Andreas Mueller & Tom Falter: HR in Digital Metamorphosis

„The times they are changin’“ – for the last couple of years in HR, we cannot stop chanting Bob Dylan’s verse. After the last major change which Dave Ulrich propagated with his idea of HR as a business partner, we are now in the middle of a transformation that seems to be even more substantial: Artificial Intelligence, Digitalization, and New Work – these themes influencing HR are shaking our businesses and seem to require urgent responses or propel us to turn our organizations into something more agile and more values-oriented.

Yet, what will stay once the dust settles? What are the images that help us see again the forest for the trees, and what could guide us in this digitalized time of unrest? 

Instead of foreseeing the future, the following visualizations might assist us to shape it or to react to these changes.

Ambidextrous Strategies

Let us take the example of a Car Dealer called CarCo, which went through several phases of growth via the various technological and business developments of the past years: CarCo expanded into a multi-brand business, moved from mechanical to mechatronic engineering, and adopted the growing demand of eco-friendly transportation. Looking back, CarCo had started to explore new opportunities while it was still exploiting the running business model. Not always did the first ideas work out successfully, but after some experiments, CarCoalways found a new and extended way to operate its business.

It is this mindset of ambidexterity that helps us to paint a bigger picture, to allocate resources in the good times and to prepare for possibly disruptive changes in the future.

Yet it is a leadership challenge to balance the two cultures of exploitation and exploration: explorers are often seen as creative but inefficient wastrels, while exploiters are seen as stubborn and slow bureaucrats. We need both qualities in the same time, which demands at least to understand if not to provoke the differences. This mutual understanding is particularly needed in two transitions:

• Bureaucratizing innovation: moving from exploration to exploitation means to save the investments of creative experiments and to turn successful new ideas into sustainable earnings. To do so, we cannot wait for the perfect and mature product, but have to start with a repetitive (industrialized) process earlier. Planning, forecasting, and optimizing start to enter the creative-disruptive minds who understand that their dominance in exploration is soon handed over to the exploiters;
• Disrupting continuity: the transition from exploitation to exploration prepares for a next wave of success, for new grounds that must be identified when current growth comes to a plateau. We should start early enough to provide enough resources for the phase of experimental creativity. The exploitive mindset of predictability, continuity, and certainty is challenged by an upcoming risk appetite and by ambiguous or scrutinizing ideas and approaches. The explorers are soon taking over.

Managing in a VUCA world of perceived volatility, uncertainty, complexity, ambiguity means managing this second transition of disrupting continuity. When these transitions occur in long cycles, then the changes are perceived as more substantial and generate more resistances and fears.

Brilliantly optimized processes are questioned, markets reject traditional products, managers start to operate with short sight. Instead of a change, we tend to talk about a larger transformation, yet we do not know where the road goes.

To reflect the human dimension of uncertainty, we prefer the term metamorphosis: like the caterpillar, we only observe a major change, but do not know in advance how beautiful a butterfly we will become. Yet in contrast to the caterpillar we are full of positive examples in which fundamental changes were instrumental to improve our daily lives.

Humans or Machines

Back to CarCo, whose owner has realized that the challenges arising from digitalization as well as from a growing demand of electromobility are only displaying a larger transformation. She invites all employees to an off-site workshop about the company’s future with two outcomes: at first, the whole company realizes the transition phase and understands the importance of upcoming new ideas for the future of the firm.

Secondly, the team already generates first ideas, e.g. to install free charging stations for CarCo clients, which enhances the offerings on an existing market. Another promising idea from CarCo’s workshop is a new offer for a new market, namely to become a mobility provider with car-sharing, chauffeur services, e-scooter rental, or old-timer rides.

Fig. 2 Ansoff matrix for CarCo

With new clients in mind, the owner sympathizes with the idea of vertical diversification to become a mobility provider in the region.

But does the company possess the required competencies and people to be successful? Can the current workforce be upskilled by trainings, or does she have to select new staff from the labor market? How can this new business idea be supported by digital technologies?

In a first step, she clusters the main activities that need to be done to satisfy future mobility clients. It turns out that the new business can make use of the current expertise in bookkeeping and the garage. Additional activities like mobility consulting to find the best means of transport (and transportation itself have to be set up from scratch.

Fig. 3 Job split forCarCo

In a second step, she reviews what could be done by humans, by algorithms (logical machines) or by robots (physical machines), and involves her HR manager, who turns into a “labor resources manager” more than a human resource manager. After a brainstorming discussion, the respective approaches are assessed financially and are subject to a make-or-buy decision.

Skill Gradient& Skill Portfolio

A traditional make-or-buy decision would search for either people or machinery to fulfill the required tasks. As automatization grows, we tend to demand more from our human resources, and in return employees grow out of their initial task set. Visualizing the skill gradient helps to identify which staff members prefer continuity (exploitation) and who prefers innovation (exploration).

The skill gradient maps the time that individuals need to acquire a specific skill. It can be based on the traditional annual feedback related to 360 feedback or on contemporary, continuous peer feedback results. You could also take into account strategic competency levels, chartered qualifications, badges, or alike.

Figure 4 shows a simplified illustration of two employees’ different learning curves with respect to one skill: while MA1 reached level 2, MA2 reached level 2 in half the time and is already on skill level 4 – which results in MA2’s skill gradient being twice as large as MA1’s.

The skill gradient not only depends on the employee’s motivation, skills, experiences etc. but also on the difficulty and on organizational parameters like learning culture, failure tolerance, career permeability, access to learning opportunities or developmental aspirations.

To find out which skills are relevant to be tracked for the skill gradient, we could employ industry skill surveys or generate aprojection about which skills are important today and in the future. Figure 5 shows a current McKinsey study. 

Skill portfolio and skill gradient extend “make-or-buy” towards “make-buy-or-learn”, and this is how CarCo made use of it:

Based on the above mentioned workshop results, the company identifies some skills which should become more important: chauffeur services, carshare management, mechatronic repair and app design. With a growing electromobility, engine repair skills and oil change skills will decrease in importance, similarly tire change skills which will be done by robots.

Assessing future skills with a skill gradient helps to decide if CarCo can invest in training activities or if it should hire new staff. At the end, CarCo upskills several employees towards chauffeur and carsharing management skills, and it recruits a user interface programmer who also gets the task to improve the overall IT setting of CarCo.

Values and Energy

The growing amount of AI and automation suggests that a future workforce will operate mainly in social or creative contexts, and less to fulfill simple repetitive tasks. Consequently, the context becomes more relevant to individual employees, and managers are more likely to manage the workplace.

As management thinker Peter Drucker already wrote: “In a traditional workforce, the worker serves the system; in a knowledge workforce, the system must serve the worker.”

We got used to the formal introduction of satisfaction and engagement surveys, but do we understand what drives every individual and how we can make the best match between organization and employee? One answer lies in the deeper roots of an employee’s behavior at work, in their self-image, values, and motives. In Particular, the values of an employee should have a large overlap with the values of an organization, not only in the interest of the employee: studies have shown that companies which are in line with all their stakeholder values are outperforming the market.

To achieve a match of values, we generate an energy fit, which represents the amount of overlap between individual values and organizational values. A job that is highly congruent to individual values will release positive energy and lead to a flow, while a missing fit draws energy from the individual due to high cost of adaptation.

Together with the skill fit, these scales generate the job fit, as indicated in figure 6. The workable area for an energy fit is between 40% and 70%, values outside this can provoke a burnout. The metrics can be accumulated to the culture visualizations of group fit and organization fit.

Our owner of CarCo is interested in a high fit of skill and energy, as employees will then achieve their highest performance. To her, skill fit is more important than energy fit, as skills can be acquired quicker than values can be changed: a salesperson who values customer orientation but is not dutiful might employ algorithms to follow-up on client conversations; a missing skill to remember pricing models might similarly be compensated by an optimization app. 

A controller on the other hand, who answers client requests without the corporate value of friendliness, would soon receive feedback or a disciplinary warning.

The changing role of HR in digitalization

The integration of digitalized work into the scope of HR can be understood by a three-level model of digitalization: 1 – digitization, 2 – digitalization, 3 – digital transformation. Digitization creates a digital representation of physical objects. Digitalization enables, improves, and replaces businesses by using digital technologies. Digital transformation then is the profound transformation of business models, individual competencies, and daily lives to fully leverage the opportunities of digital technologies.

The first step in digitization provides HR managers with more data and leaves various questions of how to make use of it. As having the data is different from using it, this availability of ‘big data’ often leads to intensive discussions with employees, as the example of Daily Telegraph showed in 2016: the company had installed motion sensors below the desks to improve space utilization, yet after their union had raised concerns, HR had to roll back on it. Yet, other companies like Barclays or Amazon are using that same technology to optimize their facility management. It is an HR issue to take care for an appropriate digitization strategy, and to find agreements with stakeholders.

When it comes to digitalization, we see an optimization of processes, e.g. by introducing apps and services based on HR information systems, or by digitalizing processes like onboarding workflow which provides new employees with accounts, hardware, and learning materials in an automated way and makes traditional forms, calls, events and even onboarding staff redundant.

Another example is Robotic Process Automation (RPA) for HR data processing, and payroll documentation from inhomogeneous sources etc., which speeds up former human work by leaps and bounds. We might also look at AI-based algorithms which help chatbots to communicate more like humans (e.g. Watson Assistant, Alexa, Bold360, Siri) or help to predict which employees are about to quit soon (e.g. IBM’s predictive attrition program). The role of HR here is to understand available technologies and their implications on the respective businesses. In particular, HR will start to optimize itself and will require more data analytics on its own.

With the notion of digital transformation, we see system-level change happening. Transformed recruiting will no more wait for paper résumés or their pdf counterpart to identify suitable candidates. Instead we will see an inverted workflow, where A.I. scans online sources of active or passive candidates (e.g. mindmatch.ai) and gathers more specific data in a first virtual interaction (e.g. HireVue), before it generates a short list to the hiring manager.

The same hiring manager has a dashboard with all employees and their strengths, flight risk, and performance (e.g. kronos.com), and can assess how the proposed candidates fit to their team in terms of skills and values / energy (e.g. flowspace.de). 

We could think similarly not only about recruitment, but about the whole employee lifecycle including personal development, performance management, and succession, as well as career management, retention management, and offboarding – the work of managers, employees and HR is changing dramatically.

As traditional work is fading out, future HR is confronted more with a three-fold challenge: 

• how can we better understand what drives the working individual and generates flow;
• which algorithms and robots can automate what humans have done so far;
• how can individuals and groups best be re- and upskilled in the era of digitalization.

As for CarCo, the owner and her HR manager are in the beginning of their digital transformation; their human-machine job split matches mainly with the second level of ‘digitalization’. While they feel the growing changes in their metamorphosis, both have started realizing that the next step would be facing the organizational setting and its leadership.

Additionally, once they have collected more data about customers, employees and machines, they might come up with even more radical ideas to transform their business, but this is another story…

Further Literature:

Drucker, P.: The’re not employees, the’re people, HBR 2002

Pinder, C.: Work Motivation in Organizational Behvior, Prentice-Hall 1989

Sisodia, R. / Wolfe, D. / Sheth, J.: Firms of Endearment, Wharton School 2007

Unruh, G. / Kiron, D.: Digital Transformation on Purpose, MIT Sloan 2017

Zulian, A. / Bouza, A.: API Product Management 2018

Prof. Andreas Mueller, Prof. Thom Falter