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.

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.

 PHOTO: HONDA 

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.

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 

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, Cornerstone, LinkedIn, 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.