Kevin Benedict Ponders the Future

Digital Technology and the Greater Good

Adam Smith wrote about the concept of rational self-interest, which posits we work together for the greater good when it benefits ourselves. Does this concept have relevance in the context of robots, automation and employment? I think it does.

I believe most of us would agree that replacing large numbers of humans with machines that result in wide scale unemployment and suffering is not in our rational self-interest. Having massive numbers of jobs terminated by the Terminator does not result in a safer, healthier civilization or vibrant economy; therefore, it is not in our best interest.

Just because something is possible, does not mean it is good. A powerful king that takes all the food, property and means of production away from his people resulting in their suffering, quickly becomes a target of their wrath, and on a quick path to poverty.

Businesses that replace human workers with machines and software, out of self-interest, will over time find it increasingly difficult to sell their products to their unemployed or underemployed consumers. At what point do businesses seek to expand employment opportunities out of a rational self-interest rather than decrease them through automation? Is it a realistic option for profit maximizing businesses to seek the greater good?

In the short-term, factories hope to benefit from automation faster than their competition in order to gain advantages, while there are still sufficient numbers of consumers employed elsewhere to provide a market for their goods. In the mid-term, entire industries will automate and terminate large numbers of jobs, but hope other, slower-to-automate industries will employ their consumer base. In the long-term, however, when digital transformation has swept through all industries, who is left to employ the consumers and provide them living wages, and who is left with capital to buy goods?

As jobs that require little training or education diminish in numbers, we have two choices, 1) Increase education levels to equip our population for the digital future, or 2) subsidize the unemployed and underemployed with a sufficient income to survive and maintain their dignity. If there are still not enough jobs for those that work hard to increase their level of education, then we are reduced to one choice.

There are plenty of problems left on this planet to be solved. Solving these problems could employ many. Today, however, not all of these problems have economic values assigned to them. Fresh water sources, clean air, forestation, peace, better health, better education, etc., all of these have the potential to generate enormous economic benefits, but they need society to place a value on them and reward innovations and employment in these areas.

A vibrant economy, and a safe and secure society depend on healthy employment numbers, adequate wages, property ownership and rights, hope, peace and purpose. Digital transformation must foster these goals, or risks accelerating a break down in our society and economy – two things that can dampen a New Year’s celebration.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant

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***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.

New Report: 40 Months of Hyper-Digital Transformation

Forty months is not a lot of time to design, develop and deliver something monumental. Consider that it took 182 years to build the Notre Dame Cathedral in Paris, 20 years to build the Great Pyramid of Giza and 10 years to build the Panama Canal. Executives from digital-leading companies, however, tell us that in just over three years – the year 2020 – 17 different digital technologies will dramatically impact the way they work, and transform the work that gets done, so we don't have much time.

This means that within the next 40 months the exponential growth of digitization and machine learning will fundamentally change how businesses create value, satisfy customers and outperform competitors. This also means that in this same time period, companies must take actions that position them for the next level of success. If they don’t embrace digital, for many it will be game over.

To better understand the strategies and technologies that digital transformation winners require, Cognizant’s Center for the Future of Work, in partnership with the renowned economist Nouriel Roubini, surveyed 2,000 executives, 500 managers, 150 MBA students and 50 futurists across 18 countries. What we found is that today’s and tomorrow’s winners will need to think differently, follow different strategies, deploy different technologies and make cybersecurity a top priority – all while realizing that time is slipping away.

Guiding a business through the turbulence and friction of digital transformation is perhaps the great challenge of our time. Just as John Ellis’s invention of high-temperature oils unleashed the power of railroads, industrialization and economic growth in the 19th century, data is the lubricant of today’s digital age. More to the point, today’s hyper-digital transformation will be driven by optimized information logistics systems (OILS) and IT infrastructures capable of facilitating real-time digital interactions.

Our research revealed several findings that we detail in the report:

Digital leaders think and act differently.
Digital leaders recognize that digital transformation is the key to their future relevance and success, and they act as though their lives depend on getting transformation right.
Recognition alone is not enough, though. Digital leaders also execute the right actions and technologies in the right sequences to successfully navigate resource constraints.
Digital technologies are the keys that unlock a golden door.

The 2,000-plus executives we surveyed have already generated $328.7 billion in total revenue through the strategic deployment of digital technologies in the last year. Respondents believe there’s much more to be had, though: We found businesses could unlock another $151.6 billion in value if they take full advantage of the digital opportunities at hand. Download and read the full report here https://www.cognizant.com/FoW/twa-hyper-digital-transformation-codex2478.pdf.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin'sYouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

Join the Google+ Community Mobile Enterprise Strategies

Virtual Reality Moves to Real with Sensors and Digital Transformation

I was on a high-rise construction site 34-floors above the city. I was talking to the construction crew when a fight broke out. There was an explosion and the floor collapsed. I removed the virtual reality (VR) goggles and laughed. It was so real. The VR solutions provided an incredible experience, almost like being there. As good as my experience was, it was not reality. It was a controlled pre-programmed experience - a notional idea. Today, however, VR, sensors and sensory feedback technologies enable a notional idea to become reality – a Real-Reality.

IoT sensors extend our physical senses beyond our physical reach. Haptic feedback systems enable us to physically feel distant objects and experience events, digital odors can be collected, profiled, transmitted and recreated locally on odor printers, 3D infrared scanners can capture and scale physical shapes and environments and transmit them anywhere to be used by 3D printers or in digital scenes. We can visualize, sense, and mentally and emotionally experience events. These experiences, which are data centric and data heavy, go far beyond virtual reality, to Real-Reality (RR).

Wikipedia defines reality as, ”The state of things as they actually exist, rather than as they may appear or might be imagined.” With enough sensors collecting data, and with the right VR solutions and sensory feedback systems in place, we can step across the line from virtual to real reality. These technologies enable us to sense, feel, and experience actual events - potentially on the other side of the world. This is a big deal. This can enable experts to be anywhere on the planet, with maximum understanding of local situations.

Although many of these technologies are still in their infancy, they hint of a time very soon when the ability to bring distant realities into homes, industry and boardrooms are not only possible, but commonplace. The keys to making this future a reality, are measuring and collecting the right data, transmitting it, and optimizing the sensory experience.

How do we understand distant situations, activities, operations and events today? In many cases a human tries to capture and recreate the situation in words, and then forwards as an email or message. There are obvious limitations to this means of communication when fast moving and complex situations are involved. RR solutions with the required level of digital transformation implemented, including real-time sensors and sensory feedback systems, can communicate these experiences directly to our minds and bodies from anywhere in the world in real-time, thus bypassing the limitations of intermediary humans and written languages.

RR enables experts from anywhere in the world to conduct medical surgeries, repair specialized equipment, manage factories, farms and other kinds of operations from distant locations. The obstacles of the past that resulted in operational blind spots and incomplete information are quickly going away as RR becomes a reality - a real-reality.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin's YouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

Join the Google+ Community Mobile Enterprise Strategies

How Good is Your Mind at Predicting?

My friend, Peter Rogers, who lives in the UK was wrong at predicting Brexit, but right at predicting Donald Trump would win. How did he get one wrong and the other right? Read about his experiences here.

Guest Blogger - Peter Rogers

Peter Rogers Predicted Donald Trump

I always thought I was particularly good at prediction as a result of me working as a technologist most of my life, but my world was turned upside down after Brexit. It took a long time for me to work out why I got Brexit so wrong, but eventually I brushed myself off and started to read a lot of material on Super-Forecasters.

It learned I had been misleading myself for many years. I thought I was good at non-technical decision-making. I recall looking at the Ladbrokes Swingometer for Brexit and being so sure of a "remain" vote, that I was going to place a large bet. I was however, wrong. I made the classic mistake of polluting the decision-maker-mindset.

In order to forecast accurately I needed to consider a wide range of diverse opinions without being overly drawn to any one particular source. This of course, is where social media makes fools of us all. We are typically drawn to a small group of close friends for inspiration, and these friends typically share our opinions. People rarely fact check on social media. We also read newspapers, which have an increasingly political bias, and a high percentage of us fail to fact check.

I decided if I was going to truly escape from newspapers and social media bias, then I was going to have to train myself to be able to forecast independently. As a first step, I built a website that enabled me to place forecasts and to track whether I was right or wrong. I added a scoring system so there was feedback for my predictions. This was important as most people don't keep track of their predictions and the results.

Every day I made forecasts on politics, sports, weather, finances, entertainment, and just about anything else I could think of. I thought anybody can make correct guesses in their own field of expertise, but how many people can make correct predictions outside of it? Even that prediction was wrong! In fact, it turns out that Subject Matter Experts (SMEs) are bad forecasters in their own field!

I learned there are two parts to being a good forecaster:

A good gut feel
Being able to show your "thought process." Show how you worked through an "Outside Model" that is refined by an "Inside Model."

I started out remarkably bad at forecasting. I soon learned to differentiate between the things I wasn’t so sure of, and mark these at a lesser percentage, from those that I was quite sure about, which I would place at a higher percentage. I also began regularly adjusting my prediction when new evidence became available. It actually started to feel a lot like betting, because I used a simple gamification hook with an avatar who gets weaker or stronger depending on my average score.

The bottom line, after 50 bets, I was actually able to predict with 95% accuracy that Donald Trump would be the next President many months before the actual election.

My goal now is to help other people improve their predictive powers as I did. The system still needs a lot of work. Today it helps people improve their gut instinct, which is an improvement as I went from 25% accuracy to to 75% in just three months of time. My plan now is to roll the system out to the general public as a beta. You can register by simply emailing peterzrogers@hotmail.com, and I will send you the website address and a secure login token.

I am also very interested in talking to people who would like to take the system forward because I strongly believe that digital systems to enhance forecasting are in demand.

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin'sYouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

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Digital Technologies Must Disappear in 2017

Almost a year ago, I wrote these words, "Technology has reached the tipping point for me, it moved from a help to a hindrance." The plethora of adrenaline and endorphin inducing mobile apps, 24x7 news, notifications, alerts and updates, drip fed my brain and hindered my "deep work and deep thoughts." In Cal Newport's new book titled, "Deep Work" he posits that most knowledge workers need concentration and substantial time, dedicated and uninterrupted, to produce their best work. He argues that a lot of technologies and open office layouts today inhibit creativity, "deep work" and "deep thoughts," and are the very things that are most highly valued, and one of the key differentiators between humans and robots.

Newport argues that we must understand and optimize the conditions that enable our brains to work best. To sum up his argument, constant drip feeding technologies serve to prevent deep thoughts and deep work, our most valuable assets. He recommends that we restructure our working environments, schedules, times, activities and technology uses to provide substantial "deep thought" times so we can maximize our brain's thinking.

A phrase I like to use is, "Just because technology can do it, doesn't make it useful." Don't get me wrong, I am a huge fan of technology and have dedicated my career to understanding, teaching and using it, but we must all realize that technology has not been designed to maximize our brain's potential. Often technology is designed to replace or degrade our brain's function, or to appeal to our addictive vulnerabilities. Have any of you, like me, lost themselves in a computer game, and then realized it was 4 AM? I did that when Doom first came out decades ago. I realized early on my brain was vulnerable to these games, and banished them from our home ever since, at least until Angry Birds came out on my iPhone and I welcomed back 4 AM.

In our professional life, it is so easy to let our email inbox and calendar invites become our boss and dictate our day's focus. Do any of us really believe this is the most productive behavior? Does our inbox recognize our priorities, goals, focus, deliverables and ambitions? I don't think so, so then why let it boss us around?

If we added up all of the mobile apps we have on our phones, then list all the possible alerts and notifications they each can provide, plus add in how many emails, messages and updates we see, and then add our social media and news feeds, it will literally be hundreds or even thousands of distractions daily. Do these distractions make us more productive or efficient? I don't think so.

In 2017, we need to reevaluate technology and take back our brains and purpose. We should be guiding our technologies, not the other way around. Technology needs to disappear into the background, while productivity and purpose should be our siren's call. We have approximately 700,000 hours between our birth and our death. About 350,000 of those hours are spent in our careers. How many of those hours do we want to waste on technology enabled distractions? I first published some of the following list nearly a year ago, but I needed the reminder, and perhaps it would be helpful for you as well. I propose the following:

Our schedules and activities must reflect our purpose and goals, not our inbox and social media feeds.
We must recognize what activities offer value, and what activities do not.
We shouldn’t have to read through hundreds of useless email messages to find the three necessary to complete our job. Communications need to change and email must disappear behind a veil of utility and productivity.
Someone emailing us, does not mean we need to respond.
We shouldn’t have to check dozens of different locations, apps and websites to communicate with our work colleagues and friends. All of these various collaboration and communication platforms need to disappear into a consolidated and efficient aggregated solution like Slack.
Communication technologies should disappear into the background, and the quality and utility of the message improved by technologies.
Email and meeting driven schedules must disappear, in favor of schedules that honor purpose and deliverables.
Prioritize thinking time and mental productivity, and dedicate the time they deserve.
Scientists agree that the creative parts of our minds work better at different times of the day. Those times need to be reserved, blocked and honored on schedules, to optimize productivity.
The requirement to develop, store and retrieve dozens of different passwords and user names must disappear. The ability to accurately authenticate a user must become more efficient and secure.
Trivial messages and alerts from hundreds of different sources arriving 24 hours a day must disappear. Trivial messages and an urge to immediately respond must not be allowed to intrude on our thinking, creating, planning, sleeping, loving, relationship building, driving and the handling of dangerous equipment.
On-premise IT solutions, hardware and apps that serve to distract from the business, and offer no additional business value, competitive advantages or market agility must disappear into the cloud.
The 200+ mobile applications on my iPhone must disappear into an artificial intelligence engine that will access their functionality and assist me even before I ask.
Mobile applications that are not personalized, and are not contextually relevant should disappear. I don’t care what you sell, if I am not interested, or it is not relevant to me, I don't want to see it.
The routine process work I do on my computer must go away. Intelligent process automation should be pushed down to individuals. An AMX mobile app should process my expenses without me. It should only alert me to exceptions, not the routine.
Technologies and the use of technologies that hinder creativity, productivity and innovation must disappear.

In the lifecycle of any technology, there is a time when we should be enamored and distracted by how it works, but these times must quickly pass and the technology should disappear into the background. I propose that digital technologies should improve and optimize our brain power, and make the human experience richer, deeper and more purposeful than ever before. This year, I am more committed than ever to making technology work for me, not against me, by being less intrusive and distracting. What do you think? Message me.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin'sYouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

Join the Google+ Community Mobile Enterprise Strategies

The Day Big Data Analytics Died

The Huffington Post gave Donald Trump a 2% chance of winning, The New York Times 15%. The best polls, prediction markets and analytics predicted a Hillary Clinton victory in the days before the election, yet they were all wrong. The national media’s predictive analytic systems failed catastrophically. Why?

Analytic systems require timely data on all the variables that impact a system and measure its performance. Analytics requires support from an optimized information logistics system (OILS), which describes the a system that manages the full lifecycle of data from collection, transmission, processing, analysis, reporting, data driven decision-making, action and archiving. An OILS is only as good as the data. It can only function correctly if it is collecting the necessary data inputs. For example the sensors in an Internet of Things (IoT) system must be attached to the right “things” that impact operations, to provide full system visibility and insight. The pre-election big data analytics systems used by pundits, media and prediction markets used incomplete data that resulted in operational blindness, a massive failure for those responsible.

A simple phone poll may not measure the degree of sentiment, neither does it measure those not on the phone. It appears from reports this morning that large numbers of folks whom rarely if ever voted - voted. This unmeasured, invisible group, that was an important data input, was not measured and analyzed in the OILS.

When I meet with business and IT strategy leaders and discuss data analytics and OILS, I always ask them, "What data are you NOT collecting that potentially could be important to your plans and operations?" Many have never considered this simple question. They look at their available data, but not their data gaps. Today in a world of hyper-connectivity, bots, real-time operational tempos and decision-making, having the right data at the right time is critical. What data are you not collecting?

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Subscribe to Kevin'sYouTube Channel

Join the Linkedin Group Strategic Enterprise Mobility

Join the Google+ Community Mobile Enterprise Strategies

Merging Humans with Enterprise AI and Machine Learning Systems

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. Code doesn't get tired. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time). Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.

I believe the first step is to admit we have a problem - the problem is a difference in the speed that computers can operate and the speeds us humans can operate. The second is to understand what a solution might look like - how humans and computers can best integrate and operate together for business success, and the third is to have a strategy and plan.

Imagine a scenario where you arrive at your desk on a Monday morning and find hundreds of recommendations for business process improvements from your AI and machine learning systems. Each of the recommendations might take weeks to socialize across the organization and implement. Now, imagine that happens every day of the year. We humans would be completely overwhelmed! In this scenario, humans are the weak links in business process optimization.

How are we then to best utilize AI and machine learning in a manner that benefits humans, and human powered organizations? I propose the answer lies in developing a powerful AI and machine learning platform that understands our goals and aspirations, can filter findings, run and test simulations, and then present a few select prioritized recommendations to human decision-makers. Simply feeding leaders with unlimited numbers of recommendations may actually paralyze, rather than empower them. What are needed are systems that won’t overwhelm humans, but rather augment them.

In order for any of these AI and machine learning systems to actually work, the data that fuels these systems must first be available, accurate, normalized and timely - for many that is the essence of digital transformation. A digital transformation initiative often includes upgrading IT systems to become “optimized information logistics system” (OILS). Systems that ensure the right data is collected, available, analyzed and its meaning and context understood and utilized.

Once an organization has an OILS in place and the data available, then leaders must ensure their organizational structures and business processes are capable of responding at an operational tempo sufficient to capture the benefits and competitive advantages available from the insights derived from AI and machine learning. That is no small feat. It does no good, as we pointed out, to have recommendations that cannot be implemented.

Massive volumes of new data from sensors, mobile devices, embedded computers and online activities means there are enormous opportunities to learn and gain competitive advantages as a result of it, but only if our leaders, IT and business systems, and our organizations are capable of responding fast enough to capture the value. Understanding how to create OILS, how to utilize AI and machine learning to effectively augment our leaders, and then knowing how to create an organization able to respond fast enough to capture the competitive advantages in the data are the monumental tasks before us today.

Follow Kevin Benedict on Twitter @krbenedict, or read more of his articles on digital transformation strategies here:

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Kevin Benedict

Senior Analyst, Center for the Future of Work, Cognizant Writer, Speaker and World Traveler

Kevin Benedict Ponders the Future

Digital Technology and the Greater Good

New Report: 40 Months of Hyper-Digital Transformation

Virtual Reality Moves to Real with Sensors and Digital Transformation

How Good is Your Mind at Predicting?

Digital Technologies Must Disappear in 2017

The Day Big Data Analytics Died

Merging Humans with Enterprise AI and Machine Learning Systems

Interviews with Kevin Benedict

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