• 21 Nov 2019 3:48 AM | Anonymous member (Administrator)

    You can’t afford to be part of a faceless organization in the new world of work. That’s why tomorrow’s highflyers will use talent as their brand. Martin Belton reveals more.

    The world hates faceless organizations. If, for some reason, you at all doubt that, type the phrase into Google check out the comments. But if you’re keen for a reference it’s worth looking up the brilliant trend curator and keynote speaker Rohit Bhargava and his lectures on the very subject.

    Traditionally, the way to overcome that facelessness was to create a strong organizational brand. Marketing experts have long wrestled with the subject. But only more recently have they recognized that an organization’s talent is also a key constituent of that brand.

    With the advent of the new world of work and the increasing use of AI and robotics, I’m betting that this focus will ramp up. It is already widely accepted that AI and Robotics will offer amazing benefits and savings. But those effects will be wider. Those very efficiencies will erode the differences between organizations. By using more automated tools, operating on matching algorithms, based on industry wide KPI’s, we can assume the organizations themselves will become more similar. So, it is to the talent within them that we must turn to express those differences.

    Talent branding is not a new subject in HR. It has been defined as being ‘the voice of your employees’. In short, it’s how they act, think, feel, and operate within your business. Your talent brand will match your organization’s mission and values to the experiences of your employees. That is, how your team talks about your organization to friends and family and the way they express what it means to work for your business. All of these are aspects that live within your talent brand.

    Connecting your talent brand directly to your organization’s public face engenders trust and understanding. These connections become touch points and references for talent pipelines, marketing campaigns, customer buying journeys to providing customer service.

    Developing talent as a brand requires leadership and HR to understand and explain how talent helps to create and support the brand. There are several issues inherent in doing this:

    • Defining your talent brand. Are you a technically advanced organization? Are you a caring sharing type of organization? Are you all about better value? How does that represent itself with your employees?
    • Defining what that talent brand is in relation to competitors, both terms of product and services, is also important. This is true for both local and national markets if you are to source the ‘best fit’ employees.
    • Importantly, applying the understanding of your talent brand when recruiting and upskilling your workforce. That means understanding how your talent brand effects the organization internally and externally.
    • Discover how you can measure the success of your talent branding can be a challenge but useful if you are to maintain its effectiveness in the long term.
    • Working with existing employees to support and educate them into the organizational brand goals becomes more important when the focus is on your talent brand.

    Of all those aspects, it is recognizing and defining the first of these – what your brand is – that has the most power to influence your future directions. This is about more than just understanding individuals’ capabilities. Talent as a brand means getting to grips with areas traditionally seen as more challenging to manage as part of the recruitment and development mix. Aspects such as behaviors, emotional intelligence and personality traits. This is of course one of the key reasons why the Talent Transformation Guild is so attached to the Talent Transformation Pyramid. This new model is specifically designed to embrace these broader measures of talent and provide HR and C-suite executives with the data that can make these critical decisions in the future.

    Finally, if you are in doubt whether talent can really be the key constituent of your brand, think of this. In a world which increasingly values real corporate responsibility over facelessness monoliths, real faces – your employees’ faces – and their comments and attitudes will be the most honest and powerful way to express who you are and what you represent. And that is branding.

  • 6 Nov 2019 3:48 AM | Anonymous member (Administrator)

    AI is presenting amazing new opportunities for organizations. But it also adding new layers of complication to planning, managing and engaging workforces. Eric Shepherd suggests employee engagement centers can be the answer.

    For organizations to stay competitive and relevant in the 4th industrial revolution, they will have to automate to catch up and get ahead. In the age of advanced analytics, data analysis, robotic process automation, IoT, and other technological advancements, AI (artificial intelligence) has now become a must-have tool. Leadership is critical to make the most of this opportunity to maximize its full potential. To achieve this, in-house capability building, and reskilling programs are the best ways to prepare the workforce.

    There are of course real obstacles to adopting AI. Not the least of these are the changes to the organization’s thought process and work culture. Leaders, managers, sales teams, manufacturing teams and more all have to think in new ways. Instead the new watch words are interdisciplinary, data exploration, analytics and agile development if we want to deploy AI.

    In the long run, organizations must transform to be successful. History shows us that this cannot be achieved with ad-hoc solutions. Hiring capable employees will give the newly formed AI practice a necessary boost. But for organizations to keep up, AI capabilities must be built across all practices encompassing all employees. And this is where training existing employees come in. Third-party training courses and knowledge bases may deliver an overview of the subject, but they cannot provide organization-specific learning experiences that allow for rapid scaling, lasting cultural changes, cross-functional collaboration, and agile development.

    Adoption strategies

    In-house training is one of the most powerful tools an organization can use to encourage this change. It enables you to build on repeatable, cohesive practices to allow employees to dive deep into the subject matter, learn from others, and achieve the organization’s objectives. Some organizations have incubated new styles of training centers to provide learning environments as part of their adoption strategies. We can think of these new training centers as ‘AI Employee Engagement Centers’ – an environment designed to provide forums for discussion, collaboration, learning, and training. The advantage of this approach is that it can be baked into the organization’s structure, work culture, and needs. Such training centers might soon become one of the core elements of an organization-wide AI transformation.

    One of the primary responsibilities of these engagement centers is to bring AI to scale as rapidly as possible. This requires engaging and reskilling employees who are impacted by the transformation. As AI becomes widely accepted, a critical challenge will be to provide job security for employees whose tasks will be automated.

    For an AI deployment to be successful, three critical building blocks must be developed. An AI Engagement Center can educate employees regarding AI and create the means to support these building blocks.

    Shared Vision

    All training and reskilling efforts must align with a shared vision, common objectives, protocol, and language. This enables everyone to be in sync when it comes to the core elements. It also helps everyone understand each other’s roles and responsibilities and utilize the same methodology while looking for and implementing a solution.

    This alignment across the organization paves the path for a successful AI deployment. This process also facilitates retrospectives from previous AI deployments and curating this knowledge for the organization. This cross-fertilization of ideas and alignment gives leaders more information on the business, talent, and training needs, which enables them to deploy the right people into the correct positions.

    AI Employee Engagement Centers

    In-house employee engagement centers can customize content based on the organization’s objectives, starting position, industry context, cultural roadblocks, and skill gaps. Learning programs can be designed to align with the organization’s transformation road map. Engagement programs can layout the required skills to achieve the transformation and provide the means to learn these skills.

    Employees are provided with technical know how to achieve what the leadership has envisioned. Engineers, data scientists, and analytics experts hone their skills via collaboration to work alongside other business teams. This allows stakeholders to focus on actual business problems and create value for the organization. Employees whose jobs become AI-driven learn how to make the transition from older practices to new AI-based work practices.

    Hands-On Experience

    AI Engagement Centers also provides a forum for employees to mix their classroom knowledge with real-time hands-on expertise. This allows them to transition from being a learner to a practitioner who can deliver capabilities and ultimately develop into an expert who can lead a function.

    Factors to Consider to Build an AI Employee Engagement Center

    AI Engagement Centers will differ in structure based on the organization’s vision, objective, and their phase of AI transformation. While there are differences, the successful centers will have things in common:

    • Building relationships and improving the organizational design, not just learning new skills.
    • Synchronizing the academy’s activities with the strategic objectives of the organization.
    • Providing learning experiences for all stakeholders for the board room to the workroom.
    • Engaging employees in the benefits of digital and talent transformations in the context of their role.

    To extract the best value from AI-driven analytics, some new practices need to be developed. Without them, the speed, scale, and depth of value derived from analytics are bound to suffer. Organizations that plan in terms of engagement rather than training tend to fair better.

    Without coordination, the organization may not be able to identify the right mix of learning materials, group activities, and real-world problem-solving.

    Transformation Centric

    AI Engagement Centers must integrate their programs with the AI transformation road map of the organization. This will ensure the right people, skills, and talents are engaged to prioritize the critical activities for AI adoption.

    Once the leaders are armed with an understanding of AI and the opportunities, they can drive better support for use cases aligned with their organization’s goals. Analytics teams can get a broader and more in-depth understanding of the business challenges that enables them to look at the scenario from a holistic perspective rather than a technical viewpoint. Leadership must understand AI concepts so they can lead efforts in the right direction.

    It's important that all stakeholders are engaged: An effective engagement center focuses on educating the organization’s stakeholders irrespective of their designations, seniority, and role. AI Engagement Centers should plan to engage executives, managers, business analysts, translators, technical developers, business teams, and business functions.

    Going Beyond the Technology

    An AI Engagement Center is more than a technical training center. Training centers will surely cover AI foundational courses, and how to adapt to the technology changes. Employee engagement centers may also focus on the cultural and organizational changes needed to build the AI solution to scale. These centers help create a knowledge repository and introduce best practices that allow the re-engineering of critical use cases.

    Here are some elements of a successful AI Employee Engagement Center:

    • Leaders learn how to drive value, restructure the organization, and ensure a shift towards a data-driven culture.
    • Developing best practices helping everyone identify opportunities, test the readiness of teams, implement the use cases, and codify the learnings.
    • Soft skills enable effective communication, which is critical for interdisciplinary teams. Business processes and challenges have to be explained to AI experts for them define and prioritize the automation.
    • Interdisciplinary collaboration is key to implementing and creating automation necessary for success.
    • Effective change, program and project management are essential for successful AI adoption.
    • Leaders learn to converse with each stakeholder to ease use case adoption.

    Combining Training with Real-world Experience: Successful AI Engagement Centers combine classroom learning, e-learning, and workshops to brainstorm, define, and prioritize solutions. These centers can oversee the successful transition of a learner to an expert. In the classroom, learners might be provided with real-world business challenges. They are required to come up with business initiatives to combat these challenges. By learning in this way, trainees are more likely to adopt the solution that will create the highest business value.

    Introducing currently active use cases and guiding how to solve them, and learners grow through problem-solving. Learning is taken to the next level via social and community engagement.

    AI Engagement Center Keep Evolving

    AI as a technology is still far away from realizing its full potential. AI-driven solutions are changing and evolving at rapid rates. As graduates handle real-time challenges, new talents are hired, and interdisciplinary collaboration improves, more and more knowledge is gathered within the organization. It is important to feed this knowledge back into the AI Engagement Center. Centers put into place the following:

    Top performers from the first batch of learners become faculty. As learners mature and gain more experience, they can lead to learning sessions and workshops. Based on their experience working and teaching, these performers are given more and more responsibilities, eventually becoming senior facilitators.

    AI Centers create a team of individuals to continually monitor and improve the curricula and activities based on real-world experiences and changes in AI technology.

    Involving the C-Suite to visit. When executives visit, it creates unprecedented excitement and highlights the importance of learning.

    As the implementation of AI becomes a necessity, capability building has emerged as the prime requirement. Many organizations have understood that merely building the technical platform, leveraging opportunities, and hiring data scientists is not a surefire way to success. Instead, upskilling and reskilling all stakeholders, starting from seasoned managers to fresh-faced end-users, is critical for bringing AI-driven automation to scale. AI Engagement Centers will not be static enters of training but dynamic organizations that will evolve with the change in technology, use cases, organization culture, and feedback from real work experiences that will be the cornerstone for AI implementation success.

  • 31 Oct 2019 4:51 AM | Anonymous member (Administrator)

    Halloween is an ancient festival and supposed to be frightening. But scarier things are coming in the shape of artificial intelligence. Martin Belton tries not to be alarmed.

    Halloween is meant to be a bit scary. But fun as well. Some believe the festival originated from ancient Celtic harvest festivals and has pagan roots. Still, others believe that Halloween began solely as a Christian holiday, separate from any ancient rites.

    But little on Halloween this year will match the scariness of the article recently featured in the UK national newspaper, the Sunday Times. With dextrous timing, the newspaper published a piece entitled ‘The End of Humanity?’. It pointed out that artificial intelligence is already taking over our jobs, asking if it will free us, enslave us - or exterminate us. After many a scary fact, including the assertion that that autonomous weapons are more dangerous than nuclear weapons, it concludes by suggesting ‘we all better watch out because we don't know what we're playing with when it comes to AI.

    Social Unrest

    One thing is certain about AI; it is already playing a part in our lives and it will only increase going forward. For organizations, it brings many new opportunities. But that also that our HR function will be wrapped in an array of challenges. Almost every month, new reports predict more unemployment and social unrest as machines replace humans. Our article in the Sunday Times cites a PWC prediction which says that nearly a third of UK jobs could be automated away in just 15 years. A further new report from Mercer revealed that executives believed that AI and automation will replace one in five of an organization’s current jobs. 73% of executives predict significant industry disruption in the next three years. On the plus side, the World Economic Forum believes that there will be 58 million new jobs created by 2022.

    Of course, automation has been with us for centuries. But now, more and more jobs are falling under its remit. This is bound to create higher levels of change and uncertainty. And it’s the HR departments that will bear the brunt of this. The challenge is to understand and stay on top of these changes. Not so easy when the experts advise is so at variance.

    Build, buy borrow or bot?

    AI has already affected those components needed to build and extend an organization’s talent capabilities. Traditionally you could define them as “build, buy or borrow. Build, as in take inexperience and develop and train it. Buy as in recruit great but expensive talent. Borrow as in using consultants and harvesting their expertise instead. But the new world of work means we must add ‘bot’ to those options as well. We now, increasingly, have the choice of automating tasks instead of using humans.

    Support organizations such as the Talent Transformation Guild have been established to support executives to get to grips with these challenges. Challenges that may be hard to imagine, but need to be faced if we are to optimize organizations' workforce. Those challenges will demand a better understanding of the contribution of talent so this is no simple ‘one size fits all’ solution. Being part of such a support organization gives you access to the latest, fast-developing thinking on these subjects. Not to mention the opportunity to contribute to the development of the solutions themselves. And that would benefit everybody. Then nobody will need to be scared. Except on Halloween of course…

  • 16 Oct 2019 5:03 AM | Anonymous member (Administrator)

    by Eric Shepherd

    The history of humankind is one of continuous technological improvement. Ever since the first tool was crafted from stone and the first wheel was rolled, there have been innumerable technological breakthroughs to land us in this age of smartphones and the internet of things (IoT). However, real industrial and technological progress began in the mid-eighteenth century, and it is still in procession. Experts and historians divide this period of roughly 250 years into four industrial revolutions.

    With the advent of each new industrial revolution, the anticipation of better lives and the fear of diminishing jobs ran parallel. History has shown us that the concerns were unfounded as the revolutions created numerous new jobs and changed every societal factor for the better. So what would the story of the current 4th industrial revolution, the age of automation be? Will history repeat itself, or will our worst fears finally come true?

    The predictors of doom often quote a McKinsey report that predicts a job loss for over 800 million people, roughly a third of the world’s workforce, across 42 countries. While the report may be another one of those fearful cries that followed the onset of each industrial revolution, it cannot be denied that the 4th industrial revolution is much different from its predecessors.

    With technologies such as 3D printing, robotic process automation, blockchain, AI, robotics, and cloud-based computing, there are entire economic, political, and social systems being transformed, at some places, slowly and at other places at a rapid rate. The nature of this technology is unpredictable and challenging to measure in terms of growth and coverage. As more and more repetitive low-skill jobs get automated, there are job losses for sure. And the trend is significant in developed countries. However, even developing countries have begun to adapt to the new age of automation.

    3D printer automating the task.

    Depending on how sophisticated the technology becomes, even high-skill jobs are not safe from the grasp of automation. But there are, thankfully, limits. At the fundamental level, a job is nothing but a group of tasks to be performed based on many factors. As long it is technologically and economically nonviable to automate certain tasks, those jobs shall remain safe. And thankfully, a large number of such jobs exist even if the situation seems dire prima facie.

    A salient example could be the job of a chef. Scientists might be able to feed a robot with recipes, but the correct movement of the skillet, skillful whisking, and an appetizing presentation is still beyond the grasp of machines and is likely to remain so for a long time to come. Hence we can safely assert that the job of a cook or a chef remains safe.

    This is just one example amongst numerous. One caveat of these job predictors is focusing on the gross job number instead of the net number of jobs. The introduction of automation shall require a higher amount of supervision and quality control. These are tasks that require humans to be at the helm. If the employment of more supervisors does not exceed the savings made from automating part of the production, there will be a reduction in the price of the product due to a cost cut. A lower price may increase demand requiring more production, which in turn shall require more supervision. Hence even though there were some jobs lost when part of the production was automated, new jobs were created. Based on the demand for the product, the final job count may be higher than before.

    There are other dimensions of job creation to be noted as well. With higher inter-industry collaboration today than ever, the adaptation of automation in one industry might lessen the cost of raw material in another. This, in turn, reduces the cost of production when it comes to the end product. Such input-output linkage can lower prices and increase demand resulting in the creation of more jobs.

    Reasons Behind the Naysaying:

    One of the prime reasons behind the negative press the 4th industrial revolution is getting is the inability to see the bigger picture. We have already discussed the gross vs. net job scenario. Often it is easy to take stock of the events happening right now rather than which the current trend might lead to in the future. While some experts take into account the jobs being lost right now, they are unable to identify the emerging job market as a direct result of this.

    Another reason could be the media’s tendency to sensationalize negative information. That and the fact that most of the jobs being lost in the preliminary stages of the 4th industrial revolution will be at the lowest skilled levels. Most of the people losing jobs belong to the low-skilled spectrum of the population. This often creates a significant wave of protest towards the current trend.

    Finally, while there are possibly more jobs to be had in the future, experts at times feel safer overstating a risk rather than understating. All of these factors combine to create a more pessimistic view of the situation. But the light shining through the crack is visible. The most significant change this industrial revolution might bring about is a paradigm shift in terms of the nature of the jobs and the skills required to perform them.

    Low-skill repetitive jobs may soon be a part of history. High-skill, specialized job training will slowly become mandatory. Governments, educational institutes, and organizations need to change along with the time and ensure better preparation for potential workers. Such preemptive steps and awareness may help see through another industrial revolution without the employment doomsday that always seems to be lurking around the corner.

  • 14 Oct 2019 5:50 AM | Anonymous member (Administrator)

    Automation is replacing tasks previously performed by people, changing the workplace creating new ways of working for everyone” says Martin Belton.

    Organizations everywhere will benefit from embracing automation. The Executive Chairman of the World Economic Forum Professor Klaus Schwab believes the fourth industrial revolution will create new work opportunities and connect countless more people to the web. He says it will dramatically improve our efficiency as individuals and in business. These new technologies will impact all job roles, disciplines, and even challenge us about what it means to work.

    Inevitably, it will mean new challenges for leaders and executives. And huge changes for, HR and L&D teams as they reposition their workforce for the changes ahead. As with any future, we can only imagine the shape and structure of these changes. But there are good reasons to believe in at least five real changes:

    1. Automation will be embraced by organizations. Given the efficiencies that will be yielded from automation tasks previously performed by people will be performed by machines. The justification for automation is compelling. Initially this will require partnering between employees and machines which might be cause discomfort or even discontent if not managed effectively. Developing and communicating the plan to accommodate employees’ jobs post automation will be an important part of this communication.

    2. Finding training solutions for fast-changing processes: Clearly, employees will need to be trained on how to partner with machines and then learn how to operate new applications and machines. The challenge for HR will be keeping up with demands for these new training programs and sourcing the required subject matter experts when few exist.

    3. Supporting employees in a disruptive ‘agile business’: The rapid changes caused by automation will mean organizations will need to provide support to help employees embrace this constant disruption. History is littered with examples of upset employees, upset and disorientated by unfamiliar new tools, machines, and practices. That means new support processes will be needed that are focused on understanding change and adapting to it.

    4. Finding new and more diverse talent: Work will become ever more specialized. This will mean HR will need to find and attract specialized talent of all demographics from all over the world, developing and engaging new people. HR itself will need to build new networks and methods to find this expert talent.

    5. Managing the increasing use of external teams: With teams becoming diverse and specialized new systems of identifying and managing talent are going to be inevitable. Key team members will not necessarily be employees or work onsite. HR will need to invest in new forms of communication and collaboration to give the management the necessary tools to coordinate virtual teams.

    6. Understanding the robotics and systems versus employment equation: The increasing use of robots and robotic processes on the shop floor and office will mean new ways of evaluation will be needed. As machinery becomes more complex and capable, so the variables of a job roles’ requirements. This will make planning for staffing and employment needs more complicated. Leaders and HR executives will need new and smarter tools to plan and evaluate their success.

    Managing these transitions and new forms of talent will need new IT systems that engage with these new working arrangements. New HR roles will emerge. Functions will arise that we can’t imagine now but will be commonplace in a few years.

    Solutions such as the Talent Transformation Pyramid are shaped to help organizations understand these changes and react to them. Without this holistic view of talent we are going to find it difficult to understand and support the organization’s future needs.

    We have evolved today organizations with complex hierarchies and decades of legacy infrastructure. If they are to become tomorrow’s agile businesses, and bring about these seismic changes, then digital and talent transformation is key to achieving that. And that starts with the HR department.

  • 25 Sep 2019 10:41 AM | Anonymous member (Administrator)

    On the face of it, spotting rising talent and improving performance is easy.
    Yet for many organizations, this is a complex and elusive quest. Martin Belton thinks some of new business books may have hit upon why.

    “Knowing what we don’t know is better than thinking we know what we don’t,” says Philip Tetlock in Superforecasting: The Art and Science of Prediction. This book was first published a couple of years ago. But it is now enjoying a new round of publicity which led me to its contents. This was a timely thought for me while I’ve been working on establishing our new model, the Talent Transformation Pyramid. More on that later because it’s also worth looking at the book’s underlying theme. That is, while most of us are quite poor at forecasting the future, there exists a small merry band who fare so much better than the rest of us. That cohort is far and few between. But statistics show it exists. The book goes on to reveal how these ‘superforecasters’ manage this.

    Another popular business press read at present is Malcolm Gladwell’s latest, Talking to Strangers: what we should know about the People we don’t know. Interesting as always, Gladwell points out how bad we are at assessing strangers. In one observation, he cites the case of Judges assessing bail applicants in New York. They believed that face to face assessment was crucial. That was until a young researcher fed bare-bones information about applicants into a computer. It transpired the machine was superior to the judges at spotting re-offenders.

    Misleading subjective assessments

    I read them one after the other. I imagine that’s why I was struck by the similarities of their premises. The first similarity is hardly a revelation: both say that, when predicting future events, the more objective data you gather, the more chance you have of getting it right. But secondly, and more surprisingly, both point out how misleading subjective assessments can be and just how easy it is to be deceived and deceive oneself.

    Going back now to talent and performance measurement, it’s not hard to see how such issues matter. Gathering, applying and managing data for these functions can be onerous. But without it, we're relying on subjective judgments. The Talent Transformation Pyramid model was created to enable us to counteract this. It provides a solid view of what we know, and what we don't know. Designer Eric Shepherd set out to address these issues after hundreds of conversations with professionals in the sector. They revealed the lack of a recognizable model that could pull together all the relevant factors that enable us to identify and grow talent.

    12 separate factors

    The Talent Transformation Pyramid recognizes as many as 12 separate factors to enable us to do that. It clarifies the relationships between those factors. That enables us to document them within competencies and group them into a competency model. The model helps us describe what is needed to be ready to deliver performance. If we are to create an effective talent and performance system, it is to this kind of detail we must turn.

    Of course, the model also admits that it may not always be possible to gather all the data we would wish to. But as Tetlock noted ‘knowing what we don’t know’ can be useful as well. That alone can give us a far stronger platform to evaluate our Talent.

  • 1 Sep 2019 12:12 PM | Anonymous member (Administrator)

    by Eric Shepherd

    How we feel, behave, and think can vastly differ from other people in society. And we experience this every single day, even when we are not interacting with them directly. Being highly evolved mammals, we do share a set of personality traits, but other than that, one person is entirely different from the other. Understanding our personalities can be a great exercise towards bettering our lives, but understanding others' can be even more rewarding. Unlocking the psychological mysteries that shape our personalities is not only a fascinating endeavour  but also helps us create an understanding and empathetic society. In this article, we attempt to lift the veil slightly and set you on a path of further studies and ponder. We will also focus here on intrinsic personality characteristics. Our behaviors are entwined with so many external factors and variables that it is often challenging to separate one factor from the other. Therefore the study of psychological characteristics can be challenging and rewarding at the same time.

    Psychology of Personality

    According to psychologists, 'Personality' refers to a set of psychological characteristics or traits that defines how we interact with the world around us. All of us have a distinct yet effective and stable way of responding to external situations and stimuli. While none of us behave the same way all the time, if we observe our behaviors and responses over a long period, specific patterns seem to emerge.

    Stable and socially well-adjusted people can change or model their personalities according to the demands of a situation. We exhibit different personas depending on the situation. For instance, our social persona might be different from our work personal. And we will probably exhibit different behaviors when we're stressed.

    Despite differences in how we perceive and respond to things, we have certain tendencies in terms of how we view certain events or actions, what motivates us, how to handle different emotions, and how we behave under certain situations. Some of these tendencies are common and shared between virtually all human beings across the globe. But to get a complete picture of your personality, it is paramount to understand those that are part of the basic human nature.

    Apart from the common traits, many other characteristics differentiate us. Whether you are a morning or an evening person, your choice when it comes attending a party or staying at home, and whether you are quiet or talkative all contributes to the kind of person you are and how you lead your life. Despite those differences seeming to be inconsequential, their influence creates many significant differences in the lives of individuals, which is why psychologists study the differences in peoples' thoughts, values, preferences, behaviors, actions, and emotions.

    To understand someone's behavior, it is critical to understand their psychological traits along with the demand of their situation. Regardless of the situation, our complex set of psychological characteristics always accompany us. These include our belief, traits, thoughts, preferences, values, emotions, and motivations, prime us to respond in specific ways.

    Based on the situation, your psychological traits trigger responses; however, your actual behavior will depend upon your level of emotional intelligence. You will act based on the social situation, your role in that situation, other people present, their relationships with you and each other, whether your actions are appropriate or not, in the context of the situation. All this information is factored in before ascertaining whether you will be welcomed or shunned.

    Personality psychology focuses on the inner psychological characteristics and mechanisms. Social psychology focuses on the social context and demands behind a behavior. More than the number of variable factors is the fact that most of these are ever-changing. Hence our personalities often take us to new experiences where certain social stimuli prompt a response that we have never encountered before.

    Psychology as a Science

    Psychology does not snugly fit the conventional notion of scientific disciplines such as chemistry, physics, and biology. One of the primary reasons behind this is the sheer volume of factors psychological researchers must deal with in each study. The science of psychology must deal with the ambiguity of several factors being in play at any given moment. And even when separated, many of them are difficult to measure accurately and study. Unlike chemical compounds that always have the same molecular structures or physical events where the same laws of physics work every time, when psychologists deal with a person, they are working with a complex set of personality traits modeled by years of internal and external influences that cannot be isolated, understood or measured fully.

    But that does not immediately diminish psychology as a science. Psychological studies are based on scientific principles where subjects are carefully and systematically studied to arrive at science-based conclusions. Via these studies, we have understood a great deal about human personality mechanisms and the psychological processes that take place inside a human brain. It is now possible to predict how a person would behave under certain predesigned situations. But error rates are significantly higher than other disciplines due to people being vastly different from one another and their thought processes and personalities changing constantly.

    Due to these factors, psychological science is fundamentally probabilistic, much like weather forecasting. Meteorologists try to understand a dynamic, ever-changing system that interacts with each other and try to come up with a model to predict the near future events. Via decades of studies and advances, it is more accurate than ever before but still with significant errors.

    Influenced by Proportions of Variables

    There is an ongoing debate as to the amount of influence each personality factor has on our behaviors. Some psychological researchers claim that the social context or pressure is the primary influence, and others suggest that individual personality traits are at the helm. But the current consensus is that it depends on the situation. For example, imagine a fine dining restaurant. There are social pressures of behaving in a certain way in a restaurant. Hence everyone behaves similarly. On the other hand, on a beach, you see all sorts of people doing different things and interacting differently because the social pressure is less.

    How a person responds to a social demand is also a personality trait. Some people are more flexible in adhering to the current social context, while others mostly rely on their personality characteristics to behave in a specific situation.

    To identify which factor is more critical in determining our responses, scientists use statistical analysis. In studies called the variability of proportions, scientists take emotion and calculate its variability to determine whether the personality traits or social demands was the cause of that emotion. When we take an emotion like anger, we see some people who seldom get angry and others get angry regularly. This gives us a variability that ranges, let's say from 0 to 100%. Studies have observed that the proportion of variability for emotions such as anger is close to equal for internal and external influencers. This means our behaviors are equally influenced by external social situations and inner personality.

    Situational and Personality Factors Work in Conjunction

    Situation and personality are more complex. In most cases, one factor can't exist without the other also influencing the outcome. Several studies have observed that people respond differently to the same situational factors eliciting completely different issues. Which means that both situational and personality factors are at play at any given time.

    A pertinent study was concerned with teenage delinquency. Two significant factors behind juvenile delinquency are assumed to be growing up in a poor crime-ridden social environment and adolescent impulsiveness. Impulsive teenagers are more prone to commit crimes and kids who grow up in poor neighborhoods littered with criminals. But the study observed that impulsiveness did not factor much in delinquency when it came to more desirable neighborhoods. A more impoverished neighborhood contributed more to delinquency but only in impulsive kids. This is a classic example of personal and social factors working in conjunction because the social stimuli of living in a poor neighborhood affected only the kids with a particular personality trait. Non-impulsive kids did not respond similarly even under similar social pressure. Given the difficulty of isolating one factor from the other scientists must study and consider both influences to paint a clearer picture.

  • 23 Aug 2019 11:51 AM | Anonymous member (Administrator)

    by Eric Shepherd

    Competency models are frameworks that act as guides for governments, schools, colleges, universities, employers, students, employees, and job seekers. Industry-specific competency models are produced by government agencies and trade groups to provide a starting point for the other stakeholders. Competency models are formulated for different industry sectors, roles, jobs, careers, or job groups. By expressing the specific skills and knowledge requirements of an industry, they create the roadmap for career growth. Organizations then use these industry models to produce their versions of the model. These models help us clearly express the behaviors, capabilities, knowledge, skills, and abilities required for jobs. Job seekers can prepare themselves for their industry of choice by using publicly available competency models. Learning and development content creators can use these models to create relevant training courses. Industry competency models also inform the competencies required for licenses, credentials, and certifications. Employees can upskill to be up-to-date with industry expectations for their role.

    Macro View

    As the diagram below shows a competency model brings many uses and benefits.

    National and regional government use industry models to inform their policy and funding decisions. Schools, colleges and universities use models to drive curricular and the competencies required to win a credential. As stakeholder Awarding bodies are key. Awarding bodies are also known as test publishers or certification authorities. Awarding bodies use models to define the competencies requires for the certification programs that might be used to documents qualifications and grant credentials.

    Competency models underpin critical HR functions such as writing job descriptions, recruiting, interviewing candidates, learning programs, employee development, performance management, selection, promotion, upskilling, certification, and so on.

    Finally, individuals use competency models to inform their career choices.

    Competency models are dynamic

    Competency models are not stagnant management models. Industry and business experts regularly update these models based on economic, business, and technology changes. Competency models must remain current to inform stakeholders. Industry competency models provide governments, and regional economies, insights into trends to develop strategies, policies, and funding to grow the talent pool required for prosperity.

    A Competency Model is a collection of defined competencies is known as a competency model. A competency describes what an individual should know and do to perform a specific role or a task. These are used to define one or more job roles within an industry or a particular organization.

    Behaviors vs. Capabilities

    There are two broad categories of competencies; behaviors define how an individual should behave, and capabilities represent what an individual should know or be able to do.

    The actual number of behavioral competencies referenced with a competency model varies from organization to organization but is in the order of 4 to 15.

    Capabilities define knowledge, skills, and abilities that an individual must be able to use to complete a task successfully. Sometimes granular levels of details define capabilities, and in some models, only high-level definitions are documented. The number of capabilities referenced with a competency model can range from tens to hundreds.

    Competencies serve as a standard and define how to assess and measure performance via differentiating levels such as “Needs Improvement or Support”, “Meets Expectation”, and “Exceeds Expectation.” A good competency definition includes:

    1. Behaviors and capabilities required
    2. Defined benchmarks used to measure the competency
    3. Conditions in which an individual will have to perform
    4. Learning and development opportunities

    Competence describes an individual’s ability to perform a specific role or a task successfully within a predefined workplace setting.

    Defining a Behavioral Competency

    While a competency definition may assume many forms, they always have some shared elements such as the “Competency Name” and “Competency Definition.” For example, a behavioral competency:

    • Competency Name: Teamwork
    • Competency Definition: To be able to complete tasks while coordinating and collaborating with others

    Each competency will have a list of desired behaviors outlining the desired abilities and assessment criteria. For example, the desired list of activities for teamwork might be:

    • Staying committed toward the teams’ goals.
    • Facilitating team interaction.
    • Focusing on teams’ goals.
    • Ability to delegate and utilizing each other’s strengths to complete tasks.
    • Mitigating weaknesses by collaborating.
    • Handling conflicts effectively.
    • Staying open to group opinions and suggestions.
    • Motivating group members to submit ideas.
    • Supporting and following group decisions.
    • Effective handling of work-style differences.

    Some competency models also contain information regarding the level of mastery required at different organizational and occupational standards. They inform the abilities to be demonstrated to achieve each level of competence. Such information helps create learning and development content and performance measurement. A competency definition typically contains the standards which are used to measure “Needs Support”, “Meets Expectation” and “Exceeds Expectation” performances.

    Defining a Capability Competency

    A capability competence establishes the knowledge, skills, and abilities to perform a task. Just as with a behavioral competency, these competency definitions also have a “Competency Name” and “Competency Definition.” For example, a capability competency:

    • Competency Name: Install and Configure a Model XL Router
    • Competency Definition: Given a functioning Model XL Router install it within a 19” rack and configure for route IP traffic securely on an internal network

    For this capability, a list of tasks would be specified; for example:

    • Determine that the work area is safe.
    • Determine if the new router is to be installed in a “hot” or “cold” rack.
    • Take the necessary precautions if the router is to be installed in a hot rack.
    • Install the router into the rack safely and securely.
    • Apply power to the rack and ensure that it powers up correctly.
    • Using configuration software to ensure that the router passes all internal diagnostic tests.
    • Using configuration software to configure and route IP traffic securely on an internal network.
    • Insert network cables and run security tests to ensure the router is performing correctly.

    A capability competency definition will typically contain the levels which are used to measure “Needs Support”, “Meets Expectation” and “Exceeds Expectation” performances. In this example, the individual’s actual performance might be determined by a supervisor or by using virtual reality.

    Effective and Targeted Learning

    Competency definitions often specify the learning needs of a person who is currently below the expected performance level. By following the learning guidelines laid out by a competency, an individual can access targeted learning opportunity which allows him to upgrade himself to above expectation or meets expectation performance levels.

    Competency Models and Upskilling

    Individuals today must continuously upgrade their skills, adapt to, and learn to stay relevant in the job market and provide the necessary skills organizations need to remain in operation. This constant need for improvement is driven by competition at the global level, massive technological changes, and the need for environment-friendly and sustainable solutions. These factors affect the economy directly, and businesses are supporting these trends.

    Under such a climate, employers, the government, and the education system are motivated to come together to perform the following critical functions:

    1. Develop industry-specific and open competency models.
    2. Create an education system that is adaptive to the fast-changing world of business.
    3. Decrease income inequality.
    4. Increase workers’ salaries.
    5. Train and prepare workers for better job opportunities.
    6. Allow low wage workers to upskill and find better jobs.
    7. Develop capabilities and behavioral skills to allow workers to succeed in the 4th industrial revolution.
    8. Help workers succeed in gaining entry into the emerging and fast-growing sectors such as the “alternate energy” industry.

    Of course, these responsibilities go hand in hand where one cannot be achieved without the other. Unless someone documents and publishes the skillset and aptitude necessary for high-skill positions, educators would not be able to create curricula and guidance systems to prepare potential workers. Competency models provide the framework for this documentation. Business and industry experts come together to develop comprehensive industry competency models. These models document in great detail the desired skillset for crucial economic sectors and emerging high-growth industries.

    When properly formulated, these industry competency models serve as reference frameworks that provide the interconnectedness between governments, academia, employers, and individuals. These models allow employers to define clear job descriptions and describe the topics for upskilling. They help prepare curricula, guidelines, and assessments required to measure the behaviors and capabilities required to perform the tasks of the job. By providing information on the desired credentials and licenses needed to fulfill the competencies, the models offer a clear pathway for career progression and growth.

  • 21 Aug 2019 12:44 PM | Anonymous member (Administrator)

    by Eric Shepherd

    Our perception of work has gone through a quantum shift in the last couple of years. This is partly due to the introduction of new technologies such as machine learning, AI, drones, automation, robotic processes, autonomous vehicles, and so on. These new paradigms have been driving significant changes across industries. Experts estimate that within in the decade, the traditional notion of work will be completely transformed.

    No one knows what our work will look like in the future, but by studying changes today, we can probably develop some excellent ideas. We are experiencing a significant transition. Such transitions bring about tremendous uncertainty in terms of job security, availability, and requirements. Experts weigh in on both sides of the equation. Experts weigh in on both sides of the equation. Some despise the changes due to the potential tsunami of unemployment and the stress it might put on society. Others welcome this transition predicting a future where machines will fulfill our basic needs.

    Whether we like it or not, the transformation has already begun, and it is here to stay. At this point, no one can correctly predict what the future holds. But it is certainly interesting to observe the changes we are already experiencing. A closer look at them might reveal how the future of work will shape up to be.

    Humans and Machines Working Together

    Close to 45% of the jobs performed by humans today can be fully automated. In some industries, such automation has already started. The emergence of these trends makes employees insecure about their jobs. The situation is comparable to the wide adoption of computers. Employees across the planet were sceptical that the introduction of computer and internet would make them redundant. But the adoption of computers created a whole new set of roles that did not exist before. Jobs that require employees to interact with the computers, fix them, program them, and ensure the tasks are performed correctly. Not to mention the thousands of jobs created in the network engineering industry to keep the internet secure and up and running.

    We are watching the same scenario unfold again. Despite 45% of the workforce feeling that their jobs could be at risk, the adoption of AI, machine learning, and automation will require skilled employees to interact and communicate with these technologies. Recent studies paint a clearer picture. Despite there being more automation than ever before, there are close to 7 million well-paying jobs in the US alone that employers are finding it difficult to fill. The real reason behind the current talent crunch is a skill-gap that is being caused by organizations engaging in digital transformations.

    As technology progresses, organizations need more and more individuals to fill mid and high-skill roles. To fill these roles, employees must be conversant with big data, OLAP, ML, AI, automation scripting, robot deployment, drone piloting, and other high-tech skills. Exposure towards STEM (science, technology, engineering, and math) subjects is necessary to achieve these skills. But the participation of American students in these high-tech and engineering courses has been dwindling for years.

    Some organizations have already identified the challenges and are taking corrective steps. Amazon, for example, is investing close to US$700 million to upskill a third of its low-wage US workforce. As these 100,000 employees are brought up to speed to work with the latest technologies, we expect other companies will organize similar initiatives.

    Just as with previous industrial revolutions, the introduction of new and sophisticated technologies does not necessarily strip way jobs. Instead, it creates opportunities that were not possible before. One possibility is for humans to be more human. And for machines to take on the dirty, dangerous, dull, repetitive, demeaning, disliked, detestable and physically tough tasks.

    In this day and age, a modern job seeker needs to have skills that allow them to co-exist and collaborate with machines. But if the current trend is a marker, we can see the skill gap widening for a good part of the next decade.

    The only solution is for governments, companies, and educational institutes to adapt to the changing times. Governments to create environments to incubate the talents required for the future. Education to teach cutting-edge and cross-cutting skills as are necessary for this new age. Companies will have to share the burden of and preparing their workforces for the technology they have to master.

    The Freelance Work Culture

    While freelance consultants working from gig to gig have been a part of the economy for many decades, this work model has seen a significant uptick in the current years. Studies show that 75% of the millennials now prefer the work-by-project model instead of a steady job with an organization. This is a considerable change in terms of the workforce, economy, and current job market.

    Some experts have expressed their concerns over this model since a gig-by-gig career cannot guarantee job security or sustained income. The solution is for gig workers to recognize the skills they need and upskill themselves. With a better and updated skillset, gig workers can win high-skill projects that would otherwise be impossible to secure. This not only translates to better pay for the worker but also allows employers to find the right candidate for challenging to fill positions.

    Gig platforms that accommodate employers and employees can detect skill shortages and upskill their members to benefit from emerging opportunities. They provide learning opportunities to their members and monitor their progress via assessments. This allows better opportunities for the worker, high-skilled employees for the employers, and build commissions for the platforms. By introducing training programs, we can create a win-win scenario where everyone gets what they want and deserve.

    Embrace, not Resist

    The changes to working as we know it is here, and it is inevitable. Introduction of new technologies and growth of the gig economy poses challenges, but there are benefits to be derived if we overcome them. History has taught us that industrial revolutions take away tasks and jobs, but it creates far more opportunities to take their place. It is up to us to embrace the changes and choose to adapt to this fast-changing work environment. What the future holds, we don’t know for sure. But it sure helps to stay prepared.


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