Category Archives: Automation

So far, it's safe to say that the predominant trend in 21st-century business has been digitalization. Every industry, organization and individual has been touched by it one way or another.

As we head towards 2020, we are moving to the next level of digitalization. Now, what has already been digitalized will increasingly be automated -- whether it's the way we work, trade or connect with each other. Automation is becoming increasingly prevalent as computers gain in processing speed and power, and as the amount of data available for computation continues to grow exponentially. At the start of the Internetage, very few things were connected and available for analysis. But with the rise of the Internet of Things and the implantation of computers into all walks of life, from driving to warehousing, more and more facets of our world can now be mapped from within dedicated software.

As a result, a whole range of new abilities and efficiencies are becoming available to companies in all kinds of industry. Automated analyses and tasks can provide actionable insights that previously would have taken a human hours or even days to produce. Whats more, handing over mundane tasks to computers can free up humans to focus on more creative tasks such as strategy and planning.

In light of this, Exact has put together some predictions for the years ahead. Weve all heard the horror predictions that automation will eventually take over the human job market, but in reality it has the potential to be a highly effective tool if used in the right way. Here are some of the key areas of business that will benefit.

Automation of trust: Blockchain

Financial transactions are the core of our global economic system. A lot of effort goes into making sure that those transactions can be completed in a safe and trusted way. We need central banks to allow money to be transferred from one person or organization to the other, with every piece of stock that is traded registered by a Central Security Depository (CSD).

Or at least, we did until recently. Over the last year the digital currency bitcoin has showed us that it is possible to completely decentralise trust. When money is transferred from one person to the other, the transfer is registered in a central administration, and every participant in the network has a copy of the data. As such, Bitcoin automates the trust of a financial transaction.

Blockchain, the technology behind Bitcoin, has the potential to disrupt many more elements of our economy and society. In the accounting industry, for example, working capital financing and triple-entry accounting are very tangible applications of blockchain technology. For working capital financing, blockchain technology can combine and register data that is locked inside the systems of companies and organizations. This increases the value of information, leading to an increase in security and a lower risk level, making financing easier and cheaper. New financing solutions then become available as a result.

In triple-entry accounting, traditional auditing will also change because of blockchain. The reputation of customers, based on historical and actual payment data, will be available in blockchain. Also, transactions between organisations will be matched via this technology in real-time, simplifying the traditional annual audit or even making it redundant.

The finance world is traditionally slow to change, as its important to make sure new technology is secure. With blockchain, that concern is covered, and as such, financial companies are in a good position to benefit from automation in this area.

Automation of manufacturing processes: the IoT

2017 will see increased access to real-time production and order data, allowing for direct orchestration of activity rates on the assembly line. Coupled with increased plant automation enabled by the industrial Internet of Things (IoT), machinery will become ever less reliant on direct human intervention, allowing for more centralized management and control. This will also support more ad-hoc production runs, allowing manufacturers to be more agile and reflective of customer demand and current stock levels in the supply chain.

Automation of personalization: machine learning

In the old days, most consumers would buy their meat at their regular butcher and bread at the baker at the corner. These retailers knew our specific tastes well. This allowed them to optimize customer experience by suggesting new products based on our preferences, or even presenting a tailored, personal offer. The rise of supermarkets and shopping malls made retailing less personal; we could no longer rely on a befriended shop owner.

As contradictory as it may sound, e-commerce holds the promise to bring back that personal flavor in customer experience. Thanks to machine learning, Netflix can predict the kind of series that you like and Spotify offers a weekly personalized playlist with suggested songs based on your historical preferences. Machine learning allows computers to discover patterns based on big data; thanks to these algorithms the services they offer get better and more personal.

This is not only applied in e-commerce, but also in financial and business software. For instance, machine learning can recognise bank statements and automatically book the correct general ledger account as a result.

Automation of financial processes: standardization

In 2017 we expect to see more rationalization of financial and accounting red tape, with a strong shift towards electronic filing and standardized data. Electronic transactions move more of a companys financial dealings onto a fully digital, real-time basis. Since the output is standardized, the source must be too. For example, e-invoices will already be coded in line with the output requirements for filing.

This will necessitate changes to accounting, book keeping and banking processes, but in return will provide much more clarity and certainty of cash flow, financial health and trading outcomes. It will also lower operating costs by further reducing cash handling for many organizations. The digitalization of financial processes also paves the way for a shift towards real-time banking --financial transactions can now be processed instantaneously. This allows companies to have constantly up-to-date insights into their financial balance.

Gavin Fell, general manager UK, Exact

Published under license from ITProPortal.com, a Future plc Publication. All rights reserved.

Photo Credit: Wright Studio/Shutterstock

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Automation is the next level of digitization - BetaNews

(TurboPatent Photo)

Seattle startup TurboPatent has raised $1.45 million in a convertible note as it gets ready to release the second version of some products and expand its documentation automation capabilities.

TurboPatent focuses on corporations and law firms of all sizes, automating taskslike formatting or document preparation, for example, freeing up people to work on more complex, high-value work. The service is designed to cut costs, save time and lead to more accurate patent documentation.

The 25-person company said it has the same impact on the patent industry as CAD (computer-aided drafting) did for engineering, the cloud for IT infrastructure, and robotics for manufacturing.

The latest funding comes from existing institutional, strategic, and founder investors as well as new private investors. The company raised its first outside funding in 2015,a $2 million seed round led byVoyager Capital.

TurboPatent recently released a new automation product, SmartShell, that helps paralegals speed up responding to office actions and reduces human error.

Formerly known as Patent Navigation, TurboPatent boasts an experienced team led by co-founders James Billmaier and Charles Mirho. Billmaier was previously CEO of Melodeo, a cloud-based media platform company that sold to HP in 2010. He also teamed up with Paul Allen in 1999 to launchhome-entertainment technology company Digeo, which was eventually sold in 2009 to ARRIS Group Inc.

Mirho, meanwhile, is a patent law veteran, having worked as a patent counsel at Intel and later as a managing partner of a patent law firm. He also has a computer science degree from Rutgers.

Read more:

Seattle startup TurboPatent raises $1.4M to expand patent automation capabilities - GeekWire

Key Takeaways

In todays world of disruptive technology innovation, needless to say that Lean Principles apply to any field of IT, and as we will see now, Lean Principles also apply to more than just manual processes in IT environment.

About Ericsson: Ericsson is a global leader in delivering ICT solutions, carrying over 40% of the world's mobile traffic through its networks. It has customers in over 180 countries and comprehensive industry solutions ranging from Cloud services and Mobile Broadband to Network Design and Optimization.

In our service delivery unit IT & Cloud (SDU IT&C), we commenced the Lean Journey with small steps around five years ago. We selected a few important KPIs aligned with the organizations strategy and initiated lean transformation programs on those areas which helped us by delivering consistently on the following parameters:

After successfully completing these lean programs, we focused on implementing the improvement levers across the organization to maximize benefits.

However, in recent times our management had been receiving feedback from stakeholders and customers that we need to further lower cost and reduce cycle time in order to remain competitive and win more business. Their points of concerns included:

At this critical stage we initiated the Lean Automation Program with the aim of improving delivery speed, quality and efficiency through automating repetitive and effort intensive tasks performed in different customer projects across the organization.

By commencing the Lean Automation Program, our sponsor set the following expectations clearly for the organization to deliver:

We will take a deeper look into how these goals were evolved in the next section when we discuss the Voice of Customer to Critical to Quality exercise for this program.

As per our Lean Deployment Framework (ref. following diagram), we first defined a charter for aligning with the sponsors expectation and started conducting our workshops across locations through virtual collaboration with the deployment team which consisted of the program driver, lean coaches, automation SPOCs as nominated from each delivery unit, development lead, and developers (as needed from shared resource pool). This exercise is referred to as workshop 0 in Current State Assessment.

Fig1: Deployment Road-map - Current State Assessment of Value Stream

In the following workshops (workshop1), the deployment team championed a multitude of activities, some of which are:

Fig2: Cause-n-Effect Analysis (Fish Bone Analysis followed by Pareto) to find out vital few top probable causes

Fig 3: Table Initial Voice of Customer to CTQ analysis to Identify Primary KPI to Improve Upon

Thereafter, during workshop 2, the team conducted multiple interviews with different stakeholders for all process areas and came up with a detailed value stream map of the end-to-end process. Through these exhaustive workshops, the team acquired a comprehensive understanding of the process, analyzed wastes and earmarked improvement opportunities in repetitive manual tasks or to address high defect rates.

Fig4A: A sample current state value stream map on a typical system integration/installation/upgrade project

Fig4B: A sample current state value stream map on a typical testing cycle

The team further collected data on each of the activities and then prioritized them in order to identify the top contributing areas in manual repetitive tasks and high defect percentage.

The significant inferences that were summarized as outcomes of the current state assessment workshops are:

Fig5: Analysis of activities from different processes to identify top-most contributing factors for high lead time and high defect percentage

After the data was collected and baselined, the VoC to CTQ exercise was updated to have the baselined KPIs with improvement targets for the same. Please refer to the following tables on baselined data and revised CTQ with improvement target:

Fig 6: Table Modified VoC to CTQ with baselined KPI and Improvement Target

Fig6: Deployment Road-map Designing the Future State and Implement Solutions

During workshop 3, the lean automation team designed the future state of the value stream, eliminating wastes, addressing probable causes and bridging gaps through short term and long term solution levers.

Finally, as a part of workshop 4, the team came up with innovative ideas for improvement through different techniques (namely - six thinking hats, affinity diagrams, and blue sky), translated them into practical solutions with proper definitions and then prioritized them for implementation with defined measurement criteria.

Key steps followed:

Fig7: Solution Selection Matrix to prioritize and select most impactful solution levers for implementation

Fig8: Important Solution Levers across technology areas that were developed through the Lean Automation Program

Fig9: Exercise on pilot implementation to identify possible occurrences in projects and projected efficiency gain against baseline

Fig10: Designing Improved Process State: Proposed Lean Automation Process Flow: setting up a continuous process of PDCA cycles

Benefits from this program have been quite significant so far. Pilot Implementation results that were reported include:

Fig11: Month-on-month savings of effort (hrs.) through pilot implementation

Fig12: Maximizing program benefit with sustenance plan through continuous PDCA cycles

While there was an imminent benefit in saving redundant manual efforts and increased delivery quality and efficiency, all of us in the organization could experience the changes in some of the cultural aspects, namely:

In some way or another, all of us experienced challenges and resistance to any change from the status quo. And it is never easy to bring in changes to overcome such resistances. In general, the resistances to lean automation may be perceived under two categories: cost and people. And two of the most common benefits are efficiency and effectiveness. Both of these factors are convincing arguments to proceed with lean automation programs.

The companies that implement lean automation early often see positive bottom line results from their efforts. However, cost savings are not the primary reason to automate IT operations. The focus should be on improving service to the clients and end users. As the quality of this service improves with automation, the costs associated with it also improve.

A few learnings our team experienced from our lean automation programs:

Some of the other lessons the team learnt through their journey in the lean deployment program:

With technology and services evolving faster each year, and growing customer demands, mastering the delivery has become the key to success - speed, quality and efficiency being the essential components. Innovation is the critical driver to succeed and survive in this competitive market; however both innovation and technology evolutions have increasingly become disruptive day by day.

According to experts at The Boston Consulting Group (BCG) and Wharton faculty, lean and innovation can indeed complement each other, and its about time they came together. Lean brings structure and predictability to innovation, and sharpens the distinction between idea generation and the development process, they say. Both share a common goal: to meet customer needs in a cost-effective manner. And lean can help empower researchers and reduce uncertainty in the innovation process itself.

While lean principles enable us to be effective and innovative everywhere we work, finding automation opportunities across every technology and customer focused processes can unlock a bigger potential for repeatedly delivering value to customers.

Nevertheless, when implementing lean principles for automating IT operation, it's important not to forget the human element. Any company that is fully automated still has people working there, and as we all know, any lean initiative should focus on that human aspect carefully. Lean is about involving people and using their brain power to bring in further improvements in the system.

No matter how we deliver IT software and services to our clients, the people part of lean are always a key piece of it. People are the key to identifying when something has gone wrong.

With automation, it's easy to forget that IT processes are only as successful as the people behind them. By contrast, the Toyota Production System seeks to maximize the utilization of people.

The goal of lean automation is to accelerate the frequency and impact of experimentation, thus to make more possibilities for disruptive innovations. And we, the IT engineers, must do as much experimentation as possible early in the process, during the lean automation design stage.

Sudip Pal is Head of Lean Implementation & Execution for Ericsson India Global Services, SDU IT&C. With 18+ years of experience, his areas of expertise span across a wide spectrum of IT landscape from System Integration to IT Service Delivery, from IT Advisory Services to Program Management, from Lean in Agile to Automation.He is a recognized Lean Expert by Asian Productivity Organization Japan, Quality Council of India and National Productivity Council India. He has mentored many big Lean deployment programs for some leading global IT companies in last eight years, such as improved cash flow & profitability, increased win rate, sales engagement process, scaled Agile, etc. In the past two years, Sudip has completed several high potential and critical transformation programs in the areas of Agile, Automation and IT Service Delivery Transformation Model. He has also groomed many candidates to become successful Lean Coaches in the industry.

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Automation and Lean: Scaling up the Lean Value Chain - InfoQ.com

Drawing on your phone or computer can be slow and difficult so we created AutoDraw, a new web-based tool that pairs machine learning with drawings created by talented artists to help you draw, wrote Google Creative Labs creative technologist, Dan Motzenbecker, earlierthis week.

AutoDrawis one of Googles artificial intelligence (AI) experiments, working across platformsto let anyone, irrespective of their artistic flair, create something super quick with little more than a scribble. It guesses what youre trying to draw, then lets you pick from a list of previously created pictures. So you cant draw? No worries! is the general idea here.

Above: AutoDraw

First up, AutoDraw isa super fun tool that gets increasingly addictive that much is clear. But whats alsoclear is that the tool is morea display of AI smarts than it is a tool to improve your artwork, because it would be just as easy to embody the exactsame functionality withina text-basedsearch engine. I mean, why bother drawing a crap dolphin with your finger when you could just type in the word dolphin? Because itwouldnt be nearly as much fun, and Google wouldnt get to show off its fancy new toys.

A few days after Google debuted AutoDraw, it revealed some otherresearch its scientists have been carrying out, designed to enablecomputers to generate simple sketches using artificial intelligence (AI). In effect, theytrained a recurrent neural network (RNN) on sketches that real people made, which emanated from an experimental app calledQuick, Draw! that launched last year (again it is really fun).The app tells you to draw things, like a giraffe or a butterfly,and thenit guesses what youve drawn. So what Google is doing is training machines to sketch like real people, with all the line overlaps and crappy squiggles included.

What thishelps demonstrate is the growing crossover between art and algorithms. But does this hint at a future wherehumans have little incentive to be creative at all?

As part of the so-called fourth industrial revolution, millions of jobs will be lost to automation, according to a recentWorld Economic Forum report. The net loss is expected to be as many as five million jobs by 2020, though of course a whole bunch of new jobs will be created, including positions in IT and data science. Jobssuch as manufacturing and production are expected to be heavily affected, while another recent report indicated that more than 100,000 legal jobs will be automated over the next 20 years.

But art art is sacred. Art is an expression of human sentiment and emotion. Computers stand zero chance of consigning human creativity to the history books. Right? Well, maybe. Were already seeing the early signs that art will be disruptedby machine intelligence and automation.

Why bother learning to paint a landscape or pay someone to sketch your newborn when you can download Prisma to your smartphone and transform your snapshots into ultra-realistic pieces of art in seconds?Prisma, for the uninitiated, usesneural networks to analyze each photo and then appliesa style the user selects. And it really is rather good.

Based on deep-learning techniques, we redraw the image from scratch,said Alexey Moiseenkov, Prisma Labs cofounder, in an interview with VentureBeat last year. We analyze tons of photos and get the typical forms and lines, then take a style and draw your picture with those lines in a taken style.

Above: Prisma: Bottle with Prisma effect applied

The point here isnt that these tools are better than human creators. The point is that such tools are pretty good just now, and theyll only get better. If someone can press a couple of buttons to get an instant hand-drawn family portrait, using little more than a DSLR camera, tripod, and a Prisma-style AI image-rendering app, why would they bother employing the services of a professional artist?

Its not beyond possibility that artists and art retailers will one day have to sell their services based on their authenticity 100% hand-painted pictures could becomethe only visible marking that separates human creations from those produced bymachines.

But technologys algorithmic arm stretches far beyond that of photography and artand into other creative realms.

For years, automated web design services such as Wix and Weebly have offerednovicesan easy-to-use web development platform that makes it simple to buildHTML5 sites using drag-and-drop tools rather than code. For basic websiteswithout much deep functionality, such tools work fairly well. But the formulaic, simplistic, template-based approach leavesmuch to be desired, which is why professional designers and developers still manage to eke out a living.

Last June, Wixlaunchedan automated web design service built on artificial intelligence, called Wix ADI. Using data garnered from its existing user base to feed into this new AI offering,the creator basically answers a few questions and provides the platform with cues as to what theme the website should be based on and what category it exists in, and then Wix pulls in relevant photos, words, and layouts based on the business type and location.

Wix ADI isnt just a new website builder it sets a new market standard for web design,said Wix ADI head Nitzan Achsaf at the launch. We have been at the forefront of this market for nearly a decade, and now as one of the leading AI technology providers, we will make website creation accessible and easy for everyone.

Wix promises that no two websites will look the same.

Other similar AI-focused web design platforms have blossomedin recent times and raised significant venture capital funding, including TheGrid, which has been operating its AI smarts for a few years already, and B12, which launched a similar proposition in beta last year with more than $12 million in funding.

The credibility ofDIY web- and app-design tools that promiseto turn noobs into designers and codershas been questioned for years. And now that AI is going the extra mile to remove any further effort from the process, it will only ruffle the naysayers feathers even more. But the usefulness of such tools really depends on what the purpose of the website is. Why pay for a professional designer and developer when you can hit a few buttonsand have a simple, informative, Google-friendly site made with next to no spadework?

Again, the point hereisnt that the machines are now good enough to replace professionals in building fully functional websites and online services. The point is that AI is encroaching further into creative professions and, more importantly, its improving all the time.

Could an algorithm ever be able to produce something as exquisite as Lennon & McCartney, Jagger & Richards, or even Mozart? Maybe. But probably not, at least for a while.

Back in September, headlines across the web screamedthat the first AI-written pop song had been made. It made for alluring headlines, but it wasnt strictly true. Sony researchers, using specialist Flow Machines software, were able to train a system on different music styles using a gargantuan database of songs. Then combiningstyle transfer, optimization and interaction techniques, the system is able to compose music in any style.

So what we have here is a song called Daddys Car,written in the style of The Beatles. And hey, its not too bad.

However, a more accurate description of this composition would be that it was AI-assisted. French composer Benoit Carr wrote the lyrics (which are pretty nonsensical) and arrangedthe song all the computer did was identify commonalities across this style of pop music and providedCarr with the parts to play around with. Sonys researchers have actually been working on AI-assisted music creationsfor a few years already, and anentire album of suchmusic is expectedlater this year.

Sony isnt the only company dabbling in this field.Last year,Google announcedMagenta, a project from the Google Brain team thats setting out to discover whethermachine learning can create compelling art and music. And earlier this year,the internet giant released aworking interactive version of AI Duet, an app that lets you play a virtual piano with accompaniment from a computer system thatriffs offwhat you play.

Elsewhere, London-based startup Jukedeck is working on an AI-poweredmusic composer that writes original musiccompletely on its own volition. Aimed at video creators on the hunt for original background music, Jukedeck has beentraining deep neural networks to understand how to compose and adapt music,with the end-user able to customize the sound theyre looking for.

All the guitar bands, DJs, and orchestras of the world can perhaps rest easy for now. While computers will improve at songwriting, artists biggest worry for the time being is how to make money in the age of on-demand streaming. Speaking of which.

Spotify snapped up music intelligence and data platformEcho Nest back in 2014, and off the back of that acquisition has been doubling down on its music recommendation efforts. The star of the show is Discover Weekly, a personalized playlist of music built around songs youve previously listened to on the platform.

In effect, Spotify analyzes your history and meshes it withthe listening behavior of othersto see what songs commonly appear next to each other, then based on this information itrecommends new music. Andit is more than pretty good it is pretty excellent. While Apple is banking on human curators via the likes of Apple Radio, Spotify is arguably winning the music-recommendation battle using algorithms and automation.

Whats most interesting about this is that it is infinitely more scalable than a humanDJs ability to recommend new music. Playlists built on algorithms are always tailored to the individual, while human recommendations will always have biased subjectivity weighted against it that will never appeal to everyone at all times.

Similarly, Shazam analyzes song structure to tell you what the name of the song is and who performs it.All you need to do is hold your phone up, tap a button, and voila. It really is a great way to discover new music and build up a library of tunes that you encounter on your day-to-day business, be it in a shop, at a football stadium, or while watching TV. Such technologies make everyone an expert, without having to become an expert. Youdont need to knowanything except how to tap a button to identifya song, while Shazam links in directly with Spotify and iTunes to make it easy to stream or buy music.

Together, the likes of Spotify and Shazam could put a sizable dent into the knowledge-powered smarts of music writers and DJs around the world. People have instant access to all the information they need on the music they hear around them. And why listen to the top 10 charts on the radio, or read the top 5 albums of the week inthe NME, when you know that Spotify has all the best new music? And why turn to your music-obsessed buddy to ask what the name of the song in that TV advertisement is when you can just Shazam it?

With algorithms at work, the need for human knowledge and expertise diminishes.

Above: Lego robot typing

Its difficult to envisage a time when a machine will be capable of crafting a best-selling novel, but lord knows geeks have been trying to make that happen for a while. Its not overly difficult to create something that is formed of words and roughly comprehensible in parts, but generating something with a proper narrative that flows beautifully from start to finish and is infused withwit and passion well, that could be a long way off yet.

But we are already at a stage where machines are producing journalistic content (for want of a better phrase). Last summer,the Associated Press (AP) revealed it was expanding its baseball coveragewith automated stories generated by algorithms through a partnership with Automated Insights.The AP had worked with Automated Insights for years already, generating thousands of computer-generated corporate earnings reports.

Automated Insights uses artificial intelligence to analyze big data and transform it into stories. Chicago-based Narrative Science offers something similar, with a specific focus on business intelligence for the enterprise, or data storytelling, as it puts it.

Heres an AP report from a baseball gamein the New York-Penn league, powered by Automated Insights.

STATE COLLEGE, Pa. (AP) Dylan Tice was hit by a pitch with the bases loaded with one out in the 11th inning, giving the State College Spikes a 9-8 victory over the Brooklyn Cyclones on Wednesday.

Danny Hudzina scored the game-winning run after he reached base on a sacrifice hit, advanced to second on a sacrifice bunt and then went to third on an out.

Gene Cone scored on a double play in the first inning to give the Cyclones a 1-0 lead. The Spikes came back to take a 5-1 lead in the first inning when they put up five runs, including a two-run home run by Tice.

Brooklyn regained the lead 8-7 after it scored four runs in the seventh inning on a grand slam by Brandon Brosher.

State College tied the game 8-8 in the seventh when Ryan McCarvel hit an RBI single, driving in Tommy Edman.

Reliever Bob Wheatley (1-0) picked up the win after he struck out two and walked one while allowing one hit over two scoreless innings. Alejandro Castro (1-1) allowed one run and got one out in the New York-Penn League game.

Vincent Jackson doubled twice and singled, driving in two runs in the win. State College took advantage of some erratic Brooklyn pitching, drawing a season-high nine walks in its victory.

Despite the loss, six players for Brooklyn picked up at least a pair of hits. Brosher homered and singled twice, driving home four runs and scoring a couple. The Cyclones also recorded a season-high 14 base hits.

This story was generated by Automated Insights (http://automatedinsights.com) using data from and in cooperation with MLB Advanced Media and Minor League Baseball, http://www.milb.com.

And heres an earnings report in Forbes, powered by Narrative Science.

Over the past three months, the consensus estimate has sagged from $1.25. For the fiscal year, analysts are expecting earnings of $5.75 per share. A year after being $1.37 billion, analysts expect revenue to fall 1% year-over-year to $1.35 billion for the quarter. For the year, revenue is expected to come in at $5.93 billion.

A year-over-year drop in revenue in the fourth quarter broke a three-quarter streak of revenue increases.

The company has been profitable for the last eight quarters, and for the last four, profit has risen year-over-year by an average of 16%. The biggest boost for the company came in the third quarter, when profit jumped by 32%.

Earnings estimates provided by Zacks.

Narrative Science, through its proprietary artificial intelligence platform, transforms data into stories and insights.

Such reports wont be winning any Pulitzer prizes yet, but theyre perfectly readable and the algorithms are constantly improving. Theres no evidence that machines will be capable of producingsomething akin to Dickens or Proust, but who knows what another 10 years worth of data could do to improve their writing smarts?

A machine will win a Pulitzer one day,noted Narrative Science chief scientist KrisHammond, in the Guardian. We can tell the stories hidden in data.

While fears abound that algorithms will kill off human journalists, figuratively speaking, the AP has previouslystated that embracing machine-written stories is more about expanding its coverage than replacing journalists. Through this method, it can cover many more Minor League Baseball games it would not have previously covered, simply by using data provided by news and statistics body Major League Baseball Advanced Media (MLBAM).

Augmented content was never intended to replace human-generated content, explained Joe Procopio, Automated Insights chief innovation office, in an interview with VentureBeat.Its another tool, another arrow in the journalists quiver, so to speak, and it should be used in places where it can take a lot of the data science and number crunching off the journalists plate. That frees up the journalists time to be able to do more of the investigative and reasoning work inherent in their jobs.

What will ultimately decide whether an artistic endeavor is replaced by an algorithm or set of algorithms, in a business setting at least, is whether its more efficient. The question is: Does it save time and money without compromising on quality?

There are basically two boxes that need to be checked when deciding to use automation to tell a story, added Procopio.One,is the data available to write something compelling, and two, is the business case there in other words, does automation save enough time and resources to make it worthwhile?

So can a machine be trained to amend its style of writing depending on whether its writing an earnings reports, a baseball review, or an obituary? Absolutely this is already happening. Could a machine write a review of a music gig? Or write up an interview? Potentially, but it all comes down to the quality of the data the platform is given,and whether its actually cost effective totrain a system to become efficient at such write-ups.

Automation can be used when writing the types of pieces you describe feature, interviews, reviews, etc., where automation makes sense, continuedProcopio. How much of the piece should be automated depends on the scope of the piece.

Whats emerging here is that such tools could be more about assisting the journalist than replacing them. It might not make sense to attempt entire computer-generated write-ups of a music gig, for example, if it already requires a human to attend the gig and form an opinion. But it maymake sense to use a machine to fill in the gaps in the final review, or even to format it properly. For example, automation could generate paragraphs on a particular bands sales and downloads, or maybe ticket sales, through tapping existing databases that contain up-to-date information. Its not really important whether a human or a machine finds and compiles such data, so long as its accurate, but using an automated approach could save a journalist a lot of time.

Away from the journalistic sphere, the global translation and interpretation industry is reported to be worth around $40 billion. And contrary to whatsome may think, the process of converting words and meanings between languages requires a great deal of creativity.Often words or sentiment dont convert well between languages and vernaculars, leaving the translator to trawl the nuanced depths of their linguistic abilities to communicate the intended meaning in another tongue.

Historically, machine translation tools have had a bad rap, but they are getting better. Its now possible to plug any foreign-language newspaper article into Google Translate and receive a pretty faithful interpretation in another language, thoughthere are many colloquialisms that will still trip up the best machine translation tools out there.Google has started using its AI-based neural machine translation across more of its public-facing services.

Skype also has a real-time voice translation tool, which lets you speak with someone (verbally) in a foreign tongue such as Japanese,in real time. Skype Translator uses AI smarts such as deep learning to train artificial neural networks, meaning it should improve over time as it listens to more conversations.

Any business worth its salt would not rely 100 percent on machine translations for mission-critical communications with customers. But we are certainly fast approaching a stage where machines can be called upon for less important stuff, and perhaps used in tandem with a proofreader to correct mistakes and clarify any ambiguities made by the machinefor use in more important communications.

So, as with Automated Insights, we could have a situation where 100 percent automation is used in some instances where it makes sense, but in cases where the nuanced understanding of a human is needed, the two would work in conjunction with each other.

Its clear that the threat from automation to human jobs is real for many industries, and that includes the creative realm: streaming services that serve you the perfect playlist, apps that turn a family photo into something straight from Van Goghs easel, real-time translations and interpretations, robot-written news reports, and websites created automatically simply by answering a few questions.

This leads us to one stark question. Creativity isacore defininghuman trait, something that truly separates us from the machines, sowhere is the incentive to get creative when all these tools out there are setting out to save us from doing it ourselves?

There are a number of positives here. If a computer was to get as good as, or better than, humans at drawing in a natural style, then it could become the teacher, or assist an artist in their own creative process. Plus, there is a strong line of argument that says that people will always have a creative streak and will want to do things themselves. If you can click a button to turn a photo into a work of art, where is the fun in that?

And that is something that humans will never lose: a desire to have fun and make things themselves. Whether they will be able to get a job off the back of it in 20 years time is another question, of course.

When technology is constantly fixing human errors, be it a typo in a Word document or a wonky line in a drawing, humans may gradually lose the ability to perform certain creative taskswithout computer intervention.Its no longer necessary to remember facts, or phone numbers, or routes to your grandmas house in the next town, because we know its all instantly accessible through a phone. This surely has an impact on a brains ability to remember things. Similarly, if kids grow up with tools to help them draw on their phone or computer because itsslow and difficult otherwise, this cant bode well if it becomes the norm.

But lets not get too carried away. Machines have yet to prove theyre up to the job of many creative tasks; all theyve shown so far is they can chip away at the edges and even then they still need human assistance. Highly creative projects such as writing novels, writing investigative journalism, or penning an entire album of original music with heartfelt, meaningful lyrics its difficult to see a time in the near future where computers will trump humans.

A good example is this cool little short sci-fi film produced last year, called Sunspring. It stars real actors, but the script was written by a machine. It was inspired by Alphabets AlphaGo AI system beating a pro player at the age-old strategy game Go.

The script for the short film was authored by a recurrent neural network called long short-term memory, or LSTM for short, according to a report in Ars last year.It is actually really funny, and makes little sense, but it serves as a reminder as to how far behind machines are in terms of creating genuine works of art that humans would wish to enjoy at scale.

Its also important to distinguish between artificial intelligence and algorithmic intelligence. The former is more about computers being able to think, understand, and adaptin way a human might, while the latter is more about usingmathematics to help people and machines work together.

Phil Tee is chairman and CEO of Moogsoft, a company that specializes in bringing algorithmic intelligence to enterprises Moogsoft basically helps them adoptalgorithms to address mundane operational tasks. He told VentureBeat:

Artificial intelligence is the ability for computer systems to perform tasks that traditionally have required human intelligence, such as visual perception, speech recognition, decision-making and language translation. Algorithmic technologies such as Algorithmic IT Operations (AIOps), on the other hand, leverage mathematics to help operators navigate dynamic, and highly unpredictable settings such as enterprise IT environments. There isnt anything artificial about algorithms.

And this is a key point. Using algorithms to predict what music youll like on Spotify or what movies you should watch next on Netflix is smart for sure, but its not creative in itself. It may be better at doing its job than a human is, but it doesnt exist as part of the arts. So while well see businesses increasingly turn to algorithmic intelligence to optimize and streamline their operations and differentiate themselves from the competition, art itself may not be directly under threat.

But will we ever reach a stage where a computer could write a completely coherent book, song, or movie of its own volition?

Absolutely, but the advances necessary are quite imposing, added Tee.The typical neural network today has roughly hundreds to tens of thousands of neurons, which makes it even less intelligent than a sea slug, which has 18,000 neurons in its brain. This journey to a creative thinking machine is vital, but a long one. Perhaps we should be more focused on intelligence as an aid to creativity rather than a replacement. After all, creativity probably is ultimately what defines humanity.

Art needs humans, and humans need art. Machines may increasingly help the two work together, and it may even replace some jobs, but as one of our defining characteristics, humans and art will continue to be inseparable.

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The art of algorithms: How automation is affecting creativity - VentureBeat

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To accelerate the development of autonomous cars, General Motors will spend $14 million on a new facility in San Francisco for Cruise Automation.

GM bought the driverless tech startup last March, and is making a major push to expand its resources. The new R&D facility, which will be ready by the end of the year, will more than double Cruise Automations current research and development space. California has provided GM with an $8 million tax credit to help with the expansion. Over the course of five years, Cruise Automation will add more than 1,100 new employees.

Founded in 2013, Cruise Automation originally sold aftermarket self-driving systems on its own, but it now operates as an independent company within General Motors. Currently, its testing more than 50 autonomous Chevrolet Bolts on public roads in San Francisco, Arizona, and Michigan. Although its unclear when the self-driving tech will make its way to market, we can expect it to arrive first on vehicles for Lyft. Last year, GM announceda partnership with Lyft to create a network of on-demand autonomous vehicles across the U.S.

Expanding our team at Cruise Automation and linking them with our global engineering talent is another important step in our work to redefine the future of personal mobility, said GM CEO Mary Barra in a press release. Running our autonomous vehicle program as a start-up is giving us the speed we need to continue to stay at the forefront of development of these technologies and the market applications.

GM recently came in second place in a study ranking the top players in autonomous driving technology. It jumped ahead of Nissan, Daimler, Volkswagen, BMW, Tesla, and a host of other companies, but slotted just behind Ford.

Source: GM

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GM Expands Cruise Automation With $14 Million Investment - Motor Trend

There are IT jobs that you just know are built for failure. They are so big and cumbersome and in some cases are plowing through new ground that unforeseen outcomes are likely. Then there are other situations where an IT pro might just say whoops when that unforeseen result should have been, well, foreseen.

UpGuardhas pulled together a group of the biggest instances in the past few years in which the well-intentioned automation of a companys IT systems facilitated a major breach instead.

Healthcare.gov: How an oversight broke the U.S. governments healthcare website

When the U.S. government rolled out the Affordable Care Acts web enrollment tool, Healthcare.gov, in October 2013, it was expected to be a monumental undertaking; and with the delivery of millions of citizens health insurance on the line, the stakes were high. So, when a major software failure crashed the website a mere two hours following its launch, the White House administration suffered a sizeable backlash. Due to a lack of integration, visibility, and testing, the project had significant problems from the start beginning with over 100 defects with Healthcare.govs account creation feature, dubbed Account Lite.

Given its function, Account Lite was a crucial piece of the Healthcare.gov site, serving as the mechanism by which people would create their accounts and gain access to their healthcare options. This particular module had so many problems that it was assuredly a disaster waiting to happen. Nevertheless, contractors moved forward with it as it stood.

The software release failed, preventing millions from securing healthcare coverage. Whats more, the outage had political ramifications as critics of the Affordable Care Act began citing the outage as evidence of the administrations inability to develop a successful healthcare program. The site was eventually stabilized, but the work that should have been integrated before the release was completed only after the crash occurred.

Dropbox: The buggy outage that dropped Dropbox from the web

No IT team enjoys the experience of an outage, especially when it kicks off a race for your team to implement its emergency procedures. In January 2014, Dropbox found themselves scrambling in this very scenario, when a planned product upgrade took down the sites for three hours.

When a subtle bug in the Dropbox script automatically applied its updates to a small number of active machines, it affected Dropboxs thousands of production servers and caused the companys live services to fail. Fortunately for Dropbox, its emergency procedures were well designed and largely effective.With its backup and recovery strategy, the IT team was able to restore most of their services within three hours. For some of the larger databases, however, recovery was slower taking the company several days for all of its core services to fully return.

Amazon/DynamoDB: When the DynamoDB database disrupted all of Amazons infrastructure

Just as physical services like freight haulage require physical infrastructures like roads and highways, companies digital services depend on underlying digital infrastructures. When some of Amazons automated infrastructure processes timed out in September 2015, their Amazon Web Services cloud platform suffered an outage. Cascading from a simple network disruption into broad service failure, Amazon experienced a network outage like those traditional on-premise data centers experience, despite its very advanced and integrated cloud platform.

Amazon had a network disruption that impacted a portion of its DynamoDB cloud databases storage servers. When this happened, a number of storage servers simultaneously requested their membership data, exceeded their allowed retrieval and transmission time. As a result, the servers were unable to obtain their membership data, and subsequently removed themselves from taking requests.

When the servers that became unavailable for requests began retrying the requests, the DynamoDB timeout issue manifested itself in a broader network outage. Just like that, a network disruption started a vicious cycle and affecting Amazons customers as it took down AWS for 5 hours.

Opsmatic: recipe for disaster

When managed under traditional server administration, automation often faces the same set of age old IT problems. One of those classic, faulty assumptions is if it aint broke, dont fix it assuming that all systems are operating the way they should be. When Opsmatics routine server maintenance shut down its whole operation, it was because things werent exactly as they had thought.

In Opsmatics case, a Chef recipe called remove_default_users had been created during the early stages of the companys Amazon Web Services experimentation. Now, long after the test, that recipe was somehow still running against the production servers, unbeknownst to the staff maintaining them.

Like many major outages, this incident was the result of a long, causal sequence of mistakes, none of which were caught until they added up to a giant problem.

Knight Capital: How one tiny mistype cost Knight Capital $1 billion

Knight Capital automated not only its administrative IT processes, but also its algorithmic trading. Unfortunately, this meant that changes and unplanned errors in handling real money could happen very quickly. This is the story of how a single error caused Knight Capital to lose $172,222 per second for 45 minutes straight in 2012.

When operating a data center at scale, clusters of servers often run a single function. This distributes the load across more computing resources and provides better performance for high traffic applications. This model requires all the servers in a cluster to use the same configurations, no matter which particular server in the cluster they are using, so that all the applications will behave the same way. However, configurations even if identical at provisioning always drift apart.

Despite all of its automation, Knight Capital was still manually deploying code across server banks, and an inevitable human error caused one of its eight servers to have a different configuration from all the others. When one of Knights technicians made this mistake during the deployment of the new server code, no one knew. Thus, from that point forward, the IT staff were operating under the misconception that these servers were identical.

At the same time, a decommissioned code remained available on the misconfigured server. As a result, this server began sending orders to certain trading centers for execution, and the error triggered a domino effect around algorithmic stock trading costing Knight Capital $465 million in trading loss.

Delta Airlines: automated fleet of flightless birds

Large logistics operations rely on automated systems to achieve the necessary speed to perform at scale. Some airlines struggle to keep those systems functional. Just like traditional, manual methods of systems administration, automated systems suffer from misconfigurations. In the worst-case scenarios from recent years, failure of these systems has cost airlines hundreds of millions of dollars and more in their customers goodwill.

When misconfigurations occur, they are pushed out quickly through automated mechanisms and can bring entire systems down. For airlines, this means flight operations are interrupted, planes are delayed, and money is siphoned out of the business. In one such case in January 2017, Delta told investors that one glitch in their automated system caused an expansive outage, costing the airline more than $150 million.

Google Gmail: Youve got mail?: Gmails 2014 bug-induced failure

When technology giants experience the occasional automation-related outage, an hour of downtime can mean a lot more. For these huge organizations to make any sort of change, they have to do so across thousands of servers. Having always been on the bleeding edge of technology, its no surprise that Google has automated its configuration management. Although employed to make operations easier, when the wrong change is executed in an automated system that means it can propagate far and wide within a matter of seconds.

In 2014, a bug in Googles internal automated configuration system caused Gmail to crash for around half an hour. The incorrect configuration was sent to live services, causing users requests for their data to be ignored, for those services, in turn, to generate errors.

The lesson is that configuration automation is not the same as configuration management. Automation ensure that changes get pushed out across all systems.

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The 7 worst automation failures - CSO Online

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There are two things we know for sure about how automation impacts jobs. First, we know that some of the work being done by humans today will be done by machines tomorrow. The robots are, in fact, coming for our jobs. However, another thing we know for sure is that this has been happening since the industrial revolution and the economy has always adjusted to accommodate the changes. The job market continues to expand even though automation changes the landscape. Will this continue to be the case going forward? Or, could things be different this time around thanks to advancements in automation that allow machines to replace more than just human muscle?

Its good to know what to expect, no matter what the future holds. One of latest studies on how automation could impact the job market, worldwide, comes from the consulting firm PricewaterhouseCoopers. This study revisits data from two other studies (one by Oxford University and the other from OECD analysts).

According to this analysis, 38 percent of U.S. jobs are at risk of being lost to automation by the early 2030s, compared to 35 percent of jobs in Germany, 30 percent of jobs in the U.K., and 21 percent of jobs in Japan.

Analysts explained that the differences exist because of variance within specific industries. For example, within the financial and insurance sector, 61 percent of U.S. jobs were deemed high-risk, while only 32 percent of jobs were labeled as such for the U.K. It all comes down to education and skill level, which has always been a huge factor in how much jobs are threatened by automation.

The jobs of these US retail financial workers are assessed by our methodology as being significantly more routine and so more automatable then the average finance sector job in the UK, the report stated, with its greater weight on international finance and investment banking.

Theres nothing new about trying to understand how automation might impact jobs and the economy or even fearing the impact. In the introduction to this report, the authors reviewed the history of the Luddites, who protested automation back in the early 19th century. In hindsight, the Luddites were wrong. While newer technologies did change the job market, they also created far more jobs than they eliminated. In the U.K., people who still worry that technology will negatively impact the economy are often dismissed and labeled as believers in the Luddite fallacy.

However, some argue that this fallacy might not hold true for much longer. Thanks to improvement in technology, more and more jobs are being replaced that require more than a physical investment. Robots can now do jobs that used to demand a humans brainpower.

The authors of this report also acknowledge that the technology thats emerging today is a bit different than what weve seen before. Todays smart technology is able to replace more than just human bodies. Now, machines can stand in for humans on an intellectual level, too.

Will this just have the same effects as past technological leaps short term disruption more than offset by long term economic gains or is this something more fundamental in terms of taking humans out of the loop not just in manufacturing and routine service sector jobs, but more broadly across the economy? researchers asked in this report. What exactly will humans have to offer employers if smart machines can perform all or most of their essential tasks better in the future?

The answer to these questions, to some extent, is unknown. Well have to wait and see how these technological advancements play out over the course of years across our economy. However, as long as certain constraints are in place, the authors of this report seem optimistic that the job market will adjust and grow stronger as a result of this progress, as it has done in the past. Here are a couple provisions they highlighted.

Also, here in the U.S., labor economists say that the transition can be eased through things like stronger unions, the creation of more public-sector jobs, more college degrees, and a higher minimum wage. These factors are deemed much more impactful than some of the other issues that get attention.

Over the long haul, clearly automations been much more important its not even close, Lawrence Katz, an economics professor at Harvard told The New York Times when discussing how automation impacts our job market compared to threats from offshoring and immigration.

He went on to add, Just allowing the private market to automate without any support is a recipe for blaming immigrants and trade and other things, even when its the long impact of technology.

There are still a lot of unanswered questions when it comes to understanding the future of automation and what it means for tomorrows workers and the job market. However, it will help to be prepared for whats to come. Workers, companies, and state and federal governments should be proactive and work toward adjusting around these changes. Thats the best way to ensure that technology continues to play a positive role in our economy, as its done in the past, rather than the reverse.

How do you think we ought to prepare for a future with smart automation? We want to hear from you! Leave a comment or join the discussion on Twitter.

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Smart Automation Poses New Challenges to the Job Market - PayScale Career News (blog)

Executive Summary

Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology.In all, 1.2 billion full time equivalents and $14.6 trillion in wages are associated with activities that are technically automatable with current technology. This automation potential differs among countries, with the range spanning from 40% to 55%. Four economiesChina, India, Japan, and the United Statesdominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting currently demonstrated technologies.

Around the world, automation is transforming work, business, and the economy. China is already the largest market for robots in the world, based on volume. All economies, from Brazil and Germany to India and Saudi Arabia, stand to gain from the hefty productivity boosts that robotics and artificial intelligence will bring. The pace and extent of adoption will vary from country to country, depending on factors including wage levels. But no geography and no sector will remain untouched.

In our research we took a detailed look at 46 countries, representing about 80% of the global workforce. We examined their automation potential today whats possible by adaptingdemonstrated technologies as well as the potential similarities and differences in howautomation could take holdin the future.

How it will impact business, industry, and society.

Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting demonstrated technology. As wevedescribed previously, our focus is on individual work activities, which we believe to be a more useful way to examine automation potential than looking at entire jobs, since most occupations consist of a number of activities with differing potential to be automated.

In all, 1.2 billion full-time equivalents and $14.6 trillion in wages are associated with activities that areautomatable with current technology. This automation potential differs among countries, rangingfrom 40% to 55%.

The differences reflect variations in sector mix and, within sectors, the mix of jobs with larger or smaller automation potential. Sector differences among economies sometimes lead to striking variations, as is the case with Japan and the United States, two advanced economies. Japan has an overall automation potential of 55% of hours worked, compared with 46% in the United States. Much of the difference is due to Japans manufacturing sector, which has a particularly high automation potential, at 71% (versus60% in the United States). Japanese manufacturing has a slightly larger concentration of work hours in production jobs (54% of hours versus the U.S.s 50%) and office and administrative support jobs (16% versus 9%). Both of these job titles comprise activities with a relatively high automation potential. By comparison, the United States has a higher proportion of work hours in management, architecture, and engineering jobs, which have a lower automation potential since they require application of specific expertise such as high-value engineering, which computers and robots currently are not able to do.

On a global level, four economies China, India, Japan, and the United States dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting demonstrated technologies. Together, China and India mayaccount for the largest potential employment impact more than 700 million workers between them because of the relative size of their labor forces. Technical automation potential is also large in Europe: According to our analysis, more than 60 million full-time employee equivalents and more than $1.9 trillion in wages are associated withautomatable activities in the five largest economies (France, Germany, Italy, Spain, and the United Kingdom).

We also expect to see large differences among countries in the pace and extent of automation adoption. Numerous factors will determine automation adoption, of which technical feasibility is only one. Many of the other factors are economic and social, and include the cost of hardware or software solutions needed to integrate technologies into the workplace, labor supply and demand dynamics, and regulatory and socialacceptance. Some hardware solutions require significant capital expenditures and could be adopted faster in advanced economies than in emerging ones with lower wage levels, where it will be harder to make a business case for adoption because of low wages. But software solutions could be adopted rapidly around the world, particularly those deployed through the cloud, reducing the lag in adoption time. The pace of adoption will also depend on the benefits that countries expectautomation tobring for things other than labor substitution, such as the potential to enhance productivity, raise throughput, and improve accuracy and regulatory and social acceptance.

Regardless of the timing, automation could be the shot in the arm that the global economy sorely needs in the decades ahead. Declining birthrates and the trend toward aging in countries from China to Germany mean that peak employment will occur in most countries within 50 years. The expected decline in the share of the working-age population will open an economic growth gap thatautomation could potentially fill. We estimate that automation could increase global GDP growth by0.8% to1.4% annually, assuming that people replaced by automation rejoin the workforce and remain as productive as they were in 2014. Considering the labor substitution effect alone, we calculate that, by 2065, theproductivity growth that automation could add tothe largest economies in the world (G19 plus Nigeria) is the equivalent of an additional 1.1 billion to 2.2 billion full-time workers.

The productivity growth enabled by automation can ensure continued prosperity in aging nations and could provide an additional boost to fast-growing ones. However, automation on its own will not be sufficient to achieve long-term economic growth aspirations across the world. For that, additional productivity-boosting measures will be needed, including reworking business processes or developing new products, services, and business models.

How could automation play out among countries? We have divided our 46 focus nations into three groups, each of which could use automation to further national economic growth objectives, depending on itsdemographic trends and growth aspirations. The three groups are:

For all the differences between countries, many of automations challenges are universal. For business, the performance benefits are relatively clear, but the issues are more complicated for policy makers. They will need to find ways to embrace the opportunity for their economies to benefit from the productivity growth potential that automation offers, putting in place policies to encourage investment and market incentives to encourage innovation. At the same time, all countries will need to evolve and create policies that help workers and institutions adapt to the impact on employment.

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The Countries Most (and Least) Likely to be Affected by Automation - Harvard Business Review

Nutanix: How Automation Changed Your IT Department Forbes We hear a lot about IT automation and machine learning these days. It is mostly at the software application level where the emphasis is very much on automating blocks of code that can perform the same (or similar) function in different application use ...

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Nutanix: How Automation Changed Your IT Department - Forbes

Rockwell Automation (NYSE:ROK) is a pure-play Industrial Internet of Things (IIoT) company that is working hard to increase its revenues from selling software. It's focused on the right priorities of increasing its software sales and integrating with data analytics platforms and ERP systems. In the long run, there may be greater convergence of industrial automation software, robotics and enterprise software. This may lead to Rockwell acquiring robotics companies or even it being acquired.

Industrial Internet, which enables smart factories, exists to best serve the needs of the customer. The integration of Industrial Internet and enterprise software will help manufacturing companies respond faster to changing demands and reacting faster to product defects. Companies can achieve this flexibility while keeping costs low. Eventually, there may be very little distinction between Industrial Internet and ERP systems when tightly integrated.

The IIoT integration with rest of the enterprise software stack is still in its infancy, but the convergence of Industrial Internet and enterprise software will enable a software company to help their customers gather, analyze and act on data from the factory floor to the customer. It is an enticing prospect for any enterprise software company to be able to sell products that cover every aspect of their customer business. For these reasons, Rockwell Automation may be an ideal acquisition candidate for an enterprise software company such as Microsoft (NASDAQ:MSFT), SAP (NYSE:SAP), Oracle (NYSE:ORCL), Salesforce (NYSE:CRM) or even a company like Cisco Systems (NASDAQ:CSCO).

From the customer perspective, it is easier for them to deploy and maintain industrial automation systems from a single vendor. There is business value in making near real-time production decisions by analyzing sales data, warranty data and raw material costs. The integration of the factory and enterprise data can help bring about greater efficiencies to the company. Moreover, automated factories can help lower labor cost, improve operational efficiencies and help with preventive maintenance.

There are multiple trends driving the adoption of the Industrial Internet.

Exhibit: Trends Driving Industrial Automation (Source: Company Filings)

The cost of semiconductors and wireless hardware has dropped dramatically. It is now cost effective to add sensor technology and wireless connectivity to very low-cost industrial equipment. Now, data from every aspect of a plant can be gathered and analyzed. That data can be stored in elastic cloud environments hosted on premise or by a public cloud vendor. The cost to store and retrieve data from computing platforms has dropped dramatically too. The availability and robustness of today's Big Data tools to quickly analyze vast amounts of data and present actionable intelligence is making it easy for companies like Rockwell Automation to manage data. Demographic trends such as population growth and increased urbanization is increasing the demand for products. The income levels in many emerging countries are less than that in developed countries. The large population coupled with lower income levels necessitates efficient and flexible operations while lowering the unit cost of products. Products and services offered by Rockwell Automation help in making products efficiently.

Exhibit: Products That Rockwell Automation Helps Produce (Source: Company Filings)

Rockwell Automation operates under two business segments:

In 2016, Architecture & Software operating segment accounted for $2.64 billion or 45% of the total sales. Its Control Products & Solutions operating segment accounted for $3.24 billion or the remaining 55% of the total sales. Rockwell Automation has customers in consumer products, resource-based and transportation industries.

Exhibit: Rockwell Automation Annual Revenue (Source: Company Filings)

Software Has Larger Margins, But Has Shown Inconsistent Growth!

Exhibit: Rockwell Automation Revenue from Architecture & Software (Source: Company Filings)

Exhibit: Rockwell Automation Control Products & Solutions (Source: Company Filings)

Rockwell Automation enjoys a higher operating margin in its Software business compared to its Control Products operating segment.

To achieve a truly smart factory that can deliver on lower costs and increased efficiency, one would need an integrated approach to data management and analysis.

Exhibit: Data Management Capabilities to Truly Enable a Smart Factory (Source: Author)

Rockwell has products that enable a connected machine, help in gathering data and provide the network connectivity required to transport data to Manufacturing Execution Systems (MES) and to ERP and data analytics systems. Broadly speaking, Rockwell offers products in these categories:

It has also teamed up with Microsoft and integrated with its business intelligence software and Azure cloud to provide end-to-end system for a smart factory. In essence, Rockwell has capabilities in enabling connected machines and in data gathering via use of its networking technology, but lacks the ability and infrastructure or compute cloud to store and analyze vast amounts of data. Microsoft has expertise in the areas of Big Data storage and analysis. Its partnership is complementary.

Even though Rockwell has a stated goal to increase its revenues from software, the reality is much different. Its revenue from software hasn't shown any consistent growth. Until it starts showing consistent revenue growth in software, it may not be enticing for an enterprise software company to acquire it.

Exhibit: Revenue Growth in Architecture and Software Segment (Source: Company Filings)

There could be multiple reasons for this lack of growth in software sales:

Rockwell Automation Heavily Dependent On Revenue From Raw Materials Production

Exhibit: Revenue from Various Industry Segments (Source: Company Filings)

But there may be signs that things are about the change. Industrial companies closely monitor economic trends and pay close attention to Industrial Production (IP) Index, Manufacturing Purchasing Managers' Index (PMI), Industrial Equipment Spending and Capacity Utilization (Total Industry).

Exhibit: Economic Data on Industrial Spending and Manufacturing Trends (Source: Company Filings)

The industrial production index and capacity utilization were slightly down, but industrial equipment spending and PMI were up in the first quarter of fiscal year 2017. The economic data is positive sign for Rockwell Automation in the short term, assuming the current geopolitical risks don't dampen consumer spending and lead to a decrease in industrial production.

Rockwell Automation has built a solid foundation, but has to start showing good consistent organic growth in its software operating segment to extend its leadership position in IIoT. The eventual convergence of industrial automation and enterprise software is inevitable in the long run. There will also be further close integration between robotics and industrial automation software. For example, Rockwell and FANUC have a global collaboration agreement in place to create integrated manufacturing solutions. Rockwell may even acquire robotics companies in order to help it gain a larger share of industrial spending and further its goal of increasing software sales. Or, it may be an acquisition target for a large enterprise software company looking to gain a strong foothold in the Industrial Internet. It may first have to prove that it can be major force in industrial automation software by showing consistent growth.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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Rockwell Automation: An Enterprise Software Company - Seeking Alpha