Returned from the IOM Conference in Cologne last week, which is mainly concerned with the “Human” side of the Future of Work that the Digital Transformation will bring. The focus is really on how white collar, mainly fairly knowledge oriented workers, will do their work in future. It’s all about improving human potential – expanding knowledge, increasing collaboration & co-ordination, engaging & enthusing, a move from hierarchy to flat organisations. It’s an optimistic vision, a Human-centric model. What’s not to like?

But here’s the rub – The Humanist, People-Centric approach is just a subset of the whole “Future of Work” trope – you don’t have to go far before you find three other very different futures being created:

While I was at the conference, there was news of other “Futures of Work” splashing over my Twitterfeed – Transport for London has put up a consultation document aimed at limiting Uber’s use of non-regulated approaches to run taxi services in London at prices the regulated cabs can’t match. Uber of course has retaliated with its normal tactic of a well funded PR campaign & petition, which quickly got c 100,000 of its faithful signing up after the call to arms. As readers of this blog knows we have also been tracking the rise of companies using fractional assets & people’s time. allied with market making technology, of which Uber is just one example.

Also, when I returned to London I saw a link to an article by Andrew McAfee in the Financial Times wondering why Humanists don’t like Technology (essentially if you are suspicious of Tech, and want to put people first, you are for Ludditism 2.0). And you don’t have to go far on the ‘Net to see worries about all sorts of white collar jobs being offshored to lower wage countries. Last night’s Social Business & Digital Transformation Meetup that we run in London also explored a lot of these themes after Rawn Shah’s presentation, and made me realise that as well as the Human centric view of the Future of Work, there are a number of other “Future of Works” that are occurring today:

• Automation, ie using ICT to replace human work (See McAfee & Brynolffson’s “The Second Machine Age” for a starter)
• Digital Offshoring – moving skilled work to lower cost, less regulated environments (this isn’t necessarily to low cost country – a lot of “Mechanical Turk” work is done by underemployed people in rich countries it’s not called the Digital Sweatshop for no good reason.)
• “Uberisation” (for want a better word) is partly automating the process (the App) and partly commoditising the process provider – the use of self employed (often unregulated below-minimum wage workers) for just the fractions of their time required, with no payment of their costs of working, nor any form of employment benefits. Read the “Work – The Future” set of essays to get a good idea of the thinking in this arena. Ditto the increasing use of “fractional assets” – rented private rooms on Airbnb, pop-up shops etc.

The people impacted by these worlds are going to have a very different experience from the happy human-centric vision painted at the start of this article.

But here is the second rub – a lot of those impacted will be those that thought they were slated for the happy, hierarchy-free humanist world of work. How so? Well, if your job can be:

• Automated (even partly), you can be replaced by an AI and an apparatchik, or
• Sent to a cheaper person elsewhere (And this will happen to everyone – Legal work once down by qualified professionals in the OECD is being sent to India), or your time can be
• Bought in small slices when required. The IT Contracting market is already like this – right now it can be a good living as there are still relatively few doing it. When there are far more people doing it as their full time jobs disappear, per diem payments will plummet for most).

Work will be impacted differently depending on where in the value chain one is, and where one is geographically. To understand this there are 2 useful models – the Value Chain model, and the Product/Process model:

The Value Chain Model 

A useful simple model is the “4-box” model of a simple value chain, and see how it changes in a Digital shift:

Creation – Automation of creativity is still hard, but mass copying, algorithmic approximation and other ICT techniques are rapidly making inroads and the truth is that a lot of “creative” work is just re-mashing existing stuff. True creatives cannot be replicated, but their value is largely marginal, rewards are distributed according to power laws – a very small number of creatives make most of the money. They have thus always lived in the lowest rent areas (Artists in Parisian garrets) unless they are of the tiny fraction that make it, but what does today’s Parisian artist do in a global world where the lowest rent garrets are in Paraguay or the Philippines?

Aggregation – The part of the business that organises & co-ordinates supply with customers – once it had to be “close to the customer”, can be increasingly easily automated and/or offshored. “Uberisation” reduces this to an automated function (eg an App).  Winners in the Aggregation stage are in a strong position in the digital world, as they specialise in aggregation of supply or demand and network effects tend to give the spoils to the frontrunners and damn the also-rans. The ideal is a market maker that not only aggregates both sides, but makes a market, hence the sudden rise of “Unicorn” businesses where this occurs.

Distribution – getting product from producing area to consumption area. Anything that can be digitised can be transmitted around the world at the press of a button which has already crashed many media businesses, and increasingly manufacturing will be possible at point of consumption. What is harder to automate is physical delivery, especially of the last mile, but “Uberisation” is essentially the fractionalisation and commoditisation of the labour of the people who man the physical delivery assets (cars, spare rooms etc). They too will apparently (we shall see…) be automated out by delivery drones and robot cars etc.

Customer Facing Environment – the main impact of automation is customer self service, allowing people to bypass local sales and delivery organisations, and companies to use less sales staff but this is till a relatively safe area as many of these tasks are extremely varied – too complex to automate, impossible to offshore, no easily scalable “Ubermarket”, often quite “high touch”. A lot of this is done in cash, with one to one organisation that cannot be aggregated easily in market making apps as existing social nets & comms platforms carry the load. Our view is that this “Dark Market” will grow as people seek to protect themselves from the Automation and Uberisation of much of the rest of the value chain.

The Product/Process Model

This model notes the essential truism that high value products tend to be made in small volumes by highly flexible workers and processes, whereas high volume products tend to be mass produced in large, highly structured workplaces where automation and/or highly repetitive work is the norm. In the middle is a mix of processes and practices, in a continual tradeoff between cost of process vs value achieved –  a continual battle between automation or labour costs, cost of supply chain vs cost of assets, access to skills vs regulation vs enforcement etc.

In essence we can say that:

• High value work will still be “safe” for humans (Green blob) – automation will still be very hard, the volumes are too low for “Uberisation” to skim a percent or two off each transaction, and (in general) the value of the goods means labour costs are relatively inconsequential, plus location close to customer is often essential to sell and service the product.
• High volume commodity work will go to automation and/or cheap labour, whether the work is white or blue collar, if it is repetitive, standardisable and programmable it will disappear from human work options (Red blob)

Which leaves the middle ground (Orange blob) to be fought over between all these models – history suggests a hybrid will occur in most cases (just as manufacturing best practice today incorporates offshoring. lean production & worker cells, automation and elements of business process re-engineering).

The discussion about how the next shift will play out politically, economically & socially in detail is for a later discussion, but – for now – a quick look at the Industrial Revolution is instructive to think about the next 20 years. During the Revolution, while it is true that the changes created new jobs, better lifestyles and a better world for those countries that went through industrialisation, it was not an easy transition:

• It is also true to say that it took 1-2 generations, and the transition was not pretty to live through- mass migration, impoverishment, starvation, riots & massacres, smashing of factories, appalling pollution, alcoholism, what we would today call “mental health” issues. Many thinkers do not believe a modern democracy would survive the riots, starvation, massacres and huge movements of people that happened the last time round, so for example some leading economists propose a basic citizen wage to buffer people from the worst of the shift to this Future of Work.
• Things only really got better when this new Working Class organised itself as a movement to prevent the excesses of exploitation and gained political power. Marxism, Unions and other Labour movements, Labour Days worldwide seem a bit arcane today, but that was the way most of your great-grandparents ensured they captured some of the benefits of that New Way of Work transition. We can expect some fairly radical resistance from workers again, but this time with social tools to back them up. We live in Interesting times

If you are interested in exploring these issues and the overall Digital Transformation in more depth, why not come to our Enterprise Digital Summit workshop & conference in London on October 21st/22nd where these issues and others will be discussed

Last Wednesday night we had our September Social Business Meetup, with Euan Semple being our speaker for the evening (he spoke on Pigs, Lipstick & Dinosaurs – see our summary here, and his relevant blog posts his relevant blog posts here, here and here)

Anyway, in the Q&A&D afterwards, we started to talk about “clicktivism”, and people noted the way Twitter seems to be increasingly being used by people to try and drive “mob opinion”, and even signal they are “more X than thou”. I have seen the effect, and it does seem to be an increasing “thing” on Social Media – but I can’t prove it as it’s very hard to write algorithms to sort genuine opinions from these, so I started looking for a reason as to the “why”.

So, I was scrabbling round the Web to see if there was any information on this, and one term I came across was “Virtue Signalling” and that made me realise that this was covered already in a classic piece of game theory in signalling “weak tells”.

In Game Theory, you can signal an intent – a “tell” in the parlance – that may or may not be genuine. The way you tell if the “tell” is genuine or not is to measure its strength, and this is measured as the cost to the signaller. A “strong tell” is typically by someone who will “put their money (or other assets, reputation, time etc) where their mouth is”. A weak tell is a signal with little cost to the user.

In general, weak tells are easy ways of generating a lot of traffic – they are, for example, the method of choice for “clicktivism” – a low cost of supporting something with little comeback. Facebook “Likes” are a simiar example of a Weak Tell.  I did an analysis on my Broadstuff blog of how this plays out in Social Media, especially about making it harder to measure true intentions and sentiment.

From an Enterprise point of view, as well as making it harder to discern meaning on social media that one may be monitoring, this has an impact on internal social business systems as well, given that similar conditions apply. (And then there is the impact of payoff distortion by non-business goals, as the Dilbert cartoon above notes). But in general, “Virtue Signalling” has 3 main effects:

• Exaggerates a particular (popular, or advantageous) view
• Puts off potential for disagreement
• (Possibly) allocates social capital to the wrong people

The net-net is that certain arguments are overplayed, certain arguments are not made as hard as they should be, and possible the wrong people get allocated resources or authority. This can have a negative impact on performance – studies have shown that suppressing dissent can drive hidden costs such as wasted and lost time, reduced decision quality and decreased engagement/motivation.

To be fair, this is hardly a Digital “thing”, this has happened in organisations from the start. The risk with electronic media though is that the effect is amplified, so the question is how to prevent it. In face to face environments, it is usually the role of a moderator, facilitator or chair to ensure that all voices are heard, but how does one replicate that on a business’s social communication systems? Some thoughts:

• Good news – the “mob storm” effect is likely to be far less intense, so less social pressure to say nothing
• More Good News – unilike public social systems, it is possible to “set a tone” on a private network that just cannot be achieved easily on more public ones
• There are a number of approaches that have worked in allowing a wider set of views to emerge, from suggestion schemes to “rewarding dissent” policies (this is quite a good brief summary from Wikipedia) – all have plusses and minusses, and seem better or worse in certain conditions, but it can be done.
• However it takes design and ongoing dedication to ensure the network can welcome and handle dissent and unpopular views
• Chances are, it also needs some form of moderation on occasion, albeit thse can be voluntees rather than full timers

Something to be aware of, at any rate….

We have been spending quite a lot of time looking at organisational structures, and what works and what doesn’t – and a rather fascinating analysis of the social networks of the later Byzantine and emergent Ottoman Turk leaders came up. The diagram above shows the 2 networks at a turning point, the period c 1310-40 when an already weakneed Byzantium erupted in civil war (hence the bi-modal Byzantine structure) and the Turks took advantage of this. We look a lot at the past for lessons, as the “what next” is known, so it is to some extent predictive.

The network analysis is blogged in more detail on my Broadstuff blog but the upshot is that while it’s easy to jump to the idea that a small, nimble, flexible structure ran rings around the ossified Empire, and victory was thus certain, this is simplistic for 3 main reasons:

Firstly, Byzantium at this time was a bigger, far more complex state than Osman’s Turkish statelet so needed more people to run it and – very unusually for it – was running with 2 co-Emperors (older one + nephew) and broke into a ruinous civil war in 1328  (half way through the analysis’s timespan)  so the network would be massively bi-modal, with 2 opposing camps, by definition.

Secondly, the Byzantine system had been relatively stable for 800 years (just how they managed that  that would really be worth studying) and had just re-unified after being broken up by the 4th Crusade so was still weak. Byzantium had seen the likes of Osman (and far worse) come and go many times over the centuries, so its not a slam dunk that Osman’s system was “better”. Arguably he got lucky, being around just at the time the weakened Empire was busy tearing itself apart (and Andronikus II was by all acounts one of the crappest Emperors they ever had), and other challengers – the Venetians, Bulgarians and various Latisn – were also attacking it at the same time.

Thirdly, as the paper notes, in Osman’s network “the potential flows of power and resources are more centralised in the hand of the ruler”. This works if the leader is able, and can keep on top of the decision flow. Not so good a system if the leader is not so able, and/or the system becomes more complex.

If there is a real lesson, its that nimble upstarts need quite a lot of things going their way to succeed, and organisation structure is not really the key determinant. That the Ottoman state system started to look more and more like the Byzantine as it grew is a salutary lesson. (Or, to rephrase for today, that the [Google Insert your favourite Unicorn] system started to look more and more like the [Microsoft insert your favourite Corporate Dinosaur] as it grew is a salutary lesson.

Last Thursday I was lucky enough to visit IBM’s Big Data Crunching Operation behind (amd underneath) the annual Wimbledon Tennis tournament, courtesy of fellow Tuttler Andrew Grill of IBM (who says nepotism never pays) and also courtesy of the knowledgeable and enthusiastic (despite me being the Nth tour guest of the week) Sam Seddon who escorted me through the databunkers.

Now I am far from the first Old Tuttler blogger that Andrew has invited, my colleague David Terrar went last year and Neville Hobson went on the Tuesday last week (here is his post). Thus the “what happens behind the Datascenes at Wimbledon ” is already more than well covered, just look at David’s blog post from last year – to quote:

Sam [Seddon] has a team of around 200 supporting Wimbledon using an impressive array of terminals and technology on site, supplemented by an enormous amount of Cloud compute power from data centres in Amsterdam and the USA. They are providing a service to the All England Lawn Tennis Club to help make Wimbledon the premier sporting event, but in doing so they are serving the audience at the ground, fans around the World, the radio and TV broadcasters of the event, the event sponsors, the Club itself, and even the players directly

David did a good analysis of the overall IBM operations last year, and had some cracking photos – so what can I add to this? Probably the best is to talk about it from the point of view of things I know about in some detail – in this case, high end datacentre infrastructure, real time big data & analytics, and system automation (in this case, Watson)

High End Datacentre Infrastructure

Firstly, from the point of view of datacentre infrastructure, this is a high pressure operation – real time, high visibility, peaky data flow. High embarrassment if things go wrong. Also, the web based systems get quite a few attacks 24/7. And as well as running all the tennis data feeds discussed below, IBM also run the ticket checking and security operations of the whole site.

Secondly, from an Operations & Logistics point of view this is further complicated as the whole setup is also completely transient – for 2 weeks a year the Tournament Tennis circus descends on Wimbledon. The entire IBM system rolls in, the porta-datacentre and its operatives are wheeled into the empty bunkers from locations elsewhere in the world (along with all the TV broadcast trucks, meedja personalities, commentators in their glass boxes, tennis line callers, buckets of strawberries and flagons of Pimms etc etc) – and then two weeks later the whole panjandrum disappears and the whole site is emptied and mothballed again for another year.

This is non trivial stuff, kudos to IBM for making it so seamless!

The IBM Dataflow – camera had too much Pimms by then….

Big Data & Real Time Analytics

From a “big data fan” viewpoint there are 3 main data handling operations (see flowchart above, and weep) – in summary::

Player Statistics

Every shot played by every player is captured, not just physically but with rich metadata – tennis experts analyse every shot and record what it was, why it was played, did it work/fail. Interestingly IBM captures data from every major tournament so you can pick up a rich data picture of every player and the permutations of their matched with others.

Added Tennis Metadata

Not only player match stats, but their historical metadata and also Wimblestats are collected – for example, veteran Leighton Hewitt hit his 1500th ace the day I was there, and they can play comparison games with stats back to the 1870’s. Much of this metadata is for the consumption of the output feeds – websites, TV & Radio  and social media, to add richness.

Social Media Analysis

The 3rd operation is the monitoring of Twitter feeds off the Gnip firehose. To my readers much of this is familiar ground – find, scrape, focus, process, analyse, use output to inform and focus further coverage etc. (David covered it last year if you’d like more detail) However, there were a few counter-intuitive things I learned:

– Court Prowess and Twitter are loosely linked – Rafa Nadal was the most talked about all player week, despite being an early exit
– Tennis stars endorsing brands is pretty pointless, on Twitter anyway – the online conversation and attention follows the person, not the brand, no matter how many hashtags the PR people throw into the Wimbledome.
– Well heeled Wimbledon fans are by and large not Twitterers, those who can pay to watch live tennis are by and large not (yet) the Generation Who Tweet – but it grows every year
– Increasingly, fairly respected online retailers are clocking onto on the twitter trending #tags and ad-spamming them (Wimbledon is clearly a better class of hashtag)

I was a bit taken aback by the last one, but I guess when you think about it, in media there is a general trend that where porn blazes a trail, advertising soon follows 😉

To a data modelling/simulation wonk like me this was all fascinating, and I hit Sam with all sorts of “what ifs” but he reminded me that It’s all about understanding the business of tennis – that you have to look at who the customers are, and what they want. I may want to run the Moneyball Crunch of all crunches on the tennis world and create virtual avatars so I can play Bjorn Borg against Rafa Nadal, but realistically the data is required by:

– Media organisations looking to enrich (and pad out….) their commentary teams’ output, especially as the days progress and the early round flood of games subsides
– A “value add” that Wimbledon.com alone can provide the legions of the tennis–nuts from its website, they aim to be the best tournament in the world.
– Coaches/players who want to look at their performance to improve (apparently not all do this, so data-led training has not got to the level many of the big team games – odd in my view given the spoils to the victors involved, but hey….)

Automated Intelligence

‘You know my methods, Watson.’

….said Sherlock Holmes in The Crooked Man (he never said “Elementary, my Dear Watson), and to me this is what is starting to set apart what IBM is doing vs other big iron data operations.

For those who don’t know about Watson, it is a very powerful Machine-Learning system initially focused on natural language processing. It’s key role is Question Answering, so underlying the language processing is are powerful deduction chain algorithms. In short, if you can feed it information and deduction methods, it can form its own hypotheses and reasoning chains This makes it useful for quiz shows (it won at Jeopardy in 2011), medical diagnoses, and – well, tennis among other things.

Data for Watson

Right now it is being taught the basics of Tennis geekdom (What is love? – answer – a score of zero), being force fed Wimbledon tennis history (to draw parallels etc) and is starting to look at the social media chatterflow. But if you look at the data processing going on at Wimbledon, there are a LOT of workflows, methods and deduction chains that are ideal for a natural learning machine learning system.

So when afterwards the IBM people asked me what my main thoughts were from the tour, I told them that in my view, many of their 200 or so operatives here will disappear in the next 5 – 10 years as Watson and other AIs start to take over the rote data processing going here, and then the less rote, and then the really high value add..

And yes, it felt traitorous to say that, sipping Pimms and watching the bright young meedja things gambolling on the grass, but at the end of the day the automation of dataflow & analytics is just the next step in an ongoing process.

Wimbledon is just part of a trend I see everywhere right now, wherever previously unstructured data is being digitised. First comes the rudimentary digitisation of hitherto unstructured data (often from manual or semi manual processes ), then comes the early analytics which creates huge value from the low hanging datafruit, and increasingly we now are starting to see the application of automation, both in data capture (IoT) and analytic reasoning (MI/AI). The future will come one automated data feed and workflow method at a time.

Below – courtside datacapture – how many years till it’s automated?

Automating the Tennis Tournament Production Factory?

Wimbledon makes 70% + of it’s money from selling Media rights for the two weeks of the tournament (and as more and more courts get their own datafeeds this revenue will only increase).

And as all those assets, that span acres of prime London real estate – the main courts, the techie bunkers and media circus rings, the player palaces, the buildings for the huge catering and logistics operations – all largely lie fallow for 50 weeks of the year, Wimbledon will always need to maximise income and minimise costs from those 2 weeks a year.

Thus they will inevitably be pushed into the economics of ongoing automation of Tennis Tournament production. The need to both reduce operating costs and produce ever more value-add from the datafeeds is probably inevitable. The competition for media $ will only intensify, other tournament operations will up their value add year by year. So that means more and more AI for the data handling and analytics tasks, to produce more value add for Wimbledon and its customers.

Unless, unless…..the unquantifiable human input can be shown to make the critical difference between an experience that will delight, versus one that is merely artificially inserted.

Maybe automation will have it’s limitations, we may not like a smart Watson, but instead prefer a nice-but-slightly-dim Watson to our human Holmes – quoth Mr Holmes:

A confederate who foresees your conclusions and course of action is always dangerous, but one to whom each development comes as a perpetual surprise, and to whom the future is always a closed book, is indeed an ideal helpmate.

We shall see. I’d place my money on Smart Watson though….

Article by McKinsey on what seems to work for companies trying to drive the Digital Transformation. They found that while in general transformation has a low success rate, some companies beat the odds. They identified 24 specific actions that companies that succeeded support five stages of a transformation – noted below:

• Senior managers communicated openly across the organization about the transformation’s progress and success
• Everyone can see how his or her work relates to organization’s vision
• Leaders role-modeled the behavior changes they were asking employees to make
• All personnel adapt their day-to-day capacity to changes in customer demand
• Senior managers communicated openly across the organization about the transformation’s implications for individuals’ day-to-day work
• Everyone is actively engaged in identifying errors before they reach customers
• Best practices are systematically identified, shared, and improved upon
• The organization develops its people so that they can surpass expectations for performance
• Managers know that their primary role is to lead and develop their teams
• Performance evaluations held initiative leaders accountable for their transformation contributions
• Leaders used a consistent change story to align organization around the transformation’s goals
• Roles and responsibilities in the transformation were clearly defined
• All personnel are fully engaged in meeting their individual goals and targets
• Sufficient personnel were allocated to support initiative implementation
• Expectations for new behaviors were incorporated directly into annual performance reviews
• At every level of the organization, key roles for the transformation were held by employees who actively supported it
• Transformation goals were adapted for relevant employees at all levels of the organization
• Initiatives were led by line managers as part of their day-to-day responsibilities
• The organization assigned high-potential individuals to lead the transformation (e.g., giving them direct responsibility for initiatives)
• A capability-building program was designed to enable employees to meet transformation goals
• Teams start each day with a formal discussion about the previous day’s results and current day’s work
• A diagnostic tool helped quantify goals (e.g., for new mind-sets and behaviors, cultural changes, organizational agility) for the transformation’s long-term sustainability
• Leaders of initiatives received change-leadership training during the transformation
• A dedicated organizing team (e.g., a project management or transformation office) centrally coordinated the transformation

McKinsey found that when organizations follow a rigorous approach and pursue all of these actions during a transformation, the overall success rate more than doubles from the average (26 percent), to 58 percent.

To people who have been around the business transformation / change management / big system inplementation space awhile, there is one thing that really stands out about these points – that they have been around a long time. What Works has been known for a very long time.  (Actually, the biggest thing I took away from this article was that even if you go by the book you get at be a 60:40 chance of success – there has to be a better way, as Do Nothing looks like a perfectly rational option at these success rates).

To no-one’s great surprise, Top Management Involvement is key. According to survey respondents, leadership matters as much during a transformation as it does in the company’s day-to-day work. It can’t be delegated to a project-management office or central team—the presence (or not) of which has no clear bearing on a transformation’s success—while executives carry on with business as usual.

From a Social Business point of view, the real take-away of the research is this: Across all 24 transformation actions, communicating—especially about progress—links most closely with success:

It also helps when leaders develop a clear change story that they share across the organization. This type of communication is not common practice, though. When asked what they would do differently if the transformation happened again, nearly half of respondents (and the largest share) wish their organizations had spent more time communicating a change story.

Though, as Norman Augustine noted, one can overcommunicate – see the quote at the top of this piece (Augustine’s Laws, 1984 ).  And for those who have long suspected it, there is a risk of overplanning:

Few initiative leaders—only 22 percent—say they would spend more time planning the transformation if they could do it over again. Instead, these respondents most often say they would spend more time communicating a change story (49 percent) and aligning their top team (47 percent).

And again, to no great surprise, its rather important to tell staff what work looks like after the transformation:

According to respondents, it’s important to define clear roles so employees at all levels are prepared to meet the post-transformation goals—a factor that makes companies 3.8 times more likely to succeed

…as well as making it clear how what they do actually contributes to the overall organisation’s goals.

In short, this stuff has been known as the best practice solution for at least 40 years (the 30 years I’ve been doing this sort of work plus the 10-year old books I was reading when I started) so to me the real question that further research needs to answer is why 74% of transforming companies suffer from Santayana’s Law – Those who forget the past are doomed to repeat it

(Cartoon above courtesy Manu Cornet at Bonkersworld)

There are many thoughts about the Future of Work these days, and many suggestions of how companies should respond – advice abounds on how to structure a business for example, from tinkering with the way people are treated in hierarchies, to new structures, to dispensing with hierarchy altogether.  Working out which (if any) of these new Ways of Working are likely to succeed is non trivial, but may we propose an approach as a first cut?

There is a fairly arcane principle in System Dynamics (and other branches of probability modelling), known these days as the Anna Karenina Principle (aka AKP) after Tolstoy’s observation that:

Happy families are all alike; every unhappy family is unhappy in its own way.

In essence it is the principle that,  for any complex system,  multiple factors are required to go right for success to occur, but it only has to fail in one key factor to be non-optimal or even useless.

It was renamed the Anna Karenina Principle after Jared Diamond’s  book Guns, Germs & Steel used it to explain why so few animal species of all those that exist are domesticated. In short, many traits are needed to make an animal domesticable, any one of which if failed means the animal is not suitable – for example the Zebra, whose equine counterparts in Eurasia were domesticated, are such ornery beasts that it has proved impossible to domesticate them (probably been around humans longer than all the others…).

A corollary is what I call the Reverse Murphy’s Law Principle – Murphy’s Law says “If something can go wrong, it will go wrong”. The Reverse Principle says “For something to go right, nothing must to go wrong all the time. i.e.  even partial success is often not enough. This is also known as the “The Principle of Fragility of Good Things” .

The principle can be seen at work systemically in the early frothy days of innovation of a new technology. Take any you choose – say aircraft, or ships, or PC’s, you choose  – and the early days of the New New Thing’s evolution sees all permutations and combinations of approaches, some fail fast, others linger on but eventually only those with the minimal failure modes remain. And it doesn’t take much. For example, before the iPhone and iPad was the Hewlett Packard iPAq – it did everything the first Apple products did, and more, but it failed on one key feature – it, like all it’s generation of mobiles that had IP capability and were smart, had a frustrating user experience, so it failed. (OK, you could argue that it wasn’t beautiful either, but I contend if it was easy to use a lot of sins could have been forgiven). In general the AKP principle would argue that the early failures failed in multiple ways, survivors had fewer failure modes and were weeded out as their fewer shortcomings showed themselves over time.

Anyway, applying the AKP principle to a comples ecosystem like an organisation is possible and also, as noted by the University of Leicester looking at organisational behaviours in stock crashes, allows some levels of prediction

…[Analysing] the dynamics of correlation and variance in many systems facing external, or environmental, factors, we can typically, even before obvious symptoms of crisis appear, predict when one might occur, as correlation between individuals increases, and, at the same time, variance (and volatility) goes up. … All well-adapted systems are alike, all non-adapted systems experience maladaptation in their own way,… But in the chaos of maladaptation, there is an order. It seems, paradoxically, that as systems become more different they actually become more correlated within limits.

It has also been used to explain the emergence and long survival of the peer review process in academia, modern Marketing techniques, and, of course, social networks.  So, applying this principle to new Ways of Working is a valid approach.

Now to be fair, in real world multi-factor complex systems, the sporadic failure of one subsystem is not enough to bring it down, specially if redundancy (ie routes around the broken system) are available. The bad news is that redundancy has an operating friction and cost all of its own, which is a mode of failure in some situations.  So the question is – what are the factors that will drive failure

First, it’s worth looking at what exists today to see what has worked so far – the hierarchy. The mere fact that it exists, and has for a very long time, says it is successful as a mode of organisation structure.  In the evolution of ways we organise ourselves, hierarchy was an early approach and a venerable survivor of the dangerous plains of the fitness landscape of human organisational structures. There are clearly inefficiencies in the system – Parkinsonian ossification and Peter principled incompetence for example – and tweaks in modern times have included matrix management, attempts at some form of upwards influence, creating hybrid structures (e.g. work cells), structured inefficiency to obtain engagement (e.g job enrichment).

It is argued that the hierarchy is no longer fit for purpose, for a wide variety of reasons across the board –  from being too slow to respond to strategic pressures, too ossified in its structure and systems, culturally unability to attract, retain, engage & motivate the right people, move knowledge through the organisation, and (silo syndrome), too prone to internal inefficiencies, the digital transformation (and Coasian transaction costs) will do for it….there is a long list of reasons for impending doom. But in essence it fulfills the AKP for organisation structure today, there is as yet no disastrous mode of failure and a long history of survival.

What is less clear is what can replace it, and whether they will actually be better – and this is where we think the AKP comes in.

Most of the newer contenders involve more heterarchical (non-hierarchical) ways of working, from the very structured, based on Sociocratic principles (e.g. Holacracy) to the almost chaotic (e.g anarchy as organisation ) and many flavours in between. But these are all largely unproven – examples have occurred in the past but they have generally not been adopted by competitors – more the opposite case in fact. How can one have any confidence of success, and is it possible to differentiate the potential of the various approaches?

What does the AKP principle imply for them?

One approach is to think of the potential key modes of failure, any one of which will bring an enterprise to its knees.  The literature abounds, but putting a systemic hat on,  one could look at a  set of modes of failure of an enterprise end to end,  and list them in some order of importance – or at least when they are encountered Here is a “Starter for 10”

1. Create a desirable product or service (clearly if you can’t do this, there is no enterprise)
2. Market and sell this into a competitive environment (build a better mousetrap, and….)
3. Source and/or manufacture and deliver a working product or service into a competitive envelope of cost, time to deliver, and quality (amateurs talk strategy, professionals talk logistics)
4. Service and support existing customers to retain them (cost of new customer acquisition >> costly than existing clients)
5. Attract, retain and motivate the right people at the right price (high staff turnover = lost turnover and high costs)
6. Create a culture that at best motivates these people to their highest levels of engagement (or at least prevents too much  free riding and other parasitic behaviours that destroy group cohesion).
7. Continuously improve the core capability to maintain competitiveness (Do Things Right) but also….
8. ….Sense the winds of change, keep on innovating to maintain product desirability, and change strategy and operation in time to avoid obsolescence  – “Do The Right Things” in the parlance of leadership theory….
9. …while still being Agile enough to shift position to face new realities (Even the best strategy fails at first contact with the enemy)
10. Finally, deal with large and sudden shocks to the system sufficiently well to live to fight (or at least trade) another day (Don’t let the Black Swan’s cr*p on you…)

Applying this “10 Factor Test” should give some interesting answers, so this is a “first cut” approach we will be using to analyse the various “New Ways of Working” theories currently abounding vs the venerable old hierarchy going forward. Of course, to ensure we don’t suffer from survivor bias, its is also necessary to look at hierarchies over time to see what factors have caused failure in the past and been dropped. That will be the subject of the next post in this topic.

It has become a truism that Social Business tools will replace/remove/redundantify (if such a word exists*) email.

It’s never going to happen. Email will be here for a long time still, so get used to living with it.  Social Business systems that can’t cope with email will die a long time before email will.

The reason for this is that, in the entire history of new media from the invention of speech onwards, newer and  further developed types of media never replace the existing modes of media and their usage patterns. Instead, a convergence takes place in their field, leading to a different way and field of use for these older forms. The diagram above shows how mewdia generations have gone in News, it will be no different for Business communications. (Source Baekdalmedia.com)

This observation is called Riepl’s Law.

This was first noticed by Wolfgang Riepl. Riepl was the chief editor of Nuremberg’s biggest newspaper at the time, and was stated as above in his dissertation about ancient modes of news communications.

He wrote his dissertation in 1913.

The rule had held good from Rome (and before) to 1913, and has held good till 2015, it’s not going to give way anytime soon. By the way, as an indication of email pervasiveness, when Groupware first arrived in the heady days of the TextNet, it was via email (Listservers). Attemps to make “Web Only” Groupware sites largely failed on  the DotComWeb, and purveyors soon learned to ambrace email. Ditto the first collaboration sites like Notes, Basecamp etc.

Email is here to stay in Business, for quite some time to come. There are some things it does way, way better than Social comms systems, and some it doesn’t. Riepl’s Law predicts it will be used for the things its good at, and the things it is less good at will be be done by newer comms systems.

* It does now….