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Doug White

ATD Links Archive - ATD Links

Effective Delegation Enables Continued Growth

Managing Workers: Four Steps to Improving People Management Skills

Doug Stone is a founder and partner of Triad Consulting Group and a lecturer on law at Harvard Law School, where he has taught negotiation for almost 30 years.Through Triad, he does consulting for a wide range of organizations, including Fidelity, Honda, HP, Merck, and Turner Broadcasting, and has lectured at Apple, Google, Microsoft, and Pixar. Stone has worked with journalists in South Africa; diplomats at the former Organization of African Unity; police and community leaders in Springfield, Massachusetts; and doctors at UN/AIDS and W.H.O. He has also done consulting for Teach for America, the Ford Foundation, the Nuclear Regulatory Commission, the US State Department, the US Department of Justice, and the White House. Stone has taught difficult conversations for the justices of the Massachusetts Supreme Judicial Court as well as for 80 federal district court judges.Stone is co-author of two New York Times bestsellers: Difficult Conversations: How to Discuss What Matters Most, which has been translated into more than 25 languages, and Thanks for the Feedback: The Science and Art of Receiving Feedback Well. His articles on negotiation and conflict resolution have appeared in Harvard Business Review, the New York Times, Educational Leadership, Rotman Management Journal, and IT Metrics. He has appeared on many TV and radio shows, including Oprah, and was a keynote speaker at the 2006 World Negotiation Forum in Brazil.From 1988 to 1998, Stones was an associate and then associate director of the Harvard Negotiation Project, where he worked with Roger Fisher and other colleagues on the development of negotiation theory.Stone graduated from Brown University in 1980 and Harvard Law School in 1984. Prior to returning to Harvard, he practiced transactional and regulatory banking law at firms in Boston and New York.

Douglas Stone

Doug Ferreira Director of Sales Training and Development Blackboard Doug Ferreira has enjoyed a 20-year career in sales, sales management and sales training. Prior to joining Blackboard as the Director of Sales Training and Development, Doug led the sales training function at Oakwood Worldwide, the largest provider of corporate temporary housing in the world. While at Oakwood, Doug was recognized as a finalist for the 2010 Stevie Award for National Sales Training Manager of the Year, spoke at industry conferences hosted by The Conference Board and Miller Heiman, and had his work featured by Corporate Executive Board’s Sales Leadership Roundtable.

Doug Ferreira

CGS Immersive's Training & Performance offering provides enterprise-ready learning, training, and onboarding solutions designed to support the evolving demands of the modern workplace. Leveraging cutting-edge technologies such as Generative AI, XR (Extended Reality), RPA (Robotic Process Automation), and IoT (Internet of Things). CGS Immersive empowers L&D (Learning and Development) teams with as-a-service implementations of emerging tech, along with dedicated support, consultation, and content creation services - both custom and on demand via its SaaS platforms. The result is a transformative approach to learning and development that enhances employee performance while reducing onboarding and downtime, fosters revenue growth, and ensures the retention of top talent.

Future-Proof Your Workforce: Building Tomorrow’s L&D With AI and Immersive Tech

“It is not about what they know; it is about what they can do.”—Bob MosherMany years ago, learning scientist Roger Schank stated “learning is the work.” Today learning in the workflow is getting renewed interest as organizations realize the opportunities new technologies provide to make assets available at the point of need. The nature of work is changing, and therefore the need for access to learning assets is changing. The modern worker wants learning that is personalized just for their needs and available on a variety of devices. They are more adaptive, collaborative, and self-directed than earlier generations of learners. They use smartphones to search for answers at any time, and expect immediate access to information.Several years ago, Conrad Gottfredson and Bob Mosher of APPLY Synergies conducted studies on learning and authored Innovative Performance Support: Strategies and Practices for Learning in the Workflow. Their research identified five unique moments when performers are learning: learn new, learn more, apply, solve, change. The first two are associated with formal training while the last three exist in the realm of performance support. Unlike traditional approaches that encourage learner dependency and begin with a training solution framed by the deeply ingrained assumption that performance challenges are best ameliorated by new learning, the five moments of need methodology encourages independence and launches from the moment of application (when learned skills are applied to the performance of a job task). The corresponding assumption in play is that performance challenges are best addressed by providing performers access to cascading levels of support, from simple (for instance, a reminder of the basic steps required to complete a task) to comprehensive (targeted training oriented around job roles and tasks for which the result of failure is catastrophic).These moments include:1. When people are learning how to do something for the first time (learn new).Example: A first-time supervisor, promoted from within, needs to learn the progressive discipline policies of the organization.2. When people are expanding the breadth and depth of what they have learned (learn more).Example: The first-time supervisor needs to learn how to conduct a verbal counseling session under the progressive discipline policies of the organization.3. When they need to act on what they have learned, which includes planning what they will do, remembering what they may have forgotten, or adapting their performance to a unique situation (apply).Example: The first-time supervisor needs to perform the task of conducting a verbal counseling session (acting on what they have learned). This requires the supervisor to plan the encounter, remember what they may have forgotten, and possibly adapt their performance to a unique situation.4. When problems arise, or things break or don’t work the way they were intended (solve).Example: During the verbal counseling session, the employee presents the first-time supervisor with new medical documentation and notice that legal counsel has been retained.5. When people need to learn a new way of doing something, which requires them to unlearn deeply ingrained skills and relearn to adjust to new practices, requirements, and so forth (change).Example: In response to legal challenges, the organization has significantly changed its time and attendance policies and procedures. The first-time supervisor, formerly a line employee of several years, must overcome familiarity with the way it has always been and internalize the new approach.Learning new and more is the traditional stronghold for L&D professionals. It is what we have done for years and years. Learning used in apply, solve, and change moments is performance support; it is needed when it is needed—just in time, not when someone is attending a class. It is also needed in the right amount, not the amount deemed important for the length of a class.According to Gottfredson and Mosher, all learning needs to focus on application. This means addressing the entire journey performers make, from the beginning stages of learning through the full range of challenges when learners are called upon to actually perform, to use what they’ve learned to take actions. If organizations are interested in advancing employee performance, they must understand the moments when employees need access to learning.The Complete Learning Journey is depicted as three phases—training, transfer, and sustain, as illustrated in this image. Designing for this model requires pairing team members who are certified in the five moments of need methodology with practitioners who are experts in the role for which content is being designed. While in concept this approach is not so different from a traditional designer–subject matter expert collaboration, in practice it becomes more of a relationship between the designer and the larger professional community being served. Approaches differ based on the moment (for example, new is taught differently than more), and content associated with formal instruction is generally limited to those tasks that, if performed incorrectly, result in significant or catastrophic outcomes. Tasks carrying less risk are addressed through instruction or performance support in the workflow.This is a framework that various modalities can easily fit into. For example, there are ways to support the moments with e-learning, microlearning, videos, gaming, and so forth. However, this design requires a complete change of mindset about the learning portfolio and an understanding that five moments of need is not a different version of ADDIE. Some tips from experience to better navigate your “new” moment with this methodology were recently provided to ATD Forum members and include:Invest your time and professional development dollars in getting up to speed holistically on the five moments of need methodology. Allow yourself to be confused as you learn; it all makes sense eventually.Don’t waste your time trying to explain the shift to a performance-based approach. Just assume that the broad community of stakeholders won’t actually get it until you have a no-kidding demonstration product to show them that’s applicable to their work.Target an individual stakeholder in an underserved area of your business as your partner. The underserved are more likely to be willing helpers and will appreciate getting top-of-the-line, impactful learning support in return.Get started, but start small.Work under the radar.It’s much harder than you think. Stick with it anyway and trust the process. As Mae West is quoted as saying, “I never said it would be easy; I just said it would be worth it.”ATD Resources:Technology to the RescuePerformance Support Certificate ProgramScience of Learning 101: When to Build Performance Support, Part 1

Learning at the Moment of Need

 _“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

As the CEO of Emergent Performance Solutions, Jennifer is a sought-after trust coach and organizational change expert with 25 years of experience in high-level change consulting. She helps organizations understand the impacts, processes, and feelings associated with change. She has a notable reputation for innovation, creativity, and reliability. Her entrepreneurial spirit balanced with years of practical experience provides opportunities to create fun and exciting experiences that produce desired results. She is considered an expert in emotional intelligence and uses this knowledge to coach, teach, and consult with uniquely complex organizations such as the Pentagon, Department of Homeland Security, HPE, Cisco, Indeed, Coca-Cola, tech start-ups, financial firms, and ministry leaders. She works to transform organizations and teams during critical times while ensuring the fostering of excellent relationships and collaboration. She considers herself a servant leader and enjoys that role both in business and her community. She is a published author, executive coach, Vistage Worldwide speaker, mother of two awesome adult children, and a dog and cat lover. She is a proud Radford University grad and member of Alpha Sigma Alpha. She lives with her (retired Army 1SG) husband of 29 years in Loudoun County, Virginia Learn more about her company at emergentps.com | Jennifer Stanford | LinkedIn

Jennifer Stanford

Instructional Designer, Rosendin Electric Kayla has six years of experience working in the L&D function at Rosendin, starting as a summer helper in 2017 and returning in 2018. Kayla graduated from San Jose State University, California, in 2019, earning a BA in psychology and a minor in human systems Integration. At that time she began her role as a training coordinator and progressed to her current role as an instructional designer in 2023. Kayla facilitates and manages two organization-wide programs, including CoreClarity, a strengths-based program for individuals and teams, and the SURGE program, an assistant project manager development program designed to help APMs progress toward the role of project manager. In addition to program facilitation, Kayla facilitates and develops content for nontechnical training classes, both virtual and in-person, while continuing to support L&D leads in the West. Kayla lives in San Jose with her son, dog, Maxx, and box turtle, Cletus. One wouldn’t guess, but it’s the turtle you need to watch out for (sneaky little guy).

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