AI And The Secret To Employee Happiness

When I started working as a mainframe operator in IT in 1988, I felt like I was part of a secret club. None of my family understood what I was doing; my friends would ask, “what’s a mainframe and why do you have to work nights?”

My onboarding took months, and a typical workday began with staring at a blank screen. Since mainframes didn’t come with a mouse, I would enter memorized commands like “=3.4” and “Sys3.AF*” to navigate the data sets I needed to find.I don’t think many workers today would put up with that.

Any manager who has tried to hire an employee today will agree the war for talent is real. Job perks like free lunches and on-site laundry just don’t cut it anymore. To recruit talent today, there’s really one thing that every enterprise needs to do: Make work better.

Make work easy

I’ve found that companies invest in digital transformation for three reasons: To work faster, to work more efficiently, and to change or expand their business models. But the end result of any digital transformation should be a better experience, and leaders often neglect the everyday experience of the workers who actually achieve these goals.

Consider this. Outside of work, most people have grown used to finding a new home, getting pet care, and organizing travel all with just a swipe of their finger on the touchscreen. They expect the same level of ease when it comes to the technologies they use at work. It’s no coincidence that the latest release of the Now Platform invested so heavily in improving user experience.

Sure, the interface looks beautiful. But the experience goes deeper than the surface by making the usage more intuitive. Good user experience is about simplifying and hiding complexity so that using it comes naturally to anyone. Make work easy.

Flex on flexibility

Many workplaces have returned to on-site or hybrid work, but I don’t think we’ll bring back the rigid workday schedule. The last two years have taught us that, while face-to-face and real-time interactions are invaluable, many other tasks can be done just as well, if not better, asynchronously.

Yes, it wasn’t fun to work from a makeshift standing desk in the kitchen while keeping one eye on a freakishly fast toddler. It’s no wonder why some employees have eagerly returned to the ergonomic office stocked with free snacks. But some of us love attending a meeting without sitting in traffic, having lunch without navigating a packed cafeteria, or taking a two-hour afternoon break to compensate for that evening call with Tokyo. You have to accommodate both types—and everyone in between.

Leaders learned the hard way in 2020 that you can’t just flip a switch and change the way a business is run. You have to stay ready with workplace technology that can support various—and changing—work models.

Flexibility, supported with a solid digital foundation, is no longer a choice. Clearly communicate what your employees need to deliver and let them decide where, when, and how. Or you can try to force a rigid work model and watch your talent flock to another employer.

AI and human intelligence aren’t mutually exclusive. They work best when they work together.

Automate the mundane

Automation has freed employees from many repetitive tasks, making work more fulfilling and creative. The digitization of work can go a step further by tapping artificial intelligence that effectively sorts through massive amounts of data and makes prescriptive recommendations. AI can even be used to make it easier for employees to be promoted internally—a huge factor in retaining and rewarding your workforce.

There’s a misconception that AI is designed to replace human workers. But for me, artificial intelligence is actually about the interface between people and machines, making lives more interesting by automating the mundane, removing friction, and presenting the right information and insights.

Better together

Knowledge workers thrive when they can harness technology to make more effective decisions. These decisions aren’t only reactive but also proactive—something that AI enables through its predictive power, which can anticipate and adjust to a world full of constantly changing variables.

When it comes to digital transformation, we think of how it impacts the bottom line by improving speed and efficiency. But how do we improve speed and efficiency? By empowering our talent with the delightful and intuitive experiences they deserve.

AI and human intelligence aren’t mutually exclusive. They work best when they work together.

Dave Wright is ServiceNow’s chief innovation officer and acts as an evangelist for how to improve workplace productivity. He has worked with thousands of

Source: AI And The Secret To Employee Happiness

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More contents:

What is a Digital Transformation Strategy? »

What is a Digital Business? »

How to make a Digital Transformation Strategy? »

Digital Transformation Strategy in 2020 »

Where to Start with a Digital Transformation Strategy »

Who should be involved in creating a Digital Transformation Strategy? »

What happens to businesses that don’t have a Digital Transformation Strategy? »

What are the top 5 Digital Transformation Strategy Frameworks?

How do I measure if my Digital Transformation Strategy is working? »

What is Digital Transformation?

The Difference Between Digitization and Digital Transformation

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How to Turn Your Company’s Purpose Into Action

The best advice is easy to understand, but difficult to execute, according to Marshall Goldsmith, executive coach and author of Triggers, Mojo, and What Got You Here Won’t Get You There.

In a virtual keynote address to Inc. 5000 honorees this week, Goldsmith explained that while coaching leaders at companies such as Ford, Pfizer, and the Mayo Clinic, he learned that it’s easy to dismiss the simplest of leadership strategies because they sound too easy. But it’s often the simple strategies that make the biggest difference for founders because they’re easier to commit to long-term.

“You’re a CEO, you’re a very busy person, you don’t have a lot of time. If I gave you stuff that sucks up too much of your time, you’re not gonna do it anyway,” Goldsmith says, adding that this tried-and-true method is still one worth teaching today because of its proven success.

Here, Goldsmith shares a simple method to becoming a more effective leader.

1. Get in the habit of asking for input.

Goldsmith argues that leaders don’t ask one simple question enough: How can I be better? Leaders should get in the habit of asking how they can be a better manager, team player, and salesperson. Many times, your employees and peers will point things out to you that aren’t even on your radar.

Something he learned from management consultant Peter Drucker stood out to Goldsmith when it comes to asking for feedback. “He said, ‘The leader of the past will have to [explain] to leaders of the future when they ask why we manage knowledge workers when they know more than we do,” Goldsmith says. In other words, never stop learning from your employees and peers.

Read More:

2. Listen to the input–don’t debate it.

Once you ask for input, Goldsmith says to fight every urge to give your opinion and to instead listen intently. Whatever feedback you get, take notes, say thank you, don’t judge, and don’t make too many promises. Instead, Goldsmith suggests you say, “I’m going to involve you and the others involved and follow up with you.”

One important thing for leaders to keep in mind is that leadership is not a popularity contest, and therefore you shouldn’t feel obligated to satisfy everyone. “You never promised as a leader to do everything people suggest,” Goldsmith says. “You promised to ask and listen.

3. Follow up.

This is where you act on what you promised. The key to making change, according to Goldsmith, is that you have to follow up and stick with it.

“You don’t get better when you listen to a speech. You don’t get better because you read a book,” he says. “You have to work at it, follow up and stick with it.”

By: Teresa Xie, Editorial intern, Inc.@resate_z

Source: How to Turn Your Company’s Purpose Into Action | Inc.com

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3 Issues To Consider Before You Introduce Recurring Revenue Streams Into Your Business

All business owners understand and appreciate the importance of revenue to the success of their businesses. At the outset, revenue is critical to the ability of a business to pay its expenses and satisfy any payroll obligations. Investors will examine the history of revenue of a business as a benchmark to evaluate the future profitability and potential growth of the company. Revenue is also an important criteria that lenders use when assessing whether to extend credit to a business  the lifeblood of every business.

With revenue being so important to the success of a business, it is often a surprise how little time most business owners spend on exploring how their businesses can meet  if not exceed  their revenue-generating potential.

The reality is most business owners are so focused on the day-to-day realities of running their businesses that they simply do not have the time to consider if their businesses are generating as much income as they should or if there are other opportunities to increase revenue-generating potential.

Related: 17 Passive Income Ideas for Increasing Your Cash Flow

What is a recurring revenue stream?

A recurring revenue stream is simply a way of conducting business that results in customers paying the business on a regular basis in exchange for some value. This value can either be the right to receive goods or services from a business or the right to access or use the property of the business for a given time.

This is very different from non-recurring revenue-generation business models, such as the sale of a product or the provision of a service, where a business has no expectation that a current customer will be a customer in the future. Recurring revenue streams enable business owners to better predict how much revenue their businesses will generate in the future. Savvy business owners use these recurring revenue streams to attract investors, obtain credit and grow their companies.

It is no wonder that the foundation of many successful modern businesses today often relies on recurring revenue streams.

Related: Why You Should Use a Subscription Business Model

What are some examples of recurring revenue streams?

You may be intimidated by the idea of a recurring revenue stream. You have no reason to be: Recurring-revenue business models are all around us. Here are three common examples of recurring revenue streams that you may be familiar with and ought to consider implementing in your business.

  1. Renting or leasing. If you have ever leased a car or rented a home, you are familiar with this business model. Leasing is a form of generating revenue where a business collects money from a customer in exchange for giving a customer the right to use a physical asset for a specified time.
  2. Licensing. Do you pay for any online services? Do you use any form of social media? Your relationship with those online services is often governed by a license agreement, which sets out terms for how intellectual property of one party can be used by the other. If one party is required to pay for the rights to use the intellectual property of the other party, those payments are often calculated based on how often that customer uses that intellectual property or on the amount of money the customer generates using the intellectual property of the business.
  3. Subscription. This is the model you are most likely familiar with. Whether it be your account to the latest video-streaming platform, your fresh coffee subscription or even your subscription to a pizza service, subscription-based business models are everywhere. The success of most subscription-based business models relies on providing ongoing value to customers in exchange for recurring payments for as long as possible.

What to ask before integrating a recurring revenue stream

While introducing a new revenue stream for your business is certainly attractive, recognize that not every recurring-revenue business model is the same. The reality is that each type of recurring revenue stream needs to be tailored to the capabilities of each business and the needs of each customer. Here are some questions to ask when considering the opportunities to integrate a recurring revenue stream into your business:

What value from your business are your customers willing to pay for on a regular basis? What price will customers pay for that value on a regular basis? What changes in your business operations need to happen to make these revenue streams a reality?

Related: 3 Simple But Effective Strategies to Create Consistent Income Online

Don’t go it alone

While I hope this article illustrates some of the benefits of integrating recurring revenue streams into your business, I must emphasize that this is simply an introduction to the concept. Don’t underestimate the amount of time, money and energy that may be required to create a new revenue stream for your business.

I would encourage you to find lawyers, accountants and other advisors to guide both your assessment of the suitability of a recurring-revenue business model for your business and the implementation of your strategic decisions. After all, a little time and energy invested in preparation often pays dividends in the long term.

Romesh Hettiarachchi

By: Romesh Hettiarachchi  – Entrepreneur Leadership Network Contributor

Source: 3 Issues to Consider Before You Introduce Recurring Revenue Streams Into Your Business

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Teaching Diversity And Inclusion To The Billions Of Intelligent Systems Making Autonomous Decisions

Engineers Meeting in Robotic Research Laboratory

The idea that diversity and inclusion should be core drivers of the new economy and the emerging global society are mostly understood at a human level. The more people who are part of one system, being offered the same opportunities regardless of their gender, race, ethnic origin, and many other diverse variables, the higher the tide rises for everybody. But even with that intrinsic understanding of the idea that diversity and inclusion will generate a different and better world, significant barriers still exist to making this human truth a practical reality in our daily lives.

The power of all genders, all races, and all languages can change the world. Even each of these pieces, though, has its blind spots if taken as a standalone viewpoint — in effect, by seeing the world through a single lens, that lens can act as a deep barrier. Imagine how much is lost with only a single way of seeing, thinking, learning, and maybe even applying those learnings.

Digital companies talk about the power of the individual or the customer to be the center of the service. Yet how can we build around individuals without recognizing and servicing the unique combinations of needs or opinions that diverse thinking and actions entail? McKinsey has, since 2014, leaned into the idea of measuring diversity and inclusivity as a driver of business value creation. The intent is to show every year that companies that live and deliver diverse and inclusive strategies outperform their industry peers.

The gap (between diverse and inclusive leaders and the poorest performers) has gotten bigger year by year, growing from 33% percent in 2018 to 36% in 2019. Even with clear and longitudinal data, we still struggle against many inherent biases to accept and act on the fact that diversity and inclusion widen the lens for viewing ideas, thinking, processes, and customers in an increasingly global market.

The world will get more diverse over time. By 2044 it is projected that over half of Americans will belong to a minority group. We will in effect be a collection of diversities, with one in five of us not being born in the USA but living here. Multiply this American future by the nuances of each of the 195 countries in the world and together we will be the largest collection of diversities the planet has ever seen.

Now imagine a world of not just 7 billion people, but 40 billion devices computing, connecting, sensing, predicting, and running autonomously in an intelligent systems world. PwC estimated that 70% of all global GDP growth between 2020 and 2030 will come from this machine economy (AI, robotics, IoT devices).

U.S. GDP is expected to grow $10T between now and 2030. If 70% of that is from these machines sensing, predicting, computing, and connecting on the intelligent edge, then that is a $7T economy. Will these machines be more capable than humankind has been to think about diversity and inclusion in the way they work with data, humans, and other machines?

These devices don’t have a McKinsey to explain to them where and how inclusion and diversity will drive a better result. They make decisions in milliseconds based on the programming instructions they receive, and they learn as they execute their many, often complex and intelligent, tasks.

How these machines learn to think (constantly) are driven by rules set by humans and by other machines that were in part or wholly programmed by humans. How can the right behaviors be instilled in these intelligent systems? Think of two basic dynamics we must pay attention to in an increasingly intelligent systems world:

Human experiences drive diversity and inclusive design

Learning — and applying — how to be aware of the needs of diverse groups has more value than ever before. This acquired knowledge will act as the codex for how we program the devices that live and work with us globally by 2030 and beyond. There is a narrow time window in which to take our own personal experiences and the experience of others around us into account in the design and programming process for intelligent systems that will manage autonomous vehicles, medical devices, and manufacturing environments where cobots will be working alongside humans.

All machines might look and behave in the same way, but the humans around them do not, so what machine biases will exist in the intelligent systems world? Understanding how to design and program for inclusive and diverse thinking without bias means intelligent systems need to have a progressive learning ability (e.g., machine learning and digital feedback loops), as well as mission-critical capacities that mean they can safely and securely function around humans who may look, sound, move, or think differently from those whom the machines have been designed or operated around.

Machines will be diverse too and will need to be inclusive of each other

Once we live in an intelligent systems world, we will need intelligent systems to recognize each other in near instant time. These systems might be doing completely different tasks, but they might need to share data, space, or compute capacity in milliseconds. Knowing when, where, and how to have that network effect in an intelligent systems world (for example, consider autonomous vehicles) requires a capacity for inclusiveness and maybe even a clear comprehension about the power of diverse data sets from different devices to create value far greater than the sum of all parts.

Nurturing that capacity to create systems for a diversity of design and operations, as well as for an inclusiveness to allow constant learning, is a challenge that will be essential in an intelligent systems world.

We will not be able to make the right world for these intelligent systems and all that they can bring to humanity if we do not design, operate, and build them to be inclusive, diverse, and without bias in how they operate. While not suggesting that there should be a soul to an intelligent system, we should recognize that the moment in our own human world to encourage as much diversity and inclusion in our thinking is right now, and how well we do it will have major consequences for how we teach our intelligent systems to thrive in a world dominated with a diversity of machines and humans.

PRESIDENT AND CEO

With more than 25 years of experience driving digital innovation and growth at technology companies, Kevin Dallas is responsible for all aspects of the Wind River business globally. He joined Wind River from Microsoft, where he most recently served as the corporate vice president for cloud and AI business development. At Microsoft, he led a team creating partnerships that enable the digital transformation of customers and partners across a range of industries including: connected/autonomous vehicles, industrial IoT, discrete manufacturing, retail, financial services, media and entertainment, and healthcare.

Prior to joining Microsoft in 1996, he held roles at NVIDIA Corporation and National Semiconductor (now Texas Instruments Inc.) in the U.S., Europe, and the Middle East in roles that included microprocessor design, systems engineering, product management, and end-to-end business leadership. He currently serves as a director on the board of Align Technology, Inc. He holds a B.S.c. degree in electrical and electronic engineering from Staffordshire University, Stoke-on-Trent, Staffordshire, England.

Source: Teaching Diversity And Inclusion To The Billions Of Intelligent Systems Making Autonomous Decisions

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Mental Models: How to Train Your Brain to Think in New Ways

You can train your brain to think better. One of the best ways to do this is to expand the set of mental models you use to think. Let me explain what I mean by sharing a story about a world-class thinker.

I first discovered what a mental model was and how useful the right one could be while I was reading a story about Richard Feynman, the famous physicist. Feynman received his undergraduate degree from MIT and his Ph.D. from Princeton. During that time, he developed a reputation for waltzing into the math department and solving problems that the brilliant Ph.D. students couldn’t solve.

When people asked how he did it, Feynman claimed that his secret weapon was not his intelligence, but rather a strategy he learned in high school. According to Feynman, his high school physics teacher asked him to stay after class one day and gave him a challenge.

“Feynman,” the teacher said, “you talk too much and you make too much noise. I know why. You’re bored. So I’m going to give you a book. You go up there in the back, in the corner, and study this book, and when you know everything that’s in this book, you can talk again.” 1

So each day, Feynman would hide in the back of the classroom and study the book—Advanced Calculus by Woods—while the rest of the class continued with their regular lessons. And it was while studying this old calculus textbook that Feynman began to develop his own set of mental models.

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What users *think* they know about your system will determine how they interact with the design. Understand users’ mental models to design something that’ll work well in practice. #UX #mentalmodels #userpsychology #HCI

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“That book showed how to differentiate parameters under the integral sign,” Feynman wrote. “It turns out that’s not taught very much in the universities; they don’t emphasize it. But I caught on how to use that method, and I used that one damn tool again and again. So because I was self-taught using that book, I had peculiar methods of doing integrals.”

“The result was, when the guys at MIT or Princeton had trouble doing a certain integral, it was because they couldn’t do it with the standard methods they had learned in school. If it was a contour integration, they would have found it; if it was a simple series expansion, they would have found it. Then I come along and try differentiating under the integral sign, and often it worked. So I got a great reputation for doing integrals, only because my box of tools was different from everybody else’s, and they had tried all their tools on it before giving the problem to me.” 2

Every Ph.D. student at Princeton and MIT is brilliant. What separated Feynman from his peers wasn’t necessarily raw intelligence. It was the way he saw the problem. He had a broader set of mental models.

What is a Mental Model?

A mental model is an explanation of how something works. It is a concept, framework, or worldview that you carry around in your mind to help you interpret the world and understand the relationship between things. Mental models are deeply held beliefs about how the world works.

For example, supply and demand is a mental model that helps you understand how the economy works. Game theory is a mental model that helps you understand how relationships and trust work. Entropy is a mental model that helps you understand how disorder and decay work.

Mental models guide your perception and behavior. They are the thinking tools that you use to understand life, make decisions, and solve problems. Learning a new mental model gives you a new way to see the world—like Richard Feynman learning a new math technique.

Mental models are imperfect, but useful. There is no single mental model from physics or engineering, for example, that provides a flawless explanation of the entire universe, but the best mental models from those disciplines have allowed us to build bridges and roads, develop new technologies, and even travel to outer space. As historian Yuval Noah Harari puts it, “Scientists generally agree that no theory is 100 percent correct. Thus, the real test of knowledge is not truth, but utility.”

The best mental models are the ideas with the most utility. They are broadly useful in daily life. Understanding these concepts will help you make wiser choices and take better actions. This is why developing a broad base of mental models is critical for anyone interested in thinking clearly, rationally, and effectively.

The Secret to Great Thinking and Decision Making

Expanding your set of mental models is something experts need to work on just as much as novices. We all have our favorite mental models, the ones we naturally default to as an explanation for how or why something happened. As you grow older and develop expertise in a certain area, you tend to favor the mental models that are most familiar to you.

Here’s the problem: when a certain worldview dominates your thinking, you’ll try to explain every problem you face through that worldview. This pitfall is particularly easy to slip into when you’re smart or talented in a given area.

The more you master a single mental model, the more likely it becomes that this mental model will be your downfall because you’ll start applying it indiscriminately to every problem. What looks like expertise is often a limitation. As the common proverb says, “If all you have is a hammer, everything looks like a nail.” 3

When a certain worldview dominates your thinking, you’ll try to explain every problem you face through that worldview.

Consider this example from biologist Robert Sapolsky. He asks, “Why did the chicken cross the road?” Then, he provides answers from different experts.

  • If you ask an evolutionary biologist, they might say, “The chicken crossed the road because they saw a potential mate on the other side.”
  • If you ask a kinesiologist, they might say, “The chicken crossed the road because the muscles in the leg contracted and pulled the leg bone forward during each step.”
  • If you ask a neuroscientist, they might say, “The chicken crossed the road because the neurons in the chicken’s brain fired and triggered the movement.”

Technically speaking, none of these experts are wrong. But nobody is seeing the entire picture either. Each individual mental model is just one view of reality. The challenges and situations we face in life cannot be entirely explained by one field or industry.

All perspectives hold some truth. None of them contain the complete truth.

Relying on a narrow set of thinking tools is like wearing a mental straitjacket. Your cognitive range of motion is limited. When your set of mental models is limited, so is your potential for finding a solution. In order to unleash your full potential, you have to collect a range of mental models. You have to build out your decision making toolbox. Thus, the secret to great thinking is to learn and employ a variety of mental models.

Expanding Your Set of Mental Models

The process of accumulating mental models is somewhat like improving your vision. Each eye can see something on its own. But if you cover one of them, you lose part of the scene. It’s impossible to see the full picture when you’re only looking through one eye.

Similarly, mental models provide an internal picture of how the world works. We should continuously upgrade and improve the quality of this picture. This means reading widely from the best books, studying the fundamentals of seemingly unrelated fields, and learning from people with wildly different life experiences. 4

The mind’s eye needs a variety of mental models to piece together a complete picture of how the world works. The more sources you have to draw upon, the clearer your thinking becomes. As the philosopher Alain de Botton notes, “The chief enemy of good decisions is a lack of sufficient perspectives on a problem.”

The Pursuit of Liquid Knowledge

In school, we tend to separate knowledge into different silos—biology, economics, history, physics, philosophy. In the real world, information is rarely divided into neatly defined categories. In the words of Charlie Munger, “All the wisdom of the world is not to be found in one little academic department.” 5

World-class thinkers are often silo-free thinkers. They avoid looking at life through the lens of one subject. Instead, they develop “liquid knowledge” that flows easily from one topic to the next.

This is why it is important to not only learn new mental models, but to consider how they connect with one another. Creativity and innovation often arise at the intersection of ideas. By spotting the links between various mental models, you can identify solutions that most people overlook.

Tools for Thinking Better

Here’s the good news:

You don’t need to master every detail of every subject to become a world-class thinker. Of all the mental models humankind has generated throughout history, there are just a few dozen that you need to learn to have a firm grasp of how the world works.

Many of the most important mental models are the big ideas from disciplines like biology, chemistry, physics, economics, mathematics, psychology, philosophy. Each field has a few mental models that form the backbone of the topic. For example, some of the pillar mental models from economics include ideas like Incentives, Scarcity, and Economies of Scale.

If you can master the fundamentals of each discipline, then you can develop a remarkably accurate and useful picture of life. To quote Charlie Munger again, “80 or 90 important models will carry about 90 percent of the freight in making you a worldly-wise person. And, of those, only a mere handful really carry very heavy freight.” 6

I’ve made it a personal mission to uncover the big models that carry the heavy freight in life. After researching more than 1,000 different mental models, I gradually narrowed it down to a few dozen that matter most. I’ve written about some of them previously, like entropy and inversion, and I’ll be covering more of them in the future. If you’re interested, you can browse my slowly expanding list of mental models.

My hope is to create a list of the most important mental models from a wide range of disciplines and explain them in a way that is not only easy to understand, but also meaningful and practical to the daily life of the average person. With any luck, we can all learn how to think just a little bit better.

James Clear

 

By: James Clear

 

Source: Mental Models: How to Train Your Brain to Think in New Ways

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