How To Build A Truly "Agile" Human Resources Function

In the early 2000s a group of seventeen software developers created the agile software methodology. Agile is an approach to software development based on quicker cycles of development, faster iteration, constant feedback, and an increased speed of delivery of functional software packages.

The “Agile Manifesto” created by those seventeen developers, says:

“We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value: “1) Individuals and interactions over processes and tools. 2) Working software over comprehensive documentation. 3) Customer collaboration over contract negotiation. 4) Responding to change over following a plan"

Agile development was a response to how heavy, rigid and slow the software industry had become. With the introduction of agile, software developers could deliver more value, at a faster pace, with tested and validated functional software! Agile became a true disruption.

The agile approach resulted in increased performance, effectiveness and delivery. Research shows that by using agile approach there’s increased productivity, velocity in time-to-market projects, stakeholder satisfaction, quality, and decreased cost of development.

Because its usefulness, agile transcended the software industry and now it can be used across the board in other business areas. For example, the “Human Resources (HR)” industry.

Agile in HR

In the same way that the software industry was heavy and rigid at the time the agile manifesto was created, for many years the Human Resources industry has been extremely slow to change, heavy to operate, oriented by compliance with processes, rules and policies, and not innovative at all.

Today HR is riper than it’s ever been for comprehensive change and transformation. The way HR operates today doesn’t deliver what employees, business leaders and organizations really want. How can HR create and add more value?

The agile manifesto and methodology, and the four values and twelve principles that it establishes, can provide useful direction for the HR transformation to become a value-creating business unit.

This is how the four values can be used in the HR industry and people operations.

Agile Manifesto Values Applied To Human Resources

Value 1: Individuals and Interactions Over Processes and Tools

HR should facilitate and make it easier for people to collaborate within the organization. That means not only fostering a culture that allows collaboration to emerge and flourish, but also rewarding it. In addition, the concept of Employee Experience (EX) is fundamental in the new world of work. HR must radically shift its focus from enforcing rules, policies, processes and systems, to ensuring that employees have the best experience at work.

For that this to happen, HR has to evolve pretty quickly from compliance-oriented function to a more strategic, developmental one. I like to call this “putting people first”: ensuring the best experience for all individuals (EX) by making sure they find meaning, purpose, opportunities for growth and development, all while promoting formal and informal collaboration (interactions).

Value 2: Working Software Over Comprehensive Documentation

“Working software” in HR can be equated to “working processes”. HR has a big problem: trying to design and implement “perfect” people processes end-to-end without testing any of the assumptions of those processes along the way. Unfortunately, by the time HR reaches the second “end” of the end-to-end design, the process might be already obsolete.

For example, performance management. Most organizations and business leaders acknowledge that the annual-review rating-based performance management doesn’t deliver any real value. However, the question always comes to “what do we do then?”. Unfortunately, as long as HR focuses on creating a fully-designed, end-to-end, well-documented replacement for performance management, instead of smaller working packages, it will be difficult to change.

What if instead of following this end-to-end approach, HR designs, tests, validates and delivers smaller workable packages? HR can start with testing the idea of “coaching and mentoring”, then feedback (bottom-up, top-down, and lateral), switching from individual rewarding to team rewards, personalizing compensation and benefits that stem from performance. The point is that for each people process, there are many smaller “sub-processes” that can be improved. Instead of going “all the way” with changing a large process, why not start with smaller pieces?

Value 3: Customer Collaboration Over Contract Negotiation

Employees are customers and they should be treated as such (and better!). Unfortunately, I don’t believe that HR has clarity about this. It isn’t a surprise that HR isn’t trusted by employees and that they see it as an obstacle for the organization to do better. Sad!

“Customer collaboration” can be easily translated into “HR collaborating with employees and business leaders”. Research shows that most business leaders are aware that HR is not delivering its potential value, but neither them nor HR itself know with certainty the direction of change. So, why doesn’t HR open its arms and embrace ideas, feedback and input from others? The transformation of HR can be a very daunting process. However, if HR considered employees and leaders as partners and champions of such transformation, perhaps the process would be less complicated and painful.

For example, if we focused on the digital transformation component of the HR transformation, we can easily say that HR doesn’t have the full know-how of how to create a digital strategy. Why not invite those who do? Collaboration between HR and business leaders and employees is paramount for the success of HR and its potential to create and add more value.

Value 4: Responding to Change Over Following a Plan

HR is not only slow to change, but it seems to be stubbornly focused on creating long-term strategic plans that make people processes difficult to adapt and change. Let’s be honest, the world is in chaos right now because of the pace at which things are changing. It is impossible to control things or to truly plan too far ahead. If you’ve heard of the VUCA world (volatile, uncertain, complex, ambiguous), I like to say that we are in a VUCA reloaded world.

The alternative to heavy long-term (usually five years) strategic plans is to have a “massive transformative purpose” (by Salim Ismail in Exponential Organizations book). That is, a clear purpose of what HR is about. The MTP would become the true north of HR, without setting in stone the specifics on how to do it. Once HR is clear about what it is about, instead of creating rigid long-term strategies, it has to be ok with flexible strategies and tactics that deliver value, but can also quickly and easily respond to change.

Agile Manifesto Principles Applied To Human Resources

These are the twelve principles of the Agile Manifesto and this an idea on how they can transform HR into a real agile function.

1 Customer satisfaction through early and continuous software delivery: focus on delivering the best employee experience on an ongoing basis, rather than developing complex policies that reward employees on a yearly basis. In addition, forget about “total rewards”. Such a concept doesn’t exist anymore in a time of extreme service personalization. Use tech to help you out with the data!

2 Accommodate changing requirements throughout the development process: responding to change without making people process too complex. Adapting via flexible strategies.

3 Frequent delivery of working software: shifting the focus from controlling people processes end-to-end to delivering smaller, workable pieces that can be tested, experimented with and validated, and only then implemented across the board.

4 Collaboration between the business stakeholders and developers throughout the project: employees are customers. Include them in the design of people process. More know-how from real “users” rather than closed-door design by HR people.

5 Support, trust, and motivate the people involved: creating the conditions for people to feel that they can count on HR as their partner.

6 Enable face-to-face interactions: more collaboration!

7 Working software is the primary measure of progress: working people processes that have been tested and validated are better than well-written, “well-designed” processes that don’t deliver real value.

8 Agile processes to support a consistent development pace: the world is changing extremely fast. Don’t be an obstacle. Be a springboard that can support people’s development and fast pace of work.

9 Attention to technical detail and design enhances agility: what are the skills that your business need? Is HR working on building those skills for the future?

10 Simplicity: simple processes. Stop making every freaking process a complicated piece of paper that nobody (not even your HR people) understands. Simplicity drives better performance than complexity,

11 Self-organizing teams encourage great architectures, requirements, and designs: HR has to focus on the type of organizational structure appropriate for their business. There’s not one-size-fits-all approach here. However, what’s true for all industries: heavy, rigid, controlling hierarchies can’t deliver value anymore. Go back to your business architecture and understand how the heavy hierarchies prevent the best work.

12 Regular reflections on how to become more effective: constant feedback, coaching and mentoring. HR needs to evolve to HD: human development!


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About the author:

Enrique Rubio is a Tech and HR Evangelist. He's passionate about Human Resources, People Operations, Technology and Innovation. Enrique is an Electronic Engineer, Fulbright Scholar and Executive Master in Public Administration with a focus on HR. Over the past 15 years Enrique has worked in the HR and tech world. A lot of his research and work revolves around the digitization of the workplace and Human Resources. Enrique currently works as an HR Specialist at the Inter-American Development Bank. He's also the founder of Hacking HR. Enrique is currently building Cotopaxi, an artificial intelligence-based recruitment platform for emerging markets.

How To Use Design Thinking In Human Resources

Design thinking is an extremely powerful and versatile approach to innovation. It can be used across various areas of knowledge to solve problems that require more than a simple step-by-step, easy to find, visible solution.

This is the catch, design thinking delivers its best results at the confluence of two elements: when it focuses on problems that are complex by nature and don’t have a “knowable” or “visible” solution and when those problems affect people.

And it is precisely at that intersection where most of the problems that the Human Resources (HR) function is dealing with live. They are complex and they impact people in severe ways. 

HR is dealing with problems that I categorize in four important areas:

  • People problems: how to put people first and maximize employee and candidate experience? This means ensuring that people have their best experience at work, while delivering the results the organization is expecting from them. Also attracting the best talent in the market. Ultimately, putting people above processes and systems.
  • Alignment problems: how to align what people really want with the organization’s purpose? This means helping people find meaning and purpose in the work they do, and connecting their purpose to that of the organization.
  • Systems and processes problems: how to redesign systems and processes to become more agile? HR is dealing with approaches that have been in place for many decades (for example, rating-based, annual performance review).
  • Technology problems: how can HR leverage on tech applications to focus on the work that truly adds value? This means breaking down every single job that HR is doing today into pieces, selecting the transactional, “automatable” elements and use technology to perform them, while keeping and focusing on the rest of the work that adds real value.

These four “buckets” include most of the complex problems that HR is dealing with today. They have to do with the nature of work people engage with and the way they do it. And these kinds of problems meet the basic requirements for design thinking to deliver the best value.

Design thinking in HR problems

Design thinking usually has six steps: empathy, definition, ideation, prototyping, testing and scaling. Let’s see how each of these steps works for HR and use one (painful) example: performance management (PM).


This is the step where problems are analyzed from the perspective of how they affect people.

A design thinking team in charge of transforming performance management would begin by looking at how the current approach to PM impacts people operations. They would do so by using several tools, such as interviews, focus groups, journey maps (how the process really works beyond the ink in the paper), journaling (asking people to write down the pain points along the PM process), among others.

I am sure that upon finishing this stage, the design thinking team (working team) would find several ways PM affects people: time consuming, doesn’t drive better performance, doesn’t help people address their performance gaps, it focuses on what people didn’t do well and not on what they excelled at, among others.

This step is the essence of design thinking. Empathy means fully understanding of how a problem prevents people from unleashing their potential, finding meaning at work, giving their best self or even being happy!

Empathy has three core values: emotional intelligence; self-awareness; and active listening.


Once the working team fully understands how a complex problem is affecting people, then they move to the second step: defining what the problem is. Let me put it this way “the rating-based, annual performance management is a piece of sh…” is not a real definition. The working team must come up with something not only better, but a definition that truly captures the complexity and the intensity of the problem.

Most HR problems don’t fall in just one of the buckets defined above. Instead, they are so complex that they have content in each of the four areas. PM is just like that. It is time-consuming, costly, low beneficial, not techy and doesn’t create or add any value. So here is the trick for HR: instead of setting yourself for failure and trying to solve a complex problem all at once, break it down and use design thinking for smaller, workable pieces.

For example, instead of revamping the entire PM process, what if the team focuses on “creating a high performing culture where teams support each other by providing feedback and leaders coach and mentor their teams”? You see, I am not talking here about the tech systems to perform PM, or ratings/no ratings. I am just focusing on a small, but important piece of the problem.

Definition has one core value: synthesizing.


Now, the fun starts!

This is the step where curiosity, creativity and imagination are unleashed. The working team is free from any constraints here. They think about the best possible ways to solve the problem.

It is fundamental that in the ideation process the working team focuses just on volume and divergence, versus quality and convergence. That means that, for now, it is better to have a greater amount of ideas, even when they are extremely different from each other or even not feasible, than having a small group of ideas that are too similar. The reason is simple: you want to avoid thinking within the existing parameters of the organization. The working team needs to start the creation process anew, as if they were in a new organization that didn’t have any process in place.

In doing so, ideation becomes idea-storming. Of course, since the working team knows the organization and its current culture, they would also know that some things may be truly impossible to do. For example, “fire all managers to bring others that like providing feedback”. That won’t happen, will it?

Ideation has two core values: open-mindedness and creativity.


The fun continues!

Once the team has ideated all possible solutions, they should narrow down from volume/divergence to quality/feasibility/convergence. This means studying and understanding each possible solution and filtering them through heavy questioning to see which ones survive.

This is tricky, though, because you wouldn’t want to leave a solution out of the equation just because it doesn’t fit into the existing context. Some solutions might push the boundaries a little bit, and that’s ok.

Once you narrowed down the long list of ideated solutions into a smaller and more manageable list, you enter in a prototyping phase. This means creating a mock up (so to speak) of a smaller set of solutions selected. If the smaller piece of the “performance management problem” to be addressed is “how to foster a culture of feedback and coaching”, you may have two potential mock-ups: bringing a small group of external coaches to work with a small unit or unleash talent from within and turn them into coaches.

Prototyping should be cheap! Yes, cheap! Meaning that you won’t go all the way investing a lot of money or resources in each solution. That’s why it’s called “prototyping!”. The idea of prototyping is building something so cheap that you can change pieces of the solution over and over again without any pain. That’s iteration!

Prototyping has three core values: open-mindedness; experimentation; and iteration.


Immediately after prototyping a set of feasible and potentially valuable and viable solutions, the working team goes out to the field and test the prototyped solutions.

When the team starts the testing process they also come up with several assumptions about each prototype. This is very positive, because the testing step will give them information about those assumptions. At the end, the team is basically validating whether their initial assumptions are true or not.

The working team will learn a lot of things in the process:

  • The solutions that work and the ones that don’t.
  • The changes they should make in their original assumptions to ensure that the final solution truly works.
  • The things or ideas they have to let go (because they don’t work) to prioritize the ones that do.

Testing is the pre-final step in the design thinking process. Here, the working team decides on a final solution. Such solution could be an improved version of an original prototype, the combination of several prototypes or even a new prototype altogether, built when the original ones didn’t fully address the problem.

The end-result of testing is selecting a workable solution that can be implemented beyond the “control group” (or the pilot group) and scaled up to larger groups or across the entire organization.

Testing has two core values: letting go (don’t get emotionally attached to any prototype!) and reframing.


The last step in the process is taking the tested solution from a test to a functional, scalable package.

Scaling means taking the solution to another, higher level within the organization. At this point, the solution was validated, the assumptions were refined, and the team is ready to go for more.

As I said before, the trick for HR processes is to understand that because most problems are so complex, they need to be solved differently than, say, a machine problem. When it comes to people operations problems, the best approach is to break them down after the Empathy step into workable pieces and then work out each piece separately. This approach doesn’t have to be slow, by the way, it can actually bring about disruptive innovation to HR with solutions that truly work. 


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Time to turn the Ulrich model into a #digital delivery model

The Ulrich model of HR delivery has been the cornerstone framework of HR for the past 20 years, but in light of the newly emerging digital world, modern HR must adapt to become agile and remain effective, says Rob Scott

There is no denying that all of us are on a digital transformation journey. Our work environments and operating models are feeling the strain of being caught between more traditional business operating models and the newer, agile demands of techno-digital environments. Deciding whether to toss out the old approach or focus on a more evolutionary adaptation of your existing ways can be a daunting decision to make for HR leaders.

The Ulrich model of HR delivery, developed by Professor David Ulrich 20 years ago, has been a solid guiding framework in full or part for most HR functions globally. And even though the model has been contested over the years, the building blocks of the model; HR shared service centres (SSC) for administration, centres of excellence (CoEs) for content expertise and the HR business partner (HRBP) for business alignment, have worked – so why change something that ‘ain’t broke’?

The underlying design principle of the Ulrich model has been about effective and streamlined connectivity between the elements of HR and business operations and strategy. It was built on assumptions that were pre-digital age. But the digital work environment has introduced new technologies such as robotic process automation, cognitive computing, artificial intelligence (AI), new thinking styles such as design thinking, evidence-based decisions supported by deep-dive data analytics as well as a deluge of demographic, ethics and loyalty impacts. As HR professionals, the worse thing we can do is bury our heads in the sand and fall prey to the normalcy bias, believing things will always function the way things normally function. We need to consider how a digital environment is changing the way the workforce is empowered, interacts and connects.

“The Ulrich model as a framework is still a relevant HR operating model, but the transition from the old roles to the new ones is an important adjustment required to support digital work environments”

In a digital world, HR must respond and adapt quickly to changes which impact your business, whether that be through external competitiveness or internal innovation. This will require the roles of the HRBP, SSC and CoE to transform into ‘early warning’ detectors and predictors which can seamlessly morph into problem-solving guru’s and inform the creation of relevant and unique HR solutions. How should these roles change?

HR business partner » alignment agent
Modern HR technology, digital and automation tools fully empower line managers to be effective in hiring, managing and developing their staff. It’s time to get beyond playing the quasi-admin role for line managers. The alignment agent is externally focussed around your organisation’s supply chain and customers, ensuring HR solutions are adding customer-focussed value in line with business strategies and advising line managers and executives on required changes. The new alignment agent is seeking out business issues from a people perspective and doing problem-solving with data analytics.

Shared service centre » analytics engine room
As automation and robotic processing takes over administrative tasks and AI replaces more complex HR admin tasks, the admin centre becomes obsolete but is reborn as an analytics engine room that supports business problem solving and provides predictive capability to business leaders. Their outcomes inform future HR solutions. The future SSC employee is a data scientist or analyst. The engine room is not HR centric only, but can be part of a broader analytics entity or could be an outsourced service.

Centre of excellence » HR solution provider
The new CoE will still require deep-skilled and experienced HR practitioners who will remain the thought leaders for appropriate people practices. They will be responsible for developing and deploying solutions which are identified by the new alignment agent and use data-driven outcomes from the analytics engine room to validate their solutions. Solutions are not always standardised and can be focused on providing the best solution for a part of the business.

The Ulrich model as a framework is still a relevant HR operating model, but the transition from the old roles to the new ones is an important adjustment required to support digital work environments.  It requires forward thinking executives and HR leaders to recognise the different demands of a future workforce and workplace, and an acknowledgement that technology, applied in the right way, is empowering employees and workplaces to be super-agile, and achieve significantly more. HR must change.

Some key takeaway messages

  • The classic Ulrich model of HR has been the cornerstone of HR delivery for most organisations. It’s a good model, but it needs to be aligned to the emerging digital work environment
  • Much of what HR business partners and HR shared services centres do is administrative in nature. The available HR software, automation and AI tools now available will completely change how these mundane activities are done. The Ulrich-defined roles must adapt
  • The old HR business partner role needs to drop the line manager ‘hand-holding’ style of management – modern HR tools make line managers completely self-sufficient
  • Shared services as we know it will disappear as administrative tasks are automated or managed by AI. A major skill refocus is needed to change these entities into analytic engine rooms

Machine Learning 101 for #HR professionals: what do you need to know?

HR has a significant role to play in the emerging digital work environment, and HR professionals must empower themselves with sufficient knowledge and understanding of developments such as machine learning to effectively guide and manage this process, writes Rob Scott

I’m a strong advocate of the aphorism ‘knowledge is power’ from a positive perspective of building intuition and ability to contribute to new thinking, innovation and creativity, rather than the negative connotation of control over others. And while continuous education and learning is an absolute necessity in today’s agile work environment, learning something completely foreign to your educational framework or work experience (such as machine learning) is daunting, to say the least.

For many HR professionals, the emerging digital work environment is shining a giant spotlight on their digital and technical skill/awareness void. What makes it difficult to rectify is the fundamental differences between a social science-based education, which most HR professionals emerge from, and a STEM-based education which underpins information technology and data science jobs.

Over the next few years, the influx of advanced technologies such as RPA, robotics, bots and machine learning (AI) capability will continue to change how we work, how we respond to business challenges, how we analyse and make decisions. Together with the realisation that technology is not going to displace humans in the short term, but rather augment what we do, it’s become obvious that HR professionals must supplement their skill set to effectively operate in a digital and AI world.

“For many HR professionals, the emerging digital work environment is shining a giant spotlight on their digital and technical skill/awareness void”

Some may resolve this problem by hiring data scientists and architects into the HR function rather than upskilling current staff. There is nothing wrong with this approach, however having ‘STEM’ educated resources focussed on e.g. HR analytics, reporting to HR leaders who have little in common from an education or appreciation perspective is likely to create short and long-term problems.

Just as today’s HR professionals learn ‘finance for non-financial managers’ which promotes common understanding, insight and the ability to engage in meaningful discussions and decision making of the financial kind, it is equally important for HR professionals to put aside any concerns and misconceptions about learning a STEM-based subject. Having the right insight and understanding of data models, how machines learn, types of issues, risks and opportunity, strengthens ones’ position as an HR leader and empowers you to get the most out of your STEM educated staff.

As a person with a social science background, I decided to put this to the test and enrolled myself onto a free Google ‘Machine Learning (ML) Crash Course’. It’s a 14-hour online self-learning course which includes some technical programming. Here are my key observations and learnings:

  • In hindsight it wasn’t as difficult as I thought, although I did feel completely out of my depth initially – but I pressed on. I did opt out of trying to fully understand and remember all the maths or completing the programming tasks. However, I spent quality time understanding what the formulas and programs were aiming to achieve. As I progressed through the course, I found myself recognising maths terminology and began understanding why the equations were important.

“Current HR professionals should urgently seek out basic training opportunities to build their insights”

  • Most of my resistance to learning maths was preconceived and hinged on less-than-favourable school memories. It is possible for old dogs to learn new tricks.
  • I found understanding the ML concepts easy, and the way the course is designed (video, support notes, practice, test etc.) supports adult education practices. I feel confident having a conceptual conversation about machine learning, framing an analytic outcome, the importance of data types and sources, validation, training and testing.
  • However, what I really learnt is that there is no such thing as AI … It’s all clever maths, but there are also many reasons why a machine learning algorithm could be incorrect or biased based on a variety of mathematical assumptions as well as individual personal perceptions. Knowing the basic risk factors has empowered me to ask the right sort of questions.
  • HR professionals and data scientists need each other for digital outcomes to be successful. It became obvious to me that the skill profile of a data scientist does not lend itself to ask the right HR type questions. Just as HR professionals need to learn the basics of machine learning, data scientists need to learn the fundamentals of HR to promote meaningful discussion, decisions and beneficial outcomes.
  • There is an urgent need for AI/machine learning courseware specifically designed for non-data science leaders. While this course was good, it’s not ideal for the general HR population. We don’t need HR professionals to become data scientists, but they should have knowledge which enables confidence and full involvement in conceptual digital and machine learning design.

HR has a significant role to play in the emerging digital work environment. The more we immerse ourselves in understanding concepts of new technologies, the greater our value offering will be.

3 key takeaways: machine learning for HR

  • HR professionals must empower themselves with sufficient knowledge and understanding of technology in general, data principles and machine learning concepts to effectively guide and manage future HR teams.
  • Hiring STEM-skilled resources into an HR function where the HR leadership team is not conversant with machine learning concepts and principles is a significant risk which could lead to retention issues as well as flawed outcomes.
  • Formal social science education courses are adapting their programmes to include appropriate STEM elements, but current HR professionals should urgently seek out basic training opportunities to build their insights.

AI and Automation in HR: Impact, Adoption and Future Workforce

Artificial intelligence (AI) has been changing our lives for decades, but today its presence is bigger than ever before. Sometimes, we don’t even realize it when a new AI-powered system, tool, or product appears and outperforms us, humans. In fact, AI is affecting human life on all kinds of levels varying from:

  • The automation of tedious, time-consuming tasks to;
  • The augmentation of human capabilities and;
  • The amplification of human functions.


“While most of the use of this AI technology is stifoll elementary at the moment, it is radically transforming our everyday lives; both professionally and personally.”


The benefits of AI and automation for HR and the workforce don’t come instantly, however. It’s a journey and one can see the short-term benefits of this journey in automation, the medium-term benefits in augmentation and finally the long-term benefits in the amplification of human activities or tasks.

Short medium and long term effects of AI and automation in HR


Let’s take a look at the various effects of AI and automation on HR and the workforce in more detail. First, let’s find out what history was saying and how this shift towards AI and automation has been going on for a long time. After that, we’ll explore how we can adopt this new technology and what the basic strategies are to move forward as an organization while turning potential threats into opportunities.


AI and Automation in HR: Impact and Current State

AI is everywhere today, and there are many aspects to consider as to how it will impact the future of work.


“It’s now popping into almost every piece of software,” said Josh Bersin, principal and founder of Bersin by Deloitte. Based on research by Bersin by Deloitte, nearly 40% of companies are using some form of AI in HR alone.


According to Personnel Today, 38% of enterprises are already using AI in their workplace with 62% expecting to start using it as early as this year. According to Bersin by Deloitte, 33% of employees expect that their jobs will become augmented by AI in the near future.


Artificial intelligence is present in virtually every major industry from healthcare to advertising, transportation, finance, legal, education, and now also inside our workplaces.


We are already increasingly using chatbots and virtual assistants in our personal lives and now we can expect to use them in the workplace as well. For example to assist us in finding new jobs, answer FAQs, or receive coaching and mentoring. The use of artificial intelligence in organizations could help us to create a more seamless, more flexible, and more user-driven employee experience.


Let us take a look at a typical working day out of the daily life of the workforce so that we can clearly see some of the very common, practical uses, of AI.


Practical usus of AI in an everyday working day


These are only a few examples. Whether you are aware of it or not, AI has an immense impact on our daily (working) lives already. For most of us, AI technology is helping us do our jobs more efficiently and it’s generally making our lives – and jobs – easier.


As such, AI plays a big role today in transforming HR and the workforce; reducing human bias, increasing efficiency in candidate assessment, improving relationships with employees, improving compliance, increasing adoption of metrics, and improving workplace learning are some of the benefits organizations are experiencing today.


Jeanne Meister stated in her article, “The Future of Work: The Intersection of Artificial Intelligence and Human Resources“, how HR leaders will need to begin experimenting with all facets of AI to deliver value to their organizations. According to her, HR leaders are beginning to pilot AI to deliver greater value to the organization by using, for example, chatbots for recruiting, employee services, employee development, and coaching.


So far, recruiting and talent acquisition are the areas where AI solutions are most effective. There is a growing number of startups and service providers who target HR with artificial intelligence-based solutions for activities such as:


“Currently, these AI-based solutions for HR & Workforce are more like analytical products driven by data and powered by next generation People Analytics.”


When it comes to AI in HR, “The applications of AI basically are analytics applications, where the software is using history and algorithms and data to be smarter and smarter over time,” as per Bersin. The most interesting part of people analytics is the interface between AI and human proficiency.


Investments in AI are growing exponentially. Research firm IDC predicts that the market for AI will grow from $12.5 billion in 2017 to $46 billion by 2020, impacting all business practices across almost every industry.


The McKinsey Research Institute mentioned in its January 2017 report, “A future that works: Automation, employment, and productivity”, that automation technologies such as advanced robotics and artificial intelligence are powerful drivers of productivity and economic growth which can help create economic surpluses and increase overall societal prosperity.


According to McKinsey, automation could accelerate the productivity of the global economy by between 0.8 and 1.4 percent of the global GDP annually; assuming that the human labor replaced by automation rejoins the workforce.


On the other hand, their automation analysis found significant variations among various sectors of the economy and among the occupations within those sectors. Taking into account the technical, economic and social factors affecting the pace and extent of automation, McKinsey estimated that up to 30% of current work activities could be displaced by 2030.


“McKinsey estimated that up to 30% of current work activities could be displaced by 2030”


When the topic of artificial intelligence and its impact on jobs and the economy comes up, the principal focus of the conversation used to be on blue collar jobs. As per CB Insights and the State of Automation Report, there are 4.6M retail salespeople jobs at risk in the USA alone due to AI. The same thing goes for 4.3M cooks & waiters, 3.8M cleaners, 2.4M movers and warehouse workers, 1.8 M truck drivers and 1.2M construction workers.


According to CB Insights, a growing wave of AI-infused Expert Automation & Augmentation Software (EAAS)platforms will steer us towards a new era of AI-assisted and/or AI-enhanced productivity. These EAAS platforms use machine intelligence to replicate and augment human understanding.


This AI-enhanced productivity is starting to threaten white collar jobs as well. And it’s going to impact most of the common professions like lawyers, HR, teachers, traders, sales, marketing, researchers, accountants, software developers, etc.


Industries that can be impacted by AI

(Image is taken from CB Insights)


“Are AI and Automation going to take our Jobs? This question was raised numerous times in the past and answer is, ‘NO’ as long as we can reskill ourselves for the future. We can however expect a structural shift in our jobs.”


History and Shift

Many AI and machine learning algorithms used today have been around for decades. Advanced robots, autonomous vehicles, and Unmanned Aerial Vehicles (UAVs) have been used by defense agencies for nearly half a century.


Technology has always triggered fears of mass unemployment. Louis Anslow, a self-described solutionist, promethean, and designer explains this reaction in his publication “Robots have been about to take all the jobs for more than 200 years”. In the 1930s, it was vaunted economist John Maynard Keynes, who implicated technology as one reason for the unemployment of the Great Depression. As such, it has always been a hot topic.


BBC Capital recently published the history of unfounded fears over the future of work which states that back in 1959, the mathematician I.J. Good predicted that “All the problems of science and technology will be handed over to machines and it will no longer be necessary for people to work”.


Another recent article by the Mckinsey Research Institute, “What the future of work will mean for jobs, skills, and wages” states that this kind of skill shift or employment displacement is not new.


Employment change in the US

(Image is taken from Mckinsey Global Institute)


The First Industrial Revolution began in England in the 18th century and the economies of Europe, the United States, and other countries have undergone two intense waves of structural change since. Mechanization enabled a revolution in agriculture and in industry, encouraging a migration of workers from the countryside to cities. A second structural shift occurred in the past 60 years as the share of manufacturing employment declined in some countries while the service sectors grew.


According to research by Mckinsey, the employment shifts accompanying this process of structural transformation have been very large. Throughout these large shifts of workers across occupations and industries, overall employment as a share of the population has generally continued to grow.


Global Economies like the US, China, India, Germany, Japan, and Brazil are going to be impacted more than emerging economies like Indonesia, South Korea, Turkey etc. The impact of AI and automation varies depending on a country’s income level, demographics and industry structure.


Expectations vs. Reality

So, will AI and automation go and automate our jobs?


“So far, AI and ROBOTICS are not used to “AUTOMATE JOBS”, rather to “AUTOMATE TASKS” and “AUGMENT” human functions which in turn increases productivity and performance.”


Most of our daily jobs are associated with tasks like paperwork, scheduling, timesheets, accounting, expenses etc. (as shown below with an average percentage). Of course, it is useful to outsource these repetitive tasks to digital assistants or automated software, freeing up more time for deep thinking and creativity.


Daily job tasks that can be affected by automation

(Image is taken from PWC Consumer Intelligence Series)


When it comes to the cognitive technologies that leverage AI that are currently available in the market, their main impact so far has been to augment existing job functions, not to eliminate workers. The machines or systems that can reason, learn, and interact naturally with people will likely continue to eliminate repetitive tasks, help the workforce to do their jobs better and faster, and free up time for more interesting tasks.


For most workforces, cognitive technologies will likely enable them to move into new and more rewarding roles. Therefore most organizations and their employees are likely to experience positive effects from the AI-based technology and automation.


Chart on AI exceeding human performance

(Research from Oxford and Yale Universities)


Adoption and Strategy

It becomes pretty clear from all these analyses that occupations involving (a lot of) physical work in predictable environments – including production workers and building and grounds cleaners – as well as office support roles like clerks and administrative assistants, are likely to face a significant impact on their activities as a result of AI and automation. Doctors and professionals like engineers and business specialists, on the other hand, are less likely to experience as much of an impact.


The current level of educational requirements for occupations tends to be correlated with the likelihood of these activities being automated. Occupations requiring some higher education generally include work activities that are less automatable than those requiring a high school diploma and some experience.


“Workers impacted by automation are easily identified, while new jobs that are created indirectly from technology and a shift in skill sets are less visible and spread across various industries and geographies.”


The World Economic Forum report, “The Future of Jobs”, looks at the employment, skills and workforce strategy for the future. The writers of the report asked chief human resources officers and strategy officers from leading global employers what the current shift means, specifically for employment, skills, and recruitment across industries and geographies.


They found that the current developments in AI and automation will transform the way we live and the way we work. Some jobs will disappear, others will grow and jobs that don’t even exist today will become commonplace. What’s certain is that the future workforce will need to align its skill set to keep up the pace.


Skills needed for AI and not needed for AI


According to Laetitia Vitaud, a researcher on the Future of Work & Consumption, most of the HR divisions or departments of our modern corporates have become process-driven ‘machines’ that manage people like assets, rather than unique human beings that require personalized attention.


Instead, HR departments run top-down process-engineered ‘systems’ — to recruit large lumps of resources, handle payroll, organize annual appraisals, send simultaneous batches of employees to training, etc. — that leave little room for personalization, flexibility, and creativity.


In her publication, “Can AI Put the ‘Human’ Back into Human Resources?”, Laetitia explains that what many HR professionals fail to understand is how AI provides the unique opportunity to redefine HR and give it increased relevance.


In short

So, the key will be for HR to develop an AI and Automation Strategy that starts by analyzing what job roles, processes, and workflows will be re-skilled by AI. In her recent article, “AI plus Human Intelligence Is the Future of Work”, Jeanne Meister states how one can begin thinking about the implications of AI and automation on work tasks, key job roles, and work processes. You can simply start by asking:

  • Automate: What are the key activities within this role that could be automated to provide greater efficiency and effectiveness to accomplish routine tasks?
  • Augment: How could more value be created by applying people analytics to identify new business insights for better strategic planning and actions?
  • Amplify: Which work processes and workflows could be re-designed by AI technologies to boost human activities and decision-making?


The diagram below shows the critical factors that are needed for an AI strategy for HR and the workforce. Based on these fundamentals and essential factors, a value proposition can be created for the business and its (future) talent.



Technology is not only a key enabler in creating the best employee experience. With the correct readiness, HR leaders can leverage these concepts to provide a culture of innovation. Going digital and embracing automation in the most efficient manner will certainly enhance the human performance of an organization.


The future is in our own hands and we should plan & implement the necessary strategy to make ourselves ready for our own, better future by accepting the fact that our future is about collaboration between Humans & Machines.

HR is hitting a second wall

Towards continuous analytics and maximizing your employee experience

Authors: Patrick Coolen and Frank van den Brink 

People who follow us on Linkedin know all our posts have been on either HR analytics (Patrick Coolen) or Employee Experience (Frank van den Brink). In this post, we are combining these two HR concepts, predictive HR analytics and Employee Experience. We strongly belief that the combination of the two enables us to do something called “continuous listening”, allowing us to maximize our employee experience. But before we dive into our framework for continuous listening first a few words separately on HR analytics and Employee Experience.

About HR analytics

The Deloitte Human Capital Trends Report 2018 showed that 70% of their respondents are in the midst of major projects to analyze and integrate people data into their decision making. Carefully we can say that (predictive) HR analytics is beginning to get mainstream. More organizations are using statistics or data mining techniques to create insights in order to support their decision making. So for more organizations it is becoming true that they are breaking “the wall of Boudreau”, going from descriptive analytics to predictive analytics. 

Within our organization we found about 600 different insights using predictive HR analytics in the last four years. These insights are spread over all business lines and are collected by a variations of modelling techniques like (logistic) regression, random forest decision trees ensembles, multi-level analyses and more recently survival modelling. Below you see a screen shot of some of our business research targets (on the right) and their relationships with the different input variables (on the left). As you can see, over the years we looked at client satisfaction, sales or other financial metrics, the quality of work, efficiency of work et cetera. But also at more HR topics like absenteeism, engagement, purpose and collaboration. 

Our team grew to 10 people, combining analytical consultants, data scientists and product owners for people analytics, strategic workforce management and survey management. It is appropriate to thank our partners, who as always support us along the way like Willis Towers Watson (survey management), BigML (machine learning) and specifically iNostix by Deloitte. With iNostix we celebrate this year our five year partnership in HR analytics. Both Luk Smeyers and Laura Stevens inspired and influenced us on various topics discussed in this post. 

So, back to the wall of Boudreau. Again more organizations are making that transition from descriptive analytics to predictive analytics. But to my opinion HR is hitting a second wall. Most of the predictive analytics work is done on an ad hoc or project basis. Insights from predictive HR analytics projects are not (yet) automated or productized. In order to have a more continuous approach for data collection and research and to be able to convert those insights and innovations in existing and new products, you need a more continuous analytical approach. In other words we have to move from predictive analytics to continuous analytics. This is one of our ambitions for the next two years. But this ambition is strongly related to another ambition we have, namely maximizing the employee experience. 

About employee experience

For us employee experience has been at the center of our HR strategy and transformation for the last year. Our purpose is to design and engineer a high value, integrated and relevant experience for all our employees and new hires. We strongly believe that when we are able to increase the employee experience, we will create a more engaged and productive workforce that helps our business to achieve their goals. A study from IBM and Globoforce found that there is a relation between a positive employee experience and higher work performance, discretionary effort and lower turnover. We will evaluate the impact of a higher employee experience on our business goals in our organization as we move along. 

The same as within many other organizations we organize employee experience around the employee life cycle, by creating employee journeys. This includes everything from creating a "Best Start" for new hires, to supporting “Meaningful Growth” for employees’ careers and their personal life, to modernizing the way we recognize and reward great contributions of employees and teams (“We owe You”) to creating "Great Ambassadors" when employees leave our organization. It is exactly on these “moments that matter” that we want to make a difference for our employees and maximize their experience. 

A year ago we did an extensive research to find out what our employees think about the journey and to see when and what they are actually using in terms of HR contact, HR information, benefits, HR services, HR reports et cetera. This resulted in a list of topics as presented above. We categorized them in five phases and prioritized them so we could start to improve the most important employee experience topics according to our research in an agile way. 

One example is that we had too much information on our HR portal. So our HR colleagues from HR Contact, HR Shared Services and content owners from Employee Experience started working on reducing the number of intranet pages (800 of them) and creating a more user friendly interface, making it easier for our employees to find the right information as quick as possible. And there are more examples like improving our onboarding experience, fixing the basics in our learning platform and hiring processes. So we are making a good start but we know we are not there yet. We need to increase our maturity and capability to process the different information we have from our employees and convert them in to actions and innovations. In other words we need to combine our employee experience and analytical efforts to create a continuous listening strategy in order to consumerize HR and create more employee and business value.

About Continuous listening  

Before we elaborate more on how we look at continuous listening within our organization it is good to have a definition on this topic. Because some organization refer to continuous listening when (only) talking about survey management, others refer to it in a much broader perspective using all types of employee data. All definitions are fine to work with, we however strongly build our definition on the definition created by Laura Stevens (The 4 guiding principles of a successful continuous listening program), so credit where credit is due. - Continuous listening is a coordinated and cross-functional effort to continuously collect, combine and analyze a variety of employee data sources to maximize the employee experience and ultimately drive and enhance company performance – by applying a customer centric mindset and analytical techniques.

So in order to really understand what drives the needs and ambitions of our employees we need to improve our ability and willingness to “listen” better. In the previous years within our organization we mainly listened to our employees by the use of an annual engagement survey. Where many organizations already moved away from an annual performance management approach to a more continuous dialogue between manager and employee, most organizations are still using an annual engagement survey to listen to the feedback of the employee. This is slowly changing and some organizations like Adidas (Stefan Hierl) have already successfully implemented a survey strategy where different topics are surveyed on a more continuous basis. 

When we talk about data related to employee experience we make a separation between actively and passively collected data. Both type of data are vital to better understand employee needs. With actively collected data we mean all employee feedback we actively ask from our employees. This can be via annual or pulse surveys or via polls or panels questionnaires. Passively collected data is in this context employee data that is already in our systems like transactional data (e.g. use of benefits, pay, learning, click behaviour), contact HR data (e.g. questions, number of complaints) or social media information (e.g. glassdoor data, blogs on internal social media). 

Both type of data need to be managed. Gathering active employee data should be managed by a survey management strategy together with corresponding and highly necessary guidelines to avoid GDPR breaches, survey fatigue, bad survey design or simply creating invalid data. The passively data is managed via the existing HR IT projects that improve data quality and integrate data in a more efficient way. This is why continuous listening in the broadest sense of the word is a joined responsibility of HR Employee Experience, HR Service Delivery and HR Analytics.   

Having the technical ability to listen better and more continuously to our employees is one thing, using it on a frequent basis is another. Basically it is about understanding and treating your employees as you treat your customers. HR should position itself more as the employee marketeer. We need to better understand the different groups of employees (personas), their needs and the opportunities to increase their employee experience and their performance. 

This requires a different mindset from everyone in HR, specifically from our process and product owners who are responsible for an end to end experience. A mindset that is built on curiosity to understand our employees on a regular basis, the ability to identify and act on employee leads, the ability to work with tools and data and the ability to turn the employee feedback into usage. 

The employee can be different people. It is helpful to think and work with defined (or new identified) personas. For example you can talk about a new joiner, a manager, an executive manager, client facing employees, first-time-managers and so on. Based on these personas, their career phase, family situation, needs et cetera we are better able to focus on specific solutions for a specific group of employees.

Finally, continuous listening is not only about creating or improving employee data. This is the main focus in the short term but we should not forget to invest in creating smarter and more relevant insights in order to drive innovations in new and existing products. This is why we think continuous listening should be divided in three steps. 

1) Creating employee experience data (short term). This phase is about collecting active employee data from survey, panel and pulse software and passive employee data from our transactional systems in order to immediately improve parts of the employee experience. 

For the active listening part we are using a self-service survey platform of Willis Towers Watson. Most surveys are supported via this platform. We are creating a calendar that spreads the different HR topics over the year to allow more dedicated and smaller surveys on HR topics like engagement, leadership, diversity, collaboration or performance? .  

For the panel and pulse software we are working with IBM connections and we are working with our colleagues from Customer Experience (CX) to use platforms they are already using. Here we are for example at the verge of experimenting with a community of volunteers (employees) who are willing to instantly answer a single question we might have on any possible HR topic or service.

2) Creating insights on employee experience (short and mid-term). This phase is about applying predictive analytics on integrated data sets. Because it is the data integration that is driving innovation. By integrating data and analytics we are more likely to find insights like the following; 

  • ‘What do new joiners need to do to speed up their productivity and to get that best start?’ - Combining feedback or engagement data with sales or performance data
  • ‘What are the specific needs of employees who move into a manager role for the first time?’ - Combining feedback or engagement data with transactional data (move into manager role)
  • ‘How can we best retain employees in specific critical roles? - Combining feedback or engagement data with job role data 
  • ‘How can we help our sales force to efficiently select the right learning intervention?’ - Combining feedback or engagement data with learning usage and sales or performance data

The approach, in our case, to integrate employee data sets is an experimental and agile approach. Per use case we need to assess the feasibility and potential value. In terms of analytical tooling we are using a mixture of SAS, Python and BigML. 

3) Creating innovations (mid and long-term). This phase is the most difficult one. In the end you want to convert (productize) your insights to innovations in new or existing products. This is where the real potential is to maximize the employee experience in a more disruptive way. Most HR organizations are only at the beginning of the continuous listening journey, starting to understand the potential value of an Employee Centric approach and not able yet to harvest the possible benefits of all the insights gained from integrated data analyses. This is also true for our organization, but we hope we are on the right track. The following examples illustrate some possible future innovations.

  • If we are able to find the specific needs of employees who move into a manager role for the first time, we can bring that intelligence back to the employees involved via e-Career Coach or Portal Chat bot. 
  • Let say we found that having a daily or weekly meeting with a buddy (colleague) has the highest impact on the productivity of a new joiner, we could potentially send ‘nudges’ (notifications) to the new joiners, their buddies and managers involved via WhatsApp, email, apps to ensure these buddy conversations actually take place. 
  • Feedback from client facing roles in retail in combination with NPS scores may reveal that specific learning intervention do not contribute to NPS as much as for instance closed loop feedback interventions do. E-Learning Advise the moment you log in our learning system (T2G) may push you in the direction of the most impactful personal learning intervention.

We do hope that you appreciate us sharing our thoughts on this topic. By no means we claim this framework to be the only right one. But we do hope our thoughts inspire you to start thinking about continuous listening in order to maximize your employee experience. Please feel free to engage with us and share your comments and thoughts.

About some valuable sources

During the last year we are inspired by many highly appreciated thought leaders whom we spoke to, worked with or followed on social media and blogs on the topic of people analytics, continuous listening or employee experience. Again with the risk of leaving out relevant influencers, in which case we apologize, we like to mention the following people, Laura Stevens and Luk Smeyers (iNostix by Deloitte), Sanne Welzen (Deloitte), David Green (Zandel), Jonathan Ferrar (Insights222), Tom Haak (HR trend institute), Dave Millner(HRCurator), AnalyticsinHRElliott Nelson (KennedyFitch), Jacob Morgan(thefutureorganization).