we also heard more about creating a “coaching culture” or teaching coaching skills more broadly, particularly to managers, rather than relying on traditional coaching relationships.

Interestingly, one of the challenges associated with creating a coaching tech landscape was the breadth of ways that vendors describe internal coaching. True, many stick to the traditional description of coaching: discussions between 2 individuals. However, we saw the most diversity in both offerings and users in this quadrant. This quadrant was also where most mentoring vendors make their appearance.²

Technology that falls within this quadrant is geared mainly toward either setting up an internal coaching program (without the use of external coaches), or creating a learning culture—usually by encouraging manager / employee development discussions. In our investigation of these vendors, we found they had 3 major flavors:

• Coaching platforms, many of the same that offered external coaching services, configured to utilize internal resources instead of external coaches
• Mentoring platforms that provide a way for continuous feedback and direction from mentors to be leveraged for coaching as well
• Dashboards in skills, mobility, and learning platforms that provide information to managers about how employees are performing so that they can better coach them

 

You may have noticed that our Coaching Tech Landscape shows several coaching tech solutions that hover between Quadrant 1 and Quadrant 2. These solutions have platforms that are configurable enough to be used to access either external coaches or internal coaches.

Common functionality

Defining common functionality in this quadrant is difficult because of the broad definitions of coaching used by the quadrant’s vendors. Aside from traditional coaching relationship (1-to-1 conversations where a formal coach asks exploratory and discovery questions of the coachee), we also observed several other types of coaching relationships, including peer coaching, manager coaching, coaching circles, drop-in coaching or just-in-time coaching, and reverse mentoring.

Several functionalities appear to be fairly standard across a number of the coaching tech solutions.

Matching internal coaches with internal coachees

For coaching tech solutions that focus on the 1-to-1 relationship between coaches and coachees, we saw the same sorts of functionality that we found in Quadrant 1. This is not surprising, since many of the players from Quadrant 1 can configure their solution to work for internal coaching pools, which can be helpful to orgs that want to move between external and internal coaches.

Coaching tech solutions in this quadrant generally match internal coaches and coachees in 2 ways: AI and matching assessments. We saw little difference in matching functionality as it was applied to either internal or external coaches; the only difference appears to be the coaching pool.

Platform for ongoing engagement

As in Quadrant 1, most vendors focusing on the 1-to-1 relationship between coaches and coachees also provide a platform for continued communication and discussion.

More integration, more nudges

Because of the internal focus and their broader definition of coaching, vendors playing in this quadrant are more likely to integrate with existing systems. Many identify integrations with work technologies (mainly Slack and Teams) to help coachees stay on track and check in regularly.

There are also vendors in this quadrant that provide more than just coaching tech (i.e., they’re a learning or performance tech that also does some coaching), and so more integrations to additional resources, assessments, learning platforms, and the like are also more common.

Access to additional resources

Many of the vendors in this quadrant provide access to additional resources or micro-learnings in order to enhance the coachee experience. This is not surprising, given that many of these are learning platforms with a coaching component; we do however, like the idea of extending the learning beyond the in-person engagement.

Several vendors also have onboard coaching resources as well, to provide training or help to amateur or manager coaches

Common functionality for many of the coaching tech vendors in Quadrant 2 includes matching internal coaches with internal coachees, platform for ongoing engagement, more integration, more nudges, and access to additional, usually internal, resources.

Things we saw and liked

Coaching built into other initiatives / Asynchronous coaching

Both Novoed and Intrepid offer coaching as a part of their cohort learning platforms. Both coaching tech solutions include video exercises with offline feedback, scheduling, and group coaching in their platforms.

Novoed provides a Coach’s Dashboard so that coaches can understand where each of the coachees in the cohort are and identify needs.

Figure 7: Novoed’s Coach dashboard | Source: Novoed, 2021

Depending on the program and need, Intrepid enables coaches to provide feedback throughout and are available to schedule 1-to-1 engagements with employees who want a little extra help or advice.

Figure 8: Intrepid's Rubric page | Source: Intrepid, 2021

Asynchronous coaching is used quite a bit in sales or other industries where the practice and delivery of certain skills is important. This technology allows employees to record themselves performing a task (a sales pitch, for example) and then receive video feedback based on a predetermined rubric.

Rehearsal offers an asynchronous coaching tool as its main functionality. It allows orgs to set exercises (sales is a big use case), then allow people to practice as many times as they would like with video, submit the video, and receive feedback from a coach who refers to a rubric. If orgs want, they can also allow other coachees to view, judge, and even upvote video practices.

Bongo (which, incidentally, doesn’t sell to end customers but is instead incorporated into other coaching tech solutions), also enables asynchronous coaching through video and rubrics.

Manager-as-coach dashboards

There are also a number of products that focus on providing information to managers so that they can better support their employees.

Degreed’s Skills Coach, for example, provides a manager dashboard that summarizes a team’s skills, as well as providing data on each individual, so that managers can plan the coaching for both the team as a whole and individuals.

 

Axonify and Qstream, both of which focus on front line workers, are considered learning tech but offer dashboards to give managers a sense of what employees are focusing on and where they may need individual coaching.

And Bridge, recently separated from parent company  Instructure, is considered both a learning and performance tool. It was also built with manager-as-coach in mind. It offers data visible to managers to help them provide better feedback and guidance about development and performance, and a platform for ongoing discussions. Bridge also offers a timeline of activity and a space for weekly check-ins to enable managers to act as coaches.

 

We are big fans of providing managers as much information as possible when they’re coaching employees. These dashboards provide employee development and skills data to managers, which can help them tailor feedback and guidance for further development or better performance.

Focus on DEIB

Many vendors spoke of the ability of orgs to use their tools to further DEIB efforts; many had stories about utilizing their platforms to further coaching and mentoring efforts among specific populations. Chronus was one vendor that stood out here—they pay specific attention to their matching algorithm to encourage more inclusive matching; they also provide a dashboard geared toward increasing diversity in mentoring relationships and are beginning to offer insights associated with industry DEIB benchmarks to help orgs make better decisions.

Peer coaching functionality

With the broader definition of coaching comes additional functionality. Imperative’s coaching tool, for example, provides a platform and guided discussions to help managers coach each other peer-to-peer. The platform and structure ensure that managers adhere to coaching principles (open-ended questions, for example) and that there is a place and prompts to continue conversations. Pairing managers together helps them reflect on how they perform and what they can do better.

Figure 11: Imperative’s manager peer-to-peer coaching tool | Source: Imperative, 2021

Best used for

Orgs utilizing coaching tech vendors in this quadrant generally have 1 of 3 goals for their engagement.

Scaling coaching to more people

Orgs that use the coaching tech solutions in this quadrant are interested in providing a human coaching experience to their employees, but want to leverage internal coaches in order to do that. The solutions in this space are enhancing the skills of existing resources and providing guardrails or guidance so there is more structure and consistency across the coaching experience.

Creating a coaching culture

This type of technology is also being used to help orgs build a coaching culture. In the wake of the pandemic, many orgs have realized (again) that their managers are not as strong as they could be. Many of these solutions have been implemented to give managers support and guidance around career coaching and difficult conversations.

Upskilling and supporting managers

The pandemic and related moves to hybrid and remote work settings have caused orgs to reevaluate the quality of interactions between employees and their managers. In many cases, managers are falling short. Fortunately, orgs are also recognizing that the shortfall isn’t due to lack of trying or desire, but rather a lack of skills and support. Many coaching tech solutions in this space are geared toward answering this need.

Quadrant 3: Machine coaches, internal resources

The bottom half of the coaching tech landscape focuses on solutions that mainly rely on technology or machines to deliver coaching. These tech solutions use information to drive prompts and help coachees think through how they’re doing and what they can do to improve.

The promise of AI or machine coaches is generally 3-fold:

• It offers a fairly inexpensive and unlimited way to scale coaching to more employees
• It solves one of the major challenges of coaching – consistent check in and feedback – that is difficult if relying only on the humans.
• It offers a means of standardizing data – currently one very big hole in the world of coaching tech

In this quadrant, we start to see a departure from what is considered traditional coaching. Not only do we see more automated coaching systems that don’t rely on the humans (or augment what the humans are doing), but we also see more integration and interaction with performance and learning tech.

In fact, as we look across our HR tech research over the past 5 years we’re seeing much more overlap between performance, learning, and engagement platforms. Many offer consistent follow up, spreading learning over time, identifying ways to personalize experiences, and nudges for regular conversations with managers. These functionalities do overlap with what coaching has come to mean, so they likely have a legitimate claim to the therm “coaching tech”.

This does mean, however, that leaders need to be extra vigilant and ask lots of questions about the nature of the “coaching” offered. We mention this in the section dedicated to Quadrant 3, but this is also an important point for Quadrant 4. Do your homework. Ask lots of follow up questions (see the final section of this paper). We’re in new territory here.

We also think the use of machines that feed off internal data and provide valuable insights to employees will continue to grow as data gets better and it becomes more acceptable to use coaching data as employee development data.

 

Coaching tech solutions in this quadrant rely heavily on internal data and resources. They often absorb information from other work tech to generate insights that are delivered directly to employees. They also often point to internal resources (via a learning platform, for example) that can augment feedback or insights.

Delivering this information directly to employees—sans manager or human coach—can sometimes take the sting out of feedback and provide data in a way that can be more easily absorbed by the individual.

As with Quadrant 2, we ran across several learning tech solutions that are also targeting coaching clients. As several of them do have coaching functionalities, we have included those that tell us they’re coaching tech, even if their main functionality is L&D tech.

Common functionality

Common functionality in this quadrant was a little harder to identify. From the survey filled out by coaching tech solutions, however, 3 common areas surfaced.

Digital or AI coach – nudges

Common to this group of technologies was that all of them have a digital component and do not require a human. Some vendors call this functionality a “coach on the shoulder” that provides information when it is most likely to be absorbed and used.

Most tech solutions provide this information through nudges—small insights or reminders to coachees, based on their desired behavior changes.

Integration with work tech

Most of the vendors in this group integrate in some way with work technology. This is used for both pushing and nudging, and to gather data on performance to help the tool get smarter, which makes for better insights. Common integrations are Slack, Teams, email clients (Google, Outlook, etc.).

Data and Information pushed down

Tech solutions in this quadrant tend to push information down to the individual rather than delivering it through a manager or coach. We saw a lot of dashboards and insights summaries as a part of these solutions, making the individual—the person most motivated and able to act—responsible and empowered to do so.

The majority of coaching vendors in this quadrant say their solution:

• Provides data and information to coachees to help them understand their own progress
• Rolls up data to manager, function, organization levels, etc. to help the org better understand challenges and areas of focus

Because it is integrated with existing work and HR tech, data associated with these types of coaching tools tends to be better and slightly more granular. We observed that these tools have a greater ability to roll up results and provide individuals with progress data than solutions in either Quadrant 1 or Quadrant 2.

Common functionality in Quadrant 3 includes digital or AI coaches and nudges, more integration with work tech, and data being pushed down and provided to employees (rather than their managers).

Things we saw and liked

As we mentioned earlier, the bottom half of the coaching landscape is pretty new. As AI, machine learning, data, and comfort with sharing data about coaching gets better, we expect a lot of growth. Some of the differentiators that showed up in this quadrant include the following.

Team coaching

In most learning tech we have seen in the past few years, nudging has been used mainly as a way to help employees follow up on learning initiatives. Within this quadrant, nudging is being used in a much more sophisticated manner.

Humu, for example, uses nudges to help teams improve. After understanding the goals of the team and how they’re working toward them, Humu utilizes a series of actionable nudges that promote continuous improvement. Nudges are personalized to the individual team member, but take into account the team and goals, so that teams can help each other execute.

Because nudges happen regularly, teams have opportunities to reflect and respond, allowing the tool to gather and integrate more data to better target nudges.

Digital coaching to optimize live coaching sessions

We also ran across tools that utilized machine delivery and AI to simplify the human coaching element. Sparkus, for example, has an onboarding and self-coaching component that requires those interested in having a coach to think through what they want to accomplish and what help they need in advance. This prework has a couple of benefits.

First, it helps orgs understand the level of commitment of coachees; since they are not assigned a coach until they finish the self-coaching component, orgs can quickly understand who is serious about the experience and who isn’t. Second, it helps to minimize the amount of live coaching needed. One challenge many orgs have is understanding when a coaching engagement is finished.

The self-coaching component helps the coachee and the org to bookend the human coaching engagement, optimizing the number of sessions with a live coach. This helps organizations scale because they are able to offer a finite number of coaching sessions to more individuals.

 

AR/VR

While there aren’t many, we also ran across a couple of vendors that leverage augmented / virtual reality to help with coaching. Mursion, likely the most well-known tool in this space, provides an immersive coaching experience. Coachees complete a simulation where they need to handle a difficult conversation (our demo experience was about creating a safe place for employees with very different opinions to discuss whether a woman should be promoted).

 

Mursion utilizes a combination of technology and real players (actors) on the back end to give an accurate simulation. While the experience from the user side is slightly animated, coachees have the opportunity to read body language, respond to tone, and evaluate all players in the scenario. It also provides a deeper experience than coachees can get with a static, linear role play, and accounts for some of the current shortfalls associated with tech simulations and AI.

Coaches on the shoulder

Cultivate is a great example of a tool that absorbs data from other systems—in this case, email—and provides feedback to managers about how they are communicating with their team members. It uses a combination of data and insights to make managers aware of behaviors and suggest ways to change them. For example, a manager may get a prompt telling her that she is sending less than 5% of emails to her team after work hours—which is good—but that prompt may also identify the individuals to whom she is sending after-hours emails.

Cultivate also offers ideas that can be tried immediately; in this case, onboard learning snippets can give the manager information on improving communication habits, adjusting priorities, or simply delaying sending messages until business hours.

 

Because the tool is integrated with a particular manager’s communication tech, the insights as well as the feedback are personalized. Because the coaching is integrated into the flow of work, managers are more likely to be able to use that feedback immediately.

Emplay is another coach-on-the-shoulder type tool. While its current strength is in sales, it is also branching into manager training. Emplay has the philosophy that successful coaching is a combination of sharing the right information and instilling discipline.

 

Emplay provides coaching in 4 areas: First, it can surface experts within the organization based on the experts’ assignment and data recommendations. It can also use conversational tech to ask questions that prompt coachees to reflect and find answers to their own questions. Third, Emplay surfaces behavior patterns in salespeople that can provide insights that can help them close deals faster; finally, Emplay can absorb long-form content and then utilize it to respond to queries via Teams and Slack. Emplay also creates dashboards and provides access to both manager and coachee for more insightful discussions.

Best used for

Because the tech falling in this quadrant is not traditional, utilizing this type of tech requires a non-traditional mindset. While coaching of this type will likely never fully replace human-delivered coaching, it does provide some unique opportunities that have yet to be solved by human coaches. We see 3 major use cases.

In-the-moment awareness for creating new behaviors

One of the promises of machine-delivered coaching tech is the ability to insert coaching opportunities into the work itself. Many of the tools in this quadrant operate on of the assumption that making people aware of their behaviors is the first step in helping them to modify their own behaviors.

Support of other initiatives

Because of its ability to provide in-the-moment data and insights, this type of technology may be very well suited to reinforce other types of learning, performance, or change initiatives. For example, once information about behavior has been shared explicitly (manager shortcomings, for example), a tool from this quadrant may be very helpful in drawing attention to unconscious behaviors in the work flow.

This type of coaching tech may also be helpful in providing common language and expectations for initiatives such as DEIB and extending the useful life of initiatives; regular reminders or opportunities to practice can reinforce principles learned earlier and build skills for using that new knowledge in the context of work.

Scalability

Finally, as with every other quadrant, one of the primary purposes of these tools is to scale. Machine-delivered coaching, while not as personal as human-delivered coaching, is infinitely scalable and fairly inexpensive. Organizations looking to provide something may be able to start here.

That said, machine-based coaching, particularly in this quadrant, does not provide the human touch and may not elicit the positive reactions or engagement of employees whose org invests in a real live coach.

Quadrant 4: Machine coaches, external resources

As with the vendors in Quadrant 3, those in Quadrant 4 rely heavily on machines for delivery of coaching. However, they also utilize external resources—meaning coaching insights are based on external data, models, usually proprietary, and additional content that can be used by coachees for extra growth.

 

Orgs taking advantage of coaching tech solutions in this quadrant are generally looking for a solution that scales very easily and has some programmatic aspect. As this type of coaching tech generally uses proprietary models and resources, it can be seen as more of a turnkey solution than coaching tech in some of the other quadrants.

Common functionalities

The common functionalities in this quadrant are not surprising: machine delivery, and the provision and consumption of data for the purposes of creating insights. We detail some of them below.

Digital or AI coach

Most of the technologies in this section pride themselves on providing insights to coachees with little or no involvement of a human. Digital or AI coaches work with existing data in organizations to provide those insights in ways and at times that can be most impactful to coachees. They may come via Slack, Teams, email, or other systems that are already used for work.

This quadrant distinguishes itself from the other quadrants by the fact that almost all of the tech in this quadrant uses humans to augment the tech, not the other way around.

Roll up data

As we mentioned above, one of the benefits of utilizing the same models and having access to more automated data is that it is easier to roll data up. A characteristic of many of these vendors was the ability to roll up data to the manager, function, and organization level, to help the org better understand the challenges and areas of focus.

Integration with HR systems

Vendors in this quadrant also integrate more readily with the HR systems that are already being used in an org. Because they are often using standardized models and data to drive insights, it may be easier for these coaching tech vendors to also standardize their integrations. In some cases, they may also be key to the functioning of their solution. We think this may be in its infancy as well – and there are privacy and ethics concerns that must be addressed – but linking coaching in with other HR systems, such as performance management, can help orgs tie coaching to broader goals.

Things we saw and liked

As with the other quadrants, there were several ideas or functionality provided by vendors that we found interesting.

Focus on wellbeing

meQuillibrium was one of the few vendors we spoke to whose solution provides coaching on well-being. meQuillibrium’s tool starts with an assessment based on their internal research. From there, coachees receive a personalized resilience program based on their assessment of how they deal with stress.

meQuillibrium provides data, feedback, nudges, activities, and content to help coachees understand what they need and then take care of themselves.

 

Listening – AI speech coaching

Orai is one example of a AI speech coach that listens to the coachee as they speak and then provides feedback on pace, tone, and filler words. It has prompts for the coachee to practice extemporaneous speaking, a prepared speech, or a presentation. While it’s not perfect, we had fun trying this out. We found that it gave some great feedback on filler words and speed (2 of our biggest challenges).

Orai is a good example of where we see this quadrant going: humans were nowhere in sight; the tech leveraged natural language processing (NLP) and AI to provide actionable feedback and insights, and there were daily prompts over a period of time for continued improvement.

 

Programmatic coaching augmented by group coaching

Pilot is a perfect example of a tech that utilizes humans to augment the machine coaching. Pilot focuses on managers and has utilized research and years of experience to identify the biggest challenges to managers. Its program walks managers through information, nudges, and activities to help them practice using new knowledge and skills online. It then augments this learning with planned group coaching events, where all coachees in a cohort join a call to receive extra instruction and get their questions answered.

Best used for

Coaching tech in this quadrant is probably the most experimental; several of the tools acted more like guided learning for the time being, but we see a lot of potential in this space. As AI and natural language processing (NLP) get better, we see the current coaching tech—and the possibilities for new coaching tech—getting better.

Orgs experimenting with some of these tools likely do so for 1 of 3 reasons.

Scalability

Because few, if any, humans are involved, tools in this space are usually infinitely scalable. It generally costs just a few dollars to add another user to this type of tool, compared to the hundreds or thousands of dollars it would take to add a user to a tech that uses human coaches.

Orgs looking for something that will meet immediate needs, or even act as a stopgap, may have success using some of the tools we’ve discussed.

Standardization

Orgs looking for standard off-the-shelf models for leadership, wellness, or other subject-matter coaching may find what they need in coaching tech within this quadrant.

Additionally, because vendors in this quadrant leverage the same model and the same resources across all users, data can be standardized and leveraged. With standardized data, these vendors will likely have an easier time improving the tool, its nudges, and its AI than those vendors that rely on human input.

Experimentation

Coaching tech in this quadrant is generally low risk. Because of that low risk, orgs that want to experiment with coaching, or more particularly machine coaching, might want to start here. Since the majority of tech here use their own models and there is not much on- or off-boarding cost, orgs may want to give a few technologies a try as they firm up their philosophy on coaching in general.

Choosing the right coaching tech for your organization

As with all of our tech reports, we try very hard not to make decisions for orgs or guide orgs toward particular solutions. We don’t give top 10 lists or indicate that one tech is better than another because, in all honesty, it entirely depends. Context matters. Culture matters. Goals of the initiative matter. What works for one org may not work at all for another. You can’t cheat off your neighbor.

In this final section, we provide some help to those looking for the right coaching tech for their orgs. We are hopeful that the 4-square model introduced earlier can help narrow down the choices at a high level. However, as we explored the tech, we found ourselves asking deeper questions to completely understand the solution, what it could offer orgs, and in what context it would be most appropriate. Even with the 4-square chart, orgs will need to be thoughtful about their own unique needs and their choice of a vendor to meet them.

The following sections outline 3 fairly general steps to take when looking for coaching tech.

1.     Define your purpose

The very first step in deciding which tech is going to work best for your org is to consider the goals of the coaching initiative. Different goals may require different tech.

For example, consider the following vignettes:

1. You are trying to simplify the process of finding and assigning coaches. Right now, your org has to continuously vet coaches and then add them to a binder (yes, one of those things that holds papers), and then contract with each one separately. Coaching tech will help you to simplify the system you already have in place and ensure a level of quality you don’t have right now.
2. Coaching in your org is seen as a coveted developmental experience. Since COVID, there have been fewer opportunities to provide rich, “special” employee development to HiPos and future leaders. You’re looking for a coaching tech that can help you scale. Your goal is to ensure that coaching is still seen as a premier experience, but with a lower cost point and wider availability.
3. Your org has recently been acquired and you find yourself working with the org development team from the acquiring company to build common ground and standardize management practices. You need to offer some sort of education / change management support / coaching to all employees who are manager level and above. You want to provide a low-cost, yet somewhat personalized experience that can provide insights to those managers and help them develop a shared vocabulary and understanding of management practice.
4. You’re trying to create a coaching culture. Rather than looking at coaching as something that only certain individuals get, you want to create a culture where peers can coach peers and managers can coach employees. You know that consistency and discipline are the keys to making this happen, so you’re looking for a tech that can help provide the necessary structure.

Tech appropriate for one of these scenarios may very likely not be appropriate for one of the others. The goal matters. Coaching tech, just like any other learning tech, has to fit the situation and the ultimate desired outcome.

It’s important for leaders to understand the purpose for their coaching initiative – it can greatly affect the type of coaching tech solution they should choose.

Orgs may also find themselves in situations where they have more than one purpose at any given time. Unfortunately, we found no single coaching tech that can meet all of the different coaching needs an org may have. Orgs may need to choose 2 or 3 to serve the needs of different coaching initiatives.

2.     Choose a quadrant

We hope that the major themes with coaching tech that we’ve described so far will help you narrow down the search for your particular initiative. To determine which quadrant you may want to consider exploring further, ask yourself a few questions:

• Human vs. machine?
• Internal vs. external resources?
• Scalability?
• Control?

Figure 20 provides the bones of the Coaching Tech Landscape. The double-ended arrows represent the 4 questions posed above and can guide you toward the quadrant you should be considering, depending on where you fall on each line, and we provide more detail about each question below.

Human vs. machine?

Do you understand your org’s tolerance (or acceptance) of tech? In some organizations, coaching is seen as ONLY a human activity, while in others, scaling with tech is viable and jives well with the culture. Understanding where your org lies on that spectrum can help to determine which quadrant you should be looking at. Vendors in the upper part of the Coaching Tech Landscape model rely more on human coaches, while those in lower part rely on machine-delivery coaches.

Internal vs. external resources?

Do you have the resources to launch the coaching initiative internally, or will you need external resources? When you consider resources, think not only about humans, but about models, data or benchmarks, and content as well. Vendors on the left-hand side of the Coaching Tech Landscape tend to use internal resources, while those on the right rely on external ones.

Scalability?

How broad is the reach of the coaching initiative? Will it affect only a small cohort of employees, or is your goal to make coaching widely available? Machine-delivery coaching tools are much more scalable than human coaches.

Control?

Does your coaching initiative require that all coachees are coached using the same model or adhere to the same leadership traits? Do you want to determine what data and content is used, or are you looking for a vendor that will provide data and benchmarks, coaches, and models? Vendors on the left-hand side of the Coaching Tech Landscape tend to offer more control to the org.

3.     Vet, vet, vet

News flash: Vendors can’t or don’t always do what they say they can do. Many of the orgs we have talked to about their use of coaching tech have been disenchanted by the difference between what they thought (or were told) the tech could do and how it actually worked in the context of their orgs.

While there is absolutely no guarantee ever, and while there is much to do inside the organization as well to make sure the tech integrates well with other systems and processes, we’re very big proponents of vetting vendors to get the one(s) that make success the easiest. We spend a good one-third of our professional lives talking to vendors, and almost all of them will tell you that we ask an obnoxious number of questions. So should you.

The best advice we have for you? Ask lots and lots and lots of questions.

The following table identifies several questions that you may want to ask coaching tech vendors. While the list is clearly not exhaustive, we provide a set of questions that apply to any vendor, and then include several questions more specific to each quadrant. This list is by no means exhaustive. Understanding your goals for coaching tech will help you to identify others.

In addition to quadrant-specific questions, those looking for coaching tech should also be concerned about the health of the companies they’re vetting. As a part of our Learning Tech Ecosystem research, leaders identified 5 things they look at when determining viability of vendors.

1. Similar challenges. Vendors can refer potential customers to clients who have solved similar challenges using the vendor’s technology.
2. Confidence of investors. While not always completely accurate, several leaders told us that they used the market to help them vet vendors. Understanding where startups were in the funding process gave them hints about the long-term viability of those vendors.
3. Robust & innovative roadmap. Vendors should be able to speak confidently to their roadmap, and learning leaders should ask for it. Understanding the roadmap helps learning leaders to understand if the technology is moving in parallel with their organizational needs and enables them to assess the vendor’s innovation and ability to respond to the market.
4. Longevity in the market. Leaders we spoke to were wary of very young vendors, worrying about their long-term viability. That said, some of the more forward-thinking leaders were also wary of vendors who have been in the market too long—worrying about their ability to react and be innovative.
5. Evidence of partnering. Many leaders also mentioned using vendors as an extension of their team and a way to push forward and adopt new ideas. They look for evidence of partnering and problem-solving, not just providing software or content. As organizations continue to adopt an ecosystem point of view, it is getting more important to not just vet the tech, but also vet the vendors. Learning leaders should look for those who go beyond just providing services and can proactively help to solve problems.³

Wrapping it up

Exploring coaching tech and identifying a model to help leaders make sense of the space has been a pretty wild ride. We started out with some assumptions, all of which were blown to hell by the 3rd briefing. That said, however, we’re excited about this space.

Why? As with other areas on HR tech, the coaching tech space seems to be reinventing itself – which is remarkable given that the coaching tech space isn’t very old to begin with. We like that many of these vendors are experimenting – not just with what tech can automate from old models, but what new tech can do that has never been done before. They are, in essence, rethinking the very idea of what coaching is and how it can affect the largest numbe of people in the org.

In fact, as we have interviewed leaders for our sister report on coaching initiatives, we can safely say that coaching is one area where the tech vendors are ahead of orgs in how they’re thinking and innovating. We imagine that coaching tech vendors will continue to push orgs to think differently about coaching and we’re looking forward to seeing how that plays out.

Download Research

In 2020, due to the COVID-19 pandemic and the social justice movements, people analytics had an unexpected opportunity to shine. Technology played a more important role than before as people analytics team looked for ways to scale and provide deeper insights to leaders on their workforce, the majority of whom were working remotely. Our goal is to help people analytics leaders succeed in that endeavor and prepare for 2021.

Through this research, we wanted to understand:

• How did the people analytics tech vendor market change in 2020?
• What are the newest capabilities leaders need to know about?
• What should leaders be thinking about when making (or expanding) a people analytics tech investment?

This study is a culmination of nearly a year of qualitative and quantitative research, that included an online poll, a vendor survey, a customer poll, and over 40 vendor briefings and demos. This flipbook highlights the changes and trends from this year, the different capabilities offered by the vendors, and the questions potential technology buyers should consider before making or expanding their tech investments. We also suggest readers check our interactive, evergreen people analytics tech tool, for current vendor information.

Key Takeaways

1. Forward-thinking L&D functions make the chaos of learning content work for their orgs. Being overwhelmed by the surging quantities, types, and sources of learning content is yesterday’s news—but still today’s problem. Learning leaders are embracing the chaos and moving from providing content to enabling it, with an eye toward making more content available to all employees.
2. Learning leaders should ask 2 questions about learning content: 1) Is it specific to the org? 2) How long is its shelf life (How durable is it)? Thinking in these 2 dimensions—specificity and durability—can help L&D functions clarify their learning content strategy and priorities.
3. We developed a new model for learning content. From our conversations with forward-thinking learning leaders, we identified a model that breaks learning content into 4 categories (defined by the 2 dimensions of specificity and durability). This model can form the foundation of a learning content strategy that’s clear on priorities, roles & responsibilities, and areas of focus.
4. There are distinct actions L&D functions can and should take to improve their learning content strategies—and those actions change based on the 4-category model of learning content introduced in this report. We provide some suggestions for immediate and longer-term actions to take, as well as examples of real orgs implementing these ideas, to help learning leaders organize the chaos and better manage learning content overall.

Why Talk About Learning Content Now?

We’ve been witnessing rapid growth in the amount of learning content available to employees. This growth started decades ago, but it’s recently turned from a trickle to a flood. There’s more learning content everywhere—inside and outside orgs; online and offline; on desktops and mobile devices; and in learning systems, shared folders, browsers, email, and chat platforms. Is it any wonder that employees are overwhelmed and exhausted by the sheer volume of all that’s available?¹

Employees are overwhelmed and exhausted by the amount of learning content.

Employees feel like they’re drowning—and it’s L&D’s job to help them find and consume the content that builds skills and drives outcomes that matter to the business . To do this, L&D functions need well-crafted learning content strategies that support org learning and business strategies.

A learning content strategy should help L&D functions answer questions like:

• How will we decide what learning content to bring into the org?
• How will we identify—and help employees identify—learning content that’ll support our business and learning strategies?
• How and when will learning content be updated? By whom?
• How will we make the right learning content easily available to employees?
• What can we do immediately and in the longer term to improve employees’ ability to find and consume the learning content they need?

In this study—which included a lit review, roundtable, and interviews—we explored these questions. Through this research, we sought to identify the leading practices that orgs are using to help employees sift through the volume of learning content to find what’s right for them, when they need it.

L&D functions need well-crafted learning content strategies that support org learning and business strategies.

In the next section, we introduce the trends we uncovered as part of this study.

What’s Happening in Learning Content?

In the course of this research, we identified 4 trends in learning content that are helping shape the learning content strategies of forward-thinking orgs:

1. More types and sources of learning content
2. More enabling, less pushing of learning content by L&D functions
3. More (and better) use of skills data to inform learning content priorities
4. More access for all employees

In the following sections, we take a brief look at these 4 trends.

More types & sources of learning content

Not only is there more learning content in more places—but there are more types of content created by a wider variety of authors. Learning content used to be primarily created and controlled by L&D functions. Now, however, employees have access to:

• L&D function-created content
• Learning content created by subject-matter experts (SMEs)
• Company reports, policies, strategy docs, etc.
• Vendor-created learning content (custom or off the shelf)
• YouTube and other social media content
• Podcasts
• Conference notes, presentations, and videos
• Trade- or industry-specific content
• Learning content libraries (LinkedIn Learning, Udemy for Business, etc.)
• Subscriptions to learning content aggregators
• The entire internet

There’s not only more volume of content—there’s more types of content, in more places, created by a wider variety of authors.

And we know that’s not an exhaustive list.

The incredible volume, variety, and breadth of the learning content that’s available—over much of which L&D functions have limited control—complicate things for learning leaders and for employees.

Through our research, though, we found that learning leaders who’ve given this some thought don’t try to control the chaos. Instead, they embrace it—or, at least, they try to work with the reality that learning content is already complicated, and it’s only going to get bigger and more complex over time.

L&D functions can create systems, processes, and policies that help employees navigate the chaos of learning content.

Savvy learning leaders think about how to create systems, processes, and policies that help orgs and employees navigate through the chaos—rather than trying to tame the chaos itself (because that’s not gonna happen).

One learning leader noted:

“Learning functions need to recognize we never owned learning content in the first place, and we certainly don’t now. We need to embrace the chaos.”

Nick Halder, Senior Director of Talent, Snow Software

More enabling, less pushing by L&D functions

Given the increasing amount and variety of learning content out there, the move toward personalized development experiences, and the sheer variety of people in most orgs, it’s almost impossible for L&D functions to push the right content to the right people at the right time in the right format—all the time. There’s also a growing recognition that often the employee knows best—or at least has a good sense of—what they need to learn.

Learning leaders are thinking about how to enable employees to navigate to the right content themselves, rather than pushing content to employees.

Instead of trying to push learning content, L&D functions are thinking about how to enable employees to navigate to the right content themselves—by giving guidance and context about, for example, the org’s strategy and direction, skills that may be needed in the future, and how learning content is organized in the company. This guidance and context can create conditions that enable employees to find and consume learning content when and how they like, in ways that align with their needs and org goals.

More (& better) use of skills data to inform learning content priorities

Learning leaders we talked to noted that, in the past, L&D functions have sometimes pushed out learning content that wasn’t relevant or helpful to employees. These learning leaders see information about the skills employees and orgs need as a potential solution:

“Without insight into what skills are in demand and what skills people have, L&D tends to focus on the learning content we think people need. That’s rarely an effective approach.”

Participant, “New Trends in Learning Content & Content Management” Roundtable

Forward-thinking orgs are using information about the skills their workforce has and the skills it’ll need in the future to decide what learning content to prioritize. Learning leaders are making investments in learning content that can help close critical skills gaps.

Skills info can help orgs better understand what learning content to prioritize and invest in.

More access for all employees

In the last year or so, learning leaders have started taking a much closer look at how accessible learning content really is in their orgs: They’re recognizing the importance of making learning content more widely available to close skills gaps—and to help the business stay agile, responsive, and competitive.²

Three ways learning leaders can improve access include:

• Removing artificial barriers. Sometimes orgs give employees access to learning content on a “need-to-know” basis. But this logic creates unnecessary boundaries that could be removed unless they’re strategically justified—for example, intellectual property, safety / security, cost, or some other significant reason.
• Making learning content more discoverable. Sometimes great learning content is hidden in pockets or silos within the company. Orgs can find ways of making learning content easier to discover by implementing organization standards and really good search capabilities. They can also create a culture of discovery by removing unnecessary passwords and encouraging employees to poke around.
• Making learning content accessible on mobile. Learning content doesn’t just live on desktops anymore. Employees, particularly frontline workers, need access on their phones. This often means rethinking accessibility to LMSs or LXPs, as well as thinking mobile-first when creating new learning content.

Forward-thinking orgs are exploring ways to make learning content transparent, accessible, and appealing to all employees.

In brief

These 4 trends are currently shaping the learning content environment. In this research, we sought to understand how learning leaders are navigating these trends—and how these trends affect their goals, focus areas, challenges, and strategies for learning content.

We developed a learning content model that can help orgs think through their learning content strategies.

This inquiry resulted in a model that can help orgs think through their learning content strategies and make better decisions about where L&D functions should focus their time and resources. The next section introduces and explores this learning content model.

A Model for Thinking About Learning Content

We looked for similarities and differences between learning leaders’ approaches to learning content—and noticed that the learning leaders we spoke with take very different approaches to learning content based on 2 factors (or dimensions) of the learning content they’re working with:

• How unique the learning content is to their org (specificity). Are leaders dealing with learning content that applies specifically to their company or content that applies across orgs?
• The shelf life of the learning content (durability). Are leaders thinking mostly about learning content that needs to be updated rarely, or learning content that’s continually changing and regularly in jeopardy of being out of date? (Note: Long vs. short shelf life may differ from industry to industry and company to company. But, in general, we consider durable learning content to last 1 or more years without needing to be updated.)

If we plot learning content against these 2 dimensions (specificity and durability), then the content generally falls within 1 of the following 4 categories:

1. Specific & Durable. Learning content that’s specific to 1 org and has a long shelf life
2. Specific & Perishable. Learning content that’s specific to 1 org but changes often
3. Generic & Perishable. Learning content that applies to many orgs and changes often
4. Generic & Durable. Learning content that applies to many orgs and has a long shelf life

The learning content model, introduced in Figure 1, outlines these 4 categories and provides examples of some common topics that each category tends to cover.

Learning leaders might consider using this model to clarify the L&D function’s (and other stakeholders’) focus areas and roles regarding learning content. When we gave one learning leader—who happens to sit in a central L&D team within a federated system—a sneak peek at this model, he said:

“I like this model because it can help our L&D teams think about who owns what content. L&D sometimes tries to be all things to all people, but that’s not possible. In my company, we’re starting to be much more intentional about where each of our respective L&D teams are best-suited to play.”

John Z., Head of Digital Learning & Design, Global Medical Devices Company

Let’s look at each of these 4 categories in more detail. For each category, we discuss:

• The focus L&D functions should have for each learning content category
• Challenges specific to each learning content category
• How L&D functions can address those challenges in the immediate and longer terms

Specific & Durable

The Specific & Durable learning content category generally applies only to 1 org and has a relatively long shelf life. It tends to include:

• Introductions to the org’s values, mission, philosophy, and how the org expects employees to act
• Info about strategic initiatives that define the org’s direction
• Onboarding training and materials
• “Crown jewels”—intellectual property that’s critical to success / competitive advantage as a company

The purpose of Specific & Durable learning content is often to shape organizational culture—helping employees understand “this is who we are” and “this is how we act.” Accordingly, the learning leaders we spoke with talked about Specific & Durable learning content most often in conjunction with organizational initiatives, such as organizational culture or change efforts; diversity, equity, inclusion, and belonging (DEIB); and strategic pivots (e.g., adapting to industry upheaval).

Specific & Durable learning content often helps shape org culture—by helping employees understand “this is who we are” and “this is how we act.”

L&D’s focus should be: Drive organizational initiatives

Forward-thinking orgs conceptualize the L&D function’s role—and related goals—differently, depending on the category of learning content at hand: They have a different focus for each of the 4 categories of learning content. As the nature of the learning content and its associated challenges change, so does the way the org thinks about where L&D functions should spend the most effort.

L&D should think about how Specific & Durable learning content can help move the needle in areas that are priorities for the business.

For Specific & Durable learning content, L&D functions should focus on driving organizational initiatives. Specifically, they should think about how Specific & Durable learning content can help move the needle in areas that are priorities for the business.

As one learning leader said:

“What’s the strategic change that’s happening? Is your learning content relevant to get to those organizational outcomes?”

Participant, “New Trends in Learning Content & Content Management” Roundtable

Importantly, learning leaders aren’t thinking about how L&D functions can drive org initiatives alone—far from it. Almost every learning leader we spoke with about Specific & Durable learning content described how they’re reaching outside of the L&D function—to other parts of HR and to leaders of other functions—to stay in sync with org priorities and use learning content to support the cultural and strategic initiatives important to the business.

Biggest challenges we heard

Because Specific & Durable learning content often links directly to key business initiatives, L&D functions typically face challenges like:

• Staying aligned with business goals. How do we stay agile and aligned with business goals in an ever-flexible environment?
• Driving change. How do we use learning content to move the org toward its goals?
• Measuring impact. How do we know if the learning content is, in fact, driving the change, creating the culture, or moving the needle in ways that align with the org’s priorities?

Intentionally linking learning content to org priorities is a critical component in addressing these challenges, particularly around measuring impact.

Forward-thinking L&D functions measure success against metrics used by the entire org, not just the L&D function.

In our research on measuring learning impact, we found that average L&D functions tend to triage based on the squeakiest wheel or easiest fix. Conversely, more forward-thinking L&D functions develop strategies and relationships to continually align and adjust learning content to support org goals—and to measure success against metrics used by the entire org, not just the L&D function.³

What L&D can do

In Figure 2, we include some ways L&D functions can start addressing these challenges. The ideas here (and in subsequent Figures 3-5) are divided into 2 sections:

• Do Now. Actions L&D functions can start on right away
• Work On. Actions requiring some time and coordination to implement

Each idea is paired with an example of how an org is implementing it.

Figure 2: Ways L&D Can Help Address Challenges—Specific & Durable Learning Content | Source: RedThread Research, 2021.

Specific & Perishable

Specific & Perishable learning content is unique to the org and changes / needs updating relatively often. Examples of this type of learning content include:

• Customer training (e.g., on the org’s products)
• Org-specific policies and processes
• Instructions and updates on internally built software / tools

A defining characteristic of Specific & Perishable learning content: The sources of the learning content exist all over the org—in policy and process documents, product release notes, wikis, etc. This truth, combined with the fact that the content changes often, means it’s exceedingly difficult (if not impossible) for L&D functions to create and update all Specific & Perishable learning content needed by the org.

L&D’s focus should be: Enable content creation

In contrast to orgs in which the L&D function tries to control learning content, orgs that deputize all employees and focus on enabling the creation of learning content—no matter who does the creating—tend to have much more success ensuring that updated learning content is available when needed.

The L&D function’s focus for Specific & Perishable learning content should be to enable the creation and curation of learning content within the org—not to create or control that learning content.

The most forward-thinking learning leaders we encountered approach learning content almost as a free-market economy problem: In their minds, L&D functions should facilitate the supply and demand of learning content. Their job is to make those supply / demand exchanges as frictionless as possible, both for the consumers of the learning content as well as the suppliers, no matter where they sit in the org.

Biggest challenges we heard

Challenges with Specific & Perishable learning content tend to stem from the fact that it needs to be updated frequently and only internal people (for the most part) can do the updating. Challenges include:

• Learning content becomes stale and is hard to keep updated
• The best learning content exists in lots of different places in the org
• Quality and consistency of learning content can vary, since a lot of the learning content isn’t created by the L&D function

We talked with several learning leaders who said their L&D teams struggle either to keep tons of content updated themselves or to incentivize SMEs across the business to keep their learning content updated.

L&D functions should provide processes, templates, and guidance to enable anyone in the org to create or curate learning content with relative ease, consistency, and quality.

To address these challenges, the learning leaders we spoke with focus on putting in place processes, templates, and guidance that enable anyone in the org to create or curate learning content with relative ease, consistency, and quality.

For example, these forward-thinking learning leaders:

• Implement basic instructional design templates and norms across the org
• Put in place tech that offers standard templates, design principles, and formatting
• Track learning content usage and communicate regularly with learning content authors about updates
• Make themselves available as consultants—answering questions and providing advice on how to create effective learning content that meets the standards they’ve set

What L&D can do

In Figure 3, we include some ways L&D functions can start addressing these challenges. Each idea also outlines how an org is implementing that idea.

Figure 3: Ways L&D Can Help Address Challenges—Specific & Perishable Learning Content | Source: RedThread Research, 2021.⁴

Generic & Perishable

Generic & Perishable learning content can apply to many orgs but has a short shelf life. Examples of Generic & Perishable learning content include:

• Training / updates on fast-changing tech skills
• How-to tutorials on common processes (e.g., how to create a QR code, how to use a function in Excel)
• Info on current events and industry / market updates

Generic & Perishable learning content is defined by its sheer volume—and the fact that it’s everywhere.

The defining characteristic of this learning content category is sheer volume: There’s so much of it, everywhere! Much Generic & Perishable learning content is available for free online, although Google and YouTube are certainly not the only ways to find it. Other sources of Generic & Perishable learning content are, for example:

• Learning content libraries like PluralSight and LinkedIn Learning
• Professional or trade publications and websites
• Tech vendors offering learning content on how to use their software

Generic & Perishable learning content also changes frequently, meaning the great video someone found last year might be 3 releases out of date this year.

L&D’s focus should be: Help employees filter to the right learning content

The nature of Generic & Perishable learning content means L&D functions’ focus should be to help employees filter. It would be incredibly difficult to provide just the right info to each employee when they need it. Rather, learning leaders’ job is to create conditions that enable employees to cut through the noise and find what they need.

L&D functions should create conditions that enable employees to cut through the noise and volume of learning content to find what they need.

In most orgs, helping employees “filter” means using some kind of tech, most commonly an LXP. We’ve yet to see an org set up a completely manual process that enables filtering at the scale most orgs need: Most orgs leverage both tech and humans to get the job done. For example, teams may share the best or most helpful learning content with one another via Teams or Slack; they may set up queries in content aggregators like Feedly and other apps.

Biggest challenges we heard

We heard 2 main challenges related to Generic & Perishable learning content, both stemming from the volume and turnover common to this learning content category:

• There’s too much noise. For Generic & Perishable learning content, the “signal-to-noise” ratio is extremely low: There’s a lot of learning content in this category, but quality and relevance vary. Although it may be easy to find some learning content on a particular topic or question, it’s hard to know whether it’s the best learning content—or what the org would want an employee to rely on.
• Finding the latest and greatest. There’s regularly more and better learning content somewhere out there. Employees have a hard time finding the most updated, most relevant stuff.

Implementing effective search, curation, and recommendation engines can help give employees direction and a place to start.

Implementing effective search, curation, and recommendation engines can help address these challenges by giving employees direction and a place to start. We explore these ideas next.

What L&D can do

In Figure 4, we include some ways L&D functions can start addressing these challenges. Each idea also outlines how an org is implementing that idea.

Figure 4: Ways L&D Can Help Address Challenges—Generic & Perishable Learning Content | Source: RedThread Research, 2021.^5,6

Generic & Durable

Generic & Durable learning content changes relatively infrequently and applies to many orgs. It includes learning content such as:

• Education and refreshers on safety, security, and ethics
• Leadership development training and programs
• Industry-specific background / context (e.g., how the banking system works)
• Learning content to develop sales skills
• Support for employee wellbeing, mindfulness, and personal growth

Because Generic & Durable learning content can apply to many orgs and likely isn’t changing at a breakneck pace, quite a few learning content vendors play in this space. These vendors offer high-quality learning content on specialty topics that an in-house L&D team may not have the expertise or bandwidth to create.

Many Generic & Durable learning content vendors offer high-quality learning content on specialty topics that an in-house L&D team may not have the expertise or bandwidth to create.

L&D’s focus should be: Facilitate consistency & quality

L&D functions’ focus for Generic & Durable learning content should be facilitating consistency and quality—setting standards for what quality learning content looks like across the org. Because so many vendors offer different learning content at varying levels of quality, L&D functions can create value by helping the org define standards that outline, for example:

• The “go-to” vendors to work with on cross-functional topics like leadership, industry context, or wellbeing
• Criteria for selecting vendors not on the “go-to” list
• What high-quality learning content looks like and where it’s coming from
• Ways to measure / understand what learning content is working and what’s not

L&D functions should define standards that outline, for example, go-to vendors, vendor selection criteria, and what “high-quality” learning content looks like for the org.

With these standards (and / or others) in place, L&D functions can provide a consistent, org-wide point of view on cross-cutting topics, like “the way we lead,” “the way we think about safety,” and so on.

Biggest challenges we heard

Consistency and quality are the primary challenges for Generic & Durable learning content because much of the learning content can apply to many different functions (think leadership development or safety / security) and so many commercial sources of this category of learning content exist.

This breadth and variety give rise to potential differences—within the same org—in:

• The content that’s used for learning on a particular subject
• The quality or efficacy of that learning content
• How that learning content is delivered or supported
• Who gets access to the learning content
• The processes used to evaluate the learning content

As an example, we’ve seen orgs that use a dozen or more different leadership models because different functions / teams brought in different leadership vendors / consultants at different times.

Forward-thinking L&D functions consider how they can foster relationships—with vendors and other functions—to ensure consistency and quality of learning content across the org.

In the lit review we did as part of this research, we read several articles on creating consistent learning content. In an org, consistent course design and visual cues across as much learning content as possible can go a long way toward helping employees understand and navigate learning content easily. However, this kind of consistency is sometimes difficult to achieve with externally created content.

In addition to these instructional design elements, forward-thinking L&D functions are also considering how they can foster relationships—with vendors and with other functions—to bring people together across silos in order to ensure consistency and quality of learning content throughout the org. This sometimes means convening cross-functional groups to align on needs, pool resources, or negotiate org-level (rather than function- or team-level) contracts with vendors.

What L&D can do

In Figure 5, we include some ways L&D functions can start addressing these challenges. Each idea also outlines how an org is implementing that idea.

 

Figure 5: Ways L&D Can Help Address Challenges—Generic & Durable Learning Content | Source: RedThread Research, 2021.

Wrap-Up

We’ve given you a snapshot of recent trends in learning content, including how the explosion in volume and variety of learning content is affecting orgs and employees alike. We’ve also introduced a model for thinking about learning content in 4 categories, based on 2 key factors:

• The specificity of the learning content to the org
• The durability (or shelf life) of the learning content

Our biggest takeaway from this study is how the 4 different categories of learning content give rise to very different focuses for L&D functions—different conceptions of what L&D functions should do with regard to that learning content—and have very different associated challenges. (Unsurprisingly, there’s no one-size-fits-all learning content strategy.)

We are grateful to those learning leaders who shared their experiences and examples with us, and are excited to share so many concrete examples in this report.

As always, we welcome feedback and discussion! Please feel free to reach out to RedThread Research at [email protected] or www.redthreadresearch.com. We’d love to hear about your experiences.

Appendix 1: Research Methodology

We launched our study in Spring 2021. This report gathers and synthesizes findings from our research efforts, which include:

• A literature review of 51 articles from business, trade, and popular lit sources
• 1 roundtable with a total of 33 participants
• 15 in-depth interviews with learning leaders about their experiences and thoughts on learning content

For those looking for specific information that came out of those efforts, you’re in luck: We’ve a policy of sharing as much information as possible throughout the research process. Please see:

• Premise: “Content in the 2020s: Enabling Learning”
• Lit review: “Learning Content in the 2020s: What the Literature Says”
• Roundtable readout: “Trends in Learning Content & Content Management”
• Q&A call: "Learning Content"

Appendix 2: Contributors

Thank you so much to those of you who participated in our roundtable and interviews. We couldn’t have done this research without you! In addition to the leaders listed below, there are many others we can’t name publicly. We extend our gratitude nonetheless: You know who you are.

Angel Rodriguez
Ann Boldt
Brett Rose
Bria Dimke
Brian Richardson
Catherine Marchand
Chris Casement
Chris Olson
Clark Shah-Nelson
Eddie Garcia
Emily Crockett
Erik Soerhaug
Gordon O’Reilly
Greg Williams
Heather Bahorich
Ian Bolderstone
Jihyun Jeong
Jim Maddock
John Z.
Laurence D. Banner
Leah Holmgren
Marc Ramos
Martin Tanguay
Melissa Lamkin
Mitchel M.
Nick Halder
Nicole Leret
Robert Young
Sarah Foster
Shaun Rozyn
Stephanie Fritz
Stephen T.
Tania Tiippana
Tone Reierselmoen
Zachary Pfau

In addition, we thank Catherine Coughlin for editing the report, Jennifer Hines for graphics, Jenny Barandich for the layout, and Sana Lall-Trail for research and project management.

DEIB Tech: Its Time Has Come

Global pandemic. Protests. Elections. Riots. (And whatever else happens between when we publish this article and you read it.) Needless to say, the last year has been rough. It laid bare our differences in stark relief. Shown how events impact diverse people differently. Perhaps it caused you some measure of disgust, despair, or even depression. At a minimum, it likely contributed to exhaustion.

But, at the same time, the last year has also revealed our underlying humanity. The extent to which we care about other people. The depth at which we hold our beliefs about our country. The potential we have when we work together (hello, COVID-19 vaccine!).

Given all this, there has never been a greater need for a focus on diversity, equity, inclusion, and belonging (DEIB) – both in our society and in our organizations. We have a need to understand each other and to work together, more than ever before.

Organizations throughout the world have recognized this, from top leaders to DEIB leaders to managers and employees. It’s for this reason companies are talking about DEIB more in their earnings reports than ever before and why the number of DEIB job openings has skyrocketed. The thing is this: organizations cannot just talk about DEIB and hire people to lead it. That is a good start, but it’s not enough. Organizations need to change their systems, practices, and behaviors. The change cannot just rely on individuals – it has to be baked into how the organization operates.

This is where DEIB technology can help, as it has the potential to build in practices, behaviors, insights, and recommendations that address bias. It can also provide insights about what is actually happening with people (versus relying on anecdote-based understanding) at the moment of critical decision-making about talent.

Tripping down memory lane

When we first began studying the D&I tech market in 2018, the #MeToo movement had thrust diversity and inclusion in the workspace under a spotlight. Stories and accounts of workplace discrimination, harassment, and unethical behaviors toward women in the workplace led numerous businesses to pledge to change their policies and take action.¹ As a result, organizations began to feel a greater need for systemwide solutions.

In 2018, we launched our first research study on this topic, and we published a comprehensive report, Diversity & Inclusion Technology: The Rise of a Transformative Market, in February 2019. The study included a list of all the D&I vendors we identified and was accompanied by a detailed vendor landscape tool (with 2 updates since). As we shared in our initial report, tech can play a transformative role.

Fast forward to today

We (still) find ourselves in the midst of health, social, and economic crises. 2020 was not an easy year for anyone, but it especially impacted diverse people in many significant ways, including:

• Women left the workforce in record numbers
• Lower-income earners saw their jobs evaporate
• The murders of George Floyd, Breonna Taylor, and others disproportionately impacted the Black community

Many companies have responded by making pledges or promises in support of the #BLM movement.² A large number of them have focused on increasing diversity levels within the companies, both at the employee and leadership levels (for examples of such corporate pledges, see Diversity, Equity, Inclusion & Belonging: Creating a Holistic Approach for 2021).

As the pressure to follow through on these promises increases, leaders must develop strategies to achieve them––and we believe that DEIB tech represents one of the critical components of the process (see Figure 2 further down). Sophisticated tech––such as artificial intelligence (AI), deep machine learning, natural language processing (NLP), and organizational network analysis (ONA)––can help leaders manage DEIB better and more easily and are increasingly becoming more accepted as essential tools for people practices.³

Through this report, we aim to achieve 4 things:

1. Help leaders understand the role of DEIB tech
2. Provide insights on the state of the DEIB tech market
3. Highlight the talent areas focused by vendors
4. Guide leaders who may be looking to make tech investments

Key Findings

The study covers three major areas and how they have changed since 2019: the DEIB tech market, talent areas vendors focus on, and what buyers should consider before investing. We also address what we see coming next. Some of the key findings from the study include the following:

1. Three major shifts punctuate the current DEIB tech market
   • In previous years, leaders were especially focused on gender; in 2020-21, the emphasis has evolved to include a focus on race and ethnicity.
   • Social justice movements and conversations around discriminatory workplace practices and behaviors have led to greater attention to inclusion than ever before.
   • The role of AI in mitigating bias to enhance DEIB has come front and center, and more approaches have been introduced to address this issue.
2. The DEIB tech market is hotter than ever
   • The global market size is estimated to be $313 million and growing, up from $100 million in 2019.
   • The number of HR tech vendors offering features or functionalities that cater to DEIB as part of their solutions has increased by 136% since 2019.
   • The total number of DEIB tech vendors increased by 87%, with a total of 196 vendors in the market for 2021, compared with 105 in 2019.
3. People analytics for DEIB has arrived
   • Lack of analytics and insights on DEIB is the primary challenge the majority of vendors help their customers solve, hence the growing number of solutions. providing DEIB analytics in 2021 compared to 2019 (28% vs 26%, respectively).
4. Small-sized organizations and knowledge industries remain the main customers of DEIB tech
   • The largest customer category is small-sized organizations (those with less than 1000 employees), who represent almost 30% of all DEIB vendor customers.
   • However, these small organizations represent a smaller percentage of DEIB vendor customers in 2021 than in 2019, and there was an increase in the percentage of customer organizations in the 10,000-50,000 range.
   • The industries most likely to be DEIB tech customers are concentrated in knowledge industries, namely technology, financial, banking, and insurance.

Check Out the Full Study and Tool

The full study (available to members) has lots more information than what we’ve detailed here, including many more details on the market, customer quotes and feedback, and checklists for leaders interested in DEIB tech.

In addition, we encourage you to check out the brand new, fully redesigned DEIB Tech tool, which is available both to members and non-members. You can look at the 196 vendors in each of the four talent areas and their relevant sub-categories. RedThread members can click through and see details on individual vendors.

 

 

RedThread members can see the areas of talent vendors focus on, the top industries served, vendor capabilities, strengths, challenges addressed, and customer feedback (see Figure 3). We provide the maximum amount of information we can, based on what vendors shared with us or what we were able to find publicly available. This tool is designed to be evergreen, so it will be updated continuously as we conduct briefings throughout the year.

 

A Thank You

This study involves a significant time investment from everyone who participated in its development. We want to thank all of the vendors and customers who gave their time, energy, and expertise to make this such a robust study and tool.

If you have any questions about this research or about becoming a RedThread member, please contact us at [email protected].