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Wedding Planner by Wedsly

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Wedbox create and market mobile apps for weddings that provides services for planning, sourcing, sharing, enjoying and capturing. Wedbox started in 2016 as a Photo-app for capturing and sharing pictures for all the guests at a wedding. Wedbox has expanded providing SaaS services for all aspects of a wedding. The services include: Wedbox PLAN Wedding planning app https://wedbox.com/planner/ Wedbox PHOTO Wedding Photo-app for capturing and sharing pictures https://wedbox.com/ Wedbox PRO Marketing platform and delivery service for professional photographers - https://wedbox.com/pro/ Wedbox PRINT Print service designed especially for weddings – https://print.wedbox.com/ OUR OFFICE Wedbox Hermodsvej 5B 8230 Åbyhøj Denmark

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How to rise above the A.I noise When trying to predict the future of A.I, a few rules must be abided by, lest one be considered “frothy around the mouth”, as I was once described by a technologically-challenged executive in his 60s (compliment taken). Below are a few principles to consider when trying to predict the future of A.I in any way, shape, or form. 1 — We overestimate the effect of Artificial Intelligence in the short run ( ) is the most relevant in the digital age as we’re won’t to abandon ourselves to , especially when it comes to A.I-induced automation. The first part of Amara’s law echoed by Bill Gates flashy headlines and clickbait Indeed, there has been a slew of research projects making a wide variety of predictions about automation-caused job loss, but those predictions differ by tens of millions of jobs, even when comparing similar time frames. This is irresponsible as new legislation might use any one of these predictions as a base for new laws, and ought to be using accurate calculations. In fact, most workers should not be in full panic territory just yet: , and we’re only riding the first one. Data analysis and theoretically simple digital tasks are already becoming obsolete thanks to the creation of “basic” A.Is trained through machine learning, but this is unlikely to go much further in the next couple of years. automation will come in three distinct waves When writing about A.I, don’t get carried away by hyperboles, lest you be categorized as yet another fanatic by those who know better (and who’s admiration and respect one should strive for, obviously). 2 — We underestimate the effect of Artificial Intelligence in the long run This leads us to the other side of the coin. SciFi enthusiasts regularly fail to fully embrace the future’s uncertainty in their analyses of the next 30 to 50 years. There are three reasons for this: too much unpredictability, not enough imagination and an approach to predictions. après moi, le déluge People in the 1950s thought that everything that could be invented had already been invented, and we somehow continue to see this with A.I. Yes, machine learning can only go so far: in fact, A.I breakthroughs have become sparse, and seem to require ever-larger amounts of capital, data and computing power. The latest progress in A.I has been less science than engineering, even tinkering. Yet it’s not beyond humanity to jump-start A.I by rebuilding its models . beyond “backpropagation” and “deep learning” The way technology has always worked is as follows: (shout-out to ). gradually, then suddenly my boy Hemingway No one knows what the future holds, so I say go big or go home. That’s the only way an amateur tech predictor might get it right. Most wild predictions for 2060 will seem quaint and antiquated by 2040 anyway. 3 — Moore’s law does not necessarily apply to A.I As much as we may like the points made above, the road to it may be bumpy and not in any way predictable. We cannot count on the reliance on the likes of to see inside the crystal ball. As mentioned above, most, if not all of the modern use of A.I are a product of machine learning, which is far from the A.Is envisioned in most popular science-fiction movies. Machine learning, in fact, is a rather dull affair. The technology has been , and the academic premises for it . What’s new, however, is the advancement and combination of big data, storage power and computing power. As such, any idea of explosive and exponential technological improvements is unfounded. Moore’s law around since the 1990s since the 1970s We might be stuck for a few years before some new exciting technology comes along. As such, don’t put all your predictive eggs in the same A.I basket. 4 — Using the right vocabulary when discussing A.I is important It often seems like very much applies to the way we discuss A.I: . But that’s not the truth, far from it. This failure of language is likely to become an issue in the future. Clarke’s third law any sufficiently advanced A.I is indistinguishable from magic articles , the vocabulary has always been a phantasmagorical entanglement of messianic dreams and apocalyptic visions, repurposing words such as “transcendence”, “mission”, “evangelists” and “prophets”. Elon Musk himself went as far as to say in 2014 that “ ”. These hyperboles may be no more than men and women at a loss for words, seeking refuge in a familiar metaphysical lexicon, as Einstein and Hawking once did. As mentioned in past Artificial Intelligence with artificial intelligence we are summoning the demon Though an advocacy project disguised as a doctrine could yet be of use, I fear we may get the wrong ideas about A.I because the language we use is antiquated and not adapted to reality. As soon as magic is involved any consequence one desires, or fears, can easily be derived. In other words, maybe we need to think a little less about the “intelligence” part of A.I and ponder a bit more on the “artificial” part. When predicting A.I, use the right vocabulary, lest the nut-cases crown you their new cult leader . 5 — A.I is nowhere near as powerful as human intelligence And won’t be for a very, very long time. Open-ended conversation on a wide array of topics, for example, is nowhere in sight. Google, supposedly the market leader in A.I capabilities (more researchers, more data and more computing power) . Similar conclusions have recently been reached with regards to self-driving cars, who all-too-regularly need human input. can only produce an A.I able to make restaurant or hairdresser appointments following a very specific script A human can comprehend what person A believes person B thinks about person C. On a processing scale, this is decades away, if not more. On a human scale, it is mere gossiping. Humanity is better because of its flaws, because inferring and lying and hiding one’s true intentions is something that cannot be learned from data. When predicting A.I in the short and medium term, don’t liken it to human intelligence. It looks foolish. 6 — A.Is are not built in a vacuum As creators, it is our duty to control robots’ impacts, however underwhelming they may turn out to be. This can primarily be achieved by recognising the need for appropriate, ethical, and responsible frameworks, as well as philosophical boundaries. Specifically, , as corporations are . governments need to step up unlikely to forego profit for the sake of societal good Writers who predict the future state of A.I must stop speaking about potential outcomes in a way that makes them seem inevitable. This clouds the judgement of people who could, and should, have a voice in how their data is used, the rules that are made with regards to robotic, and the ethics of any sufficiently advanced A.I. Speak up. Demand proper regulations. Vote. All this will have an effect on A.I, one way or another. Don’t let Silicon Valley say that their inventions are “value neutral”. They built it, and they can (and should) fix it if needed. 7 — Don’t be like Hollywood The world is unlikely to see its own / / / / bring about the Apocalypse anytime soon. Yet, movies make it seem like this may happen in our life-time. HAL SHODAN Ultron SkyNet GLaDOS , , , , , , , , … they all show that Hollywood confuses Intelligence for Sentience and Sentience for Sapience. An A.I can not ignore it’s programming. It’s simply impossible. “Ghosts in the machine” ARE possible, but only in the form of unexpected shortcuts, such as when we saw an . This was unexpected but very much within the machine’s programming, highlighting the need for a better understanding of algorithms. Ex Machina I, Robot Ghost in the Shell Chappie Her Wall-E A.I Space Odyssey Blade Runner A.I cheat by exploiting a bug in an Atari game Hollywood also ignore the difference between software and hardware. Yes, we have an A.I that can beat a human at chess, but the human can go home after the game and make tea, build IKEA furniture, then play some football. ? Do you know how much those crappy robots cost? Millions! Have you seen robots move To combat the cycle of fear induced by Hollywood’s versions of A.I, we need to understand what artificial intelligence is, and isn’t. A.I is very unlikely to ever become a monster. Hollywood already is one. Don’t fall for its tricks. 8 — We don’t want humanoid A.Is Not only can we not make our own SkyNet, but we potentially will never want to. While passing definitely poses an interesting challenge for machines (and for their engineers), it’s not actually the goal of A.I. as we are currently building it. Artificial Intelligence research seeks to create programs that can perceive an environment and successfully achieve a particular goal — and there are plenty of situations where that goal is something other than passing for a human. the Turing test In fact, passing for a human can only have a nefarious outcome, which is why one should be wary of any company claiming to be able to do so. It’s much more profitable to build something able of assisting humans rather than imitating them. What could possibly be the use of creating a machine able to pass for a human if the company that built it cannot find an ethical way to provide a decent ROI? 9 — Most CEOs are just as confused as you are Below are a few quotes that perfectly exemplify how profoundly lost many CEOs are when it comes to massive change within their industries: When Alexander Graham Bell offered the rights to the telephone for $100,000 to Carl Orton, president of Western Union, Orton replied “ ” ? What use could this company make of an electrical toy Years later, (1943) Thomas Watson, president of IBM quipped “ ” I think there is a world market for maybe five computers. When the market did expend, Ken Olsen, founder of Digital Equipment Corporation, was quick to follow Watson’s footsteps by saying in 1977 that “ ” There is no reason anyone would want a computer in their home. And finally, my all-time favourite: Blockbuster CEO Jim Keyes, when asked about streaming on 2008, proclaimed loud and clear that “ ” Neither RedBox nor Netflix are even on the radar screen in terms of competition. A.I won’t change just one industry, it will change ALL the industries, sometimes massively, sometimes just a little. If, like me, you’re a strategy consultant, pay very close attention to the words used. If your interlocutor needs to ask what backpropagation is, best restart the conversation from the beginning. Very slowly. 10 — A.I cannot solve everything Though A.I will change all industries, this in no way means it will change everything, and save the world. As previously mentioned, we over-estimate A.I capabilities hugely and tend to imbue it with qualities it just does not have. World hunger, wars, disease, global warming… All these are still very much in our hands, and anyone saying otherwise ought to feel ashamed. We need to put a little bit of our own into it before getting robots to solve it all for us: it’d be too easy to let ourselves off the hook for all our past inefficiencies. At the end of the day, A.I merely holds a dark mirror to society, its triumphs and its inequalities. Maybe, just maybe, the best thing to come from A.I research isn’t a better understanding of , but rather a better understanding of ourselves. technology Join a movement This article was originally made for , an online magazine providing in-depth analyses of today’s technological challenges. . The Pourquoi Pas Click here to access it

Top 10 Principles of A.I Prediction

People who think all Bitcoin purchases must be accompanied by KYC/AML and other are 100% wrong. Bitcoin is to any purchase of “Pay as You Go” telephone top-up credits, iTunes and Amazon Gift Cards or even Lottery tickets, which is… Security Theatre no different gambling. The photo at the top of this post was taken today in a California supermarket. Three machines stand side by side near the exit. The one on the far right is a Lottery ticket dispenser. You can insert between $1 and $20 to buy tickets and when I say, “you” I mean . The machine is Scratchers® Vending Machine anybody can do it unsupervised. From the “ . Calottery” website The Lottery ticket dispenser doesn’t ask for any ID to gamble. A can go up to this machine and buy a Lottery ticket. Think about that. It is under US Federal and state law for a private entity to run a this is child ILLEGAL lottery; the selling gambling to children via easy to access and ubiquitous vending machines. government The most common minimum age for participating in state Lottery in the USA is years. Arizona and Iowa require that players are years of age before it is legal for them play. In California you must be . There is no one supervising this machine to make sure persons under the age of 18 do not access it and buy tickets. Rather odd, isn't it? 18 21 18 The box in the middle is a movie vending machine from the Redbox company. From their literature, Can children under the age of rent R-rated movies? No, they can’t. by the Motion Picture Association of America (MPAA). 17 Redbox requires that you validate your age when renting any movies that are rated R Redbox takes credit cards as a form of payment, and they claim they require that you prove that you are 18 or older to rent an R rated movie, but in practice . If you borrow your father’s credit card and take it down to this supermarket, you can rent an “R” rated movie if you are 15 years old, because no one is checking the rentals made at this machine, and your identity is not verified during the purchase. This is another instance of a system that is wide open to abuse. this is easily circumvented On the far left is the Coinstar machine. This machine sells Bitcoin. Read this thread about what it’s like to use it... What can I say? “Wow”. Now compare and contrast this with buying $20 in Bitcoin through Azteco. Go to the store, buy an Azteco Voucher for $20. It’s issued in 3 seconds Type the voucher code into the Azteco website, paste your Bitcoin address Is the number of seconds it takes for your Bitcoin to arrive There is no fourth step. The Azteco process takes less than 2 minutes. Wait…What?! None of the three supermarket processes described above makes any sense obviously, and the questions raised by this stark juxtaposition of three services should come easily to you if you can think. How is it that buying $20 of Bitcoin with your spare change requires that you present ID, a telephone number and a picture of your face, and hours before you “take delivery”, but buying a lottery ticket requires only… handing over the money? Similarly, why are not Lottery ticket dispensers requiring all the Heath Robinson Security Theatre that Bitcoin vending machines attract? You know perfectly well that a 15 year old buying a winning lottery ticket would get her mother to cash it in for her. Why are there absolutely no controls on those machines? Furthermore, the vast majority of lottery tickets don’t win, unlike in Bitcoin, and there is no gambling involved at all. No, people who buy $20 in Bitcoin are not speculating that the price will moon, where the users of it all get the same utility you silly goose. The same applies to the video dispensing machine. Why isn’t the identity of the purchaser verified before they rent an R rated movie? Clearly, none of this makes any sense at all. The scenario described here is a modern version of “Emperor’s New Clothes”. Three machines side by side, all contradicting each other in the way that they operate and the assumptions behind how they deliver services. It is Security Theatre, nonsense, and upside down circus absurdity all at once! Thank you to Ryan Dippmann for the photograph. Follow Azteco for news and updates.

A Tale of Three Vending Machines

Qwest predicted Netflix, Hulu, and HBO GO in 1999. By Micah Rosenbloom , Partner urging people to temper the hype surrounding the Amazon/Whole Foods acquisition, essentially arguing that Amazon will be a disruptive player in the food industry, but don’t expect them to scarf down market share immediately. It’s an important point. Food represents 13% of US GDP and many of the deal’s boosters are underplaying the sheer volume of innovation in the food category at the moment. Erin Griffith wrote a great post QSRs like SweetGreen and Chipotle have redefined fast food while supermarkets like Whole Foods and Wegmans are increasingly doubling as cafeterias/restaurants with huge takeaway selections. Recent entrants in food delivery — Seamless Grubhub, Postmates, Uber Eats, etc. dramatically changed the ease and frequency of food delivery. We invested in Plated, Gobble, and SkipTheDishes (Acquired by JustEat), and with the exception of Uber, I’ve never seen companies grow revenues so quickly. In retrospect it makes sense, most of us eat every day, multiple times, but still, it’s astonishing that a group of companies that are less than ten years old now account for $10B in consumer spending. As Erin notes, many armchair analysts are beginning to write off the packaged meal category, as well as grocery stores more generally, based on the current Blue Apron share price, but let’s remember that this stuff takes awhile. The force of change in food has only just started. Change in Tech Takes Time. Lots of it. As a reminder, this commercial which premiered in 1999 at the height of the first dot-com boom. At the time, the promise of “every movie, ever made, in any language, day or night” was as fantastical as a flying car to most people. This was a time when Blockbuster, a store-sized version of Redbox popular in the last century, still issued fines to people who returned videotapes that weren’t rewound. Techies and VCs of course saw the potential and started building it, but nearly 20 years later, we’re still a ways away from enjoying that enticing value proposition. Sure, I can watch on any device that has an internet connection, but if I want to stream I need to sign into my Prime account. adds a third app, and yet another subscription fee to the mix. There is a cottage industry of websites , or , a given streaming service at any given time. A great many of our cultural treasures — and to name but two — remain in inaccessible IP vaults. Orange is the New Black The Americans Game of Thrones explaining which shows are coming to leaving ER The Fresh Prince of Bel-Air From a technologist’s point of view, video on demand is one of the simpler products to be built. It’s just packets over the internet, after all. But even in today’s world of widespread broadband, DRM, and omnipresent smart TVs and set top boxes, we’re still managing multiple services and remotes. How to think about self-driving cars, AI, and ecommerce Now imagine how long it may take one of today’s hot technologies, like self-driving cars, to take hold. If you read some pundits, they believe truck drivers will soon be as obsolete as fur trappers. Let’s examine this hypothesis critically. If it’s taken us 20 years to stream reruns, do you really think we’ll have technical solutions to route through Boston or SF’s backward traffic patterns, unexpected detours caused by exploding water mains, falling cranes, drunk drivers, and distracted pedestrians in short order? How quickly will the business case for fleet ownership be hammered out? Will 50 states be able to coalesce on laws governing the design of autonomous vehicles and licensure for the operators or coders in a timely fashion? Do we need new infrastructure, like sensors embedded in streets, to make fully autonomous driving work? Will progress be slowed the first time an AV clips a kindly grandmother crossing the road? Will the drivers of these trucks, who often pull double duty stocking shelves at their destinations, be replaced with robots? These are not problems that are going to be solved in the next year. It’ll probably take a decade or more. That’s why smart companies, who are able to play the long game, are investing in companies like Cruise today. Self-driving will only be a reality if companies are willing to do the hard work to make them a reality. Three’s Company For those panicking that AI will put most workers out of jobs, remember that Amazon’s vaunted machine learning recommendation engine is regularly a punchline in tweets because it advertises products that we’ve already purchased. Amazon has been grinding for over 20 years and ecommerce still only accounts for 10% of retail sales. Ebooks should have replaced dead trees by now, but paper has proven to be as resilient as Katniss Everdeen and their electronic counterparts have plateaued at 50% market share. Customers are weird and unpredictable. Regulation often routes technology in suboptimal directions. Established players can be surprisingly durable and loathe to adopt new technology. Food is not videos and is not AI and each will follow its own path, probably slower than you think. It’s fun to make bold proclamations about the future, but the perspective of history tells us we should be more circumspect. As Bill Gates said “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”

Tech Takes a While

[Photo: Unsplash user ] Peter Clarkson A lot of us grew up with the idea that we were beautiful, unique, utterly irreplaceable snowflakes. This is true … but there’s a negative to this uniqueness. A thousand or so years ago, somewhere in the great subcontinent of India, there was a village. Within the village were five elders: men who had traveled wide and studied much. Sadly, back in those days, it was always men; clearly they couldn’t have been that wise. But though they had explored far, they had never met an elephant. The beast was mythical to them, only a few steps away from a dragon or a phoenix. Then one day an elephant came to town. The thing about the elders is that they couldn’t see very well. They weren’t blind as a bat, per say; rather, they were as blind as old, proud men. Since they were each very much accustomed to being right and loved nothing more than to tell their friends about something they didn’t know, each sought out the elephant, looking to report back on the nature of the creature. The first elder came to the elephant and found its trunk. An elephant is a snake, only more playful! he thought to himself. The second elder sauntered up to the elephant and alighted upon its knee. An elephant is rough, round, and strong — like a tree trunk! he decided. The third elder found the elephant’s tusk. An elephant is as sharp as a spear! he understood. The fourth found the ear. An elephant is like a palm frond, so good for shade! The last found the elephant’s rear end. An elephant is very big and very smelly! he admitted. After each elder made his encounter with the ambiguous pachyderm, each told the other what he had found. They each had a different account of elephantness — to be an elephant is to be thick, to be an elephant is to be thin, to be an elephant is to smell bad. It could be said that each elder accurately reported his own experience, yet it’s obvious that each elder’s reportage was incomplete. How can we describe that incompleteness? It is perspective. It is bias. Let’s examine how biases manifest themselves. For instance: When the designer in Silicon Valley tends to make products that solve problems known in the Bay Area. When the transplanted Midwesterner does not recognize that the gender norms he grew up with aren’t as fixed on the Coasts. When the European social entrepreneur working in Africa tries to impose Western solutions into non-western contexts, completely missing the importance of the well to community building and disrupting the life of the village. A lot of us grew up with the idea that we were beautiful, unique, utterly irreplaceable snowflakes. This is true — each of us has a genius unique to ourselves, to use the Ancient Greek meaning of the term — but there’s a negative to this uniqueness. Our whole history: the family we’re born into; the town, the state, the country; the schools we attend; the friends we keep; the organizations we form or join; the disciplines we steep ourselves in: designing, writing, leading, coding. Each of these gives us a unique lens through which to view the world, but each lens refracts a different bias, gives us a different hand on the elephant. In the research of Northwestern professor Lauren Rivera, we can see that elite firms have a replication problem: while they may hire to satisfy demographic diversity — getting people with different genders and skin tones into organizations that used to be rather homogenous — they still lack in deep-level diversity. While the new recruits may look different from the old recruits, they still went to the same three schools and were born in the same three zip codes and played the same three sports. Why do these people get hired? Because the hiring managers “click” with them; they just “get along.” They could stand being stuck in an airport with them. But that feeling of “clicking” just means that we have shared experiences with the other person, commonalities in our personal histories. This can make interactions very pleasant; it can also make the processes and products within an organization monolithic. We best empathize with people — be they colleagues or customers — that we have some shared experience with. So if a company is homogenous in form, it will only be able to connect with a homogenous swathe of humans outside the organization — you’ll only be able to communicate with people who have the same hand on the elephant as you do. This leads to a lack of understanding of what value the customer wants. That misunderstanding is the seed of disruption, for the gap between the value you think your customers want and the value your customers actually want is precisely the space where a competitor can replace you. “It could be said that each elder accurately reported his own experience, yet it’s obvious that each elder’s reportage was incomplete. How can we describe that incompleteness? It is perspective. It is bias.” A Different Kind of Blindness September 23, 2010: Blockbuster, the once-mighty purveyor of video rentals, files for bankruptcy. In 2004, it had more than 9,000 stores; by the end of 2013, nearly all of them would be shuttered. What happened? It’s become a morality tale in the business world: gigantic incumbent rests upon laurels, gets upset — or disrupted if you would — by nimble young upstarts. In this case it’s Redbox and Netflix. (By the way, have you started watching House of Cards? It’s great.) The story is that Blockbuster was titanic and lackadaisical and skeptical of this thing called the Internet, and it is for this tone-deafness that the startup kids came and ate their lunch. If you’re following along, at the time of this writing, Netflix’s stock is above $350 a share. “A lot of us grew up with the idea that we were beautiful, unique, utterly irreplaceable snowflakes. This is true … but there’s a negative to this uniqueness.” Yes, Blockbuster does appear to be a modern myth about the dangers of myopia, but let’s try to take a more empathic lens to its demise. Specifically, let’s try and look at the psychology at work here. While we can’t get into the heads of its execs, we can attempt to consider their vantage point. Imagine you’re a Senior Vice President there — perhaps SVP of DVD Store Experience. (We’re making this up.) If you were the SVP of DVD Store Experience, would you want to bat an eye at this interweb thing that the nerds in IT keep talking about? And who would wait for DVDs to arrive in the mail when they could just go to the charming blue store? And again, who cares about the internet: the transfer speeds are so slow! No, you need to perfect the placement of New Releases as the customer walks into the store — get that display right and they’ll be sure to love their shopping experience. Now we can begin to understand a few of the busts lying within Blockbuster. First, there seemed to be a bias toward clinging to The Way Things Are, and a false assumption that The Way Things Are is the same as The Way Things Always Will Be. People love movies. To get the movies they love, they drive to the store and rent them — only to return them a few days late and face that late fee. What’s missing here is an understanding of the value that customers are coming to them for, the ”job” that users are ”hiring” the product to do. The guy renting the action movie just wants to lounge on his couch and watch things blow up. If he can get that same pyrotechnic delight from another avenue — like by getting a DVD in the mail or streaming it to his laptop — then he’s going to opt for that more convenient option. As will, history now shows us, everybody else. In hindsight, all of this looks so obvious. But what was it that prevented Blockbuster from actually adapting? What is it that prevents any organization from adapting? Bias is the root of that dysfunction. “That misunderstanding is the seed of disruption, for the gap between the value you think your customers want and the value your customers actually want is precisely the space where a competitor can replace you.” It’s the Structure, Stupid Let’s take Yammer, which was recently acquired by Microsoft, as an example of one way to overcome bias. Within the engineering section of Yammer, people don’t keep their positions for long — teams form, split and re-form with others. This makes the workday more about the work and less about the title. Knowledge naturally gets shared between individuals, making for a higher bandwidth organizational network. Those with natural leadership skills show themselves to be leaders. Developers get to test their abilities in a range of structures, in a range of contexts. This allows for the team to optimize for the value that users are telling them they want through their behavior; the team can be responsive, allowing strategies to emerge from the shifting tastes of users rather than having them arranged for by leadership and the organizational structure. Why is this important? Because we are creatures of our structure. Blockbuster was organized around making an awesome DVD rental experience — intentionally or not, they were fixated on the medium that delivers the value rather than the value itself. But the user doesn’t care about the medium, they care about the value, the experience they associate with your brand. It’s not that structure itself is maladaptive; but it is if the structure doesn’t flex to fit to the changing environment. Some companies are excellent in managing for that fit. One of them is Square, the payments company founded by Twitter alumnus Jack Dorsey. When someone within the organization wants to test a new product, they quickly assemble a ”fullstack” team — that is, one having a whole range of necessary capabilities present within a team: you have developers, designers, project managers, the whole lot. Management scholars call the congruence between environmental demands and organizational needs requisite variety: the information entering into the team is complex, so the constituents of the team need to match that complexity in order to operate at a high function. What’s the result? The team doesn’t need to conscript talent from another wing of the company to move forward with an idea. Similarly, they don’t need to make a pitch presentation for some manager’s approval in order to improve the product. This allows for more rapid decisions, and thus more rapid iterations, and thus better, faster, more adaptable business. While the structure may seem inconsequential (it’s only stuff that people in HR pay attention to, right?), it is, in fact, incredibly predictive of the outcome. As Blockbuster, Yammer, and Square all show, the structure itself — something often invisible to the people within the organization — predicts the function of the organization. This is a lesson that the young creative must learn again and again, for the most difficult part of composing a narrative work — be it a film or a book — is to figure out the structure of the telling. The same scenes told in a different order reveal a vastly different story. It’s similar within business: while a top-down, waterfall, command-and-control structure is amazing for mobilizing quickly, it’s not so good for coming up with new ideas and adapting to the shifting desires of users. “… the information entering into the team is complex, so the constituents of the team need to match that complexity in order to operate at a high function.” But when a structure is matched to its environment, the results can be extraordinary. Depending on where your hand is on the elephant and how your structure suits your environment, bias — when accounted for — can be overcome. [Photo: Unsplash user ] Peter Clarkson Adapted from ” (McGraw Hill, 2014). “ Everything Connects — How to Transform and Lead in the Age of Creativity, Innovation and Sustainability Copyright © 2015 by Faisal Hoque. All rights reserved. I am an . Founder of and other companies. Shadoka enables entrepreneurship, growth, and social impact. Author of “ ” (McGraw Hill, 2014) and “ ” (Motivational Press, 2015). Follow me on Twitter . entrepreneur and author SHADOKA Everything Connects — How to Transform and Lead in the Age of Creativity, Innovation and Sustainability Survive to Thrive: 27 Practices of Resilient Entrepreneurs, Innovators, and Leaders @faisal_hoque Use the Everything Connects leadership app for free .

WEDBOX

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WEDBOX

wedbox.com

1-10 employees

Since 2016

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