Imagination Expanding HR Technology: solutions that transform how people think about work

“Our understanding of technology limits our imagination about work. We do not seriously consider actions if we think they are unrealistic. But the reason we create technology is to accomplish things that are difficult or impossible to do without it.” – Talent Tectonics

The word “transformational” is often used when describing HR technology. But not all transformations are the same. Most HR technology solutions tactically transform how people perform existing work tasks. A smaller number mentally transform how people think about work itself. Understanding this distinction has significant consequences for deploying HR technology solutions. There is a big difference between getting people to act differently and getting people to think differently. This article discusses differences between tactical and mental transformation, provides illustrative examples using AI/ML driven HR technology solutions, and explores how these differences impact the adoption of HR technology.?

Tactical vs Mental Transformation

At its core all technology is automating or augmenting tasks that were previously performed by humans or envisioned by humans as being tasks worth doing. – Talent Tectonics

Technology changes work through two basic mechanisms: automation and augmentation.? Automation uses technology to perform tasks in a manner somewhat similar to how people were already doing it. Augmentation uses technology to perform tasks in ways that can be radically different from how people did it in the past. For example, bicycles and airplanes both provide alternatives to walking as a way to move around the planet. Bikes automate and slightly augment tasks associated with walking (e.g. pedaling and coasting instead striding). In contrast, airplanes radically augment the act of walking to the point that we do not think of flying and walking as being related. Even though they are both methods to move from one place to another. In general, the more technology augments tasks, the more anxiety people feel toward use of the technology. This is one reason why fear of flying is a far more common anxiety than fear of biking, even though biking is arguably more dangerous .

Understanding whether an HR technology solution works via automation vs augmentation has significant implications for solution adoption. Solutions that work through automation require making a “tactical transformation” to how people perform familiar activities. Solutions that work through augmentation require making a “mental transformation” to how people think about the activities themselves. To illustrate this concept, contrast employee adoption of cell phones versus texting. Phones augmented the task of talking so people could communicate over greater distances. Cell phones enhanced this transformation by removing the constraint of having to connect to a landline. While concerns are occasionally expressed about phone calls being less effective than in-person conversations, employees were quick to adopt cell phones because they automated the already familiar activity of talking with one another.

Compare this to adoption of text messaging. Many people who readily accepted using cell phones for voice conversations resisted using cell phones for texting . Text messaging augments the act of conversation by blending aspects of written and verbal communication that were traditionally kept separate. Texting combines elements of verbal conversations such as informal, rapid, back and forth bursts of communication in real-time with elements of written conversation such as asynchronous information exchange and use of visual imagery (e.g. memes). Adopting texting technology requires making a mental transformation about what it means to have a conversation. Employees who had not used texting outside of work often struggled to make the mental transformation required to adopt this new way of communicating.?

Tactical vs mental transformative uses of AI/ML HR Technology

Looking at HR technology solutions through the lens of tactical vs mental transformation provides a sense of how solutions are likely to be adopted and the impact they are likely to have on work. Tactically transformative solutions tend to be quickly adopted but may have little impact beyond creating efficiency gains. They rarely give companies enduring competitive advantage because they are relatively easy to deploy. Mentally transformative solutions tend be more challenging to deploy but also create greater competitive advantage for companies that adopt them early on. This is because they do not just make existing work easier. They change the nature of work to make it more impactful. To illustrate this distinction, consider some of the ways AI/ML has been applied to solve the perennial HR challenges of designing jobs, staffing positions, developing capabilities, and engaging performance[1].

Designing Jobs – setting compensation levels. A core part of job design is deciding how much people will get paid.? Compensation rates are influenced by internal employee pay equity and external labor market pay rates. Traditionally companies rarely adjusted a person’s pay based on internal equity or external market comparisons other than when they were hired, promoted or transferred. And even then, adjustments did not always happen due in part to lack of data. Innovations in AI/ML have enabled companies to analyze pay data to support proactive and continuous approaches to ensure pay is both equitable and competitive (e.g. Compa ,Syndio , ). But companies have been quicker to adopt solutions focused on ensuring internal equity than solutions focused on external market competitiveness[2]. This reflects the degree to which these solutions require mentally transforming attitudes about when pay should be adjusted.?

Internal pay inequity used to be viewed as a problem for employees to solve. Many leaders did not believe it was the responsibility of companies to ensure people received equal pay for equal work. Over the past several decades, society has undergone a mental transformation shifting responsibility for pay equity from employees to companies. Most companies now want to provide equitable pay lest their compensation practices be viewed as unethical and potentially illegal. As a result, the biggest barrier to adoption of AI/ML pay equity solutions tends to be tactical, based on whether companies feel they need these solutions to ensure fair pay.?

In contrast, there is no social edict for companies to provide employees the same pay they might get if they worked for another organization. Paying market competitive rates may help with attracting and retaining talent, but proactively adjusting pay levels based on external market comparisons is not viewed as a morally important or socially critical issue. It is no longer acceptable to underpay people based on internal equity, but it is acceptable to underpay people based on external market comparisons. Getting companies to adapt AI/ML solutions to proactively adjust pay levels to ensure market competitiveness is difficult because it requires leaders to mentally transform their existing beliefs about when pay should be adjusted.

Staffing positions – improving recruiting processes. Staffing is about getting the right people into the right jobs as quickly and efficiently as possible. AI/ML solutions that enable companies to match candidates to jobs based on skills have been quickly adopted by staffing organizations (e.g., iMocha , Eightfold ). These solutions automate a task that recruiters where already doing to decrease time to fill open positions. Using AI/ML to match candidates does not require talent acquisition departments to fundamentally shift their thinking about staffing.

In contrast, companies have been somewhat slower to adopt AI/ML solutions that enable companies to use post hire data to understand and improve staffing outcomes (e.g., Crosschq ). ?Using AI/ML to improve quality of hire requires talent acquisition leaders to mentally transform their definition of staffing performance. Instead of measuring staffing based on traditional metrics such as cost-of-hire and time-to-fill, leaders must mentally shift their focus to include post-hire metrics such as new hire productivity, reliability, career progression, and organizational commitment. Improving quality of hire has a far greater impact on organizational performance than decreasing time to fill, but staffing departments historically placed little emphasis on quality of hire as a critical metric. Using AI/ML for skills matching to reduce time to fill is a tactical transformation to existing staffing practices. Using AI/ML to improve quality of hire is a mental transformation in how companies measure staffing effectiveness.?

Developing capabilities – assigning training: Connecting employees to job relevant training resources is a core part of employee development. AI/ML has been widely deployed to provide training recommendations to employees based on their skills and interests (e.g., Degreed ). This represents a tactical transformation to tasks historically associated with browsing training catalogues and creating learning plans.

In contrast, companies have been slower to adopt what is likely to be a far more impactful use of AI/ML to convert training content to ?be delivered as micro-learning in the flow of work (e.g., Arist , Axonify). Using AI/ML to provide training recommendations is a tactical transformation to improve how people find development materials, which is not a new task for companies. Using AI/ML to convert traditional dedicated learning courses to in-role micro-learning requires mentally transforming how people think about the process of employee training at a more fundamental level. It requires shifting mindsets from the familiar concept of “sending employees to training” to the unfamiliar concept of “sending training to employees”.???

Engaging Performance - assessing employee attitudes. Listening and responding to employee concerns, ideas, and suggestions is critical to improving employee engagement. One of the main ways companies have done this in the past is through use of employee surveys. Not surprisingly, one of the first uses of AI/ML for employee listening involved automating how companies interpreted written responses to surveys (e.g. Qualtrics ). These solutions have become widely adopted because they involved a straightforward tactical transformation to an existing task.

The use of AI/ML to interpret survey responses generated little concern from employees because it was a tactical change to an existing practice. Compare this to how people react to the use of AI/ML to assess employee attitudes and ideas through analyzing data from online meetings, e-mail and text messages (e.g., Fora ). The suggestion companies might analyze internal meetings and sift through employee e-mails to understand employee attitudes can elicit strong emotional reactions about privacy. Companies have the legal right to access this data in most countries but using it will require people to make a mental transformation about how internal company data should and should not be used.???

When tactical transformations create mental transformations

The previous examples contrasted tactically and mentally transformative applications of AI/ML HR technology. Solutions that require mental transformations typically augment tasks in ways that require rethinking how people define the nature of work. It is also possible to automate work tasks to the point that it becomes akin to augmenting human behavior instead of merely replicating it. An example is the use of AI/ML chatbots to automate recruiting (e.g. Paradox ). Initial chatbot solutions automated administrative tasks like interview scheduling, which freed recruiters and hiring managers to focus more time on strategic activities related to engaging and selecting candidates. Chatbot solutions are now able to automate a broader range of recruiting activities including complex tasks such assessing candidate job fit. Some companies are using these solutions to hire people for certain jobs with no human involvement whatsoever. There is a big conceptual difference between reducing the time people spend on a task and eliminating people from the task entirely. Hiring candidates without ever talking to them requires making a mental transformation about what it means to recruit someone.?

Managing Tactical vs Mental Transformations

The fact something is valuable to do does not mean companies will do it. – Talent Tectonics

Understanding whether an HR solution achieves value through tactical or mental transformation provides insight into factors that will affect adoption of the solution. Solutions that enable tactical transformations tend to be easier for people to adopt because they create clear cost and time saving by automating familiar tasks. On the other hand, tactically transformation solutions tend to be quickly copied and commoditized and create considerable frustration if they are difficult to use or make mistakes. To sell the value of these solutions emphasize ease of use, time saving, reduced costs, and increased efficiency.

HR solutions that enable mental transformations by augmenting tasks that change how work is done tend to be harder for people to adopt, but once adopted tend to have a much greater impact on work performance. Adoption of mentally transformative solutions requires educating people on why using the solution is better than existing, familiar ways of working (e.g., why is it better to measure employee attitudes by analyzing electronic communication instead of conducting surveys?). It may also require addressing anxiety that the solution might do things in an unnatural or potentially inappropriate way (e.g., what safeguards are in place to ensure an AI/ML chatbot does not dispense inaccurate information?).

HR technology solutions that drive mental transformations are rarer than tactically transformative solutions, tend to be harder to create, and are usually more difficult to adopt. Developing mentally transformative HR solutions requires a high tolerance for risk and strong innovative spirt, and these solutions tend to be developed by smaller, entrepreneur-led technology companies. These solutions also have the biggest impact on advancing the quality of work over the long-term. This is because they are not just about doing existing things in a better way. They are about doing better things.

Building, selling, and implementing mentally transformative HR technology solutions can feel like a major uphill battle. But as has often been said, the most valuable accomplishments are rarely the easiest to achieve. The biggest improvements in the quality of work will come from development and adoption of mentally transformative technology. Over time, effective mentally transformative solutions eventually cease to be seen as transformative and just become the accepted way work is done. For example, it is hard to imagine how large companies could function if they had to manually manage their payroll processes. But there was a time when the idea of trusting payroll to a computer was considered extremely innovative, somewhat unnatural and highly risky .

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[1] Names of companies that provide different AI/ML HR solutions are provided for illustrative purposes and are not intended as an endorsement of any particular application.

[2] This is based on my observations of the market. If someone has data that confirms or disputes any of the views expressed in this article I would welcome seeing it.