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Notes from Reality. An Interview with Dr. David Bray and Michael Krigsman on Ethics and Artificial Intelligence.

By on September 28, 2016

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This is a series of interviews with notable luminaries about the state of AI, the impact AI will have on businesses, and about the ethical implications of intelligent computers. Discussing ethics is always a tough subject. The IEEEhas formed a major initiative to examine the ethical dimensions of AI.

The Global Initiative for Ethical Considerations in the Design of Autonomous Systems is an Industry Connections Program of the IEEE Standards Association. It was formed with two primary deliverables: create a document featuring key issues in AI/AS grounded in ethically-aligned design; and, make Standard Project recommendations to IEEE-SA based on these issues.

Dr. David Bray, Harvard Executive-in-Residence and Eisenhower Fellow, and Michael Krigsman, industry analyst, author, and the host of CXOTalk, are co-chairs of the EPIC/AI committee, one of the seventeen committees comprised of over one hundred global experts in The Initiative.

The EPIC/AI committee includes representatives from the government, independent research organizations, academia, and the commercial sector and we’re examining the public policy aspects of AI and autonomous systems to guide The Initiative’s work. It is essential that policymakers consider all these perspectives.

Ultimately, the document our Committee/The Initiative is creating will be released under a Creative Commons license for any organization to adopt to help prioritize ethically-aligned design in AI/AS. It will also be directly submitted to IEEE for formal consideration, re: policy decisions by their official channels handling these issues.

JT: What is broken in the business process that sets the ground for AI?

MK: The internet has, paradoxically, flooded us with volumes of information that now make current processes unable to move with sufficient agility and resiliency to keep up with this “digital tsunami” as Dr. David Bray, who is also the co-chair of the committee, likes to call our era. AI can help organizations make better decisions more quickly. The impact is profound and will affect many industries and processes, which is why this topic is so important.

JT: What are the biggest ethical concerns for AI

MK and DB: The ethical aspects of AI center on development, use, and application. AI offers its maker advanced capabilities that can be applied to fields as diverse as robotics, medicine, autonomous vehicles, weapons, and much more.

As with any technology, the developer’s goals and objectives dictate how AI technology is used and in what fields it is applied. Given the power of AI to mimic human decisions and intelligence, the question of application is crucial to consider.

For example, imagine AI technologies in the hands of a government planning to identify and target specific populations or groups for attack or discrimination. Most people would say this is an unethical use of AI.

What about companies using AI to target consumers with levels of personalization unattainable today. At what point do we cross the line between appropriate and inappropriate use?

The answers are often not easy or clear and depend on personal values, organizational policies, and a host of other factors on which reasonable people can disagree.

JT: Can AI be perceived as a threat to human dignity? If so, what is the logical fallacy that would mitigate this concern?

MK: AI is a set of technologies that we can use for either good or evil (although even stating the issue in this manner creates an over-simplified caricature). We cannot today anticipate the full scope of how AI will affect our world in the future; this quality of unknown implications is similar to technologies such as nuclear fusion in its earliest days or even the Internet when it was still just a set of protocols.

DB: Humans may choose to use nuclear technologies to create safe nuclear power plants = ethical. They may choose to make nuclear warheads as a deterrent = questionably ethical depending on your philosophy. Nuclear warheads as a way to kill their neighbors to seize more territory = unethical.

MK: Our ethical challenge is developing policies that enable AI and autonomous systems while ensuring they serve the benefit of humankind rather than accelerating that which is “evil.” The problem is significant because definitions of “good” and “evil” are relative and depend on one’s perspective.

DB: At the same time build airplanes to save lives in crisis situations = ethical. Airplanes for travel = neutral or borderline ethical if your philosophy supports growth or freedom of action. Airplanes to fly into a nuclear power plant as domestic terrorism = unethical.

DB: It’s important to separate specific tools from the broad tech in this discussion. Also getting people to articulate whether a war is ever ethical or not is a really had thing to do (it is doable, just hard). I personally think there can be just wars — they’re just rare and still done with a heavy heavy heart and mind.

DB: You’re absolutely right though. If an AI decides that they best way to save lives is at the expense of your own life (the utilitarian/Mills lens) then where is any human agency in that choice?

Or that the means justify the ends (the deontological/Kant lens)?

I am a Rawlsian personally, specifically his theory of the Veil of Ignorance and recognition that from moment we are born our perceptions of what is just are colored by who we are — our gender, race, age, health, social status, etc. all bias what we think “others should do unto others” and what the “maximum benefit to society” is. That’s what makes it so hard to have a philosophical discussion with people from different backgrounds because in their own minds they’re each right.

If we could somehow put on a “Veil of Ignorance” and not know who we would be — prior to birth — and not know if we would be healthy or unhealthy, smart or not smart, a certain gender, certain race, certain social demographic, live somewhere, etc. — what then would we want to agree to as social norms?

This gets to your “unknowable part”

Perhaps a benevolent machine learning could illuminate it. (Rawls argues we would pick norms where whatever group is worse off — and there will always be a worse off group — are “the best of” of all possible scenarios, because none of us know if we might be born as them. I am more interested in the Veil of Ignorance as a philosophy than his conclusion).

Of course if we did somehow construct a benevolent machine that could illuminate how we all could live better — if you jump back to Plato’s The Republic, he would claim (through the voice of Socrates) that fairly quickly we humans would kill any benevolent, uncompromising, single ruler. Because we want compromises and we don’t want the truth that perhaps we have too much or we need to contribute a larger share, etc. Such is human nature: politics is the art of compromise because humans want compromises vs. the truth.

So would we try to kill the machine? Cling to the last vestiges (however frayed) of political caucuses?

Fast-forward to the philosopher Rousseau who, when asked by Poland how best organize their country, recommended they “cut your country in half” and again, and again. Arguing that all politics are local, and efforts to do them broadly always dilute what individuals really need in an effort to find ever increasing complexities of compromise.

Maybe our future — much like what has happened with the ability to personalize what we each see and receive digitally via the Internet through data analytics — can only be solved when we engage each other as countries of one. We would have AIs that would share our preferences for what we “permit to be done unto us” — and those people we meet would do the same. If someone tried to do something to us we did not permit, non-lethal protection and/or of them or us from the situation would occur automatically via the AI. Same if we tried to do something to someone they did not permit. Expressing all that we permit and don’t permit explicitly would be impossible, so it would require a learning machine of our preferences, which would need to understand context and changing sentiments.

Does having every human being super-empowered as a “nation of one” via AI sound scary or anarchy? It’s worth recognizing right now the true state of international affairs (to wit the North Korea missile tests, or the Russian “war game exercises” in Crimea) is anarchy. The only difference is almost all of us have little sway over what nations do on our behalf, nor do we possess means to defend ourselves should national actors attempt to do something we do not permit.

Choice architectures. Individuals use AIs to express what they permit both digitally and physically to them in real-time. In return there are trade-offs — want to fly this plane, participate in this process, etc. then you have to make choices.

AI can help to address the ever increasing connectedness and complexity (in the truest form of the word complex meaning systems that possess multiple feedback loops) of our world.

And in some respects returns us back to the Golden Rule, adapted: “do unto others as they would permit you to do unto them.”

(Cross-posted @ Medium | John Taschek)

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Autonomous Vehicles: The Empty Re-positioning Challenge

By on September 28, 2016

Over the next 10 years it is estimated that the number of autonomous vehicles on U.S. roads will become a mainstream force. The rate of progress to this goal is accelerating, building on the success of technology-enabled safety features such as lane assist, adaptive cruise control, and emergency braking. We first saw these technologies come together with self-parking features, and 15 years of continual innovation and evolution are now at a point where self-driving vehicles are a reality.

The stress that will be exerted on already stretched infrastructure will be significant because of the very nature of this leap forward in capabilities. Roads, enforcement, regulations, and the legal system are all designed to work within the framework of a human-controlled automobile. Roads in particular are organized to protect us from ourselves, with speed limits fixed irrespective of the road conditions and flow patterns designed to move a person from A to B.

There is a consensus that car sharing services will continue to expand and with autonomous vehicles go into overdrive as car owners turn loose their vehicles when not in service of the owner. I would not rule this out but it is an overreach to assume that people will do this by default. Personal property is just that and I, for one, would not take kindly to having my vehicles used by strangers unattended. I also don’t think people realize how much the cost of maintenance and depreciation is per road mile, therefore the economic proposition of car sharing may not be a good deal for everyone.

For people leasing vehicles, the mileage restrictions alone prove onerous to car sharing use cases. This is important because lease penetration broadly is about 27% of the total U.S. car market, but as you move up the price tiers the lease penetration increases to 50%. Self-driving technology is an expensive add-on, even more when coupled with EV and hybrid drive systems. It is predictable that over half of all self-driving vehicles will be leased.

So let’s assume that the majority of the market for self-driving vehicles, acquired for personal use, will be strictly for personal use.

Putting aside the obvious challenges that autonomous vehicles present to the legal system – liability – let’s take a look at something not often talked about in the context of autonomous vehicles. Empty repositioning, the process for moving vehicles that do not hold passengers in order to retrieve and transport passengers.

I was first introduced to this problem while looking at an investment in the ocean shipping industry. The flow of trade between countries is rarely equal, the U.S. brings a massive number of full shipping containers from China while sending a much smaller number to China. Left unattended, China would eventually run out of shipping containers, so there exists a well understood process of empty shipping container repositioning. Container ships returning to China carry empty containers in addition to loaded containers. The process is not that complicated, made easier by the fact that shipping containers are standardized in size and configuration and a shipper doesn’t care whether they get a Maersk or APL branded container, they are all the same.

The empty repositioning problem in shipping is solved by loading empty containers on ships headed back to Asia. This is complicated by the fact that empty containers have to be loaded at ports which are not the final destination, but port operators and shipping companies have developed processes to ensure that empty containers are loaded optimally for weight and unloading of full containers at other ports. None of this applies to cars though.

The challenge to public infrastructure comes from the loading of roadways with vehicles that do not contain passengers. For 4 decades the investment in transportation infrastructure has favored incentives to high occupancy vehicles (HOV) over single occupant vehicles (SOV) and public transportation. As HOV lanes, which in CA see about $2.5 billion of investment each year, have proven to not increase carpooling and remain underutilized (LAO report link), toll roads have gained favor.

Unfortunately, car sharing and HOV lane utilization continues to lag projections and the typical HOV lane carries fewer passengers per mile than a non-HOV lane. The challenge that self-driving vehicles presents is enormous because in that scenario a typical SOV will be making 4 trips per day instead of 2.

Autonomous vehicles represent a cataclysmic tipping point for how governments allocate funding for roadways. The entire public transportation system is designed around a premise that increasing congestion will drive people from their cars on to mass transit. In dense regions that are land constrained, the Bay Area being representative, public officials don’t even bother to obscure the strategy increasing the pain of driving.

Here we see a technology that massively disrupts not only traditional businesses, but also public policy on mass transit. Drivers don’t have to drive during their commute, and can send their vehicles home when they are not using them, avoiding the cost of parking. Transportation planners are wholly unprepared for this future, and given the hundreds of billions of dollars that have been spent on public transit systems that are inflexible and capital intensive hub-and-spoke systems, I forecast a lot of pain ahead.

(Cross-posted @ Venture Chronicles)

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And nearby somebody finally gets it – new interaction programming models

By on September 28, 2016

I saw a tweet this morning that caught my eye.

Having written about Conversational IoT earlier this week I thought it was worth a quick follow up. I said then that

The nature of the current dominant user interface – the apps – is breaking down.

The existing screen obsessed mobile first model, where we’re all looking down at our phones like

kodak

So from my perspective Google’s Nearby platform is very interesting because it has a design principle based on interaction between a number of devices and end points. Generally development has been about this screen or that screen rather than this screen and this screen. And now we’re throwing voice into the mix with Amazon Echo, Siri, and Cortana.

Even watching TV isn’t a one screen activity any more – we’re tweeting from the other screen at the same time. I am surprised nobody has nailed the mobile for sports data, augmenting the TV experience yet. What’s interesting to me are the new models for interaction between all the devices and services we use. Amazon’s dash buttons are another non screen-based end point.

Beacons have been around a while, obviously, but Apple and Google are in unique positions to get this right. I will have to look at it more closely but at first glance Nearby looks like a really interesting framework, in that it comes at things from the interaction rather than the screen perspective.

Nearby Messages exposes simple publish and subscribe methods that rely on proximity. Your app publishes a payload that can be received by nearby subscribers. On top of this foundation, you can build a variety of user experiences to share messages, create real-time connections between nearby devices, and receive beacon messages.

Nearby Connections enables your app to easily discover other devices on a local network, connect, and exchange messages in real-time. Use Nearby Connections to create multiplayer and multi-screen experiences.

Nearby Notifications is a new feature allowing developers to tie an app or website to a BLE beacon and create contextual notifications, even with no app installed.

I can’t wait for better heads up design, interaction and programming models. The idea we’re redesigning urban signage for example to accommodate our cricked necks seems crazy to me. In Augsburg they’re now embedding signals into the road after a girl was hit by a tram.

 

 

(Cross-posted @ RedMonk)

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AI’s moment

By on September 28, 2016

halThere’s lots of talk pro and con these days about bots, AI, and intelligent assistants. A lot of this talk is not necessarily new; it’s been percolating around the industry for decades. Vinnie Mirchandani, a friend and truly gifted analyst, wrote a book recently, Silicon Collar that accepts that this automation might be eliminating jobs but optimistically holds out for the silver lining. Mirchandani firmly believes and documents how businesses and individuals are taking advantage of an opportunity to build new human mediated processes (and jobs) that leverage intelligent systems.

Another friend, Esteban Kolsky who is also a gifted analyst, says not so fast. Like many of us Kolsky has seen this movie before. He points out that adoption has been painfully slow—so slow in fact that AI fails what I call the Gates Test. You might recall that Bill Gates once said that we over-estimate what we can do in two years and underestimate what we can do in ten. Indeed, the gestation period for an overnight success seems to be ten years these days.

But as Kolsky points out in a recent post, “The latest survey, to be shared at Dreamforce 2016 and published soon thereafter, says that from the single digits in adoption they enjoyed for the entire 2002-2012 decade we are seeing adoption nearing 15% now for automated bots and intelligent assistants.” Slow indeed. What’s been holding things back has been a lack of two things: 1) not enough computing power and 2) a clear need.

We can all think up scenarios where a little help from something with AI embedded might be good but on closer inspection we realize there are other ways to get the job done. AI is a heavy lift, or at least it was once. Back when the working models of AI were set down, computing power was not up to the job but really fast processors, multiple cores, flash memory, and the cloud have made it possible to concentrate the power needed to drive AI. But this still leaves us with finding a clear need.

I offer the following analogy: we live in a spreadsheet-dominated world with a linear mindset but we are moving to a world where the lines are anything but straight. To make sense of curved lines you need calculus. It’s calculus, especially the integral variety that tells us what’s going on in a process that has plenty of funky ups and downs. In the spreadsheet era, which I firmly believe is ending or at least transitioning, we searched for averages and made straight-line derivatives from them.

This led to some dumb ideas like calculating what an average deal is and trying to fit all deals into it as if it were a straight-jacket. It also harkens back to the statistical awakening in the 19th century when the term “average man” first came into use. The average man is a fiction but a highly usable one that gives us a basis for modeling.

But when you go for an average you have to ignore some profitable outliers or other things that don’t fit your model. In the age of business by transaction, the straight-line model was good enough. Nonetheless over most of this century so far, we’ve seen that model become less effective as the vendor-customer relationship moved toward the micro-transactions of subscriptions. A straight-line model doesn’t work very well in subscriptions because at a micro level all transactions look the same. It’s only when you expand your view that you can see the micro-transactions that show trends that might be good or bad. As a result we’ve been left without a model.

A model for the vendor-customer relationship that works involves calculus, at least at the metaphorical level. Calculus gives us the flexibility to model many variables involving customer demographics, purchase history, life-cycle stage, and of course the transaction before us.

I think many people in business have a working appreciation of all this, though they are certainly still in the minority and this is where AI comes in because I see its algorithms as calculus in a box. AI gives the average businessperson who has no interest in calculus, or who might have studied it decades ago, the ability to apply more sophisticated modeling to increasingly complex business.

So this is a long-winded attempt to say that at last we have a clear need for AI as well as the horsepower to run it. The need is all around us and if you’ve ever caught yourself wondering at how sophisticated business and our supporting systems have become, you’ll likely be grateful that there’s a new weapon in the arms race.

(Cross-posted @ Beagle Research Group)

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Why We Invested in ProsperWorks CRM

By on September 28, 2016

With so many CRM products in the market, I was not looking for a new CRM startup. But when I met Jon Lee, the founder and CEO of ProsperWorks, two years ago I thought he was on to something big by solving the biggest, unsolved problem of CRM:

Sales people hate entering data

They do it because they are forced to do it or they don’t get paid.

Its not as if every CRM vendor does not know this — they all do. I have worked for 2 of the largest CRM players in the market and we all knew what the challenge was so we worked hard to make the CRM beautiful, and mobile, and social, and cloud.

But Jon had a different idea:

What if we could make the CRM disappear?

The Zero Input CRM

What if you started with the tool sales people already use the most and spend their lives in — email. And build the functionality right into the world’s fastest growing email product: GMail.

The ProsperWorks team launched the product and has gained amazing momentum. In our diligence, we compared the revenue and growth to some of the fastest growing SaaS startups in year 1 and year 2 of their lives — like Salesforce, Marketo and Zendesk — and found ProsperWorks was on to something epic.

Source: G2 Crowd User Reviews

We wanted to dig deeper into what was fueling this growth over and above a winning product that customers love — with 4.8/5 stars on G2Crowd. The key to their growth was focus.

Secret to Success: Focus

A startup must decide what it wants to do insanely better than the incumbents. Jon chose to focus on Google and making ProsperWorks the best CRM for Google Cloud customers.

Every CRM company pays lip service to email integration. Just go to any of these leading CRM companies websites and review forums and you will see a litany of broken connectors and widgets that work half the time if at all.

By choosing to focus on Google Cloud, the team made a brave but strategic choice. ProsperWorks is one of six apps recommended by Google Cloud, and Google itself is a customer.

The Google Cloud Bet

As an investor, I am convinced Google Cloud — apps and platform — is growing fast and is on the cusp of an even bigger break out. The Google Apps for Work as its popularly known has been reported to be a multi-billion dollar business for Google, and growing fast.

At Storm Ventures, we believe Google Cloud under the new leadership of Diane Greene will continue to be an ecosystem where new leaders will emerge.

ProsperWorks has an amazing head start for companies that are betting on the Google apps and cloud ecosystem and with this infusion of $24M, I am excited to see what we can do in the next 10 years.

(Cross-posted @ Medium | Anshu Sharma)

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Aligned to Achieve: A B2B Marketing Classic

By on September 27, 2016

Tracy Eiler and Andrea Austin’s Aligned to Achieve came out yesterday and it’s a great book on an important and all too often overlooked topic:  how to align sales and marketing.

I’m adding it to my modern SaaS executive must-read book list, which is now:

So, what do I like about Aligned to Achieve?

The book puts a dead moose issue squarely on the table:  sales and marketing are not aligned in too many organizations.  The book does a great job of showing some examples of what misalignment looks like.  My favorites were the one where the sales VP wouldn’t shake the new CMO’s hand (“you’ll be gone soon, no need to get to know you”) and the one where sales waived off marketing from touching any opportunities once they got in the pipeline.  Ouch.  #TrustFail.

Aligned to Achieve makes great statements like this one:  “We believe that pipeline is absolutely the most important metric for sales and marketing alignment, and that’s a major cultural shift for most companies.”  Boom, nothing more to say about that.

The book includes fun charts like the one below.  I’ve always loved tension-surveys where you ask two sides for a view on the same issue and show the gap – and this gap’s a doozy.

sm gap

Aligned to Achieve includes the word “transparency” twenty times.  Transparency is required in the culture, in collaboration, in definitions, in planning, in the reasons for plans, in process and metrics, in data, in assessing results, in engaging customers, and in objectives and performance against them.  Communication is the lubricant in the sales/marketing relationship and transparency the key ingredient.

The book includes a nice chapter on the leadership traits required to work in the aligned environment:  collaborative, transparent, analytical, tech savvy, customer focused, and inspirational.  Having been a CMO fifteen years ago, I’d say that transparent, analytical, and tech savvy and now more important than ever before.

Aligned to Achieve includes a derivative of my favorite mantra (marketing exists to make sales easier) in the form of:

Sales can’t do it alone and marketing exists to make sales easier

The back half of that mantra (which I borrowed from CTP co-founder Chris Greendale) served me well in my combined 12 years as a CMO.  I love the insertion of the front half, which is now more true than ever:  sales has never been more codependent with marketing.

The book includes a fun, practical suggestion to have a bi-monthly “smarketing” meeting which brings sales and marketing together to discuss:

  • The rolling six-week marketing campaign calendar
  • Detailed review of the most recently completed campaigns
  • Update on immediately pending campaigns
  • Bigger picture items (e.g., upcoming events that impact sales and/or marketing)
  • Open discussion and brainstorming to cover challenges and process hiccups

Such meetings are a great idea.

Back in the day when Tracy and I worked together at Business Objects, I always loved Tracy’s habit of “crashing” meetings.  She was so committed to sales and marketing alignment – even back then – that if sales were having an important meeting, invited or not, she’d just show up.  (It always reminded me of the Woody Allen quote, 80% of success is showing up.)  In her aligned organization today, the CEO makes sure she doesn’t have to do that, but by hook or by crook the sales/marketing discussion must happen.

Aligned to Achieve has a nice discussion of the good old sales velocity model which, like my Four Levers of SaaS, is a good way to think about and simplify a business and the levers that drive it.

Unsurprisingly, for a book co-authored by the CMO of a company that sells market data and insights, Aligned to Achieve includes a healthy chapter on the importance of data, including a marketing-adapted version of the DIKW pyramid featuring data, insights, and connections as the three layers.  The nice part is that the chapter remains objective and factual – it doesn’t devolve into an infomercial by any means.

The book moves on to discuss the CIO’s role in a sales/marketing-aligned organization and provides a chapter reviewing the results of a survey of 1000 sales and marketing professionals on alignment, uncovering common sources of misalignment and some of the practices used by sales/marketing alignment leaders.

Aligned to Achieve ends with a series of 7 alignment-related predictions which I won’t scoop here.  I will say that #4 (“academia catches up”) and #6 (“account-based everything is a top priority”) are my two favorites.

Congratulations to my long-time friend and colleague Tracy Eiler on co-authoring the book and to her colleague Andrea Austin.

(Cross-posted @ Kellblog)

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Accenture Acquires DayNine and positions itself at the forefront of the race for Workday services supremacy

By on September 27, 2016

Why do the eyes of HR people always light up when you mention the word “Workday”?  Has Workday become the “complete suite” for the HR function, in a similar vein that SAP did with the CIO in the 1990s? 

Well, judging by the feverish excitement from the leading service providers to scoop up the best and brightest of the star niche players, the battle for Workday services supremacy has hit fever pitch, with Accenture following IBM’s and KPMG’s recent acquisitive forays in the space with the purchase of DayNine.

So over to HfS analyst, Khalda de Souza, author of our Workday Services Blueprint 2016 report to giver her take on what this means to the future of Workday services:

What did we tell you last week? Workday Services Blueprint 2016: Lots of Changes in this still Immature Market . Yes – this market is moving fast with even more consolidation in the Workday services market!

Accenture’s latest move in this competitive game has potentially check-mated its closest competitors, at least in the short-term. Instead of just acquiring a set of specific technical or geographical delivery capabilities, Accenture has targeted one of the leading Workday services partners in the market.  Both service providers did very well in the recently published Workday Services Blueprint 2016 report. We positioned Accenture and DayNine in the Winner’s Circle because they both demonstrated depth of experience and capabilities, impressive investment in tools and technologies to support deployments, and a focus on talent retention, all supported by strong client references. In fact, we positioned DayNine as the best executor in the Blueprint.

We estimate that the combined entity has a total of 1,275 people in the new Workday practice, including an estimated 1,152 certified Workday consultants. This is bigger than the largest Workday practice we have seen so far at Deloitte. Moreover, Accenture consultants hold an average of 2 Workday certifications each, and at DayNine this figure is 3.5, among the top 4 in the Blueprint.

Of course, it’s not all about pure scale. DayNine gains access to Accenture’s broader consulting capabilities and innovation around service development. Accenture gains access to medium sized enterprises and additional skills in Europe, which is a hot growth area for Workday services next year.  This initiative also aligns neatly with Accenture’s Cloud First growth agenda. It acquired Salesforce services partner, Cloud Sherpas last year (see: Why Accenture’s acquisition of Cloud Sherpas is both an offensive and defensive move) and  more recently, Italy-based Salesforce solutions provider, New Energy Group.

The Bottom Line:  DayNine was one of the last “hidden Workday jewels” waiting to be snapped up… now Accenture needs to make 1+1=3 to deliver

By acquiring DayNine, Accenture will have the opportunity to create genuine synergy from this acquisition, if they can realise the ’1+1=3’ value proposition that this appears to be on paper. Yes, there is a lot of work to do as there always is when acquiring any IT services entity. But if the strategic alignment and energy from Accenture Cloud First Applications lead,  Saideep Raj,  Accenture Workday practice lead, Beth Boettcher,  and DayNine CEO and co-founder, Tim Ramos (who will lead the new Accenture DayNine group), is anything to go by, the main recommendation for competitors is to Watch Out!

Premium HfS subscribers can access the new HfS Blueprint Report: Workday Services 2016 here.

(Cross-posted @ Horses for Sources)

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The wild-haired one

By on September 27, 2016

einsteinSalesforce isn’t even waiting for Dreamforce to begin the drumbeat over its AI offering which is or will be called Einstein. There is so much to discuss over this turn of events that it’s hard to begin so rather than starting at a conventional jumping off point let’s think for a moment about the name.

You couldn’t have lived at any point in the 20th century and not have some idea of who Albert Einstein was. For most of that time he was regarded as special, a savant, one who could see things that no one else could. But I prefer to think of him as one of the first to build models of reality that he could test his ideas against. History is full of these people most of whom did a one off. But Einstein systematized the approach—he had to because he was a theoretical physicist.

Einstein may have invented the word “thought-experiment” because his work centered on the very small and the very large, things that happened in nanoseconds and things that have taken up the 13 or so billion years of the universe’s life and the billions more to come. Things you couldn’t easily see or handle.

In theoretical physics you don’t set up an experiment in a lab. You look for evidence in nature that you or others discover because your alternative is to build an impossibly expensive collider to smash nuclei together to simulate the Big Bang. You also do a lot of thinking because your imagination is the best modeling tool.

Perhaps Einstein’s greatest modeling effort was to imagine what reality would be like to ride a beam of light. That simple idea led to understanding that the speed of light is the ultimate speed limit in the universe and that if nothing can go faster then all communication is ultimately limited by the speed of light. This includes time or the perception of time. Traveling at or very close to the speed of light causes time to slow down. Einstein did all of this with a few equations and his mind. He was quite a modeler.

In this context I think Einstein makes perfect sense for an AI product name because it enables a business to model the salient points of its reality in dealing with customers and helps us all to make logical deductions that we might have missed with prior brute force methods. Salesforce’s Einstein brings together several types of AI/BI/machine learning/deep learning/machine intelligence and whatever from prior acquisitions including RelateIQ, Implisit, PredictionIO, Tempo, and others.

Here is why I think this is significant. AI of this caliber significantly signals the end of transaction oriented business and conventional CRM that supported it. The better the model of your business the more you can see into the processes you are involved in and importantly the less you’ll focus on any transaction because transactions will be a foregone conclusion. You’ll focus on the process that leads to a transaction, which will help to assure a transaction takes place.

As a practical matter this doesn’t mean you’ll suddenly have superstar sales people who nail every deal. But having good AI might tell you when you’re wasting your time, when a deal isn’t going to be yours. Knowing this you’ll redeploy resources with the confidence that you’ve made the right decision.

Don’t worry this isn’t only about selling, it’s equally important for every aspect of your business. AI in service can tell you not only the next best action or offer, but it can also tell you the customer’s likelihood of being satisfied with the engagement and therefore what else you need to do. AI in marketing has many opportunities but let’s look at one that seems to always get neglected, the installed customer base.

Too many businesses assume that customers will come back for more and many do, but others keep looking for better opportunities. Worse, too often businesses that do big deals with multiple tranches of purchases spelled out in agreements forget where they are in the life-cycle and leave money on the table. There’s no easier way to make your numbers than to sell to existing customers but for some reason, we neglect the low-hanging fruit. AI could be an effective agent for changing that situation.

I don’t know all of Salesforce’s plans for Einstein, although they are a client and they brief me from time to time, it’s a big kimono. But with this early signal, I expect that Dreamforce 16 will have a distinctly AI flavor. That’s not very surprising and the company has been signaling in that direction for a while without making announcements like this. What will be surprising will likely be the many different applications of AI they’ll find and the timeline for releasing it all.

(Cross-posted @ Beagle Research Group)

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Notes from Reality, An Interview on AI with Vinnie Mirchandani

By on September 27, 2016

No one covers large enterprise software ecosystems like Vinnie Mirchandani,  founder of Deal Architect, a former technology industry analyst (with Gartner), outsourcing executive (with PwC, now part of IBM) and entrepreneur (founder of sourcing advisory firm, Jetstream Group).

He is a thought leader on trends in software, outsourcing and offshoring. He has personally helped clients evaluate and negotiate technology contracts valued in excess of $ 5 billion and has consistently advised companies on IT risk management, globalization and sourcing issues.

Vinnie is also a friend and a fellow Enterprise Irregular. Here are his thoughts on how transformative AI will be:

JT: Is AI transformative? What are three things AI will do that could not be done before?

VM: Today’s AI will be transformative because it is opening our eyes to all kinds of white collar automation possibilities with massive compute power being able to crunch massive data sets and help on cognitive tasks. Previous generations of automation like robots and drones primarily made agricultural and industrial tasks more productive. Now we can bring the same to cognitive work. When you look at the possibilities IBM has raised with Watson in healthcare, in auditing and many other sectors, Microsoft with Cortana and Amazon with Echo at home with natural user interfaces, you will see all kinds of business use cases being tried out in the next decade. Benioff has already raised interest in how Einstein will evolve the craft and there is plenty of interest in what Salesforce will showcase at Dreamforce.

Having said that, in my new book, Silicon Collar, I also point out AI has raised expectations every few years and not completely delivered 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.

Yann LeCun, director of AI research at Facebook, has commented, “Despite these astonishing advances, we are a long way from machines that are as intelligent as humans — or even rats. So far, we’ve seen only 5% of what AI can do.”

Similarly, 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.”

So, I am cautiously optimistic about this generation of AI — never before have we had so much data or this much affordable cloud enabled computing power to make white collar tasks more precise and efficient. I look forward to learning more about Einstein next month!

(Cross-posted @ Medium | John Taschek)

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News Analysis: @Adobe Chooses @Microsoft #Azure For Cloud #MSIgnite

By on September 26, 2016

Adobe Move To Azure Signals Closer Partnerships Ahead

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On September 26th, 2016 at the Microsoft Ignite conference in Atlanta, Adobe CEO Shantanu Narayen, and Microsoft CEO, Satya Nadella, announced a strategic partnership between the two companies.  Constellation sees the announcement from three points of view:

  • Adobe makes Microsoft Azure the preferred cloud platform.  Adobe intends to move Adobe Marketing Cloud, Adobe Creative Cloud, and Adobe Document Cloud onto Azure over the next few years.  Adobe will gain access to Microsoft’s machine learning services through Microsoft Cortana Intelligence suite and also access SQL Server.

    Point of View (POV):    While Adobe currently uses multiple providers and data centers for its cloud operations, the move to Microsoft Azure will provide a global, Tier-1 environment for work loads as well as security.  More importantly, Adobe gains another tool for machine learning.  However, access to SQL Server remains questionable as performance issues for SQL Server on Azure remain for heavy transactional apps.
  • Microsoft Dynamics 365 Enterprise Edition adopts Adobe Marketing Cloud.  Dynamics 365 Enterprise Edition brings three Azure hosted Microsoft Services together with Office 365 and the Office Graph and LinkedIn:  Dynamics CRM Online, Project Madeira (financials), and Dynamics AX ERP suite.  As the preferred service for Dynamics 365 Enterprise Edition, Microsoft signals that they will resell Adobe Marketing Cloud in Azure.  Full details have not been disclosed on yet on pricing and general availability..

    (POV):    Observers and insiders believe that while the Marketing features acquired from Marketing Pilot will continue to be available from Dynamics CRM, the long term shift will cede Marketing capability to Adobe.  As Adobe builds out more functionality in the Adobe Marketing Cloud, expect development from the Microsoft Dynamics team to focus on intelligent business applications that integrate more tightly with Adobe for the marketing function.  One of the key features with Dynamics 365 is the ability to license by applications or role based licensing.  Constellation believes customers will benefit if this licensing model extends to the Adobe offering.
  • Both vendors agree to data model standardization.  For marketing and business applications, the two firms greed to coordinate on data model design.  Both vendors also agreed to provide shared, extensible data models to customers, partners, and developers.

    (POV):    Microsoft and Adobe have decided to counter Salesforce.com with a joint coordination on the marketing front and the artificial intelligence front through the alliance.  After Microsoft’s failed attempt to acquire Salesforce, the move to align on not only cloud delivery, but also business applications will provide a formidable challenge to Salesforce.

Figure 1. Adobe and Microsoft CEOs announce partnership at Microsoft Ignite

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The Bottom Line: The Marketing Cloud Wars Heat Up and Commerce May Be Next

While customers have a few big choices in the Marketing Cloud from Adobe, IBM, Marketo, and Salesforce, the partnership between Adobe and Microsoft hints at more consolidation in the cloud wars around marketing functionality.  Adobe gains a strong partner in cloud delivery and enterprise scale and Microsoft gains a key asset in workloads and business applications.  Customers may actually benefit from this tighter integration as CRM shifts from core components of sales, service, and marketing into commerce.  As digital technologies transform CRM, the long term focus is not on selling products and services, but on delivering brand authenticity and outcomes, or experiences, from campaign to commerce engagements.  The ultimate result is either a conversion rate or click through.  All Microsoft and Adobe will need now is to add commerce to the mix and customers will have a more complete solution.  Why? commerce is the outcome of good CRM and CX not vice versa.

See the video on Future Campaign to Commerce vision below:

Your POV

Are you ready to try Adobe as a Microsoft customer.  Will Adobe customers move more towards Microsoft Dynamics 365? How will you build your strategy with Adobe and Microsoft?  Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org.

 

(Cross-posted @ A Software Insider's Point of View)

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