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What machine learning is today, and what it could be soon

February 18th, 2019

If AI is a broad umbrella that includes the likes of sci-fi movies, the development of robots, and all sorts of technology that fuels legacy companies and startups, then machine learning is one of the metal tongs (perhaps the strongest) that holds the AI umbrella up and open.

So, what is machine learning offering us today? And what could it offer us soon? Let’s explore the potential for ML technologies.

Intro to machine learning

Machine learning is the process of machines sorting through large amounts of data, looking for patterns that can’t be seen by the human eye. A theory for decades, the application of machine learning requires two major components: machines that can handle the amount of processing necessary, plus a lot (a lot!) of gathered, cleaned data.

Thanks to cloud computing, we finally have both. With cloud computing, we can speed through data processing. With cloud storage, we can collect huge amounts of data to actually sort through. Before all this, machines had to be explicitly programmed to accomplish a specific task. Now, however, computers can learn to find patterns, and perhaps act on them, without such programming. The more data, the more precise machine learning can be.

Current examples of machine learning

Unless you are a complete luddite, machine learning has already entered folds of your life. Choosing a Netflix title based on prompted recommendations? Browsing similar titles for your Kindle based on the book you just finished? These recommendations are actually tailor-made for you. (In the recent past, they relied on an elementary version of “if you liked x, you may like y”, culled from a list that was put together manually.)

Today, companies have developed proprietary algorithms that machine learnings train, or look for patterns, on, using your data combined with the data of millions of other customers. This is why your Netflix may be chock full of action flicks and superhero movies and your partner’s queue leans heavily on crime drama and period pieces.

But machine learning is doing more than just serving up entertainment. Credit companies and banks are getting more sophisticated with credit scores. Traditionally, credit companies relied on a long-established pattern of credit history, debt and loan amounts, and timely payments. This meant if you weren’t able to pay off a loan from over a decade ago, even if you’re all paid up now, your credit score likely still reflects that story. This made it very difficult to change your credit score over time – in fact, time often felt like the only way to improve your credit score.

Now, however, machine learning is changing how credit bureaus like Equifax determine your score. Instead of looking at your past payments, data from the very near past – like, the last few months – can actually better predict what you may do in the future. Data analysis from machine learning means that history doesn’t decide; data can predict your credit-worthiness based on current trends.

What the future holds for machine learning

Machine learning is just getting started. When we think of the future for machine learning, an example we also hear about are those elusive self-driving cars, also known as autonomous vehicles.

In this case, machine learning is able to understand how to respond to particular traffic situations based on reviewing millions of examples: videos of car crashes compared to accident-free traffic, how human-driven cars respond to traffic signs or signals, and watching how, where, and when pedestrians cross streets.

Machine learning is beginning to affect how we see images and videos – computers are using neural networks to cull thousands of images from the internet to fill in blanks in your own pictures.

Take, for instance, the photo you snapped on your holiday in London. You have a perfect shot of Big Ben, except for a pesky pedestrian sneaking by along a wall. You are able to remove the person from your image, but you may wonder how to fill the space on the wall that walker left behind. Adobe Photoshop and other image editors rely on an almost-standard API to cull other images of walls (that specific wall, perhaps, as well as other walls that look similar) and randomize it so that it looks natural and organic.


This type of machine learning is advancing rapidly and it could soon be as easy as an app on our phones. Imagine how this can affect the veracity of a video – is the person actually doing what the video shows?

Problems with machine learning

We are at a pivotal point where we can see a lot of potential for machine learning, but we can also see a lot of potential problems. Solutions are harder to grasp as the technology forges forward.

The future of machine learning is inevitable; the question is more when? Predictions indicate that nearly every kind of AI will include machine learning, no matter the size or use. Plus, as cloud computing grows and the world amasses infinite data, machines will be able to learn continuously, on limitless data, instead of on specific data sets. Once connected to the internet, there is a constant stream of emerging information and content.

This future comes with challenges. First, hardware vendors will necessarily have to make their computers and servers stronger and speedier to cope with these increased demands.

As for experts in AI, it seems there will be a steep and sudden shortage in the professional manpower who can cope with what AI will be able to day. Behind the private and pricey walls of Amazon, Google, Apple, Uber, and Facebook, most small- and medium-sized businesses (SMBs) actually aren’t stepping more than a toe or two into the world of machine learning. While this is due in part to a lack of money or resources, the lack of expert knowledge is actually the biggest reason that SMBs aren’t deeper into ML. But, as ML technologies normalize, they’ll cost less and become a lot more accessible. If your company doesn’t have experts who knows how you could be using ML to help your business, you’re missing out.

On a global level, machine learning provides some cause for concern. There’s the idea that we’ll all be replaced in our jobs by specific machines or robots – which may or may not come to fruition.

More immediately and troubling, however, is the idea that imaging can be faked. This trick is certainly impressive for an amateur photographer, but it begs an important question: how much longer can we truly believe everything that we see? Perhaps seeing is believing has a limited window as a standard truthbearer in our society.


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Reaching the Cloud: Is Everything Serverless?

February 18th, 2019

As it goes in technology, as soon as we all adapt a new term, there will assuredly be another one ready to take its place. As we embrace cloud technology, migrating functions and software for organization, AI potential, timeliness, and flexibility, we are now encountering yet another buzzword: serverless.

Serverless and the cloud may sound similar, both floating off in some distant place, existing beyond your company’s cool server room. But are the cloud and serverless the same? Not quite. This article explores how serverless technology relates to the cloud, as well as, and more importantly, whether you have to adapt a serverless culture.

What is serverless?

Serverless is shorthand for two terms: serverless architecture and serverless computing.

Once we get past the name, serverless is a way of building and deploying software and apps on cloud computers. For all your developers and engineers who are tired of coping with server and infrastructure issues because they’d rather be coding, serverless could well be the answer.

Serverless architecture is the foundation of serverless computing. Generally, three types of software services can function well on serverless architecture: function-as-a-service (FaaS), backend-as-a-service (BaaS), and databases.

Serverless code, then, relies on serverless architecture to develop stand-alone apps or microservices without provisioning servers, as is required in traditional (server-necessary) coding. Of course, serverless coding can also be used in tandem with traditional coding. An app or software that runs on serverless code is triggered by events and its overall execution is managed by the cloud provider. Pricing varies but is generally based on the number of executions (as opposed to a pre-purchased compute capacity that other cloud services you use may rely on).

As for the name itself: calling something “serverless” is a bit of a misnomer because serverless anything isn’t possible. Serverless software and apps still rely on a server, it’s just not one that you maintain in-house. Instead, your cloud provider, such as Google, AWS, Azure, or IBM, acts as your server and your server manager, allocating your machine resources.

The cloud vs. serverless

While the cloud and serverless are certainly related, there’s a better reason why we are hearing about serverless technologies ad nauseum. Because cloud leaders like AWS, Google, Azure, and IBM are investing heavily in serverless (and that’s a ton of money, to be sure).

Just as these companies spearheaded a global effort to convince companies their apps and data can perform and store better in the cloud, they are now encouraging serverless coding and serverless architecture so that you continue to use their cloud services.

Serverless benefits

Is everything serverless? Will everything be serverless soon? In short, no and no.

The longer answer is that serverless architecture and serverless computing are good for simple applications. In serverless coding, your cloud provider takes care of the server-side infrastructure, freeing up your developers to focus on your business goals.

Your developers may already be working on serverless code – or they want to be. That’s because it frees them from the headache of maintaining infrastructure. They can dispense with annoying things like provisioning a server, ensuring its functionality, creating test environments, and maintaining server uptime, which means they are focused primarily on actual developing.

As long as the functionality is appropriate, serverless can provide the following benefits:

  • Efficient use of resources
  • Rapid testing and deployment, as multiple environments are a breeze to set up
  • Reduced cost (server maintenance, team support, etc.)
  • Focus on coding – may result in increased productivity around business goals
  • Familiar programming languages and environment
  • Increased scalability

Traditional code isn’t going anywhere (yet)

While focusing on your core business is always a good goal, the reality is that serverless isn’t a silver bullet for your coding or your infrastructure.

Depending on your business, it’s likely that some products and apps require more complex functions. For these, serverless may be the wrong move. Traditional coding still offers many benefits, despite still requiring fixed resources that require provisioning, states, and human maintenance. Networking is easier because everything lives within your usual environment. And, let’s face it: unless you’re a brand-new startup, you probably already have the servers and tech staff to support traditional coding and architecture.

Computationally, serverless has strict limits. Most cloud providers price serverless options based on time: how many seconds or minutes does an execution take? Unfortunately, the more complex your execution, the more likely you’re go past the maximum time allowed, which hovers around 300 seconds (five minutes). With a traditional environment, however, there is no timeout limit. Your servers are dedicated to your executions, no matter how long they take or how many external databases they have to reference. This can make activities like testing and external call up harder or impossible to accomplish.

From a business perspective, you have to decide what you value more: only paying for what you use (caveat emptor), with decreased opex costs. Or, perhaps control is tantamount, as you are skeptical of the trust and security risk factors that come with using a third party. Plus, not all developers work the same. While some devs want to use cutting-edge technology that allows them to focus on front-end logic, others prefer the control and holistic access that traditional architecture and coding provides.

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When Technology Moves Faster Than Training, Bad Things Happen

February 18th, 2019

Technology is changing how we design training, and it should. Unfortunately, many instructional designers are not producing the learning programs and products that today’s technical talent needs. Not because they don’t want to, but because many companies don’t support their efforts to advance their work technologically or financially.

That’s a mistake. Technology has already changed learning design. Those who don’t acknowledge this appropriately are doing their organizations – and their technical talent – a disservice.

Bob Mosher, chief learning evangelist for Apply Synergies, a learning and performance solutions company, said we can now embed technology in training in ways we never could before. E-learning, for instance, has been around in some for or another, but it always sat in an LMS or outside of the technology or whatever subject matter it was created to support. That’s no longer the case.

“Now I don’t have to leave the CRM or ERP software, or cognitively leave my workflow,” Mosher explained. “I get pop ups, pushes, hints, lessons when I need them, while I’m staring at what I’m doing. These things guide me through steps; they take over my machine, they watch me perform and tell me when and where I go wrong. Technology has allowed us to make all of those things more adaptive.”

Of course, not all learning design affected by technology is adaptive, but before adaptive learning came on the scene, training was more pull than push, which can be problematic. If you don’t know what you don’t know, you may proceed blindly thinking that, “oh, I’m doing great,” when you’re really not. Mosher said adaptive learning technologies that monitor learner behavior and quiz and train based on an individual’s answers and tactics, can be extremely powerful.

But – there’s almost always a but – many instructional designers are struggling with this because they’re more familiar with event-based training design. Designing training for the workflow is very different animal.

The Classroom Is Now a Learning Lab

“It’s funny, for years we’ve been talking about personalized learning, but we’ve misunderstood it thinking we have to design the personalized experience for every learner,” Mosher said. “But how do I design something personalized for you? I can give you the building blocks, but in the end, no one can personalize better than the learners themselves. Designing training for the workflow is a very different animal.”

In other words, new and emerging technologies are brilliant because they enable learners to customize the learning experience and adapt it to the work they do every day. But it’s one thing to have these authoring technologies and environments; it’s something else for an instructional designer to make the necessary shift and use them well.

Further, learning leaders will have to use the classroom differently, leveraging the different tools at their disposal appropriately. “If I know I have this embedded technology in IT, that these pop ups are going to guide people through, say, filling out a CRM, why spend an hour of class teaching them those things? I can skip that,” Mosher said. “Then my class becomes more about trying those things out.”

That means learning strategies that promote peer learning, labs and experiential learning move to the forefront, with adaptive training technology as the perfect complement. Antiquated and frankly ineffective technical training methods filled with clicking, learning by repetition through menus, and procedural drilling should be retired post haste in favor of context-rich learning fare.

Then instructors can move beyond the sage-on-the-stage role, and act as knowledge resources and performance support partners, while developers and engineers write code and metaphorically get their hands dirty. “If I have tools that help me with the procedures when I’m not in class, in labs I can do scenarios, problem solving, use cases, have people bounce ideas and help me troubleshoot when I screw up,” Mosher said. “I’m not taking a lesson to memorize menus.”

Learning Leaders, Act Now

Learning leaders who want to adapt to technology changes in training design must first secure appropriate budget. Basically, you can’t use cool technology for training unless you actually buy said cool technology. Budgetary allocations and experimentation must be done, and instructional designers have to have the time and latitude to upgrade their skills as well because workflow learning is a new way of looking at design.

“Everyone wants agile instructional design, but they want to do it the old way,” Moshers said. “You’re not going to get apples from oranges. Leadership has to loosen the rope a little bit so instructional designers (IDs) can change from the old way of designing to the new way.

“IT’s been agile for how long now? Yet we still ask IDs to design in a waterfall, ADDIE methodology. That’s four versions behind. Leadership has to understand that to get to the next platform, there’s always a learning curve. There’s an investment that you don’t get a return on right away – that’s what an investment is.”

For learning leaders who want to get caught up quickly and efficiently, Mosher said it can be advantageous to use a vendor. They’re often on target with the latest instructional design approaches and have made the most up to date training technology investments. But leadership must communicate with instructional designers to avoid resistance.

“Good vendors aren’t trying to put anybody out of a job, or call your baby ugly,” he explained. “It’s more like, look. You’ve done great work and will continue to do great work, but you’re behind. You deserve to be caught up.”

The relationship should be a partnership where vendor and client work closely together. “Right,” Mosher said. “If you choose the right vendor.”

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Reflections from a Reluctant Servant Leader

February 2nd, 2018

If today’s leaders are going to succeed they have to develop a new skill – the ability to serve. They have to learn there’s no shame in following someone else’s lead.

The idea of servant leadership would strike a traditional leader as odd. A traditional leader is brought up to think of him or herself as a kind of warrior. Not a paint black stripes on the face, or carry a spear type, but someone other people are glad to stand behind when things get tough.

That leader-as-warrior mentality carries many leaders through their early careers. They may even build a track record of success with that kind of behavior. But then life happens.

You have setbacks, you work hard to overcome them, but you still fail. And the leaders ahead of you will, at some point, fail to live up to your expectations. On the other hand, sometimes they inspire you in unexpected ways. It’s that ability to inspire, to create something meaningful out of failure that makes a servant leader.

If we’re lucky, we learn that lesson while we’re young and malleable, our leadership style is not yet fully formed. New approaches still fit, and you can learn on the job.

It’s the best kind of leadership development, on the job, dealing with real problems, in real time, with real deadlines. There are consequences for your actions. And let’s face it. Some of us need to learn the hard way.

For instance, Iet’s say your team is struggling with an important project. Brash and impatient, you push and push, demanding longer hours and more commitment, as though they aren’t already working their hearts out. It’s a bad move, however, and it blows up in your face.

The day you’re supposed to present a polished, completed software prototype, you still have bugs, errors, a mess. Worse, you’re revealing that fact the day of the deadline, with no warning.

Why? Because you didn’t listen when your teammates tried to tell you, you were moving too fast, that they needed more time for testing, that the code wasn’t ready.

So, you have to go to your boss, who you admire, and tell him that you failed. Not your team, you. You failed to deliver.

You think you’ll be fired for sure. The code’s for a tough client with no patience for missed deliverables or excuses.

You go into your boss’ office head down, ashamed. You manage to look him in the eye as you recount your tale of woe, but it’s one of the hardest things you’ve ever done. And then you wait.

He says your name.

You look up, suck in a surreptitious breath and wait for the axe to fall.

“It’s okay.”


“It’s okay,” he repeats. “I knew you weren’t going to be able to make the deadline. I asked for an

extension a week ago.”

You stare. “We have an extension?”

He smiles and nods. “Now, show me what you’ve got, then we’re gonna get the team together, and we’re gonna tackle this thing a different way.”

You can’t believe it.

“You knew all the time that I was gonna fail?”

It occurs to you that you should be angry, but you’re too surprised, too curious, too relieved for that. You just want answers.

“I did,” he said.

“Why didn’t help me?”

“You didn’t ask for help.”

At that point you have to sit down. It’s so simple, and so poignant. You failed because you didn’t ask for help.

“You drove your team to exhaustion trying to fit a square peg into a round hole. It never occurred to you to try something different,” he said simply. “To ask for a fresh perspective. You didn’t collaborate. You just barked out orders, and your team followed them.”

“To my doom,” you whisper.

He laughed softly. “Yup. To your doom. You ready to try again?”

You are. But first, you apologize to your team, admit that you led them in the wrong direction. Then you ask for their input, this time with your boss’ support every step of the way.

Ultimately, you finish the code, deliver the software, the client is pleased, and your team is happy.

And your boss? A few months later he promotes you.

“You’ve proven yourself,” he says when you ask him why. After all, your failure is no longer quite so fresh, but it wasn’t that long ago.

“This isn’t about your technical skills,” he explains. “You’ve proven yourself as a leader. The way you’ve managed your team. The way they interact with you. The speed you’re able to work and bring projects through to completion. It’s only possible because they trust you now. And more importantly, you trust them enough to ask for help when you need it.”

You thank him, and the experience becomes a frame on the reel of life’s great moments.

Leaders today are struggling between the traditional and the new normal. Change has become this weird, shaky bridge that too many leaders slip and fall off of. Only it’s not the mistakes that do them in. It’s the exit interviews with peers and coworkers, the sour reputations that follow them from company to company or into the media like a bad smell that provide that last thrust of the knife.

It’s why servant leadership works. When you’re a servant leader, the people around you are happy to share in the blame. They feel some responsibility for any failure to succeed or perform, and they’re only to happy too speak with optimism about their plans to recover, to create a new or better solution.

But when you’re a traditional leader who craves command and control, barking and yelling, pointing fingers and telling everyone around you what to do, you’re usually alone when things go wrong. Because to lead effectively, sometimes you must follow.

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Four Drivers Pressuring Organizations to be More Human

January 23rd, 2018

There are several trends in the world of work and beyond that are pressuring enterprises to humanize work. But as of yet, these factors are not changing the workplace to the extent that they should. Leaders should consider these trends when trying to engage their people.

Alienation from the workplace

People more than ever feel alienated from their places of work. Relentless organizational restructuring and downsizing, re-engineering, and layoffs are now commonplace. This upheaval inevitably unsettles and demoralizes employees, particularly those who lose their job! Throw into the mix the growing inequality of wages and the rising disconnect—reflected in engagement survey results worldwide—and insecurity understandably builds. Although people feel separated from their organization more than ever, they paradoxically yearn for a more humanizing workplace. The estrangement caused by these unsettling forces presents real opportunities for leaders to improve their interactions with team members.


The pervasive influence of technology is another driving force to humanize work through better conversations. Isn’t it ironic that we’ve never been more connected digitally, and yet—at a human level—we’ve never been so disconnected? At the click of a button, we can connect with someone on the planet in seconds. This digital connectivity and the wonderful benefits it brings is a relatively recent phenomenon. In inverse proportion, there’s been a rapid erosion in human connectivity. Regional communities have been replaced by virtual communities. We don’t always know our neighbors, let alone the people who live in the house across the street. What’s more, we’re not all that interested in knowing. We keep to ourselves. We don’t know the name of the person who services our car or the person behind the counter at the corner store. And yet, we humans still have a deep hunger for human connection; much deeper than ‘connecting’ with friends on Facebook. So, the workplace can—and does, to some extent—fill this void as our de facto community.

The workplace—despite feelings of emotional isolation—is a prime source of community. Traditional groupings for emotional bonding are evaporating. There’s a decline of neighborhoods, dwindling church attendance, disappearing civic groups, and less reliance on extended families, for example. Can the workplace community—even with its apparent insecurities—compensate for these dwindling, traditional pillars of society? For an increasing percentage of employees, the workplace offers the only steady link with other people—a constant source of ongoing human interaction.

The meaning of work

With the digital explosion comes lots of exposure to new ideas, philosophies, and perspectives. For instance, Eastern philosophies are no longer mysterious to Westerners. What’s more, Eastern philosophies have inspired Westerners to consider other forms of spirituality. There’s a growing curiosity in Buddhism and Confucianism, for example. Zen Buddhism and Confucianism promote practices like mindfulness and meditation and emphasize values such as loyalty to one’s group instead of individualism. Central to these philosophies is the discovery of one’s spiritual identity in all pursuits. These sorts of ideas are finding greater acceptance and application in our society. Time-honored beliefs such as these are shaping the way we think about our lives, including the role work plays.

With a large slice of the current workforce contemplating retirement and about to depart full-time work, baby boomers are reflecting on the meaning of their lives and the legacy they leave behind. As aging baby boomers move closer to life’s greatest certainty—death—they naturally have a growing interest in contemplating life’s meaning. I know I do! This reflection concentrates more attention on one’s work contribution.

Creativity and global competition

There’s a lot of talk and instances of artificial intelligence taking people’s jobs now and in the not-so-distant future. At the same time, escalating global competition has, in the past two decades, shifted attention from machines to people as the primary source of competitive edge. Despite this, most people—bar a select few—are increasingly being treated as a disposable commodity, regardless of their capabilities, skillset, or educational attainment. Yet it’s the technological tools that are the real commodities. Technology is easily accessible, reducing in price all the time in relative terms, and offers the customer a bewildering array of options. Technology is no longer the edge it once was.  It’s people who are still the differentiators in the working world.

Even though people are generally treated as a resource, high-performing individuals are in great demand worldwide across all industries. The relentless pressure of global competition has escalated the value of people’s creative energy; thinking outside the box is the new black. Harnessing and maximizing people’s ideas and ingenuity involves the collaboration of head and heart. Innovative thinking that translates to practice is a rich source of adaptive advantage. Innovative thinking is the fuel that drives the necessary adaptive gain in an economy characterized by accelerated change and uncertainty.

Even with these four drivers to humanize our places of work, there are factors working against this idea. The human connection to the organization is more tenuous than it ever was. Instead of a place of dignity and security, today’s workplace is one of unease and insecurity. Work was once a stable and predictable pillar in one’s life. Today, more and more people are changing jobs for a host of reasons every couple of years or less. High turnover accounts for some of the tension we experience in the workplace. Still, these four drivers offer leaders a platform to humanize their workplaces.

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A Practical Script on How to Transform Training for Technical Talent

January 9th, 2018

A Practical Script on How to Transform Training for Technical Talent

Change is rarely easy. We get stuck in ruts. We fall into patterns, and the learning industry is no different than any other. But we can still create the change our learners need. In part one of this two-part series, I discussed the danger of learning becoming irrelevant and we had the following list:

  1. Too much information is floating around at any given time.
  2. There are multiple sources and ways to get information.
  3. There is a difference between facts/data/information and knowledge.
  4. Knowledge needs to be curated and focused on maximum accessibility and usability.
  5. Knowledge needs to be made readily and easily available in multiple ways.
  6. Knowledge and performance must be connected.

Barriers to Training Transformation

In this part two, let’s take a deeper look at each point to understand how one can transform learning and development by adding a skill to the learning leader’s toolkit: curating and providing, or C&P.

    1. Too much information is floating around at any given time. Trying to find out how to do something can feel like trying to drink from a firehose. The next time you do a Google search for best practices for using a JavaScript tool, take a look at the little number in the left corner that shows the amount of info related to your query.
    2. There are multiple sources and ways to get information. These days if I want to know something or learn to do something I can read it in a long or condensed version, watch it, listen to it, call someone to talk about it, take an actual or virtual course alone or with other people, go to a chat, watch a webinar—and more. If I speak a language other than English, I can find it in my native tongue. There are many roads leading to what people need to know.
    3. There is a difference between facts/data/information and knowledge. We live in the Knowledge Era, which is driven by data; yet data by itself is useless. It takes time to transform data into facts, then facts into information and finally information into know-how.
    4. Knowledge needs to be curated and focused on maximum accessibility and usability. We need human intervention and time to transform data into facts, facts into information, and information into usable knowledge. Even then, there’s often too much knowledge to be useful. Curation can refine knowledge to produce what I call Maximum Value-Added Knowledge (MVAK). Focusing it makes knowledge useful and powerful, like when you concentrate a beam of sunlight with a magnifying glass and use it to start a fire.
    5. Knowledge needs to be made readily and easily available in different ways. There are many ways to present knowledge, and some of them work better than others. If I’m going to be responsible for my own learning, I don’t want someone else telling me how to learn; I want to decide. That is common thinking among developers and software engineers who prefer a hands-on learning approach.
    6. Knowledge and performance must be connected. Some kinds of learning really do need a push. There are times when all employees must get the same knowledge at the same time. This is often true regarding legal, regulatory and compliance knowledge, for HR, safety, etc. It’s also useful when a team of developers is learning something new.

Push training can provide a baseline of common language and definitions for conversations, communication, and collaboration. What is often missing is the ability to determine whether everyone understood the information, that everyone is using the same language, and that people can use what they learned.

To provide that missing piece, training will have to change—and quickly. Learning professionals have a lot of work to do to ensure they will be relevant and effective in the Knowledge Era, and they may need a vendor’s help to get what their learners need.

The Focus on Curating and Providing

The role of C&P will be more important to anticipate what employees might need, and ensure that employees can source knowledge at any time. Knowledge should also be available in multiple formats to accommodate different learning styles and preferences.

C&P skills learning professionals may need to develop or acquire include the ability to:

  • Treat developers as knowledge consumers; listen to what they want to learn
  • Help developers find the unknown—answer to skills gaps they didn’t know they had
  • Parse and curate knowledge to provide maximum value-added knowledge
  • Use push training to easily pivot to meet pull learning needs when self-learners are back at work
  • Ensure that knowledge is always available, accessible and useful. This includes choosing formats and languages, etc.
  • Constantly update knowledge to ensure it’s accurate, timely, relevant
  • Monitor employees to ensure they find, understand and apply knowledge on the job
  • Change or improve information as soon as necessary based on new information, learner feedback, or changes in business and employee needs

The new focus for C&P requires customization and accessibility. Learning partners need to keep an ear to the ground to find out what developers want. Invest the time to determine how busy developers need to learn.

Be creative. Stop depending on the LMS to do all the knowledge transfer work. Create a community of learners in every class, and turn those into ongoing communities of practice. Customize learning as much as possible, don’t forget to add labs, and remember different roles require different knowledge. Forget the one-size-fits-all push training model.

When employees are enabled and empowered to learn what they want and need to know, they’re happier, employee turnover drops, and the organization becomes more successful. C&P may be the next important piece in the learning leader’s role, but the mission to help people learn remains the same.

David Grebow is an author, speaker and workshop leader who, with his co-author Stephen J. Gill, wrote the bestselling “Minds at Work: Managing for Success in the Knowledge Economy.”

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Shifting from a Job Focus to a Performance Focus

January 8th, 2018


L & D professionals are called upon to design, facilitate, and evaluate training programs that are mostly job-specific. Since the document we commonly refer to as a job description clearly favors job-specific KPIs, we devalue non-job performance. Attempts to redress this imbalance have been made in recent years. but these efforts don’t go far enough.

We need to shift from a job focus to a performance focus. Having an accompanying document or a set of bullet points at the bottom of the job description listing several non-job expectations such as ‘be a team player’ is not enough. Consigning these non-job roles to the end of the document implies that the ‘meaty’ part of one’s performance is the set of job tasks we refer to as Key Result Areas (KRAs). But performance is made up of both job- and non-job tasks.

The job description—like the performance review—is a relic of the last century. Yet we still cling to this antiquated HRM tool in the hope that it aids employee performance. But we get frustrated with the job description, don’t we?

We are constantly tinkering with its format and content, hoping to make it more reflective of the work people are supposed to do in the organization. Instead of fiddling with the job description and asking how can we make it be more effective, we ought to be asking a better question: Is it still relevant?

The answer is no, the job description is no longer valid—it’s past it’s used-by-date.  It’s time to assign it to the industrial dustbin and replace it with something else. But what? What can replace the antediluvian job description?

The Role Description

The alternative to the job description is the role description. Is it just another label for the same thing? Is it old wine in new bottles? No.

The role description is significantly different—it reflects a shift from a focus on the job to a focus on performance.  Role descriptions better capture work in the 21st-century workplace.

As its name suggests, the job description is based exclusively on the characteristics of a specific job of work. A job as we know is typically broken down into six to eight job-related tasks, functions, or KRAs. The job description continues to be defined by the technical requirements of a job.

Therefore, it neglects—or at best—gives mere lip service to key non-job competencies, such as being able to work in teams. This means that the work document is incomplete and deficient. Organizational performance now is much more than successfully completing the list of technical requirements of the job. Yet we are so dependent on the job description for most HRM practices.  A more complete performance model that factors in the job and non-job dimensions is long overdue. Why isn’t a comprehensive performance model common practice? I think that the continual concentration on more measurable task-based job requirements is about maintaining a legally defensible performance appraisal system.

Legal or otherwise, the spotlight has been squarely on the performance of job-specific tasks for over a century. However, non-job work has become more and more relevant to organizational performance. But non-job roles are not embedded in the job description to the same extent as the job-related tasks. The conventional job description fails to sufficiently capture what is expected of the incumbent in the non-job dimension of work.

The role description better encapsulates the totality of work performance. Although the job description has evolved over time, it’s still pretty much centered on the job. The document is too hooked on the task-related activity of work. Put simply, the job description is too focused on the job and not enough on the individual doing the job. Some effort has gone into addressing this imbalance of job over non-job roles. Nonetheless, the job description is still too job-centric.

Non-job Roles Framework

Organizational performance—and the contributing performance of employees—is more dependent on the four non-job roles I cover in my book: Performance Management for Agile Organization: Overthrowing the Eight Management Myths That Hold Businesses Back. Yet these non-job roles are not spelled out in detail in the traditional job description.

My crucial non-job framework includes:

  • Positive mental attitude and enthusiasm role;
  • Team role;
  • Skill development role; and
  • Innovation and continuous improvement role.

Non-job Underperformance

If these four non-job roles aren’t being performed by most employees to a high standard, several negative consequences will inevitably emerge in the workplace.

For instance, a widespread lack of enthusiasm and the absence of a positive attitude will adversely affect job satisfaction, attraction, retention, employee engagement, morale, and so on. Or, an organization filled with individuals who are not ‘team players’ results in communication barriers in the form of silos and cross-functional communication breakdowns. Communication that is kaput because of no teamwork means that soon, the product or service quality suffers—customers become unhappy.

We need to rethink work and the work documents that define it. The role description as I define it covers five roles: the job role and the four non-job roles I’ve covered in this article. With this eclectic perspective of work performance, the role L & D professionals play is clearer and more directly related to a broader definition of what it means to perform at work.

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Best Practices in Train-the-Trainer Initiatives

January 8th, 2018

In a world where technology is changing constantly and new apps and tech solutions emerge daily, R&D pros need to be as efficient and effective as possible in ensuring that their staff has the training they need to perform their job duties. That can take a toll on even the most experienced and well-staffed R&D teams.

Couple a department that may already be stretched thin with employees who may bristle when their specialized training is provided by generalists, and it’s clear that creative solutions may be necessary. While not a new solution, train-the-trainer initiatives can prove to both address productivity and efficiency needs as well as the tendency for people to like to be trained by those who are “like them.”

Big Benefits from Train-the-Trainer Initiatives

“Too many R&D people suffer from the Dunning-Kruger effect when it comes to training,” says Mike Schultz, president at RAIN Group and the author of Insight Selling. In the field of psychology, the Dunning-Kruger effect is a cognitive bias wherein people with little experience in the area mistakenly think that succeeding in that area will be easy, says Schultz. R&D people, says Schultz, need to realize that training will most likely succeed when five conditions are met:

  • Goals and objectives are clearly defined in terms of how training will affect skills, behavior, and results.
  • Improvements in the performance environment—such as process, management, and performance support—are developed to support the training effort. “If you provide the training, but leave the structure, process, and tools the same, you may transfer knowledge, but you won’t get behavior change,” says Schultz.
  • Training is blended with strong reinforcement that is well-received by participants and immediately applicable to their jobs. “If the training, or the trainer, is not accepted by the participants, they will not apply it,” Schultz says.
  • Training is translated into the desired behavioral change. This may require coaching, performance support tools, technology, leadership support, etc.
  • The effectiveness of the training effort is measured in terms of its impact on skills, behavior, and results.

These factors can be positively impacted by utilizing a trainer that is part of the work team and who understands the processes, structure and support trainees will require to effectively apply what they learn on the job.

Best Practices

It’s not enough simply to designate an individual or team of individuals to “go out and train the masses.” Effective train-the-trainer initiatives require some key best practices to be optimally effective.

1. Selecting the right people to serve as trainers

Just because an employee is an exceptional developer, for instance, doesn’t make them the right choice to train others. In fact, one significant challenge that those with expertise in a particular topic most often face is a lack of awareness of the basics—the things they take for granted that others know. The best trainers will have the right combination of subject matter expertise and the ability to train and coach others.

According to Stephen Ibaraki, FCIPS, I.S.P., writing for Microsoft’s TechNet blog, some critical skills for trainers include:

  • The ability to establish a rapport with an audience
  • Expertise in the field and thorough understanding of the material
  • Ability to think on their feet
  • Ability to draw from an assortment of examples and analogies
  • Ability to express the content in terms students can relate to

Employees with a background in education are an obvious choice, but simply observing those employees who excel at teaching or showing others how to perform elements of their jobs may help to pinpoint those who might serve as effective trainers.

In considering employees who are likely to serve as effective trainers, consider opening up the opportunity to “apply” to serve in this role as you would with any job opening. Identify the knowledge, skills, and abilities that you seek in trainers and evaluate applicants based on their ability to meet these requirements. Offering employees an opportunity to volunteer in this way serves as a development opportunity for them and helps the R&D department better identify those who may be effective in this role.

2. Providing useful training materials

In any training initiative, it’s important that training content is developed based on specific learning objectives and desired training outcomes. Those learning objectives, then, serve as the framework for developing training materials. Trainers will benefit from being provided with the foundational training materials needed to deliver content to trainees—presentation materials, handouts, exercises, etc.—along with the ability to modify elements of the program to best meet their individual preferences if the key concepts are conveyed consistently across all learners.

Engaging trainers in the development of these materials, particularly if you’ve selected trainers from the group of employees to be trained, as recommended, will help to ensure they are on target for learners’ needs. In addition, seeking feedback from trainers—and trainees—on an ongoing basis will provide insights into how the training, and training materials, could be modified and improved over time.

3. Creating feedback loops

Trainers need support on an ongoing basis and clear channels of communication to allow them to seek information and provide input to continually improve the training process, content and delivery. R&D leaders and staff should make themselves available to receive input and convey an open, transparent and non-defensive posture as they interact with trainers.

Don’t forget that trainers also need training and support to help them serve in a training role. Offer training opportunities and support materials for them, as well as the ability to easily get answers and information from R&D staff.

4. Measuring, monitoring and working together for continuous improvement

Particularly in situations where a group of trainers is training employees in various locations or settings, gathering information on how well the training is received—and to what extent training impacts back-on-the-job performance—can help to identify best practices that can be shared and adopted by others.


Developing a cadre of trainers who can work directly with their peers to boost their knowledge, skills, and competencies in areas of importance to your organization is a win-win-win-win—for employees being trained, employees serving as trainers, the R&D department and the organization. Following the best practice tips presented above can help you ensure that your efforts achieve real results.

About the Author:

Can L&D Become Relevant Again?

January 3rd, 2018

Fair warning: I think the learning and development industry needs to go in another direction to make itself relevant to the organizations of the future.

Imagine the following learning scenarios:

  1. Someone in a position of authority tells you to learn a new technology they think you should know and informs you how you need to learn it.
  2. You realize you need to know something about how to use a new technology for an upcoming project, find the information on your own, and learn it yourself.

One of these two scenarios should be familiar. It starts when you’re born, continues through your college-level schooling and on into the workplace where you attend training programs. It’s often called “push” training because what you need to learn is pushed at you. It’s the default for how we learn almost everything.

In the second scenario, “pull” learning, you find the information—in any form and from any person—whenever you can locate, access and use it. You “pull” the specific knowledge and then move on to the next thing you need to do.

L&D Lives in Push Mode, The Rest of Us Are In Pull Mode

Someone else decides what they think employees should know, then designs, develops, and manages a classroom or online course or program delivery. It’s usually a scheduled event with a sage-on-the-stage. The model is traditional one-size-fits-all training, and sometimes, some effort is made to see if training improved performance.

This is Standard Operating Procedure for L&D. What I call the D4M2 model—Define, Design, Develop, Deliver, Manage and Measure—was developed around 100 years ago to help workers and companies meet Industrial Era employees’ needs. The old approach was often “you should pay attention because you may need to know this someday.” Unfortunately, if “someday” arrived more than 2 or 3 days after the training, everyone’s brains had already forgotten the new information.

When D4M2 was developed, the world wasn’t computerized or mobile. Things didn’t change at today’s frenzied pace. No one even imagined your job description. There was time to learn. That’s no longer true. So, we need to retire the model and metaphorically blow out 100 candles on the birthday cake.

In the pull model, you’re in charge of figuring out what you need to know and how to learn it. You’re a “self-learner.” The pull model relies on your personal motivation to learn—and it allows you to quickly respond to Knowledge Era needs. These needs make a critical difference in knowing how to do what needs to be done and doing it before your competitor does. When it comes to learning with the intent to apply on the job, motivation is not a problem for developers and other technical talent who often view learning as a key piece of their role; the pull model works just fine.

The question is: why continue to use the old push model?

It’s Time for a Change

A critical discussion is going on now about the future of learning professionals and providers, especially those course developers and instructional designers who work with SME’s and instructors who are the actual or virtual “sages on the stages.” These professionals are all wondering about their role in this changing dynamic.

The answer is simple. L&D, learning and development, needs to become C&P, curating and providing. Here’s how it works. Instead of all the D4M2 that goes into a training program, C&P understands the following six facts about self-learning:

  1. Too much information is floating around at any given time.
  2. There are multiple sources and ways to get information.
  3. There is a difference between facts/data/information and knowledge.
  4. Knowledge needs to be curated and focused on maximum accessibility and usability. Training programs should keep that in mind. Custom would be best.
  5. Knowledge needs to be made readily and easily available in multiple ways that suit talent needs. For instance, technical talent prefer classroom instruction with a strong lab component.
  6. Knowledge and performance must be connected and measured.

In the next article in this two-part series, I dig into each of the aforementioned barriers much deeper to illustrate what learning leaders must do to transform training.

David Grebow is an author, speaker and workshop leader who, with his co-author Stephen J. Gill, wrote the bestselling “Minds at Work: Managing for Success in the Knowledge Economy.”

About the Author:

Rethinking the Carrot and Stick

January 2nd, 2018

Stanford University researcher, Mark Lepper, and his team conducted a significant research study in the early 1970s, concerned with the impact of extrinsic rewards on performance.  Specifically, Lepper was interested in whether prizes influence behavior in young children.

A brand-new activity was introduced to the children at a nursery. The teachers issued the children with creamy white artist’s drawing paper and brand-new marker pens; the children were given time to draw with these novel materials. They had never done drawings with marker pens before. Predictably, the children took to the activity with relish. But after exactly one hour, the materials were whisked away to the disappointment of the children.

Several days later, one of the researchers returned to the class and randomly divided the class into two groups to continue the new drawing activity. One group of children were taken to another room. They were given the opportunity to continue their drawings, just as they had done before. After an hour, the researcher thanked the children in this group and took away the art material and their drawings.

The second group of children was offered a prize for drawing their pictures. It was explained to this group that some special prizes would be given to the children who draw good pictures. The children took to their task, anticipating they might receive a prize for their picture. This control group was given the same amount of time (one hour) as the other group to complete their artwork.  At the end of the session, the researcher thanked the children as he’d done with the other group. But this time, he handed out a prize to each child in the control group.

One week later the researchers returned to the classroom. The afternoon period consisted of ‘free time;’ the children could choose what they wanted to do with their time. The special paper and marker pens were placed on the tables and easily accessible for the children. However, the children had other options too. They could go outside and run around on the playground. They could play with the toys in the classroom. Or they could return to the drawing activity. The researchers observed the time the children spent on their chosen activities. To what extent would the prizes given to the children in the control group affect their choices and behavior? The researchers assumed that the children in the control group, who had received prizes, would spend more time on the drawing activity.

But that didn’t happen!

The result was one the researchers didn’t foresee. Their findings challenged conventional wisdom about parenting and education. The children who received the extrinsic rewards for their artwork chose to spend less time drawing than those who weren’t rewarded. Conversely, the children who didn’t receive a prize chose to spend more of their discretionary time on the drawing activity. The children who were rewarded seemed reluctant to continue with the activity without the promise of a further reward. The initial reward paradoxically reduced the children’s motivation rather than increased it.

But what was even more surprising is this: The artwork of all the children was evaluated by a group of independent judges with no knowledge of the experiment. The result was that the pictures drawn by the children who were rewarded were evaluated as less competent than the pictures drawn by the unrewarded group.

So, in summary: The children who received an extrinsic reward spent less time drawing when given a choice and when they were rewarded, they put in less effort too.[1]

Extrinsic rewards are limiting in promoting higher levels of performance. In some cases, they’re even demotivating. Most people want more from their work than promises of a bonus. Work can offer more than a source of subsistence. For many, it is a vehicle for personal growth, wellbeing, cultivating a sense of belonging, and fulfilling purpose and direction in one’s life.

When incentives are used to improve performance, it can unintendedly take the employee’s attention off the work the reward is designed to enhance. The promise of a bonus shifts the employee’s focus from the task to the prize.  The work, in other words, becomes the means to the outcome—a reward. Extrinsic rewards can reduce—not increase—performance! With a bonus top-of-mind, it’s common for the employee to cut corners, do whatever it takes, or even cheat, to get their hands on the prize. As well intended as extrinsic rewards are—and as effective as they can sometimes be—they can back-fire too.

Using monetary incentives to induce greater performance is, however, part of the DNA of the work-setting. Workers were once viewed—and perhaps still are in many ways—as small cogs in a large factory machine.  Bonus pay is still used to entice workers to perform their work in a prescribed way, to a time-limit. Cogs in a factory machine perform a relatively narrow range of activity. Work conformity was—and still is—the name of the game. The carrot and stick are the levers to reinforce orthodox work practices. We have continued these motivational strategies for the past century, believing it was the answer to extracting higher performance. We still endeavor to motivate employees with a suite of inducements and apply sanctions when predetermined behavior is not met. Work has transformed. But the way we kindle performance hasn’t fundamentally changed.

[1] Yeung, R. (2011). I is for influence: The new science of persuasion. London: Macmillan.