People may recall in 2014 we set up our Digital Wave model (see picture above). At the time we identified Mobile, Social and Cloud  as trends that would shape the next few years of Digital Transformation. That is playing out now much as predicted, so one of the things we have started to bring more sharply into focus for enterprioses looking at Digital Transformation is how to think about using the next “wave” using our “What Works, What Doesnt, What’s Next” approach.

Our current focus is Professional Service businesses and our support of the Global Legal Hackathon so this post is focussed on that arena – interestingly enough, McKinsey recently put this sector (and themselves, by extension?) as laggards in AI adoption (See chart below and this report) – this despite news of AI taking over low level legal roles, introduction into marketing and customer service roles etc.

This post is an initial analysis of opportunities and managing reviews in Artificial Intelligence (AI) as early systems start to come into range of being useful to enterprises other than the big data analytics based businesses like Google and Facebook. To say the sector is overhyped is putting it mildly, but there  are some babies among the frothy bathwater, but hopefully we can sort the wheat from the crap.

What Works

Maybe a better term for AI is “Automated Intelligence” – essentially it is just another wave of (digital) automation, chipping away at “white collar” knowledge work, just at the next level up compared to the previous waves. And as with all automation, it is initially being used to automate work that is simple and has:

• Bounded Solutions – Most useable AI is today is what is better termed “machine learning” or “deep learning”. To be honest, when I was at Uni  few decades ago these weren’t considered “AI” at all – there has been hype creep. But in essence “Machine Learning” systems are very bounded closed loop systems – machines – that have a limited ability to adjust their processes and outputs to react to new inputs. “Deep Learning” is reall better termed “Pattern Recognition” – it’s the ability of IT systems to just crunch far more data and find relationships in the data that humans don’t have the time to do. “General purpose” AI – AI that can learn on it’s own – is still a long way from being usable in most situations.
• Easy to Automate – rote tasks, with simple workflows and minimal variation, that are easy to write rules for and replicate in software. AI is the took that hadles that bit more variation and workflow complexity
• Worth Automating – this is important – the economics of replication have to be considered, If it can be done for less by people it will be as a human still has a higher functioning AI computer than anything yet built,  Where AI comes into it’s own is that it can work 24x7x365, and makes fewer errors with repetitive tasks.

In a way, AI is more useful compared to past “AI revolutions” that failed since today, because there is more digital real estate in most enterprises, it is easier to find work for AIs to work on – so we expect a lot of the first “AI” systems to be used to automate existing digital workflows in enterprises.

What Doesn’t Work

Many of the pronouncements about AI capabilities today won’t be around for years, if not decades. There are a number of major barriers today:

• Vertical Variability – Modern AI is still very simple, they can only really work in very tightly defined verticals where variability in conditions is low or else the environment becomes too complex (This is what has killed all previous AI “hot tech” cycles, not computer power). This why all the “gee whiz” comes from AI in games like Go and Chess, which have very constrained variability and a bounded solution space. The question for large scale deployment is always “is this vertical big enough/valuable enough per workpiece to make it worth throwing expensive automation at it.”
• GIGO (Garbage In, Garbage Out) – the same problem that besets Analytics besets AI – today’s systems have no judgement of context outside the narrow area they are aimed at, so data has to be very carefully scrubbed, structured and tagged for them to use it without making gross errors. Also, the data has to be more than just clean, it has to be correct to train an AI.
• Volumes – most AI systems need a lot of data to do tjeir thing, many enterprises bven in teh days of Big Data don’t have enough in all the necessary areas in a workflow for AT to be useful.
• Developing and training AI’s is hard and expensive – very time consuming and needs high levels of expertise. Parenting a living infant is hard and a near  full time job, for a long time. Ditto an AI system. As with live infants, the simpler the organism the less the time and work needed – but the less it is capable of. Some types of AIs can self learn very well against datasets (genetic algorithms, neural networks for example) but having used these in the past I know one quickly hits the Vertical Variability and GIGO problems

What’s Next

No doubt the technology will continue to improve, but when we look at what will be adopted in teh near future, a few clear rules emerge:

• It will have to be in high value verticals – we expect a lot of early uses will be to automate existing digital systems and workflows in high value industries.
• But these verticals will also have to be very predictable with small solution spaces – so think mass production, rote tasks rather than replacing creative roles.
• Many work roles will need to be re-engineered to use AI effectively – many work roles today have a mix of the rote and complex, the predictable and the variable, and it typically is only the “rote and predictable” that can be AI’d away. It’s not worth automating tiny bits of a person’s role, so all previous automation from Winslow Taylor (and before) seeks to split up and redefine the worflow to aggregate the automatable parts.
• It has to be a good deal better than “good enoughs” – this includes human part time effort (Mechanical Turks, Gig Workers, Offfshoring etc) or simply that the AI-able tasks in a role being easily done in the workflow by the existing workers don’t add enough value.
• In addition, we are picking up real concerns about “blind” and biassed” algorithms –  so algorithm testing and transparency are going to big issues for deployment, especially into consumer areas. We’d expect to see a lot of AI technologies deployed inter-enterprise first.

It’s no accident therefore that the McKinsey analysis of the industries most  advanced in AI (see McKinsey chart below) are IT & Comms (automating existing systems) and Finance (highly valuable benefits of automation). and the next ones are high value Manufacturing, Logistics and Energy which have been the main recipients of previous automation waves.- these are all very rules based industries, with fairly bounded processes and relatively well defined workflows. Note for example Aerospace isn’t in there in the vanguard – it’s a lot harder to automate, we can tell you!

So what of professional services and AI? Note that they are a laggard in the chart above. That’s no real surprise to us, professional services have proven very difficult to automate in the past as the work – although often very valuable – is highly variable, and the solution space is highly unbounded. If previous lessons from automation in these industries are anything to go by it’s largely the support services and rote work that are automated, so we would expact AI to come into areas that have already seen IT inroads such as:

• Knowledge management – everything from document capture, document checking, tagging/labelling, storage, sampling etc.
• Data assembly – searching for data, reports etc and assembling it against requests
• Aids in building presentations, contracts etc – and checking them intelligently for errors
• Load balancing – scheduling people, resources, work packages etc. You can check out McLeod Brock to maintain the perfect schedule to handle things.
• Workflow processes with simple rules – e.g, changing a report or contract for one client to another,  one day maybe even expenses (one can dream)
• Recruitment and retention, HR policies, IT support systems, possibly financial support
• Risk analysis (of major risks) – probably financial, contractual and regulatory initially
• Decision support – this is where we predict AI will enter the more creative, “one off” project type work areas, design support – supporting the human experts in the space. Look for very high value tasks to make it worthwhile

But that is more than enough to be getting on with for now…….

Readers of the blog will know that we have long proposed that the “Future of Work” appears to have 4 emerging trends:

• Human Centric – Better organisation of human potential via collaboraion/co-operation/communication
• Offshoring – moving to lower and lower cost labour pools, in low wage countries
• Uberisation – using technology to organise low cost “gig economy” labour in-country
• Automation – replacing labour with electro-mechanical and increasingly digital equipment

We have written about this in more detail on this blog over here, and will also be talking about this in or conference on the 24th November in London. We have argued that the economics of the various options means the Human Cenric approach is most likely to occur in complex, non mass-produced work with high added value – too variable to automate or offshore, to valuable to leave to gig economy mechanical Turks.

As input to this discussion, today Paul Mason published an interesting article on what he called Postcapitalist ways of working. In essence he argues that IT is having 3 main impacts on work:

• Firstly, it dissolves the price mechanism. Information goods — if they can be copied and pasted infinitely, and used simultaneously without wear and tear — must fall in price under market conditions to a value close to zero. This is essentially the Automation issue at ts most broad case.

• Secondly, IT automates work faster than new work can be invented. Around 47% of all jobs are susceptible to automation, say Frey and Osborne (2013). And information also makes it work modular, loosening the link between hours worked and wages; and it makes work possible to do outside the worplace — blurring the division between work and life.

Mason argues that the capitalist system copes with these twin shocks in two unfortunate ways:

a) to maximise capacity utilisation of low-skilled labour and of assets. So we get Uber, Deliveroo and AirBnB are effectively capacity utilisation businesses. [aka Uberisation and Offshoring]

b) to artificially inflate the price and profitability of labour inputs: so housing becomes the major thing wages are spent on — and healthcare and university education. [ie if labout cost is not a key input, make it pay its way in the broader capitalist system].  Things that in all previous eras of capitalism the elite desired to be as cheap as possible — to ease wage pressures — are now made as expensive as possible, and capital migrates away from production and from private-sector services towards public sector services.

He believes this, if taken to its logical endpoint, results in  a form of Gig-Economy based neo-feudalism.

He believes what we call Human Centric way of working is the way out of this, his a third impact of info-tech. It has begun to create organisational and business models where collaboration is more important than price or value. As he notes:

Networked busieness models create massive positive externalities — network effects –where the data, or the wellbeing, or the utility created by network interactions is capturable and exploitable. But as soon as technology allowed it, we started to create organisations where the postitive effects of networked collaboration were not captured by the market.

Wikipedia is the obvious example; or Linux; or increasingly the platform co-operatives where peopel are using networks and apps to fight back against the rent-seeking business models of firms like Uber and Airbnb.

He believes that the examples set by enterprises such as Wikepedia, Linux etc create a model for the Future of Work, and this is the way out of the inevitable Automation/Uberisation trap and calls for the transition to a non-capitalist form of economy which unleashes all the suppressed potential of information technology, for productivity, wellbeing and culture, by:

• Moving as much as possible of human activity out of the market and state sectors into the collaborative sector; to produce more stuff for free.
• Networked living (even physical) he believes cities are the ideal environment as they are “the closest the analog world comes to a network”.
• Reduce the input costs to labour, so that we can survive on less wages and less work (this would need regulatory or other activity usch as mass house building to reduce prices)
• Pushing forward rapidly the de-linking of work and wages

In essence moving production out of the paid economy, and replacing wages with some form of wage for living. (His last point by the way is the same argument as that Minimum Wage exponents use, arguing it is necessary to handle the displacement period from the old ways of working  to new ones without major strife).

Gartner has put forward its Top 10 trends driving the Future Digital Worlplace – the areas are nicely summarised in the graphic above.

To be honest. the “digital transformation” predictions are somewhat aggressive, even possibly hyped (surely not!) – a trend others have noticed with some of Gartner’s recent output in the space.

In the Agile Elephant view, based on the limited successes of Office Automation 1.0,  the Digital Workplace is more likely to resemble the graphics above rather than their tech automation predictions for quite some time.

But, all fuel to the debate.

A summary of a piece I did on Broadstuff on the emerging New Economy, this is germane to New Ways of Working:

I was prompted to write the post after reading Gideon Lichfield’s useful article on all the different types of systems jumbled up in the term “Digital Economy” and the muddy thinking about it that results. To summarise Gideon first, these are the different types of systems operating up in this new economy, and the reality behind the hype – the expurgation is mine:

Sharing economy – The name is apt for any service that allows a thing previously available only to its owner to be used by other people, thus making more efficient use of resources.

Peer (or peer-to-peer) economy – It’s meant to get at the more direct connection between the people on either side of a transaction, unmediated by a big faceless company, but “peers” economy” is very misleading as to the relationship between borrowers, lenders and intermediary

Gig economy – not a steady job but a series of gigs. But the gig economy could be short-lived: Legal disputes about gig workers’ rights, liability, and so forth could force the creation of a new category of worker that is neither freelancer nor employee.

On-demand economy – this is where the venture capital money is: services that offer cars, food, home-cleaning, and other services at the touch of an app button. [My note – and it relies on “Gig-economy” labour]

Platform economy – a digital platform that, whether through algorithms, a rating system, or some combination of the two, serves to connect customers with providers of goods or services.

Networked economy – See “Platform economy.”

Bottom-up economy – the newfound ability of small businesses and freelance workers to find customers or band together with other workers from all around the world.

Access (or Rental) economy – services that let you pay to use things like cars (Zipcar), movies (Netflix), or music (Pandora, Spotify) without owning them. Been going on since long before the Ubers of this world came into being,

Uber economy – “Uber for X” has become an easy shorthand, but it’s simplistic—the Uber model is only one of many.

The first takeaway is that these different “New Economies” will drive many different ways of working.

But what this summary also really made clear to me is that all these “Next Economies” (possibly with the exception of a Bottom Up collaborative system) is that they like to use the impressive (and more upmarket ) term “Economy” whereas in reality they are just good old Markets – either simple ones flogging something, or two sided markets matching a buyer and a seller via a mediator.

So it’s a lot more like the Old Economy than one is led to believe at first reading.

And this is hardly a new feature of the digital age (I am reminded that many “vertical markets” and “e-market” dotcoms were proto-Unicorns until the dotcom crash, and its hardly as if the last 15 years haven’t seen a lot of new digital markets emerge).

Some Implications..

The implications are that there is little “this time it will be different” New Economy in the next iteration – it’s the Old Economy, but mainly with added automation and more part time, low wage workers. New Ways of Working will have to obey Old Rules of Economics more than on would ideally like, so any new structures will be judges on old metrics, like:

• Efficiency
• Effectiveness
• Dependability (predictability of output)
• Reliability (uptime)
• Mean times between failure/ faiure per unit output

As  Texas oilfield accidents lawyers has stated that it is the right of employee to claim compensation for the injury caused during the course of employment.There are some following implications – regardless of the ways of working or organisatio chosen, human centred work will need to be structured at all times to be better a than a low cost “gig” worker or automation.This implies the sort of thought into work structure that Toyota put into its systems, a point made by Professor of Operations Management Zeynep Ton at the O’Reilly #NextEconomy conference last week.

If the structure can’t do that, it won’t be part oaf any New Economy.

At the Enterprise Digital Summit I did a sort of Pecha-Kucha style talk on future organisation structures but I thought it may be worth going through the talk in more detail as a blog post as well. In essence, the formal part was to show the research behind 3 hypotheses we were going to discuss:

• There are many options for effective organisations, from quite structured hierarchies to very open structures, there is no one “best” option. Some are more appropriate than others in certain situations
• Forget Dunbar’s numbers in any organisation structure at your peril
• Pure Hierarchies are inflexible, flexible Network & Heterarchical organisations have problems scaling and delivering, so the likely “future organisation” solutions are going to be around hybrid structures

Also, we have done a bit of research on long lasting alternative organisation structures, so in the spirit of our “what works, what doesn’t, what’s next” approach, we can make a few observations about modus operandii.

There have been many effective organisational structure options – the ecosystem decides which is “best” (aka “fittest”)

We have gone back into biology and anthropology as well as history to look at organisation structures that work. By this we mean structures that are resilient,  effective and flexible – the term we like to use (inspired from here)  is Responsive organisations – capable of adapting to new situations. One of the most interesting cases from nature is the difference in societal structure between Chimpanzees and Bonobos – genetically very similar apes to each other (and to humans…), yet one has a hierarchical, patriarchal society and the other is a flatter, matriarchal society. Both are resilient, both are effective (for example both species can use tools), and flexible. The reason for the difference is believed to be the different ecosystems they live in. Another interesting case study is  a troop of Baboons that changed from patriarchal to matriarchal society when the alpha males were killed off, and have remained stable in the new state for 3 generations so far.

Also, Jared Diamond’s* and others work on early human societies show that there has always been a wide variety of human ways of organising society. And again, the explanation for differences is usually the environment they are in. Human evolved a more flexible social structure than Chimpanzees or Bonobos, which ultimately has led to 9 billion of us.

As far as organisational structures go, we noted the extremely long lives of various hierarchies – the Catholic Church, Byzantine government, various flexible military organisations, to give a few examples. We also noted that modern corporate hierarchies were modelled on old 18/19th century military structures, but the modern military has in many ways moved farther away from that model than most organisations have, due to the pressures of combating asymmetric armies.

Forget Dunbar’s Numbers at your peril – scale drives hard choices

As we have explained before, there is more than one Dunbar’s number. Dunbar’s work shows that we can only hold a limited number of people at various levels of social closeness. Very close is about 5, close is about 15, general friendship group about 50, and casual friends/acquantances about 150. The amount of social transactions required to keep a person at a particular level rises as they become closer, and we have a limited capacity. To keep 150 people at the level of intimacy you keep 15 would take take all the spare time in a day. Larger groups become ever more distant.

In a social business context, this has a real impact. The level of extreme collaboration that a close team like a work cell requires tops out at the Dunbar number of c 5, it’s very difficult to run teams where everyone knows what is going on at sizes greater than c 15 people, and at c 50 people there is a transition from one person being able to run an organisation, and it has to be split between subordinate managers (this is a well known problem in startups, this is the point where the entrepreneur has to “let go” direct control and put processes in place. In many cases either entrepreneur or organisation don’t survive the transition).   At c 150 people up a company moves from being a place you vaguely know everyone to being an “organisation”, where formal structure replaces informal connections.

The reason for this is the amount of interactions required to keep a high level of trust and collaboration.  For 5 people, fully connected (networked) to each other, there are 10 links between the individuals in total (20 if you count each part of a link as a 2 x 1 way interactions). At 15 people it is 105 links, at 50 people 1225, at 150 people 11,175. This is a power law, every time the group size increases by 3x, the number of links increases by the square (The law is (N(N-1)/2 to be precise).  You can model this to show that fully linked groups rapidly become unsustainable as they scale.

To scale therefore, there are two options

(i) reduce the quality of the links, and as Dunbar explains, this already occurs as social groups scale – but so does their functionality, and therein lies the rub, for more co-ordinated activities (like business activities) to happen the links have to operate at a certain minimum level of functionality; or

(ii) reduce the number of links required. The way to do this is to introduce a structure where not everyone has to link to everyone else. The simplest way of doing this is to introduce a hierarchical structure, where everyone knows their place in it and who they link to. Arguably hierarchies also reduce the number of links which are fully bi-directional, further reducing the load.

This is why human enterprise in any form, when it scales, throughout history has moved to forms of hierarchical structures, almost irrespective of surrounding environment. That they are imperfect is not in doubt, but throughout history hierarchies have been, to paraphrase Winston Churchill, the worst form of organisation, except for all the others.  In fact, the only really notable thing about any alternative structures so far has been their relative rarity and short lifespans. (There are also a few interesting features of the ownership structure of such stable alternatives which we go into further down the page)

The argument today is that modern working conditions – a sped up business cycle, ability to work anywhere, anytime, increasing need to inovate to compete, plus new technology will force a shift from the hierarchy, and that technology will facilitate this shift to other structures, usually postulated to being more networked and heterarchical.

Hierarchies, Heterarchies….. and Hybrids

There are a number of proposed alternative structures around at the moment, mostly based around networked and/or heterarchical principles. Although these terms are often used interchageably, they are not quite the same.

Networks are the structures that connect people together, strictly speaking any connection structure is a network, including a hierarchy. But by “network” most proponents of new organisations mean a non ranked, non hierarchical structure that connects all to all, and argue that new technologies allow a more flexible way of linking people digitally.

Heterarchies are “a system of organization where the elements of the organization are unranked (non-hierarchical) or where they possess the potential to be ranked a number of different ways” – they differ from the above network definition in that “ranked in different ways” term. The idea is that any individual is differently connected to a variety of networks.

But given the limits of Dunbar numbers and link scaling power laws, how will this work? All the above comments about Dunbar numbers and scalability still apply to these networked structures – it’s not really a technology issue, its a human cognition issue. The flooded inbox problem is a symptom of being over-networked in a business sense, and newer technology doesn’t solve this – companies are finding that as email use declines, the social messaging overload grows – increasingly companies are asking how to filter business social messaging for relevance. In other words, how to impose a link-reducing structure on information flow.

Arguably a heterarchy is more scalable than a pure network, as it allows one to drop a lot of unnecessary links so in effect not everyone is connected to everyone else – in effect forming a scalable small world network structure. However, that starts to look suspiciously like a hierarchy if all links are equal, but some (those that link between the various sub networks – small worlds – for example) are more equal than others – which they tend to become.

Both these new models also require more energy to maintain than a hierarchy, the transaction costs required in maintaining these informal structures require higher time investment and personal skill levels, so at the minimum there usually needs to be a lot of education of people to work in them. There is some concern that not all people or jobs can work in this way, so they limit the labour they can use.

But Hierarchies have also adapted, implementing some features of network and heterarchical practice, and in watching this adaptation we believe the eventual endgame is taking shape – a hybrid structure. For quite a few decades Hierarchies have been trying a number of approaches to reduce their flaws:

• Breaking up into smaller units within a large structure, wher each unit gets closer to a viable Dunbar number so can opearte withoit overwhelming process.
• Increasing reporting lines across the organisations – “matrix” organisations.
• Implementing hetearchies within the hierarchy – Quality Circles, Manufacturing Cells, Ad Hoc Project teams, “Agile” processes are all examples of this.
• Giving authority to people further down the organisation.

What Works – Alternative Organisations that have scaled, succesfully and sustainably

There are a number of interesting case studies of larger companies that do use alternative approaches and have proven they are both scalable and systainable over time:

John Lewis Partnership

The UK department store retailer has been going for over 100 years, and is consistently seen as great for customers and great for staff. In structure its is at first glance hierachical, but with the major difference that every employee is in fact a aprtner, a shareholder in the company at a meaningful level, and this impacts it’s culture and approach. There are however 3 other features worth noting about John Lewis that are important:

• It is privately held, i.e. can focus on long term company goals rather than short term pressures from rent seeking investors
• Stores are “natural Dunbar structures” – they are essentially a large collection of c “150 person” elements, the ecosystem has been sympathetic to reproducing viable sized structures
• It operates at the upmarket (aka higher margin end) of its business ecosystem, where a focus on service generates more benefit than possible higher costs of its approach

W L Gore

Gore has been going for c 60 years and pioneered an Open Allocation organisation (choose what you work on, within bounds) structure based on it’s founder’s experience with ad-hoc task teams in hierarchical structures. Gore has c 10,000 associates (employees) in operations spread across the world. It looks to be poles apart from John Lewis, but Gore has some interesting features that are similar:

• Family, then privately owned
• Gore Plants also have limits to size – In each factory they limit the number of employees to c 150 people (recegnise thet number?) so that “everyone knows everyone”.
• At the high end of it’s business ecosytem, again a focus on customer service yields a larger benefit than on cost

Toyota

Toyota was founded just before WW2, but like most Japanese companies had to re-invent itself afterwards, and developed a system to optimise the conistions of austerity in post war Japan. It  became well known for this “Toyota Production System” as the exemplar of “just-in-time” production. The system focussed on minimising slack resources, and emphasized efficiency on the part of employees, and gave a lot of power to the employees on the front lines. They pioneered work cells, quality circles and modular structures. It was the go-to case study in the 1970’s & 80’s. During the 1990s, Toyota began to experience rapid growth and expansion, largely due to the culture.  Expansion strained resources across the organization and slowed response time, and the organization culture became more defensive and protective of information. Toyota’s CEO, Akio Toyoda, the grandson of its founder, noted that  “I fear the pace at which we have grown may have been too quick.” Note that it maps to Gore and John Lewis in that i:

• Is Family owned
• Although a mass market player, it strove to be at the “high value” end – quality and customer choive were built in from the design process
• There is no direct linking to the 150 “Dunbar Number” but they certainly understood small team dynamics very well, and the “respect for people” philosophy echoes a lot of the thinking of the other two
• Although it does not have the common ownership model of the other two, it does benefit from teh Japanese company corporate loyalty model, arguably an equivalent approach in terms of aligning interests

A number of new companies are often posited as being examplars of these new ways of working (Valve,  Medium etc) but as most are new we can’t tell whether they are sustainable. Also, most of these organisations are still relatively small, few are over 50 employees in size, never mind 150, so in Dunbar terms these organisations can work mainly because of human cognition capacity rather than any new structure per se. Also most are almost pure “knowledge busineses”, which have already shown over the last few decades that they are fairly easy to run in less hierarchical fashion . The interesting thing will be to see what happens when they scale, and if there are any ecosystem factors underlying the successes (or failures).

Of the current crop of new organisation experimemts, the one that interests us most is Zappos, as unlike many of the others held up as exemplars today, who are mainly too small and new for any conclusions to be drawn, Zappos is already a larger enterprise that is trying to re-engineer its culture, and it deals with physical materials and operational issues.

Zappos

Two years ago Zappos stated to implement holacracy, an approach originally based on Sociocracy, ostensibly a heterarchical system but one which has a very structured approach to the ranking of people in different ways (to the extent that sceptics observe that it looks very much like a hierarchy in all but name…).

Zappos differs from our sustainable case studies in a few ways:

• It is not family/privately run, being acquired by Amazon
• It is not clear if it has the common ownership model (at a level that makes alignment of interest top of mind), so it is up to the holacracy culture to deliver this.
• It is not “upper end” of its market, however it is obsessive about service and was an early adopter of the online channel, and can offer a huge range of niche sizes and products that are hard to find in conventional stores

So far the results are unclear, reports lauding and lamenting it both appearing in the media to date, but it’s early days and major change is never quick – definitely a cse study in the making.

What Doesn’t?

We have written already of why many earlier structures failed (see here) and have noted that all succesful organisations tend to have in them the seeds of their own destruction, but the following amusing – and educational – concepts are also worth following up on, as they will kill any organisation, no matter how structured:

• Peter Principle – every employee rises to their level of incompetence
• Parkinson’s Law – make-work expands to fill the time available for its completion. There will be the maximum number of chiefs at the point the system collapses
• Pournelle’s Iron Law – in essence that in any organisation, the people who will eventually rule it will optimise for its own survival, regardless of whether it is functional or not

What’s Next?

This was to be the rest of the workshop – publish a comment and damn the torpedoes!

*Some in the Anthropological establishment dislike Diamond’s work, its hard to tell whether its valid objection or noses out of joint, but what is unarguable is that he shows that various succesful early human societies can be organised very differently