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Chris Majer

February 2011 - TD Magazine

The Silent Killers of Productivity and Profit

Chris W. Chen is an organization effectiveness manager with Sempra Energy, a Fortune 500 company in San Diego. He also runs his own consulting business specializing in leadership training. Previously, Chen led Sempra's organization development function, where he was responsible for leadership development, organization development, training, and employee communications. His publications include New Supervisor Training (ASTD, 2002), Simply Spoken Leadership (David and Roe Press, 1998) and articles on a variety of human resource topics. He has spoken at several major conferences, including the Colby leadership conference.

Chris Chen

8.4 trillion dollars.That's how Jean-Marc Laouchez quantified the impact of the talent crunch on today's organizations. Laouchez leads the Korn Ferry Institute and the Korn Ferry Academy, and recently delivered the opening keynote at Degreed’s annual innovator’s conference, LENS.So how best to make sense of such a staggering figure?Insight #1: People are your number 1 capital.In his welcome message at LENS, Degreed CEO Chris McCarthy reminded leaders that "technology and products can always move faster than organizational change."There is still a disconnect between what learning and development (L&D) leaders communicate as a top priority to the CEO (investing in people) and what the board/shareholders prioritize (investing in technology). \[4\] That there is disparity between how these two stakeholder groups view strategic priorities is unsurprising. In fact, even in high-performing companies, it's estimated that less than 30% of employees correctly identify organizational strategy. \[5\]The advantage of clearly communicating to all audiences that "people are the #1 asset" is that this clarity empowers employees to move quickly and confidently in alignment with goals. In their book, Talent Wins, Charan et al. go further to emphasize that executives and boards must recognize that talent, even more than strategy, is what generates value. \[1\] Laouchez laid out a clear plan of action that challenges organizations to pursue change by understanding how to "maximize internal talent to capture future value." \[4\]Insight #2: Define and develop the right skills, for the right jobs, for the right talent.Laouchez shared a study that Korn Ferry conducted with the World's Most Admired Companies (WMACs), where less than half of those organizations felt their talent had the right skills and capabilities to execute strategy. \[3\] This finding aligns with PwC's 21st Century CEO Survey where "80% of CEOs say they’re worried and 38% are extremely concerned about availability of digital skills in their country." \[9\] The conversation around the skills gap is complicated by the argument that employers should focus on increased wages—oversimplifying the problem to one of supply-and-demand driven primarily by the price of labor. Gartner's Marcus Chiu explains that for the many employers who are already willing to pay more, this solution is fraught with diminishing returns. \[2\]So what happens when there's a high supply of workers? The LinkedIn Workforce Report for September 2018 analyzes migration trends between major U.S. cities and which cities present the largest skill gaps. Interestingly, although Austin, TX gained the most workers in September 2018, this city also ranked among the top 10 cities with the largest skill shortages. \[6\] This suggests that an influx of workers is not always enough to solve the mismatch between the skills workers have and the skills employers need.It is worth noting that "more than one-half of employees rated opportunities to use their skills and abilities at work as a very important contributor to their job satisfaction." \[10\] This signals a shared value: Both workers and employers want to match skills to roles. Everyone now has access to the millions of courses, books, articles, videos, apps, podcasts, social networks, and other learning content. Key to building the skills needed to win is for employers to connect that content to skills, and then connect those skills to jobs.Insight #3: Make learning work by balancing outcomes, capabilities, and purpose.Culture—this is what ties it all together. A digital learning strategy that connects content to skills and skills to jobs must be anchored in a strong culture. If the digital building blocks for a highly-skilled workforce are firmly set on the cornerstone of a talent-first agenda, the organizational culture can thrive. \[1\] A culture of continuous learning is a competitive advantage, and is key to attracting talent—especially Millennials. \[8\] Laouchez outlined a three-point framework for how employers can drive a strong learning culture \[4\]:● Purpose: Come and work with us, and be who you are.● Capabilities: Do what you are good at, or do what you like, and master it.● Outcomes: We are going to put the needed resources behind you to achieve the desired results.Technology alone is not the magic bullet. In this paradigm, technology is an enabler—one that helps drive a disproportionate return on talent. This framework is also about nurturing trust between the organization and its talent to capture future value. Shane O'Leary references Daniel Coyle to explain that organizations like IDEO, Google, and Pixar integrate purpose and profit because they "build a common purpose, share vulnerability, and create psychological safety." \[3\] \[7\] Another way to build trust is to reward the talent that generates outsize value for the organization. Developing a roster of top-skilled talent is not just about the talent at the top. Further, those value creators can be many positions distanced from the CEO. \[1\] A flash of genius almost never sparks in alignment with organization hierarchies, but instead comes "from a volume of random ideas by lots of different people with different perspectives." \[3\]Want to learn more? Check out this pathway that includes the sources cited throughout the post.\*\*\*Charan, R., Barton, D., & Carey, D. C. (2018). Talent wins: The new playbook for putting people first. Boston, MA: Harvard Business Review Press.Chiu, M. (2018, June 4). Can US Employers Close the Skills Gap With Higher Wages Alone? Probably Not. Retrieved from https://www.cebglobal.com/talentdaily/can-us-employers-close-the-skills-gap-with-higher-wages-alone/COYLE, D. (2017). CULTURE CODE: The hidden language of highly successful groups. S.l.: BANTAM.Laouchez, J. (2018, October 4). L&D Strategy: How to Win the Battle Between People and Profits. Keynote speech presented at LENS 2018 in New York, New York.Leppert, J., & Kotter, J. (2014, June 9). When CEOs Talk Strategy, 70% Of The Company Doesn't Get It. Retrieved from https://www.forbes.com/sites/johnkotter/2013/07/09/heres-why-ceo-strategies-fall-on-deaf-ears/#240067ed3663LinkedIn. (2018, September 11).LinkedIn Workforce Report | Atlanta | September 2018. Retrieved from https://economicgraph.linkedin.com/resources/linkedin-workforce-report-september-2018O'Leary, S. (2018, July 8). The surprising reason why IDEO, Pixar & Google prioritise 'psychological safety' to win the battle for better ideas... Retrieved from https://www.linkedin.com/pulse/surprising-reason-why-ideo-pixar-google-prioritise-safety-o-leary/?lipi=urn:li:page:d\_flagship3\_profile\_view\_base\_recent\_activity\_details\_all;sdxb2rg3S8mRhNOmIoT35A==&licu=urn:li:control:d\_flagship3\_profile\_view\_base\_recent\_activity\_details\_all-linkPalmer, K., & Blake, D. (2018). The expertise economy: How the smartest companies use learning to engage, compete, and succeed. Boston, MA: Nicholas Brealey Publishing.PricewaterhouseCoopers. (2018, January 22). Key findings on talent: 21st CEO Survey. Retrieved from https://www.pwc.com/gx/en/ceo-agenda/ceosurvey/2018/gx/deep-dives/talent.htmlSHRM. (2016, November 16). 2016 Employee Job Satisfaction and Engagement: Revitalizing a Changing Workforce. Retrieved from SHRM.

What the World's Most Admired Companies Are Extremely Concerned About

 _“Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.”—_Alan Kay Raymond Kurzweil, the director of engineering at Google and a renowned futurist predicted in The Singularity Is Near: When Humans Transcend Biology that by 2029 computers will be cleverer than humans. While such a mindboggling prediction can be a matter of debate, there is little doubt that in the future machines will be much more sophisticated than what they are today. Indeed, these smart machines will be able to learn on their own—without human intervention—using technology!   Erik Brynjolfsson and Andrew McAfee, co-authors of The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies say, “Technologies that used to seem like science fiction are becoming everyday reality.” Machines that can use such technologies to learn and autonomously adapt to the changes will cause serious disruption to well-established business models. Likewise, Gartner predicts that “by 2017, a significant and disruptive digital business will be launched that was conceived by a computer algorithm.” All of these predictions are based on one common observation: Machines can learn exceptionally fast, and it is possible to build machines that can learn without any active human supervision. In fact, several of the top 10 emerging technology trends of 2015, as predicted in a recent report by World Economic Forum (WEF), are technologies for learnable machines. Neuromorphic Technology With advances in machine learning and rapid processing of high volumes of data, the next generation of powerful computing will be built on chips that will process information by mirroring the brain's architecture. As explained in the WEF report, such computers will be energy efficient and will have much more computing power than a conventional CPU (central processing unit). Moreover, these computers will be able to “anticipate and learn”—instead of just responding in a pre-programmed fashion. IBM has already demonstrated this technology in a prototype form through their million-neuron TrueNorth chip in August 2014. Gartner has predicted that by 2017, 10 percent of computers will be learning rather than processing. The report also mentions, that, “The Defense Advanced Research Project Agency (DARPA) and Ecole Polytechnique Federal de Lausanne are funding the SyNAPSE and The Human Brain projects, respectively, fostering neuromorphic computing techniques intended for pattern recognition applications, including facial recognition, object recognition, drug discovery and medical diagnostics.” Emergent Artificial Intelligence We are already seeing significant advancements in artificial intelligence through self-driving cars and automated flying drones. Emergent artificial intelligence takes this step further with machines that can learn automatically by using large volumes of data. Carnegie Mellon University's NELL (the Never-Ending Language Learning) computer system "not only reads facts by crawling through hundreds of millions of web pages, but attempts to improve its reading and understanding competence in the process in order to perform better in the future." Machines with emergent artificial intelligence are not only capable of processing huge amount of information in a short time frame and arrive at an optimal solution, they also can avoid human errors and emotional biases. However, many experts are raising early warnings for potential pitfalls associated with such super-intelligent machines, which will not only replace the jobs of many human workers, but also may eventually “overcome and enslave humans.”    Programmable Material In case of 3D printing, computer readable designs are fed to a machine that can process the design and produce a finished object using additive manufacturing technology. Medical applications of 3D printing have reached such an advanced stage that even a living tissue or a cell can be printed in this process. Other fields of application of 3D printing include automotive, aerospace, electronics as well as housing and construction. Researchers at MIT have pioneered a technology in which the finished objects will be made from “smart materials” that can be pre-programmed. This new technology is called 4D printing, because it incorporates the additional dimension of “time.”   “While 3D printing is purposed around building static items, 4D printing employs dynamic materials that evolve or adapt to their external environment in real-time and in direct response to changing conditions. This ability to transform is embedded within the material itself. Objects created through 4D printing are assemble and alter their structure when the material of which they are composed comes in contact with different conditions, such as moisture or humidity. The ultimate goal is to create stimuli-responsive components, materials that modify their own form or self-assemble new patterns automatically and in predictive ways,” explains a September 2014 Georgetown Journal of International Affairs article. Advanced Robotics Next-generation robots will learn to collaborate with humans to become their co-workers. They will no longer be used only in factory assembly lines and separated from humans by safety cages. The WEF report talks about an example: “In Japan, robots are being trialed in nursing roles: they help patients out of bed and support stroke victims in regaining control of their limbs.” Boston Dynamics, a wholly owned subsidiary of Google, builds “advanced robots with remarkable behavior: mobility, agility, dexterity and speed.” These robots can walk, run, and climb in a variety of terrains and surfaces by learning to deal with environmental variations. An interesting Tech  Times article discusses how researchers at University of Maryland are looking at whether robots can learn a skill, like cooking, by watching YouTube videos.  Apart from the five technologies mentioned above, technologies that are going to impact our lives in a profound manner are Big Data, Analytics and Internet of Things (IoT). Among these, analytics will be a de-facto technology for a learning machines. Embedded Analytics According to Gartner’s “Top 10 Strategic Technology Trends for 2015,” analytics will be embedded in every app so that it will continue to learn and develop insights on a continuous basis. Such technology-based learning will lead to “advanced, pervasive, and invisible analytics,” through which the apps will be able to intelligently monitor events, gather data, and learn about the context to take better decisions. By continuously analyzing data and by developing insights, these apps will create deep understanding of the business domains. Chris Argyris, in his seminal HBR article on organizational learning, “Teaching Smart People How to Learn,” coined the terms single loop and double loop learning. Argyris explained single loop learning with an analogy of a thermostat that automatically turns on the moment the temperature of the room drops below 68 degrees. “A thermostat that could ask, ‘Why am I set at 68 degrees?’ and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaging in double-loop learning.” The article raised the issue of how highly skilled professionals can be very good at single loop learning but miserably fail at double loop learning. With the advancement of technology, we can now have thermostats that are intelligent enough to use double loop learning. Nest Labs, a Google company, specializes in making sensor driven self-learning thermostats and smoke detectors.   Bottom line: Machines will no longer remain dependent on their masters for learning. More importantly, like humans, learning machines will be able to use technology to enhance their skills. B. F. Skinner once said, “The real problem is not whether machines think but whether men do.” The time has come to seriously ponder over the implications of this remark.

Learning Technologies for Machines

 While I coined the term “subscription learning” back in 2006, I was too optimistic about its use. The industry wasn’t really ready to create subscription learning because email was the only way to deliver subscription-learning nuggets—and email didn’t enable any tracking, feedback, or interaction.But technology is finally available that can make subscription learning a reality. There are now many subscription-learning authoring tools, with more appearing all the time.CameoCameo is a subscription-learning authoring tool created by Yukon Learning. It enables learning providers to schedule a series of subscription-learning nuggets and send them through email. It is very big on using scenario-based questions in the learning thread and providing a feedback mechanism that support learner decision making.Cameo has a way to track how well learners are doing—and whether they have engaged the learning thread. Chris Ayers of Yukon Learning, one of the inventors of Cameo, told me that their clients often get very high completion rates, over 95 percent, especially on threads that are required.Merrill Collier, of Medtronic CardioVascular, used Yukon's Cameo learning reinforcement emails as follow-up to Yukon's negotiation training—and also to help reps reinforce clinical data scenarios. According to Collier, learner feedback was very positive. Learners especially appreciated how scenarios allowed them to deal with realistic customer situations.MemriseMemrise is another subscription learning authoring tool gaining momentum. Its tool is available in the cloud and features both micro- and macro-spaced learning. With Memrise, a learner will engage with content like this:receive an emailrespond to a series of micro-spaced interactions (perhaps 10 to 20)wait for another email pointing to more learning (macro-spaced in that it may arrive three, seven, or 10 days later)re-engage with more micro spaced interactions.In other words, “lather, rinse, repeat!”DuoLingoDuoLingo is the new major player for subscription-learning connoisseurs, and won Apple's App of the Year Award for 2013.  Focused on teaching languages, DuoLingo uses both micro- and macro-spaced learning. It also uses some integrated gamification and socialization to make the interactions more engaging. Every e-learning shop should study DuoLingo for ideas on how to improve their e-learning designs.Flashcard programsIn addition to these fairly sophisticated systems, there are many flashcard-type programs that employ three of the most important subscription learning features:retrieval practicefeedbackspaced repetitions and interaction.These systems theoretically can provide good learning outcomes, but authors often use them to create poorly designed learning interactions. The flashcard design pushes many authors to create cue-response learning that doesn’t reflect real-world practical situations. Still, with good instructional design, these programs can be effective. Some of the names in this category include:BrainscapeSuperMemoOpenCards.See the Subscription Learning website for a whole host of subscription learning providers, and check out in-depth explorations of two popular subscription-learning tools: QMINDshare and SimWriter.Note: This blog post is excerpted from Will Thalheimer’s newsletter; subscribe to the mail list at www.work-learning.com or read his blog at www.willatworklearning.com.

New Tools Prime the Industry for Subscription Learning

A little over a year ago, LinkedIn launched its AI-powered coaching chatbot. You can ask it for advice on workplace issues, and it will help with suggestions on how to build skills, tackle issues, or move your career forward. Could this be the future for coaching at scale?The prospect of deploying an AI-powered virtual coach that could support thousands of employees simultaneously with their workplace challenges and aspirations is an attractive one. Imagine the savings on coaching and the benefits for employees of having access to a coach, round the clock, to respond to their every need almost instantly.Can AI replace a human coach?For incoming, younger employees, this may be just what they’re looking for: A recent survey found 66 percent of 25–34 year-olds would rather talk to AI about their problems than a loved one.And it’s easy to see why. With the power of generative AI, a chatbot can communicate with empathy. People report that the experience feels just like texting with an actual human, leading to concerns that we are rapidly becoming too attached to or dependent on these AI companions.The potential of providing this kind of support to employees is not without risk. While the interactions feel real, we must remember that the chatbot has been trained to mimic empathy. It knows what an empathic response to challenges and vulnerability sounds like and gives a seemingly appropriate reply. But it lacks genuine human empathy. It doesn’t actually care like a person may.Unlike a human coach, AI can’t think, feel, or relate to a person. No matter how sophisticated its language model, a coaching chatbot lacks the nuances of personal connection—and it cannot draw on a lifetime of emotional intelligence to offer support. Without that vital human connection, AI cannot effectively deliver workplace coaching.What are the benefits of AI in the coaching process?Where AI can significantly enhance coaching is by automating some logistical aspects, such as coaching provision and mechanics, making it easier and more efficient to build strong, effective coaching relationships. For example, AI can analyze hundreds of data points to match an employee with a suitable coach, increasing the likelihood that the two individuals will work well together, rather than leaving it to chance or asking an employee to choose from a list of coach bios.AI can then manage administrative tasks, such as scheduling calls, taking notes, and curating personalized content and learning materials from the vast quantity of resources organizations provide to their employees. This helps employees take post-coaching action and removes the administrative burden of managing the relationship, driving greater engagement and improving ROI.When deploying coaching at scale across all levels of the organization, AI platforms can minimize administrative overhead for learning and development (L&D) teams while delivering a seamless, engaging employee experience.AI also has the potential to analyze data from across an organization’s HRIS, recognizing patterns and identifying employees who could benefit most from coaching. For example, AI can detect factors that increase the probability that key employees could become flight risks, thereby creating an opportunity to intervene with career development resources before they decide to leave the organization. What’s next?It’s inevitable that AI will advance exponentially, potentially even creating life-like avatars that mimic human interaction. Until we have artificial general intelligence (where machines can truly think like humans), behind those lines of code, AI-powered coaching misses the spark of humanity that makes coaching so effective.Learn how you can offer scalable, personalized career coaching—that’s powered by people and technology—to employees at all levels of your business within your budget. 

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