deal architect

TAMyopia

When I was at PwC in the early 90s, I remember we used to throw out statements like "we have 40 to 50% market share in the SAP (or PeopleSoft) implementation market" When I got to Gartner I saw their competitors were saying similar stuff. So, I started telling them - "no, actually you have less than 10%" . The tune of at least some of them turned to "we have 50% market share of deals we choose to chase". Choice - certainly their prerogative which segments or verticals they chose to focus on. But it's not a reflection of Total Addressable Market.

One of my favorite advisory projects is helping vendors challenge their previous definitions of TAM.

I have seen plenty of dysfunctional behavior from technology vendors of every shape when their TAM definition is narrow  - salespeople chasing deals convenient to them, not what the leadership would have approved, marketing claims to deliver functionality in areas which their R&D had no intention of developing in any meaningful manner, CFOs and investment committees which don't approve enough R&D and Capex needed to keep their entities competitive.

In my recent book, I outlined several trillion dollar markets that many technology vendors have missed since the late 90s. Those included smart products, digital marketing, IoT, cloud infrastructure, various verticals - - Google, Facebook, Amazon, Foxconn, GE, Siemens and a whole bunch of vertical players have thrived in those markets even as many technology vendors ignored them. Download the chapter from my recent book  in link here if you want to read about all the missed markets.

Many of those markets were brand new ones or what in MBA school we called adjacent markets so you can understand why so many vendors ignored them - even as they now wish they had entered those markets. But even in their core focus markets, I have seen many a myopic definition of TAM. Here are a few examples

a) Automation and Outsourcing

When I wrote Silicon Collar three years ago, I chose the title because it felt like traditional definitions of white, blue, pink collars were dated as technology was reshaping every job, everywhere. We all wear a Silicon collar these days.  I had examples of robotics, drones, machine learning, CGI, exoskeletons, robotic process automation (RPA) and other automation in the oil patch, in accounting, in films, in winemaking, garbage collection and many other sectors. I wasn't dreaming this up - executives at sophisticated companies were in the book describing their experiences.

So, it has been a bit disconcerting to see so many outsourcers have glommed onto RPA as their major (in many cases only) focus when it comes to automation. Even more cynically, most outsourcers do not take seriously repeated customer expectations to automate their own labor intensive services.  

b) Data Centers and networks

I cannot believe how envious many vendors are of AWS's and Azure's recent success. On the other hand, I cringe when I hear software vendors and many outsourcers say "glad we didn't have to invest billions in Capex". The reality is less than a decade ago HP(with EDS), Oracle (with Sun), IBM, Accenture, Verizon, Deutsche Telecom and other telcos populated and ran data centers and networks for the world. The emerging cloud infrastructure market was theirs to lose and they have done so - spectacularly, so far.

Many SaaS vendors view the cloud infrastructure market as a bonanza - let someone invest the Capex, we will just rent it for cheap. It's not really that cheap and increasingly they are being expected to support multiple clouds. Zoho has continued to invest in its own data centers and passing along those savings to customers. The time will come for other software vendors to similarly hedge their bets. They will have to expand, or at least, morph their market definition of infrastructure.

c) "Glo-ver" in SaaS

I have increasingly been pointing out a huge under-achievement in the enterprise applications market. After two decades of cloud and in-memory applications, only about 20% of a grid across countries and industry processes has a decent choice in contemporary architecture. Myths persist such as "verticals are a sink hole". Sure if you dont do it right, but go see how much lock-in revenue and margins SAP has enjoyed with its vertical investments in the early 2000s.

I have accelerated my coverage of applications and executives who are focused on the white spaces - see some of that coverage summarized here in a trend I call Glo-ver  expansion in focus.

Back to helping vendors challenge their previous definitions of TAM.  Those are fun projects when the vendor is willing to open up. I must be honest though - many vendors look to influence analysts, not use them much for intelligence. So, they would rather posture they have "50% market share in deals they choose to chase".  

Seriously, I hope that changes. Too many vendors are defining markets too narrowly and finding their competitors have done the same and a result too many are chasing after the same, narrow markets. That is a sure path to commoditization.  Trust me - I have seen it repeated many a times over the last three decades. There is little safety in those numbers.

The past, present and future of Work

I was at a Cognizant event last week where they introduced 21 jobs they are predicting for the next decade. Last year, they had announced their first batch of 21 jobs. See here for details. The day before, Mark Hurd went even further at Oracle OpenWorld – he projected a set of new technology-related job titles would be created and sooner – by 2025. See below for the slide he used.

Laugh at them if you want but I liked the confidence in both projections. As I told a couple of folks at the Cognizant event, it is nice to see people be optimistic about new types of work, not be gloomy that machines will largely take it over. That was the mood when I started to write Silicon Collar just 3 years ago.

But why look into crystal balls? The Bureau of Labor Statistics which keeps a very close eye on skills and titles for over 800 occupations reported many changes in its Standard Occupational Classification (SOC) in just the last 8 years

“Of the 867 occupations in the 2018 structure, 391 remained completely unchanged from the 2010 SOC, 355 had at least a definition change, 131 had at least a title change, and 115 had at least a code change.”

Work continues to evolve before our very eyes. Why are we surprised?

This year I have studied a lot of US history in the 1800s. In doing so, I kept an eye for how occupations appeared and evolved. Consumer tastes, technology impact on work, labor migrations all led to significant ebbs and flows in jobs.

If you have watched the TV show Mountain Men you may think these are modern day studs. Actually their ancestors were fighting the Beaver Wars centuries earlier and their glory days came after mapping improved with the Lewis and Clark western expedition of 1804-6.  Their fortunes subsided as European demand (in particular) for fur changed and technology allowed for development of synthetic clothing materials.   

If you have read Melville’s Moby Dick which was set in 1851, you wonder what happened to whaling, once a lucrative occupation. Improved refining technology and logistics brought kerosene cans to much of the country thanks to Rockefeller (Standard Oil branding was meant to assure consumers the quality was standard everywhere) and that reduced some of the demand for whale oil. Then Edison’s bulb and his and Tesla’s electric currents delivered a fatal blow to whaling. But just as important was that unlike Ishmael who found "nothing particular to interest me on shore," young workers increasingly found new occupations the Industrial Revolution was creating onshore.

I went to school at TCU in Ft. Worth and kept hearing of the Chisholm Trail. Much later in life I learned a technology - the railroad - had helped create the cowboy who did his famous cattle drives on that trail to railheads in Arkansas and Kansas.  From there, the herds would be be shipped by rail to eastern markets. Just as quickly, another technology – barbed wire – affected the occupation by restricting their routes and forced cowboys to turn from nomads to settled ranchers.

These days we marvel about UPS and Fedex logistics. 150 years ago, the supply chain pioneers were Russell, Majors and Waddell. They provided wagons and supplies to countless families who trekked west. Their single most prominent innovation was the Pony Express which could deliver a letter cross-country in 10 days. That was lightning fast in 1860! But the service only lasted about a year. A new technology – the telegraph – made the service look painfully slow and unreliable.

Mark Twain would likely have never become a famous  author. The Civil War interfered with a job he really enjoyed – as a steamboat pilot. His real name was Sam Clemens and his adopted moniker was a river term for 12 feet, a safe depth for boats then navigating the Mississippi. 

The Civil War, horrible as it was, similarly changed the lives of millions of other young workers. It gave a generation of American males on horses and trains their first taste of wanderlust. That along with immigrants created a mobile workforce which has become a hallmark of the US economy.  The war allowed young ladies to become nurses, tailors, farmers, even spies. Technology in the form of sewing machines, typewriters and telephones created millions of jobs over the next few decades for these ladies who had seen life outside their homes.

The railroad also led to all kinds of job related immigration. Chinese workers helped in the construction of the Transcontinental railroad. Scandinavian workers helped with the logging industry as the country’s population spread west. E. European workers helped create our giant wheat and other farms in the Great Plains.

I could go on. The 1800s are a fascinating study of countless occupations which were born, blossomed and transformed in the US.

Just a few generations later we would do well to remember that labor tends to be mobile and that occupations evolve all the time. We can fret about machines, push for regulation, and keep ignoring the signals the labor economy consistently sends out. We have been not paying close attention to those signals and have ended up with a job economy with millions of unfilled blue collar and trade jobs. In contrast, we have way too many white collar graduates with backbreaking student debt. Worse, our immigration has for the last few decades not been aligned with these labor market realities.

Time to go back to listening to the labor economy. It is an amazing ecosystem which supports 800+ occupations and keeps making workers smarter, speedier and safer while absorbing ever new forms of robotics, drones, wearables, sensors, AI and other automation.     

IBM Red Hat: Reading the tea leaves

I have certain core IT values. I like customers to buy versus build systems . Have long been a buy cheerleader. But in return, I expect vendors to build not buy.  So, I have never been a fan of big acquisitions. Certainly not at massive multiples.

So my initial reaction to IBM’s $ 34 billion play for Red Hat is that of distaste.

But the opinions of bankers and investors matter more than mine so not going to comment more on that front.

Reading the tea leaves, though I wonder if this signals

a) The end of another hype cycle for AI?

I saw a recent Fortune issue which was all about AI and I wondered if that is a signal of the peak of the current AI hype cycle. BTW, we are going through the 4th or 5th hype cycle around AI. They appear and disappear every decade or so. As I wrote in Silicon Collar

“Since the 1950s! That is when Alan Turing defined his famous test to measure a machine's ability to exhibit intelligent behavior equivalent to that of a human. In 1959, we got excited when Allen Newell and his colleagues coded the General Problem Solver. In 1968, Stanley Kubrick sent our minds into overdrive with HAL in his movie, 2001: A Space Odyssey. We applauded when IBM’s Deep Blue supercomputer beat Grandmaster Garry Kasparov at chess in 1997. We were impressed in 2011 when IBM’s Watson beat human champions at Jeopardy! and again in 2016 when Google's AlphaGo showed it had mastered Go, the ancient board game. Currently, we are so excited about Amazon's Echo digital assistant/home automation hub and its ability to recognize the human voice, that we are saying a machine has finally passed the Turing Test. Almost.”

Is IBM’s pivot from the attention it has given Watson the last several years more of a confirmation of the peak?

b) A great disturbance in the Force?

I wrote earlier about The End of Cloud History I concluded

“Yes, we are approaching a pivot in the cloud apps market. This is where the geopolitics were in 1989. The next decade and beyond will be tumultuous – exciting for some, disrupting to others.

It is the end of cloud apps 1.0.”

Last week, Larry Ellison announced a more secure Gen 2 cloud. IBM’s play for Red Hat is about “multi-clouds” and given its traditional hardware and hosting strengths, I read more of a pivot towards private clouds.  

My fellow analysts who watch hawk-like for multi-tenant, public cloud architectural purity will be having conniptions with the cloud washing we will see in the next few years. 

I have a feeling J.J. Abrams could make a next career cataloging the cloud pure and evil that are about to unfold.   

“Draw a bigger circle”

We are approaching the two-year anniversary of Silicon Collar (released on September 2, 2016).  Many readers tell me their favorite book I authored is still The New Polymath. Amazon tells me SAP Nation sold the most.

Many disagreed with the thrust of Silicon Collar that automation takes decades to kill jobs. In the interim, automation actually makes workers smarter, speedier and safer. It changes the nature of work and often generates new types of work.

To me, it was one of the most significant books I have written because it reshaped my own approach to work and life. Let me explain.

The Background

When I started to write the book nearly 3 years ago, there was near-panic about machines taking over human jobs. Gartner had projections like “By 2025, three out of 10 jobs will be converted to software, robots or smart machines.” and “By 2018, digital business will require 50% fewer business process workers.” Two Oxford U researchers had an even more pessimistic assessment: “According to our estimates, about 47% of total U.S. employment is at risk.” WEF, McKinsey, MIT and many other thought leaders had similarly large and scary projections about job losses.

I read most of the research and interviewed other market watchers. Very few of them had talked to practitioners who were approving investments in, testing and working alongside automation. Others were one-dimensional – they would only base their research on projections around one type of automation like RPA. Still others appeared politically motivated – wanted more social benefits like Universal Basic Income in their anticipation of catastrophic job losses.

So, I did what Bill Joy (the Sun co-founder among many other things) likes to say “If you cannot solve a problem, make the problem bigger. If you draw a bigger circle, you start to see several systems you can work on.”

I interviewed 50+ practitioners in as many industries/work settings. They told me about machine learning, robotics, drones, wearables, exoskeletons, self-driving vehicles and many other forms of automation. More importantly they told me what worked, what did not, how expensive the machines were. They were pragmatic, not hysterical about the machines they were working with. They told me machines are good at certain tasks, not complete jobs. They told me about unintended consequences – how automation often leads to new, different jobs.

I then spent a lot of time on a very dry subject – data from the Bureau of Labor Statistics. Our labor economy is majestically diverse – supports over 800 occupations. And it is layered – in franchises, on platforms, concentric circles around the Fortune 500, the Federal government etc. – see my posts The Alt-Job economy and The Clover-Leaf Talent economy . I found very few market watchers understand these layers in our complex job economy and so use glib terms like “gig economy”

Most importantly, I spent time looking at automation in the last century – UPC scanners, ATM machines, how cars have been taking over driving from humans. I found UPC codes actually improved inventory control in stores and allowed them to order more SKUs. Sales went up and grocery checkout jobs increased. ATM machines transformed the role of the teller – made them customer service and sales reps. 50 years later, just in the US we still have 90,000 bank branches with over half a million associates. Cars have gradually been self-driving since cruise control was introduced in the 1960s, and yet globally 50% of new cars are still sold with stick shifts.

I concluded in an article called Slow-Motion Automation  “… societies absorb automation at a pace that’s much slower than technology’s evolution. And five powerful “circuit breakers” help delay and influence the trajectory of automation in surprising ways.”

People call me a luddite for not accepting machines are becoming faster and killing jobs. I accept that technology progress is accelerating, but its societal adoption is not accelerating and the impact on jobs is still pretty gradual. 

So, how has the book research changed me personally?

Made me curious about what people do every day

Like most people, I knew from my neighbors, friends and clients we worked in oh, 30-40 occupations – attorneys, brokers etc. When I wrote The New Polymath in 2010 I thought I had researched a wide range of occupations that Renaissance Men excel at.  The BLS data opened my eyes to our much, much larger set of occupations.

Now, when I watch a movie like Arrival, I observe that the Amy Adams character is a linguist who is fluent in Portuguese, Farsi, Sanskrit and Urdu and is qualified to decode alien language. Or that the Matt Damon character in The Martian is a botanist and as he says he literally has to “science the shit” to survive. I enjoy meeting with Mike Rowe and watching his Dirty Jobs episodes which highlight jobs we don’t even know existed.  When I meet a Phil Manougian at the National Hurricane Center, I like learning trivia that he is a NOAA Commissioned Corps Officer. It is one of 7 Federal Uniformed Services (others are Army, Marines, Navy, Air Force, Coast Guard and Public Health Service). When I go to a National Park, I seek out Park Rangers. Each has a fascinating story about his/her career path and all kinds of anecdotes from the wild. On flights, I am genuinely curious about what my neighbors do for a living. If they are willing to talk, I listen especially if they happen to be metallurgists, geneticists or in other occupations I don’t encounter on a regular basis. 

Made me more cautious about academia and analysts

I was shocked that the Oxford study I mentioned above had been cited and repeated in over 600 other academic publications, none of which chose to point out the flaws in the methodology as I had in the book. 5 years after their scary projections, not a single job has been lost and yet they have not chosen to reverse their study. Given the immense influence Oxford has, I find that irresponsible. At least, Gartner has backed off from its own pessimistic projections.

Now when I see a widely cited analyst and academic study on any subject I check to see if people have truly kicked its tires.

Made me much more interested in “history of work”

Like most technologists I like to look ahead, not back. I have written plenty about the “Future of Work”.  Silicon Collar forced me to do a lot of research on automation which originated in the last century – how it originated, how it gradually got adopted, the societal impact it had. The book made me go back and study the evolution of occupations. I wrote  “In fact, many of us continue with names which reflect the trades of our ancestors. It could be the Chinese Chong (derived from bow maker), the English Weaver, the Egyptian El-Mofti (from Arabic for legal expert), German Baumgartner (related to orchard), or the Indian Bhattacharya (from Sanskrit for teacher)—and there are thousands of other names derived from occupations in various societies.”

For my next book, I have been reading a lot about how the US tamed its West in the 1800s. Again there, my keen interest is in how occupations evolved.  There I am learning about Meriwether Lewis and his already wide set of skills when President Thomas Jefferson picked him to lead the Corps of Discovery. Then the President arranged for him to get a crash education in many other disciplines “Andrew Ellicott taught Lewis map making and surveying. Benjamin Smith Barton tutored Lewis in botany, Robert Patterson in mathematics, Caspar Wistar in anatomy and fossils, and Benjamin Rush in medicine.”  

It is fascinating to read about our Mountain Men and their mapping and trapping skills. About our whalers and how the light bulb affected their fortunes. Or our cowboys who arbitraged and moved cattle from Texas to the rail junction in Kansas for sale in Chicago and the Northeast. The early logistics pioneers at firms like Russell, Majors and Waddell who outfitted the oxen wagons the pioneers took west, and the Pony Express, the “human telegraph”. So many colorful occupations adorn our history books. Did you know Sam Clemens aka Mark Twain was a steamboat pilot for a couple of years in the 1850s on the Mississippi river before his writing career took off ? Granted he was fictional, but does Captain Ahab not intrigue you about what a real whaler’s life was like when the book was written around the same time?

So, in balance I continue to be fascinated by the ever evolving job economy. We have a choice of 840 occupations the BLS is tracking. The list will be updated in 2018 and I expect it will be longer and have more STEM influence. Our workers are getting 2nd, 3rd, later acts in life. No other country has so many new gen opportunities — franchises (about 7 million jobs), platforms (Apple, Amazon fulfillment, eBay work at home, Uber etc — about 20 million part time for now but rapidly changing ), new services — alternative healthcare, ethnic groceries, pet/child care etc.(another 5 million). I am not even including Silicon Valley type entrepreneurship opportunities in energy, space, food, IT etc.

Yes, lifetime employment and pensions are elusive, but we have ended up with a remarkable new labor economy that our parents would have drooled to be part of. The book research convinced me what a great time it is to be alive and working.

I look forward to drawing even bigger circles!

Automation, “Superworkers”, ERP and CRM

I recently did a podcast with Ton Dobbe. I have know Ton for a long time in various roles at Unit4. He always impressed me with his work with organizations around the world around topics such as accelerating business change, fighting business disruption, boosting operational efficiency and adopting digital transformation.

He recently started his own consulting firm, Value Inspiration and invited me to talk about themes from Silicon Collar and how automation is helping make all of us much more productive workers and how ERP, CRM and other business software vendors should be thinking about machines and their impact on business processes.

Here is the podcast – we cover wide ground about automation and digital transformation in many industries. Ton is a very positive, optimistic person and the half an hour flies pretty quickly.

The “Star Wars” Jobs economy

Lots of people sat through the credits at the end of The Last Jedi waiting for a post-credit scene or for bloopers.

Margaret and I did to see all the job titles that went into making the movie. It is a practice we started a few years ago. It’s fascinating to recognize the people who bring these movies to life (some profiled in the video below) and how CGI, sound and other technology continues to work alongside the human element, not replace it. In Silicon Collar, I had a section which looked at the man-machine mix in the animated movie, Zootopia.

Star Wars has plenty of digital talent – SFX technicians, droid puppeteers and LIDAR techs. It also has old school carpenters, stunt artists, travel coordinators, weather consultants and costume makers.  It is a microcosm of our changing labor economy.

Before you say but this is not the real world, with every episode the science-fiction becomes science-fact. After episode 7, Infoworld wrote in 2015 about 10 technologies that are “almost here”. With this episode, US News writes about “Real-Life Tech” inspired by the movie. Wikipedia  has an bigger section on how lasers, cybernetics, prosthetics, biotech use in the movies don’t seem so distant. Think of the ripple effect on jobs in medicine, energy, defense.

Want more reality? Luke Skywalker hides in a spectacular, remote island called Ahch-To. That is actually Skellig Michael, in the SW of Ireland. Other scenes were shot in Dubrovnik, Croatia and at the salt flats in Bolivia. Should open up new tourism jobs.

Then there is the impact on jobs in the theater and concessions industry. The AMC I went to has new reclining seats, Dolby Surround Sound, IMAX screens. Lots of concession sales.

Then there are new toys like the Porgs and R2-Q5 in the movie and many others here. Lots of Christmas retail and delivery jobs.

The film has made $450 million around the world since opening overseas Wednesday. And it will not even open in China till January 5.

Long and short – plenty of jobs, digital and old school. In a galaxy pretty close to us. 

Tone down your AI expectations

We have had many previous hype cycles around AI. As I wrote in Silicon Collar : “Since the 1950s! That is when Alan Turing defined his famous test to measure a machine's ability to exhibit intelligent behavior equivalent to that of a human. In 1959, we got excited when Allen Newell and his colleagues coded the General Problem Solver. In 1968, Stanley Kubrick sent our minds into overdrive with HAL in his movie, 2001: A Space Odyssey. We applauded when IBM’s Deep Blue supercomputer beat Grandmaster Garry Kasparov at chess in 1997. We were impressed in 2011 when IBM’s Watson beat human champions at Jeopardy! and again in 2016 when Google's AlphaGo showed it had mastered Go, the ancient board game. Currently, we are so excited about Amazon's Echo digital assistant/home automation hub and its ability to recognize the human voice, that we are saying a machine has finally passed the Turing Test. Almost.”

The good news is over the seven decades, the AI community has gifted us a wide range of big words like deep learning, neural networks, cognitive computing and natural language processing.

Yale computer science professor David Gelernter thinks we have only scratched the surface. In his book The Tides of Mind, he calls it “the spectrum of consciousness,” which is “essentially a range of mental states through which all humans cycle each day. The cyclical element is crucial and underlies his metaphor of tidal motion. At the upper end of the spectrum, mental high tide, we are focused on the outer world, biased towards logical and abstract reasoning, and more likely to remember our experiences later. But as we drift down through the middle and into the lower reaches of the spectrum, we become increasingly conscious of the inner worlds of memory, prefer narrative to logic, and cross eventually into the difficult-to-remember realms of dreams.”

But we are told this time it is different. We can train machines with the gobs of Big Data we have been collecting and we have computing power in the cloud like we have never had before.

But do we really have enough of each? Listening to Ginni Rometty, CEO of IBM at Dreamforce last month (see interview below – Marc Benioff has always been a very respectful interviewer, and Ginni is in fine form), I have my doubts. Ginni makes the point that 80% of data is not searchable. I would argue that ratio for enterprise data is even higher. Most useful data is locked up in on-prem systems. Getting permissions to have broad samples of such data will be no easy feat. Then you have the issue of semantics and master data rationalization across the multiple sources of data. We are only now seeing early versions of natural user interfaces by training machines around decades of voice/accent data collected by the likes of Nuance in transcriptions ,voice mails we have been leaving each other since the early 1980s and photos we have been uploading into the cloud.

Ginni is also preparing us for a world of quantum computing because for a growing range of scenarios even today’s supercomputers (or cloud infrastructure) are not enough. Take the trajectory of weather forecasting. Decade after decade, data sets have become larger with satellites, buoy sensors and Hurricane Hunter type sorties collecting data on moisture, temperature, wind speed and other metrics. The modeling has become way more complex. So, the thirst for ever increasing compute capacity continues. Forecasts keep getting better and better – but tell that to many of my fellow Floridians who evacuated multiple times as Hurricane Irma made a mockery of most forecasts. As a renowned meteorologist said “I was very surprised not by how one model was going back and forth -- but by how all the models were going back and forth.” Exactly, now wait till Salesforce's Einstein and IBM's Watson start contradicting each other :) Customers are being told they need both.

Ginni does not overtly talk about it, but 50% of IBM revenues come from services. Go ask early Watson healthcare clients how much of the tab was in services. IBM's interest in Salesforce's AI tool, Einstein is being driven by its Bluewolf services unit. Amazon offers its engineers as consultants via its ML Solutions Lab, Google via its The Machine Learning Advanced Solutions Lab. We will need highly skilled labor to train the machines. And we will also need tactical labor to keep refining the data. Amazon has its talent networks – the Kindle author, the Flex courier, the Mechanical Turk and fulfillment networks it can mine. Apple has its iTune and iOS networks to draw from. 

Enough usable data, enough compute, enough talent? Take a hard look at each from the lens of your AI project.

Far more tactical implementations of Robotic Process Automation (RPA) are more promising. RPA is not machine learning, it uses software bots to mimic human activity – like logging in to a system, copying and pasting data across systems. The payback is much lower than what AI/ML promises, but it removes the drudgery and increases productivity of many white collar jobs. 

Mihir Shukla of RPA vendor, Automation Anywhere told me in an interview for Silicon Collar

“Think of the impact of this on real estate requirements. I can take what would need offices in six large buildings and put them on two racks. I can help consolidate global offices. A cognitive bot can process an invoice in 30 languages—I don't need centers in the Philippines and other centers in Japan to deliver diverse language skills. Think of the employee recruiting and onboarding time we can help save.”

It is a more traditional definition of savings that comes with automation, and far more realistic. But even there, many services firms are pushing RPA which is a bit of a red herring when it comes to implementation costs.

Of course, many companies are being deluded by the promises of AI (and broader automation). J.P. Gownder, a Forrester analyst says if you don’t have realistic expectations “You’ll be inclined to automate too many roles — at the expense of both customer and employee experience.  You won’t hire the right mix of new roles— or any new people at all.”  In other words besides seeing poor payback from your AI projects, you may hurt other critical parts of your business.

On the flip side, we may not need to worry as much when Elon Musk says AI is more dangerous than N. Korea. Or when Prof. Hawking says AI will end mankind. The bad guys will face the same data, compute and talent shortages that AI for good is facing.

Tone down your AI expectations. And get ready for the next generation of AI big words.

The Future of Work–in an automated world

This time, last year I felt very lonely. In writing Silicon Collar, I was one of the few voices who is not pessimistic about automation killing masses of jobs. I kept getting asked, how can you ignore the rapid pace of technological evolution?  I kept telling people adoption of technology has not evolved as rapidly. Society has its “circuit breakers” to too-rapid automation. Read about the century of automation I have cataloged in the book and how tens of millions of jobs have changed, but survived, in most sectors. Yeah, but this time it is different, I would hear back.

Last week, I finally felt some vindication. I was at a Cognizant event which celebrated work and workers. Even the background music was mostly about the joys of work. As a slide referenced, it was as Robert Plant of Led Zeppelin often called it “a song of hope”. There was nobody clamoring for a dole or Universal Basic Income. It was about how automation is transforming work or creating new type of work. And yes, it was good to hear themes common with those in my book. Automation targets 3D tasks – dull, dirty and dangerous ones. Work has been central to mankind for millennia. Our last names handed down for generations convey that fact. The handouts had emphatic statements like “But a world without work is a fantasy that is no closer to reality in 2017 than it was 501 years ago upon the publication of Thomas More’s Utopia.”

Oh, there was a lone dissenter – Prof Vivek Wadhwa who kept saying (without much data to back him up) that jobs are lost. His pessimism only made the pragmatism of the others stand out even more. And even he was optimistic about future of healthcare and renewable energy.

The Cognizant speakers like Ben Pring – most ex Gartner and Forrester analysts and now part of its Center for the Future of Work – unveiled 21 new jobs they believe technology will help create. The graph below summarizes them.  10 of the jobs are in B2C settings, the rest in B2B settings.

Some of the job titles are awkward sounding but each is believable. Who would not like to see a member of their family benefit from a “augmented reality journey builder” before dementia or Alzheimer’s potentially afflicts them? Who does not think we will need analysts who can help run our increasingly smarter  cities? How can you argue with any of the jobs (which they say they identified from hundreds of candidates) all of which carry some element of coaching, connecting or caring? This 60 page white paper describes each of of the 21 jobs in some detail.

Another session had Danish serial entrepreneur, Dennis Mortensen, founder of x.ai, which has developed two AI driven personal assistants, Amy and Andrew which schedule your calendar. His humility was breathtaking. His highly focused bots have taken him years to build and mature. He did not expect AI to  dramatically reshape the world of more complex work anytime soon.

A guest speaker was J.P. Gownder of Forrester. He had a nice framework for how work is evolving – machine-first, machine-human teaming and humans-first. Even in machine-first scenarios like at  the Goldman Sachs cash equities desk, the number of traders has dropped from 600 in 2,000 down to 2. But now 200 software engineers support the automated trading. In contrast, in a human-first scenario, Autodesk is hiring novelists to write scripts for its chatbots.  He had plenty of other examples of man leading or following machines or teaming with them. But each still has a human element.

To me, one of the best slides from the day was about this bell curve. While the day focused on the right of the curve and new jobs, I am much more heartened by the bulk in the middle that are being transformed by technology. I have written about roofers who are using drones and tools like EagleView. I have written about digital calipers, ring scanners and AR goggles on the shop floor. Gownder had examples like Robin Technologies which is bringing Roomba like robots to lawn care. Yes, as they improve in horsepower and battery life, they will take away the drudgery of cutting grass in the summer heat, but they will also allow the care taker to focus much more on landscaping. That’s what automation has always done – allowed humans to work smarter, safer and speedier.

I expect Cognizant to keep evolving its research on the topic. Try and catch a similar session like the one I attended in a city near you. It will inspire you or at least make you feel less morbid about automation. And the music will certainly cheer you up.

Two decades of HR Tech

Post updated with a change in source document

Stacey Harris sent me a preview copy of the Sierra-Cedar annual HR Systems survey. It is the 20th edition, and I particularly enjoyed the walk down memory lane when I was following HR applications at Gartner.

“20 years ago, David Link and Alexia (Lexy) Martin, the first authors of the HR Systems Survey, embarked on a journey to understand a new and emerging trend of self-service HR: the idea of giving candidates, employees, and managers access not only to data, but also to tools that they could use to help make decisions and improve their work environments. The Internet was in a state of infancy, and most organizations were still working through mainframe versus client-server application discussions. With Y2K just right around the corner, and all of the anxiety and optimism coming with the start of a new century, the industry was ripe for change. “

Get a copy of the report here for the nostalgia if you will. But especially do so for the chock full of data on the HCM market. There are 121 graphs – on average one a page. They cover wide ground from cloud adoption by HR application area, to HR analytics to mobile and social HR tools. Some of the data will surprise you – like how low cloud penetration is in many areas even though HCM has led other functional areas in cloud adoption.

After my last book, Silicon Collar about automation, I get the sense HR is in for a huge change over the next few years. Automation via AI, robotics, drones, wearables – is changing the nature of work in every profession. We will see new job titles emerge we did not anticipate just a few years ago. More of the workforce will be on platforms, in franchises and other “extended enterprise” formats. Workforce planning will increasingly factor in machines and related asset planning and maintenance. That will have a Big impact on recruiting, learning and every other aspect of HR.

I have a feeling the next few Sierra-Cedar surveys will increasingly reflect those changes. In the mean time, back to digesting all the data in the latest one. I look forward to more nostalgia as the survey reaches its 25th birthday.

“The Definitive Guide to Connected Manufacturing”

Plex has a neat publication for the new world of Advanced Manufacturing (see many examples in the resurgence that Greenville, SC is showing as a factory town) and where we are seeing the Fourth Industrial Revolution. Plex calls it Connected Manufacturing.

They sent me a glossy copy, but anyone can download a PDF version here

I am quoted in the publication

“Connected Manufacturing is about putting the modern shop floor with its robotics, sensors, wearables and 3D printing at the center of your business universe. Doing so allows you to deliver much smarter products, delights your customers with highly personalized offerings and turbo charges your supply chain.”

While most ERP systems have their genesis in the MRP world, most have drifted away from the shop floor and largely deliver back-office, corporate financial, procurement and HCM functionality and emphasize their cloud delivery models.  Don’t expect to get from just that the massive efficiency advanced manufacturing sites are reporting.