#FutureOfWork: The impact of #AI on the workplace and employee engagement

• The digital workplace merges work and life — a virtual space with applications, services and information on demand. For users, this means access to the technology they need, when they need it, on whichever device they prefer to use.

• Employees expect their enterprise systems to be as engaging, exciting and intuitive as consumer devices. Technology research company Gartner calls this a shift from technology-literate people to people-literate technology.

• Companies now have more exquisitely detailed data about how their products and services are used than they ever had before, thanks to a vast network of sensors and advanced analytic tools.

• Cognitive systems can parse all that data and learn what employees need to do their job better — even if they don’t yet know it themselves. Cognitive systems will deliver the ability to visualize vast amounts of data, curated and analyzed, for a unique task and a unique user.

• The workplace of the future will embrace emerging new cognitive and analytic capabilities. These tools can provide insights into how employees engage most effectively, what the best technologies are for each task and for each individual, and help provide a seamless work environment — an environment that will help to attract and retain the best talent.

[Source]

#futureofwork: How AI will change fintech jobs, and advice to college grads how to benefit from this

  • The automation of the finance industry will hollow out jobs in that field in the same way that robotics and other technologies have reduced manufacturing employment. That is how:
    • Floor trader, of course, has long been the archetypal job on stock exchanges. But there are precious few left of them.
      • Most trading jobs have been taken over by servers running trading algorithms.
    • Much of Wall Street’s back office operations involve the performance of relatively structured tasks.
      • Many of these could probably be taken over by tools like robotic process automation, which can reach into multiple systems for needed data and apply rule-based decision logic.
    • Regulatory compliance has been one of the few growth areas in recent years on Wall Street, but systems from Digital Reasoning are automating internal fraud investigations.
      • IpSoft’s Amelia is focused in part on facilitating compliance in customer conversations.
      • Narrative Science automates the creation of anti-money laundering reports.
      • RAGE Frameworks automates the extraction of data for credit and wealth management, and can create automated compliance reports on the process.
    • Many entry-level jobs on Wall Street involve combing through data to make a case for a particular financial transaction. That is an it job:
      • Kensho, for example, is a startup that analyzes data on markets and generates reports on their implications.
  • Another common task of financial analysts and attorneys is to prepare disclosure data on a company’s financial history for potential investors.
    • iDisclose does that automatically.
  • Another common role in the finance industry is to provide investment advice.
    • The “robo-advisor” concept / automated advice is becoming pervasive at the lower end of investing.
      • Vanguard, Charles Schwab, and Fidelity have all taken some steps in that direction
      • startups like Betterment, Wealthfront, and Personal Capital are pursuing Millennial customers with money to invest.
      • The capabilities already exist for higher-end versions of robo-advice, and a few banks like UBS have begun to explore them.
      • Investment advice is complex, data-intensive, and rapidly changing, so it seems very likely that there will be substantially fewer human investment advisors in the future.
  • There are other finance-oriented tasks that will be performed by automation, including some new ones involving ongoing financial management for consumers that should have been done by banks long ago.
  • It is a process: Job after job will be whittled away over time. Entry-level jobs will probably be the hardest hit; if you can teach a recent college grad to do a task, you can probably teach a machine to do it.
  • Advice for entry level grads:
    • There will be a substantial number of jobs that involve working alongside machines.
    • If you’re already familiar with key financial processes, you’ll have a much better chance of keeping your job if you learn to work alongside smart machines that perform key aspects of those processes — monitoring the machines, fixing them, and picking up the ball when they drop it.
    • Or you can become skilled at overseeing them, understanding when the financial world has changed and when the algorithms are no longer well-equipped to deal with it.
    • Finally, of course, there will be many jobs involved in building intelligent finance systems.

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#makeithappen – my road to make the world work better – overview

Why I do this?

I want to be a rouge pirate scientist that makes the world a better place: At the end of my life I … fulfilled the dream of the 5 year old boy that played with Playmobil how he created a great world by science (e.g. not dying, no pain) without respect for boundaries.

How to make the world work better?

It is about make people and myself more happy by help solve the big challenges (Domains):

  • Healthcare
  • Environment
  • Living (Cities …)
  • Working & Producing (Smarter Industry)
  • Lifestyle (products that make people feel better)

What is needed to make an impact? Domain Knowledge + Technological Knowledge + Soft Skills This maps to the following areas:

#FutureOfWork: Including all soft skills that make one work efficiently:

  • Agile (including Time Management and Learning)
  • Lifestyle

#BuildTheFuture: The “technical” skills needed to build products, based on your domain knowledge, that create value for people:

  • Process: Agile, Design Thinking, Lean Startup (Bluemix Garage Method)
  • Design: Domain Driven Language
  • Programming: JavaScript, OOP, Java Testing, Building
  • Domain specific skills: Data Analytics, Full Stack Development (App & Microservices), IoT & Embedded
  • Background: Algorithms & Statistics

#FutureOf[Domain]: Includes the domain knowledge that is need to use the technology in a way that creates value:

  • Health:
    • Data Driven Medicine
    • Genomic Medicine
    • Medicine, Biology, Chemistry
  • Lifestyle:
    • Science of Happiness
    • Consumer Products & Marketing: What creates value in the life of an consumer?
  • Industry:
    • Industrie 4.0
    • Integrated Industry
    • Manufacturing Challenges & Trends
    • 3d printing

Why I will focus more on the human in the future of industrie & living

In the last years and month I kept my eye on the areas of Industrie 4.0 or Industrial Internet. While I will continue this journey, and will refocus more on the area around lifestyle and healthcare and consumer products. Why?

I definitely believe that there is large potential in Industrie 4.0, I personally am quite surprised with the experience how long it takes until an idea makes it to pilot or even production in the enterprise it. In the consumer area the cycle times are much lower.

But there are two more important reasons. First I enjoy helping people, and when I thought about my dream when I was a little boy, I dreamed to help people make the world a better place by making them more happy and helping them to be healthy. Well back then I dream of healing every diseases and fixing all the world problems form climate change to mass hunger. Most likely this will stay a dream, but I love the idea to contribute a small small piece to something meaningful.

I always believe I could do something meaningful, and this does not mean to revolutionise the world, only few of us are luck enough to experience this. But do something meaningful means to putting your head to a meaningful challenge and throwing punches to wear down the challenge for a heavy hitter to punch it out.

But do something meaningful means to putting your head to a meaningful challenge and throwing punches to wear down the challenge for a heavy hitter to punch it out.

So we come to the third reason: I am currently working at IBM, and one reasons I chose to work for IBM was that they brand themselves as a company that contributes something meaningful the the solutions of large problems. Currently I integrate systems at big banks, while this is important, it seams quite meaningless compared to curing cancer or alzheimer’s or any other disease. But as it seems we IBM has not given up on making meaningful contributions. When they announced in April 2015 that “healthcare will be our moonshot”, I knew that I have to be a part of this. Therefore I will refocus on this area, and learn what is important about this topic and how it can be combined with the Internet of Things. I see a large overlap between Industrie 4.0 and the consumer side. I think we will get a much larger acceptance for Industrie 4.0 to be not just another buzzword, when we focus on the customer and identify areas where we can generate immediate benefit to the people.

What this will mean for my focus areas, well I will have to keep learning to develop and maintain apps on a cloud platform. This includes connecting things to the cloud with Arduino & Node.JS. Those Applications will follow a modern agile Microservice architecture. But modern application need also some analytics to generate value from data. To do this I will useApache Spark, Java, Python, R and Statistics.

This will be continued!

Rethink Industry 4.0: Lessons learned after 3 years of Industry 4.0 experience

Back in 2013 when I started at IBM and I first heard about the topic of Industry 4.0 I was enthusiastic about the vision of a new industrial revolution. I had not much background in production, but I believed that the manufacturing industry was 20 years behind what IT guys like me do. What are Daimler, VW and BMW compared to Google, Netflix and Amazon? The one push new versions of their products weekly, the others yearly. Internet companies are agile and pivot, while production companies are six sigma, process focused. The internet companies have a return on invest much higher than any production companies. So the first conclusion was simple let us take the IT technology that made the Googles, Netflix, Amazons and Facebooks (the GNAFs) so successful and push it into production companies. Did this work? It did not. But why, the assumptions of a young industry entry like me were wrong. Where were I wrong?

  • It is not about technology it is about processes.
  • The reason the GNAFs are so successful is not technology but culture, automation and agility.

There were many more things I was wrong about, like the level of technology maturity production companies have reached, they are by far NOT 20 years behind! But all those are details compared to the two main misunderstandings above.
Was I the only one that was wrong? No I still see us pushing for IT solutions that go for technology but not the key reasons why the role models in the internet age are so successful. How can we expect german production companies become the GNAFs or better Apple’s of the production industry if we offer them technologies that are not used by the GNAFs because they do not fit their culture and hinder innovation and agility. You don’t think we do offer this to companies?

Well look at every Industry 4.0 reference architecture, it is a heavy layered combination of systems, from with most of them are not agile, not open and have release cycles of years. Then they have propriartary APIs, the need for month and years of consultancy to implement, they are complex and they hinder by there licensing models the design of resilient/antifragile architecture. This list goes on!

The key to be innovative and efficient like the GNAFs is to have agile process and software. Don’t built systems that can not innovate daily! You say the internet is something else than you have, that was what Kodak and many companies said: And they are out of business today, because someone thought, it is not something else and made the effort to prove it and make it work. So I urge us to rethink how our Industry 4.0 architectures, strategies and products look. And go for the lean approach. But let’s start with the most important thing first, agile culture: Let us not go back to the drawing board and rethink and design for a year how our agile culture, products and industry 4.0 vision looks: let’s start now doing it.

Leave me a comment or discuss with me on twitter @smaterindustry. What is your opinion, where am I wrong? Let’s discuss and bring Industry 4.0 forward!

Future articles will continue this one in the following weeks:

  • API Industry 4.0: A hypothesis put to the test as a starting point to implement this kind of Industry 4.0. This is not something new or revolutionary, it just something to try. Let’s start failing fast, learn and improve.
  • Role models: Where Germany manufacturing companies should look to “borrow” for their Industry 4.0 strategy
  • The german tragedy: What happens if we can change our culture

Contribution to Blogparade: #Blogparade zum Thema „Industrie 4.0: Chancen, Risiken, Ideen und Umsetzungen – was hat Deutschland zu bieten?”