3 ways HR can gain micro-competitive advantages in a VUCA World

HR plays a critical role in gaining and sustaining competitive advantage through people, and Wayne Brockbank explains that there are three practices HR must adopt in order to optimise the likelihood of realising sustainable competitive advantage in a VUCA world

As has been clearly and powerfully argued, we live a VUCA (volatility, uncertainty, complexity, and ambiguity) world. It is a world where product and capital markets, demographics, geopolitics, technology, and the competitive landscape are becoming increasingly volatile, uncertain, complex and ambiguous.

In such a context, it will be increasingly difficult for companies to create and sustain fundamental sources of competitive advantage. The obsolescence or exchange of technological insights, operational best practices, and here-to-fore proprietary knowledge across organisational boundaries is becoming the norm.

Within such a world, the likelihood of sustainable competitive advantage drops like rock in a VUCA world. It is unlikely that a company will ever again have 20 years of competitive advantage through the traditional means of patent and other legal constraints. On the other hand, it will be possible to create a 20-year fundamental competitive advantage through alternative means, some of which are directly related to a business-focused HR strategy.

A company can create and sustain a 20-year monopoly but it will have to be earned through a continual series of short-term competitive advantages. For example, if a company can innovate a new product or service, it will enjoy a monopoly position for, say, 6 months. It will take its competitors six months to catch up. During those six months the company will earn monopolistic profits. By the time the competition catches up, it will then innovate again, thereby creating another 6 months of competitive advantage.

“Within such a world, the likelihood of sustainable competitive advantage drops like rock”

If a company continues this cycle, then it can create a 20-year monopoly with its accompanying profitability but in a VUCA world the 20-year monopoly has to be earned in 6-month increments. Kathleen Eisenhardt at Stanford University refers to this phenomenon as a “continuous flow of competitive advantages”. This is exactly what companies such as Apple, 3M, Alphabet, Amazon, and Intel have accomplished.

3 steps for HR
HR plays a major role in making this aspiration a reality in a VUCA world. Within this context, I suggest three actions that HR must undertake.

First and foremost, HR must work with senior management to conceptualise, frame and champion the culture that is required. The culture that creates and sustains micro-competitive advantages will need to be fast-moving, agile, and highly opportunistic. Organisational silos will need to give way to cross-unit synergy to optimise opportunities that are provided by ever morphing markets. Such cultures will encourage improvising and experimenting with new products, services and business models. Improvising will not be entirely random; rather, it will be based on the best judgement of future market opportunities.

Second, HR will need to adjust its collective talent and organisational tools to be consistent with the inconsistencies of change. Workforce configuration will need to be fluid and flexible. People will shift across market opportunities. Individual talent will not be owned by individual businesses; rather, talent will be owned by organisation-wide considerations. Individual talent will reflect the need for deep specialists with the simultaneous need for broad generalists.

Measurements, reward and accountability will need to be continuously reevaluated and redesigned to reflect evolving pockets of short-lived competitive advantage. Finally, as indicated in earlier article in Inside HR, HR will play a strong role in encouraging the total flow of information from the outside in, from the future to the present and from within silos to across silos.

“The culture that creates and sustains micro-competitive advantages will need to be fast-moving, agile, and highly opportunistic”

Third and, perhaps, most challenging, HR itself will need to embody the culture and practices that will be required in a world of micro-competitive advantages in a VUCA world. In some companies, HR claims to be the facilitator of change but concurrently is seen as the source of resistance to change. An honest self-examination of individual HR talent and department capabilities may need to be undertaken to position HR as a role model with the credibility and ability to support and sustain the requisite culture.

Without these HR tools and practices in place, firms will simply be unable to succeed in the VUCA world. With these HR tools and practices, however, firms may optimise their likelihood of sustainable competitive advantages.

Action items for HR to gaining competitive advantage in a VUCA world

  1. HR professionals must understand the reality of the VUCA environment and the role of sustainable micro competitive advantages as a key to success in such a world.
  2. They must forge an agreement with senior management about the culture that is required to flourish in a VUCA world.
  3. HR tools and practices must be reformulated to reflect the business mandates of flexibility, adaptability cross-boundary collaboration and speed.
  4. HR departments must themselves be evaluated and held to the same cultural standard that is expected of the entire firm.

Artificial Intelligence Is Making Job Descriptions Obsolete, Executives Say

A recent study suggests that seeing tasks taken away by machines has a demoralizing effect on human workers. The study, conducted by researchers at Cornell and Hebrew University of Jerusalem, details how humans were pitted against machines in a series of games. (Dan Robitzski provides a nice summary of the results in Futurism.)

As one (human) study participant put it: “I felt very stressed competing with the robot. In some rounds, I kept seeing the robot’s score increasing out of the corner of my eye, which was extremely nerve-racking.”

AI, at your service.

AI, at your service.


The key is to help employees get comfortable, and trained and ready to collaborate -- not compete -- with AI systems. Business leaders are hoping that their employees are going to be accepting of AI in their worklives, because companies have big plans for AI -- and many hope that it will ultimately extend -- not replace -- worker capabilities. A recent study of 1,200 business leaders and 14,000 workers, conducted by Accenture Research, suggests AI will bring about significant changes to peoples' daily tasks, essentially redefining or redesigning their current jobs. Nearly half of the executives surveyed (46 percent) said that "traditional job descriptions are obsolete" as machines take on routine tasks, and as people move to project-based work. Executives at 29 percent of the most advanced AI companies  leaders report that they have already extensively redesigned jobs.

The Accenture  survey found close to three quarters of executives (74 percent) plan to use AI to automate tasks to a "large" or a "very large extent" in the next three years. At the same time, almost all (97 percent) also say its purpose is to enhance worker capabilities. They envision their people productively collaborating with intelligent machines.

At the same time, executives underestimate the willingness of employees to acquire the relevant skills,  according to the report's authors, Ellyn Shook and Mark Knickrehm, both with Accenture. On average, executives deem only about a quarter (26 percent) of their workforce as "ready for AI adoption," and cite resistance by the workforce as

a key obstacle. However, from the workers' perspective, 68 percent of highly skilled workers and nearly half (48 percent) of their lower-skilled peers are positive about AI’s impact on their work. Overall, 67 percent of workers consider it important to develop their own skills to work with intelligent machines.

So, executives are optimistic that AI will enhance jobs, but don't seem to quite grasp that their employees want to begin learning how to work with AI. To address this disconnect, Shook and Knickrehm provide the following advice to "reimagine work" and pivot the workforce in the coming age of AI:

Continually assess tasks and skills, not jobs.  "Companies need to identify the new kinds of tasks that must be performed," and "allocate those tasks to people or machines." Such an effort is ongoing and requires constant re-evaluation, "some companies are finding that they need to correct their initial allocation of work to machines. After all, many AI systems are not fully autonomous and require considerable input and adjustment from humans."

Create new roles. This is essential, as "AI enables people to take on higher-value work," Shook and Knickrehm state. "Operational jobs will become more insight-driven and strategic, while mono-skilled roles will become multiskilled." For example, "consumer brands will become increasingly dependent on AI chatbots to represent them in the mass market. Personality trainers will be required to develop the appropriate tone, humor and level of empathy needed for different situations. A health care AI agent must appreciate the sensitivity of patients in a different way than a supermarket AI agent would need to appreciate the mood and mindset of a groceries customer."

Map skills to new roles. In many cases, employees  whose roles have been automated can take on higher-value work, "using AI and other technologies to provide more informed services to clients," the Accenture authors state. "Take order processing and accounts payable collections. One Accenture client has produced a human–AI hybrid workforce where algorithms predict which orders have issues, such as a risk of cancellation or payment disputes. Employees can therefore spend more time paying attention to high-risk situations and be more proactive in mitigating negative outcomes. This approach has required training people to help them develop a range of expertise and capabilities — from industry sector knowledge to analytics and data interpretation, to the soft skills required to work with customers in new ways."

Prioritize skills for development.  In the Accenture survey, the most important skills for effective AI deployments include resource management, leadership, communication, complex problem-solving and judgment/decision-making. "Among the most valuable human skills required to collaborate with AI will be the judgment skills needed to intervene and make or correct decisions when machines struggle to make them," Shook and Knickrehm state.

Employ digital learning experiences. "Digital learning methods, such as virtual reality and augmented reality technologies, can provide realistic simulations to help workers master new manual tasks so they can work with smart machinery," the authors state.

The learning journey at Siemens towards shaping digitalization

For several years now, the topic of digital transformation has been a constant focus of the business community. We’ve all heard about the risks of digital disruption and the need for transformation in the digital world. But what does digital transformation really mean applying it to your business? Foremost it requires new ways of thinking, acting and a willingness to disrupt well-established patterns. Read on to discover how Siemens is coaching its people to drive the transformation. 

Make digitalization work!

This goal is one of our top priorities for 2018. The message is clear: It’s not about the why anymore it is about the how. So how to make digitalization work in a company with a workforce of 380,000 people? Our CEO Joe Kaeser empazises the importance of further education and training of people in this context. Much of it would boil down to learning new skills and unlearning patterns that stand in the way of digitalization. Subsequently, our Board set the ground and trusted us with the task of launching a global initiative to accelerate the digital transformation.

After forming a global, cross-functional team of experts and business leaders, we co-created a learning solution built on five pillars:

-       Inspire customers: Make sure everyone speaks the same language when it comes to digitalization and brings across a consistent story when connecting with customers.

-       Understand technology: Brief our people on the fundamentals of IoT, cloud technologies, artificial intelligence, cybersecurity, data analytics, and discuss potential use cases

-       Experience technology: Hands-on experiments with digital technologies on realistic use cases relevant across businesses: building chatbots, “hacking” into a system, programming of cloud applications, connecting sensors to MindSphere (the Siemens open IoT operating system), and analyzing data with platforms like Knime

-       Design business: “Build-test-learn” for rapid business prototyping taking the customer’s perspective. Exploring business opportunities in co-creation with customers and a joint view on the entire value chain.

-       Implement and scale: Discuss and brainstorm the required changes in organization, processes, tools and competencies when implementing digital business.

Trickle down or bubble up? 

First, our top 200 managers attended two days of classroom training; 3,000 more leaders covering more than 60 nationalities in customer-focused functions followed. These leaders are acting as ambassadors, inspiring their teams to join, learn and share their insights. At the same time we put together a massive open online course to be delivered to the employees worldwide.

This learning program is voluntary and available by invitation. The objective was for 20,000 employees to complete it in 2018 spreading the word across the organization. More than 24,000 are already part of the community. Many more will follow as the global rollout picks up momentum and the program expands to more and more countries in more and more languages. An additional 50,000 have already started their learning journey.

Better together!

There are several reasons why our team was able to rise to the challenge of successfully implementing this initiative. With its global reach, Global Learning Campus was able to conduct 100 face-to-face workshops around the world and is delivering the e-learning package to its far corners within a few months. But the bottom line of what made this story a success story is teamwork and the strong spirit to make it happen.

A truly global and cross-functional team with strong support from our top management created a learning experience that ties in with our business strategy. It provides real-world insight that our people can put to good use in the context of their business. A big thank you to the team for the commitment and a big thank you to our management for the strong support we received during the journey toward making digitalization work.

Learning to love it!

Siemens colleagues have taken this learning opportunity, going at it with enthusiasm, curiosity and the open-minded attitude making this global community so special. They liked the interaction with our “tech experts”, experimenting and super hands-on learning about the opportunities of digitalization.

For me on a personal level, the biggest kick I got out of this is the feedback from our participants about their successes implementing of what has been learnt. Seeing them sharing the digitalization story with customers and partners, continuing to passionately shape the way into the future with their teams.

They are connecting the dots, sharing with us the feedback on further learning needs and engage in co-creating the next topics to further raise the bar!

Now that’s the kind of learning I love!

Dr. Matthias Reuter, Siemens Global Learning Campus 

Learning Experience Platform (LXP) Market Grows Up: Now Too Big To Ignore

The learning experience platform (LXP) market is growing up fast.  Only a few years ago startup companies like Pathgather, Degreed, and EdCast pioneered the idea of a platform to make corporate learning content easy to find. These next-generation portals took off and now thousands of companies are looking to put their Learning Management System in the basement.

Since then this market has exploded (it’s over $300M and growing at 50%+ per year) and it now shows signs of age: vendors are getting bigger; products are becoming complex; the players are moving in different directions.

Will the LXP market replace the $4 Billion+ LMS market?  I think this is starting to happen, which is why every Learning Management System (LMS) vendor has jumped into this space.

Why Do We Have Learning Experience Platforms?

The original problem these products solved was what I would call discovery.

I want to learn something or take a course and I simply cannot find it in the course catalog (LMS).

LMS systems were never designed to be employee-centric. They were developed as “Management” systems for learning, focused on business rules, compliance, and catalog management for courses.

The LXP, which looks more like YouTube or Netflix, is a true content delivery system, which makes modern content easy to find and and consume.

Fig 1:  What an LXP Typically Looks Like

Both systems are needed (LXP and LMS), but over time these lines have blurred. To make it simple to understand, consider the picture below.

Fig 2: LMS vs. LXP

In the early days of the LXP, the green layer was very thin. All these products did was serve as aggregation portals.

As the product category grew, however, they became more functional. Not only does the LXP need intelligent methods to recommend content (discussed below), but they can also be used to recommend third-party articles, find people who are experts, and potentially index documents, videos, and other digital assets. In a sense, they are content management, knowledge management, and learning systems all in one – which is why the market is growing so quickly.

The Problem of Discovery Is Complex

The core problem, that of discovery, is very complex. (This is what made Google a multi-billion dollar company.) Inside a business, there are thousands of courses to find, and as you build more content (using tools like 360Learning), the library explodes. And the LXP reaches the internet, where every video, article, podcast, and webpage is possible a way to learn.

As the founder of EdCast puts it, the real LXP mission is to “index the world of learning,” to make it easy to find just what you need.  (Sounds like “The Google of Learning.”) Much harder to do than it is to say.

I’ve talked with many of the early users of LXP platforms and they love the systems at first … but as they become full of content the problem of “intelligent discovery” becomes hard. And that’s where the market is going next.

How Will Content Discovery Evolve?

Let me suggest this market is moving in six directions at the same time.

Fig 3: How LXP Products Recommend Content

1. Skills-Based Learning.

Vendors like Degreed, LinkedIn, EdCast, Percipio, and IBM have started to add skills-based discovery tools into their systems. While all LXP systems have skills-based tagging, these vendors are starting to build skills assessments, skills inferences, and skills-based learning paths. Companies have always wanted a skills-based approach to learning. It’s just much harder than it looks.

IBM’s Watson Talent Frameworks, for example, one of the most robust skills frameworks in the world, has over 3,000 job profiles and 2,000 skills integrated. An LXP that tries to map all these skills to content has a lot of work to do. Degreed has launched its own internal skills assessment engine, and I believe others will as well.

Pluralsight, one of the leading providers of software technical skills programs, has its own learning portal and has built a very innovative skills assessment engine called Skill IQ. This offering came through the acquisition of Smarterer, which was a very innovative system to crowd-source assessment based on the skills of others. While Pluralsight is not in the LXP market, one may ask “how can the LXP use the skills data from a content provider?”

2. Usage-Based Recommendations.

Vendors like EdCast, SkillSoft, CornerstoneLinkedIn, and Fuse, aggregate massive amounts of data to recommend learning based on the usage of others. While this sounds like a good idea (this is the idea of Google PageRank or Facebook EdgeRank), it creates problems. When one program is widely used it becomes widely recommended, and it starts to “crowd out” other content that might have more value and credibility. And what if I”m in the Paris sales office, should I see the most popular content in China?

Vendors will build smarter recommendation engines over time, but I’ve heard stories of buyers who purchased an LXP and found it recommending incorrect content on the public internet because it had high rates of traffic. I’m not saying this approach is not a good idea, it just takes lots of R&D to make it work well.

When EdCast started working with NASSCOM and other major institutions its goal was to create faster and better machine learning recommendations. They now do it for each client, as do Degreed, Valamis, and CrossKnowledge. Degreed’s new OEM relationship with Harvard Publishing promises to help them better understand patterns of usage for Harvard’s content. Wiley, the owners of CrossKnowledge and one of the biggest book publishers in the world, is embarking on a data lake project to better recommend content.

And there are some important subtleties to this. Fuse lets teams segment themselves into communities, so the system can recommend the most popular content within that group. Their clients found this to be far more relevant than any type of enterprise-wide usage tracking.  (ie. If I work in a certain hotel, I may want to see content most useful among my peers, not necessarily across an entire global network.)

3. AI-Based Content Analysis.

The third approach to discovery is far more innovative and powerful. These vendors (the leader here is a company called Volley.com, followed by Valamis, IBM, and Docebo) actually injest instructional content (text, video, audio) and then identify the instruction within. 

In other words, they read the content and figure out “what is this trying to teach people?”

Volley.com is widely used on Wall Street, for example, and the company’s system can crawl through CyberSecurity documentation and “create” training, micro-learning, and assessments on security procedures for the bank. This approach has enormous potential as these systems identify “level of expertise” and “credibility” of content through pedagogical analysis.

IBM and Valamis’s new learning platform does this as well. Valamis, which is used by Boeing, for example, can fast-forward to a video segment and show you precisely what to watch based on instructional needs. The domain of content analysis is going to be huge.

4. Chat With and Understand The Learner.

The fourth way to recommend content is to ask the learner.

In the best of all worlds, the Learning platform should know your role, your experience in that role, what content you have consumed, your learning preferences, and of course what aspirations and goals you have. 

Most of the LXP products let the user define their interests when they log in. But that’s not enough. The system may need to know your tenure, aspirations, levels of expertise, and even your “way of learning.” In my case, I like to read a lot so I want more books; others may want podcasts or videos.

LinkedIn Learning has a lot of potential in this area, but companies like Filtered, Degreed, and Valamis are working on this too. Degreed, for example, has worked with clients like Bank of America to map specific job roles to learning recommendations. IBM’s YourLearning platform does this using IBM’s Watson Talent Frameworks.  Filtered’s product Magpie, asks you lots of questions when you get started to make sure the engine knows your role, interests, learning needs, and skills profile.

Fig 4:  Filtered’s Magpie Recommendations

5. Expand The Learning Business Rules.

The fourth approach to discovery is the boring, well-known, but badly needed model of “business rules for learning.” Every LMS has lots of this.

Every quarter we need people to take sexual harassment training; salespeople have to finish their new hire training by the end of the quarter; engineers in a pharma company need to be recertified on equipment; and on and on.

All these business rules, which we have heretofore left in the LMS, are creeping into the LXP. And why not?  If we are going to put hundreds of content objects into the LXP, why can’t we get it to formalize, recommend, track, and manage curricula and programs too?   This is a heavy lift for the LXP players, but hey whoever said being a learning platform company was going to be easy.

(PS the conversational interface is a new way of managing this, look at Valamis’s Digital Learning Assistant.)

Fig 5:  Digital Learning Assistant from Valamis

6. Store and Manage Data and Analytics.

The sixth new dimension of the LXP market is data. Where are all the utilization, history, tracking, assessment, and compliance data going to be stored?  Right now most companies put their LXP in front of their LMS. It’s only a matter of time before companies start saying “why do we have two platforms anyway?”  Can’t this LXP store the data in our LMS?  Or can we just buy one system?

Right now LXP systems don’t store much learning history data, but they are collecting a lot. You as a buyer have to decide where you put all this information, and how the machine learning can best use it all.

What Happens To The Business Rules of Learning?

There’s still a big open question in the market:  where do the business rules go?

A Few of the Business Rules for Learning:

  • Compliance: Some content is mandatory, so we have to track completion and manage compliance. When the content changes we may need to “recertify” people.
  • Career programs: Onboarding, new hire training, and career development are learning tracks. They may have pre-requisites, branching, and tests at the end.
  • Customer education: Customers may have to pay for certain courses; they may only see content which is externally licensed; we need e-commerce, training credits, and discounts.
  • Performance support: Sometimes employees just want to search for the answer to a question, they want to search within the content.
  • Development plans: after a performance review and an employee puts together a custom development plan, where do they store it?
  • Manager approval: do managers have to approve this course? Do they need to know someone has completed it?
  • Ad-Hoc learning: Sometimes employees don’t know what they need to learn: they just want the system to nudge them to read or learn something that’s important or adjacent to their current jobs.
  • There’s the whole new world of “learning in the flow of work.” How do I take learning content and make it relevant to me from my job?

One could argue that most of this functionality is in the LMS, so let’s leave it there. I think this will change. Much of this functionality will migrate into the core HR system over time (this is certainly Workday’s strategy), leaving the LXP as a more and more important system in the architecture.

Will the LXP Become an LMS?

The LMS vendors would have you believe that the LXP is a simple and easy system to build. Companies like Cornerstone want to give the LXP away.

I believe the opposite may be true. The LXP is the learning delivery platform of the future, and the level of investment in that platform is growing exponentially. Vendors with legacy LMS systems are trying to catch up, but they’re finding it’s harder than they thought. 

And now this market is too big to ignore. Most LMS vendors are building this type of functionality, including companies like LinkedIn, SkillSoft, Saba, and even Microsoft. 

Long ago when I was at IBM I learned something important from one of the most senior leaders in IBM research. It’s hard to make old software do new tricks.

Software is hard, but hardware is soft.

In other words, it’s not easy for legacy LMS vendors to turn their old systems into new platforms. In most cases, they have to start over (this is certainly what Skillsoft has done with Percipio), and I could expect to see acquisitions start this year.  (Including LXP vendors buying LMS vendors.)

The Economics Really Matter

The final point I want to make is about the economics of this market.

As the LXP space grows in size and value, training departments have to decide where they want to spend their money. One of our clients, a pioneering user of an LXP, has a plan to replace their LXP with the planned solution from their new LMS vendor. Why?  They just don’t want to pay for two systems.

In the other direction, there’s a lot of demand to reduce spending on the LMS. I know of two global enterprises who plan on “turning off” their LMS in the next few years.

The market is dynamic, exciting, and filled with innovation. If you are searching for an LXP or need help figuring this all out, just let me know. I’m here to help.

AIHR Live with Dave Millner "The @HRCurator"

In this short interview, Erik discusses the most important HR and HR analytics topics with the one and only “HR Curator”.

Find out about the digital future of HR from the expert, Dave Millner!


Erik van Vulpen: Hello, and welcome to AIHR Live. I’m Erik van Vulpen, and with me is Dave Millner. Hi Dave. How are you doing?

Dave Millner: Hi. I’m fine, Erik. Thanks.

Erik: Fantastic. Today we’re going to talk a bit about HR analytics. Dave, what I want to ask you first is… What are some of the areas of HR analytics that you’re most excited about, that trigger you the most?

Dave Millner: I think the things that are most exciting is the opportunity predictive analytics gives. I think particularly when you’re able to draw on past information and data, that then starts to shape the opportunities in terms of, “What if we did this, that could happen,” and for me, I think that’s the most exciting because I think to then put that into a business context and to bring that to life, starting to say, “If we recruit in this way, or we develop in this way, it will bring us this from what we’re projecting,” that for me, I think, is a really exciting opportunity to will take time for HR to get its head around, but I think that’s really exciting.

Erik: I agree, and I think it will take quite some time for HR to wrap its head around it because there’s still a lot of talk about predictive analytics and everything it can do. What you see in practice is a lot of organizations are still at a basic or an advanced reporting level, not even thinking about making predictions, even though all the value in the end is in making predictions and knowing what’s going to happen in the future.

Dave: And I think it comes down to data quality, which we’ve always talked about as being an issue. It takes time for that to happen, and people data is not as good as it should be, but you know what? I think over a time we will get to that point, but that is the area of analytics that excites me the most because I think it makes sense when you project it back to a business leader. They get it. They understand it, and it’s not too hypothetical if you’ve grounded it in research that you’ve done previously.

Erik: Now, it’s interesting that you also mentioned data quality because something I’ve been very excited about in the last few months is how you can adjust your HR systems in a way that you can ensure that the data will come out having a higher quality, like very simple things like certain mandatory field stats. Some business partners or some administrative people in HR just don’t put any data in because they don’t have the data, but it’s a mandatory field, so they have to put in a zero or one, and you get a big row of messy data. I think that’s also something that excites me very much … The data integrity and how you can maintain a quality of data through just smarter system design.

Dave: I think you’re right, and I think also that getting the HR practitioners to be a little bit more numerically orientated and understanding the implications of what their decisions are having on that data set I think is important, but it takes time. And you probably didn’t join HR because you like data and technology and analytics, so it will take a little bit of time for people to understand that we’re not trying to transform you into another finance person, for want of a better word.

Erik: So one of the areas that a lot of people are very excited about lately has been ONA, organizational network analysis. What are you thinking about it? Is it a bit over-hyped? Is it something that has tangible benefits for the business? What is your view of it?

Dave: Yeah, there’s been a lot of noise about it, and I think it’s got definite benefits, particularly when we’re looking at things such as mergers, such as collaboration, and trying to break down why is this not happening, and why should we be doing things differently. So I do think that ONA can help in that respect. I’m just wondering whether it’s a little “big brother is looking at you.” I’m just a little concerned about that perhaps, and I’m just worried that people might get seduced by the sexy new stuff, when I think there’s a lot of work that HR has to do to get the basics right, whether it’s reporting, metrics, advanced reporting, predictive. I think all of those sorts of things we really need to focus on.

I don’t want them to get distracted by ONA, but I also don’t want them to forget that that is an opportunity to give you a different lens through which to look at the way that people do things. I guess my word of caution is that complex analysis doesn’t always give you complex and sophisticated outcomes. So we just need to be, I think, mindful of what it can do and also, “Is it really the right thing for you to be focusing on?”

Erik: No, I think that that’s an excellent point and something that… You have the analytic maturity model, and as they say, you have your descriptive analytics and then your advanced analytics, and then you got predictive analytics. That’s at the high level of maturity. Well, some business questions you can just simply answer without needing that higher maturity level, which implicitly says, “Hey. It’s more mature. It’s a better version,” even though you might not even need it.

Dave: And who’s to say in three or four years’ time with this sort of cognitive, smart technology … Who’s to say that maybe a lot of that routine-ness will be done for us, which that would be amazing, but I think we’re a long way away from it, but never the less, we got to be mindful that hopefully one day we will be in that position.

Erik: Yeah, that would be very interesting. So analytics is evolving, and in the last few years, we’ve seen analytics get a foot in the door in the HR departments, and now for most larger companies, analytics is, I see, getting taken more and more seriously. Do you see analytics going to the heart of the organization? Is analytics going to be a core part of HR?

Dave: I think it’s going to be a fundamentally important part. I don’t think it is the center of everything for HR. I personally think there are three elements that derive and shape the future of HR. I think one is certainly the data and analytics. It’s an important part of the jigsaw. I think technology is a critical part because that’s the thing that will feed us with data, with insights, with information, and provide an experience that employees will say, “Wow. This is almost like being at home,” type of thing. And I think the third and probably the most important is the capability of the HR practitioner themselves.

It’s pointless having great analytics, great technology, if the HR practitioners are still operating in a rather more old fashioned, second class citizen partner orientated way. I think they’ve got to be regarding themselves as thought leaders. They’ve got to be challenging the business and driving the inner direction that will add value, and I think those three elements complement each other, but I don’t think you can do one without the other. That’s my take on it.

Erik: That’s interesting because those three, I think, are the key enablers to everything you do in HR because technology is maybe the most massive enabler. So is the data that feeds into … The output of the technology should be the data that feeds into the algorithms and the process of making smarter decisions, and the capabilities are really, “What do you do with the technology? What do you do with data? And how do you implement the policies in HR? And how do you bring them to fruition and get the most out of them?”

Dave: And I think the digital transformation and the digital agenda is definitely raising its head and giving CHROs who’ve been talking to me … Been saying, “Do you know what? I think we may need to do something to help our HR practitioners on that journey,” because it’s not gonna be simple. It’s about change. It’s about technology, challenging, really promoting, looking at the outside and bringing some new ideas. So it’s a really complicated role that HR has both today and tomorrow, but we really need to give them some support and help. And I think that’s why I’m starting to see quite a lot of help being asked for in the HR world, which is only good news for analytics in HR obviously.

Erik: No, that’s true. What I want to focus on is two of those enablers that you mentioned. You mentioned technology, and you mentioned analytics. How do those two integrate with each other? Because I think there’s potentially a huge synergy between the two.

Dave: Yeah. I’m absolutely with you. I think part of this data quality, that we talked about a little earlier, is because the technology is not aligned. It’s not simple. It’s not providing us with the information we want when we need it. It’s hardly surprising that people don’t want to do analytics when they’ve got 20 different spreadsheets to pull together. And I think we’ve gotta try and find and work on systems that are built for the future and just not today. And I think the technology vendors have got a big role to play in not just alluding to what the future looks like, but actually providing them with concrete evidence that, “This is where we are today. This is the roadmap for tomorrow. This is how we’re gonna get there. This is what it will then do, and these are the features, and this what it’s got to provide.”

And I think that’s where there’s an interesting dilemma between HR startups and the more traditional vendors, and I can see a lot of the startups are making a lot of traction because they’re able to be incredibly responsive, in a very quick way to the needs of what some of the large organization and more medium size are looking for. So I think there is definitely a connection between the two, but we’ve gotta get the technology right because otherwise, we’re continually questioning the data and having to go look for it, and that’s not good for anybody.

Erik: Yeah. I agree, but on the other hand, what you also see is that those smaller HR technology firms are creating very tailor-made solutions, very specific solutions, so they do them really well compared to the big vendors who offer one size fits all, and that just doesn’t work some areas of learning and development or other HR disciplines.

So what’s, on the other hand, also worries me about technology is that you see… I know a company here in the Netherlands that I used to have, I think, 80 different HR apps that they were using for the employees so they could do everything in a self-servicing matter. All the data coming out of those tools wasn’t thought about, “Hey. What are we going to do with the data and how can we integrate the data in a way that we can do, in a couple years, analytics and all the more advanced predictive analytics that you mentioned before.

So I think there should be, in a way, a shift in the thinking about, “Hey. How are we going to leverage those technologies, and how are we going to align those technologies, in the end, to map the employee journey?” to now, “Hey. These employees coming in, so we have the Applicat Tracking System. We have all those fancy new selection tools that we are using now. How can we get all the data aggregated and use it in our analytics department to make better decisions about recruitment, about hiring, about sourcing, all the way to the exit interviews? And how can you map that employee journey and do that as well as possible using those technologies?”

Dave: I think you’re right. I think the aggregator, or the platform integrator, or whatever word you want to use, is definitely the key to all of these apps, and I think the other thing we need to be mindful of is that we’ve also got to be able to bring in non-people data, such as business outcomes, KPIs, whatever it may be because that will then ultimately make the analytical process easier for the team or the HR practitioner who has to do it. So it’s not an easy journey, and there is a “no one size fits all,” but I just think that the ultimate solution of having access to the data when you need it, in the type of ways that you can use it just sounds really exciting.

But I think part of the challenge here is, “Does HR want to get to that point?” Because it means, therefore, that the administration will have disappeared. It therefore means we’ve got to focus on these critical processes, talent, and all of that, workforce planning, et cetera. We’re gonna need to focus on those things really properly, and we’ve just got to make sure that we’re not relying upon HR people who are in the comfort zone of liking to do things the old way. And that’s no disrespect, but life is moving on, and maybe the profession is also moving on hopefully.

Erik: Yeah, let’s hope so. There’s always a funny story that I like to tell that when I was still studying, and I was doing some, and the father of one of the university students came in, and usually, you never talk to parents of university students, so it was always a bit funny for the one specific course. And he asked me, “Hey. What do you do?” And I said, “I study I&O psychology.” And he said, “Oh, there’s something funny because recently I had to apply for a job again because I quit my company,” and he had stayed there for 20, 25 years, and he had made a number of internal promotions, and he said, “I applied to a different company for the first time in his life because he had been working so long at the original company.

And he said, “You know what’s so funny? That the process I went through, the recruitment process, it hasn’t changed a bit since I first did it 25 years ago,” and actually he said, “That’s so mind-boggling that nothing changed in 25 years. I’m still doing the same conversations. I’m still doing the same assessments. It’s still the exact same process, and nothing really has changed.” And I think that for me has always been a motivator of, “Hey. How can we, in the end now, change the HR department for real?” Because if you haven’t been changed for 25 years, in today’s world that’s kind of unheard of.

Dave: Yes, and it’s funny because I was working in a bank in an internal HR function, our big thing was about having interviews that were evidence-based, and we’re talking today about evidence-based HR, and I’m like, “That’s just the same thing we were talking about years ago,” but we will get there. But I think there is a big education piece we’ve got in terms of how do we get line managers to understand that this is a better way of doing things, and I think I’m not sure we’ve really actively focused on how we can educate leaders and managers about why we believe this is the best way to do it, but anyway, that’s another day’s conversation probably.

Erik: That’s true, and I think you hit the nail right on the head. One final question I had, and I think you already partially answered it, is about the future of HR. Where do you see analytics develop in the near future?

Dave: I think it’s interesting. You’ve got a lot of, what I would call, leaders in the field, so a lot of the larger corporates have got analytical teams, and they’re very much pushing the analytical agenda, and that’s amazing. They’re the ones that present at all of the conferences. They’re the ones that are telling their story, and that’s great.

I think the thing that is still a bit frustrating is that we have a lot of smaller organizations, which are probably 500 to 2000 employees… Those for me are the ones where if you introduce analytics, they could really make a huge impact, and I’m not quite sure that we’ve really captured hearts and minds for that group yet. I know the datasets will be smaller. I know it’ll be more difficult to get correlations because we haven’t got the number or volume of data available, but I just feel that when we start to get analytics into organizations of that size, then we’re starting to really build momentum for this as being a core part of the HR function, but we will get there, but I think we’ve still got a lot of work to do. We shouldn’t just be promoting the fact that the large corporates are doing it and doing it amazingly well. How can we integrate this down into the smaller organizations? Because I think they can make radical changes very quickly, and I’d just love to see analytics driving that within their organizations.

Erik: Yeah, it’s interesting. We see a similar thing on the website, where we get a lot of traffic on the pure HR analytics stuff. People want to know about predictive analytics and how to do it, and for us, that’s a mainly a population that’s working at the larger companies. We also get a lot of traffic simply on there. How do we measure performance with our performance metrics? What are the most common metrics you can use in your recruitment process? And I have a feeling that a lot of the interests that driving those metrics questions are coming from slightly smaller organizations that also what to get going and go to Google and type, “Hey. What are the recruitment metrics I can use?” So yeah, I definitely think that that’s a next step of the analytics evolution in a sense of, “Hey. How can we bring” … Maybe not necessarily analytics, but at least a data-driven approach with smaller companies as well.

Dave: Definitely. It’s all about the evidence, and the data that you’ve got to back it up. So it is important. Please don’t get me wrong, but I think it’s more than just the analytics and data. I think if we get that technology right and get the HR people thinking and behaving in a different way, that for me are the three elements that will really drive the future of HR.

Erik: Perfect. So it’s data and analytics if I summarize this correctly. It’s the capability, in the end, that’s most important. Then it’s the technology. Those are the three main enablers or drivers of everything we do in analytics, and let’s nail them.

Dave: And that will give us a commercial HR function. What more do you want?

Erik: Dave, thank you very much for participating, and thank you very much for watching AIHR Live. And I think Coco, our office dog, also made an appearance. She’s a beautiful dog. I hope you like this episode, and see you in the next one!

Debunked: 8 myths about AI's effect on the workplace 

The interplay between technology and work has always been a hot topic.

While technology has typically created more jobs than it has destroyed on a historical basis, this context rarely stops people from believing that things are “different” this time around.

In this case, it’s the potential impact of artificial intelligence (AI) that is being hotly debated by the media and expert commentators. Although there is no doubt that AI will be a transformative force in business, the recent attention on the subject has also led to many common misconceptions about the technology and its anticipated effects.

Disproving common myths about AI

Today’s infographic comes to us from Raconteur and it helps paint a clearer picture about the nature of AI, while attempting to debunk various myths about AI in the workplace.

AI is going to be a seismic shift in business – and it’s expected to create a $15.7 trillion economic impact globally by 2030.

But understandably, monumental shifts like this tend to make people nervous, resulting in many unanswered questions and misconceptions about the technology and what it will do in the workplace.

Demystifying myths

Here are the eight debunked myths about AI:

1. Automation will completely displace employees
Truth: 70% of employers see AI in supporting humans in completing business processes. Meanwhile, only 11% of employers believe that automation will take over the work found in jobs and business processes to a “great extent”.

2. Companies are primarily interested in cutting costs with AI
Truth: 84% of employers see AI as obtaining or sustaining a competitive advantage, and 75% see AI as a way to enter into new business areas. 63% see pressure to reduce costs as a reason to use AI.

3. AI, machine learning, and deep learning are the same thing 
Truth: AI is a broader term, while machine learning is a subset of AI that enables “intelligence” by using training algorithms and data. Deep learning is an even narrower subset of machine learning inspired by the interconnected neurons of the brain.

4. Automation will eradicate more jobs than it creates 
Truth: At least according to one recent study by Gartner, there will be 1.8 million jobs lost to AI by 2020 and 2.3 million jobs created. How this shakes out in the longer term is much more debatable.

5. Robots and AI are the same thing
Truth: Even though there is a tendency to link AI and robots, most AI actually works in the background and is unseen (think Amazon product recommendations). Robots, meanwhile, can be “dumb” and just automate simple physical processes.

6. AI won’t affect my industry 
Truth: AI is expected to have a significant impact on almost every industry in the next five years.

7. Companies implementing AI don’t care about workers
Truth: 65% of companies pursuing AI are also investing in the reskilling of current employees.

8. High productivity equals higher profits and less employment
Truth: AI and automation will increase productivity, but this could also translate to lower prices, higher wages, higher demand, and employment growth.