Academic Conference: “Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency and the Performativity of Technology” (IFIP 8.2)

Dear All,

I am proud to be on the programme committee for one of my favourite academic conferences IFIP 8.2. The conference will be at San Francisco State University, December 11 & 12, 2018 with Lucy Suchman (Lancaster U.) & Paul Edwards (Stanford U. & U. of Michigan) at Keynotes.  I very much hope to see you there!

Submission Deadline: May 27, 2018


Our evolving digital worlds generate both hope and fears. Algorithms, using big data, identify suspicious credit card transactions and predict the spread of epidemics, but they also raise concerns about mass surveillance and systematically perpetuated biases. Social media platforms allow us to stay connected with family and friends, but they also commoditize relationships and produce new forms of sociality. While there is little agreement on the implications of digital technology for contemporary work and social life, there is a growing realization that information technologies are performative (MacKenzie 2006) in that they no longer merely represent the world, but also produce it. And given their growing interdependence, the ability to control any given technology is increasingly limited. Stock market flash crashes, induced by algorithmic trading, are highly visible examples of such algorithmic phenomena (Scott and Orlikowski 2014). Have the things we have made become out-of-control juggernauts? Are we living with monsters?

IFIP WG 8.2 has a distinguished history in shaping research agendas around information technology and organisation. For the 2018 working conference, we call for papers from scholars studying information technology and related practices to reflect on the worlds that we help create through our research, debates, and teaching. The metaphor of monsters is intended to stimulate a rethinking of our orientation by compelling us to consider whether, when and why our creations turn against us, and with what implications.

(Image CC – Kevin Dooley – with thanks!)

The Enterprise Kindergarten for our new AI Babies? Digital Leadership Forum.

I am to be part of a panel at the Digital Leadership Forum event today discussing AI and the Enterprise.  In my opinion, the AI debate has become dominated by the AI technology and the arrival of products sold to Enterprise as “AI solutions” rather than the ecosystems and contexts in which AI algorithms will operate. It is to this that I intend to talk.

It’s ironic though that we should come see AI in this way – as a kind of “black-box” to be purchased and installed. If AI is about “learning” and “intelligence” then surely an enterprises “AI- Baby”, if it is to act sensibly, needs a carefully considered environment which is carefully controlled to help it learn? AI technology is about learning – nurturing even – to ensure the results are relevant. With human babies we spend time choosing the books they will learn from, making the nursery safe and secure, and allowing them to experience the world carefully in a controlled manner. But do enterprises think about investing similar effort in considering the training data for their new AI? And in particular considering the digital ecosystem (Kindergarten) which will provide such data? 

Examples of AI Success clearly demonstrate such a kindergarten approach. AlphaGo grew in a world of well understood problems (Go has logical rules) with data unequivocally relevant to that problem.  The team used experts in the game to hone its learning, and were on hand to drive its success.  Yet many AI solutions seem marketed as “plug-and-play” as though exposing the AI to companies’ messy, often ambiguous, and usually partial data will be fine.

So where should a CxO be spending their time when evaluating enterprise AI? I would argue they should seek to evaluate both the AI product and their organisation’s “AI kindergarten” in which the “AI product” will grow?

Thinking about this further we might recommend that:

  • CxOs should make sure that the data feeding AI represents the companies values and needs and is not biased or partial.
  • Ensure that the AI decisions are taken forward in a controlled way, and that there is human oversight. Ensure the organisation is comfortable with any AI decisions and that even when they are wrong (which AI sometimes will be) they do not harm the company.
  • Ensure that the data required to train the AI is available. As AI can require a huge amount of data to learn effectively so it may be uneconomic for a single company to seek to acquire that data (see UBERs woes in this).
  • Consider what would happen if the data-sources for AI degraded or changed (for example a sensor broke, a camera was changed, data-policy evolved or different types of data emerged). Who would be auditing the AI to ensure it continued to operate as required?
  • Finally, consider that the AI-baby will not live alone – they will be “social”. Partners or competitors might employ similar AI which, within the wider marketplace ecosystem, might affect the world in which the AI operates. (See my previous article on potential AI collusion). Famously the interacting algorithms of high-frequency traders created significant market turbulence dubbed the “flash-crash” with traders’ algorithms failed to understand the wider context of other algorithms interacting. Further, as AI often lacks transparency of its decision making, so this interacting network of AI may act unpredictably and in ways poorly understood.
Image Kassandra Bay (cc) Thanks

Why you should study Information Systems within a Management Degree.

The following is a lecture I gave to young people applying to study Management at the LSE. The aim of the lecture is to discuss why studying information systems is vital if we are to understand modern management practice. 

At the start of 2014 Erik Brynjolfsson and Andrew McAfee at Sloan Management School (MIT) published a book titled “The second machine age” (Brynjolfsson and McAfee 2014); a title drawn from the idea that information technology is progressing at such a speed that digital technology is likely to reinvent our economy – just as the industrial revolution and steam engine did in the first machine age. Picking up this books’ argument The Economist used a picture of a tornado ripping through offices to illustrate how in the near future information technology is likely to reinvent all aspects of work – including management jobs.

In some ways their thesis sounds like another tired outdated argument for how wonderful Information technology is and how it will change everything. Similar pronouncements were made in the 1960s as computers began to be used for banking and travel agencies; in the 1970s as computers began to be used for office work such as publishing; in the 1980s as the Personal Computer arrived on every desk and entered the home; in the 1990s as the Internet emerged as an “Information Superhighway” connecting the worlds information; then again in the 2000’s as eCommerce, Websites and Mobile telephony exploded.

What is different today? Should we accept these authors’ pronouncements and their hyperbolic claim of a “second-machine age” today? And if we do accept it what does that mean for the study of management?

Let’s begin by considering where we are with information technology in business today.

Within modern industrialised economies most businesses already rely on a suite of information technology applications to support their activity. We cannot conceive of businesses without IT to manage all the different information flows they need.

From a small shop’s stock management system and accounting package, to a large business running thousands of applications IT is everywhere. For example AstraZenica, a global pharmaceutical company runs 2144 applications for its 50,000 users.

Large businesses like these will run applications for many different management tasks – to manage their key resources (staff, stock, warehousing etc); applications to manage their relationship with their customers (for example to manage call-centres, websites, online-orders, customer-email), supply chain management systems to manage suppliers and partners; and finance packages to undertake their accounting. They will also run specialist applications created for their particular business – for example the LSE has applications for timetabling all the classrooms and lecture halls, and applications to book halls of residences rooms for you.  These complex information tasks have existed for years –indeed the “computing department” predated the computer as shown in this photo – it was a department in a company responsible for processing information – so what is different today?

If these have existed for years the how then can Brynjolfsson and McAfee justify the argument that we are entering in a second machine age?

To consider this we need to think about the role of technology in our economy more generally.

The first machine age was when factories and machines (e.g. steam engines) were create in the “industrial revolution” and in a short period the entire economy evolved and changed.  Yet in 1976 Daniel Bell (Bell 1976) argued that we were a “post-industrial society” in which the number of employees in “first-machine-age” businesses were rapidly declining as computerised systems took over the role of people for repetitive manual labour (for example simple robots screwing nuts into the chassis of a car during its production). IT evolved and changed our industrial landscape during this period…

However at about the same time Peter Drucker (Drucker 1969)  (a famous management guru) coined the term “Knowledge worker”, describing how knowledge was the key economic resource and so showing that the human (as central to knowledge creation and application) would continue to rule supreme for knowledge work. The argument was that information-technology and machines might replace and mechanise simple repetitive jobs (making a more efficient first-machine-age) but knowledge-work would remain and would be vital for designing, building and supporting these machines.

While a PC might get rid of the typewriter and replace it with a wordprocessor application, or get rid of the calculator and replace it with an accounting application, the knowledge-worker would continue to direct and use these machines. They were supporting knowledge work not supplanting it. Knowledge Work wouldn’t be capable of mechanisation.

This view of knowledge-work also aligned with the shifting nature of economies as labour shifted from primary industry and secondary industry (e.g. Agriculture and manufacturing) to tertiary industries (like financial services) in which information technology was increasingly vital to support the knowledge work. Essentially knowledge-workers in tertiary industries – Lawyers, Financiers, Teachers, Doctors, Managers, Designers, Architects and (thankfully for me) Academics jobs’ were safe. Information Technology might support us in our work – and so make us more productive (and perhaps even more prosperous) – but ultimately we would still be needed!

Recently though we have seen a number of significant shifts in the way information technology is used in businesses and between businesses which might challenge this assumption.

To understand this though we need to stop talking about Information Technology – and begin to talk about Information systems. Because Information technology – computers, printers, applications, smartphones, networks – are only one part of an information system.

An information system is an organised system composed of technology and people which collects, organises and processes information. It is about how people are managed and coordinated to use information technology to do some task which is useful; whether it is a shop owner looking at their past-sales on a spreadsheet in order to make decisions on what stock to buy, or something more dynamic, globally distributed, and complex.

Take for example the Lotus Formula 1 racing team (which I had the pleasure of researching last summer). One of the crucial management tasks they face is to make decisions on racing strategy during a race. To do this they have a complex information system consisting of the driver, the car (which relays data about its performance from 150 sensors every 100th of a second) the pit-crew (who can see this data in real-time as it is processed by the 36 computers it keeps “Track-side” running specialist applications), and then a secondary crew sat in an office in Oxfordshire who see the same information as the pit-crew but also have access to all the engineers who designed the car, and another supercomputer to process this data. The information system then is all of this – the people, their expertise, the task (deciding what race strategy should be taken), the technology (the car, its instruments, the computers, software, the screens, the networking etc). The management decision on race strategy is effectively taken by all these components coordinating together and its success can have profound implications for the team’s financial performance.

Less extreme Information systems are everywhere… they got you into this lecture theatre this morning (remember the people with clipboards checking your name against a list), remember the Oyster-Card you used to get the tube here, remember the booking system you used to book the hotel.

What then might have changed recently to justify the name the second-machine age, and to justify why you need to study information systems within a degree on Management?

I would like to argue that two things might justify this argument today (these are drawn from my own research rather than from Brynjolfsson and McAfee’s book:  Digital Abundance and Connections and coordination.

1) Digital Abundance

Until quite recently business information systems managed information at a local level. Information was a scarce resource and mangers were employed to produce it (for example by getting people to walk around the shop floor counting things), analyse it (putting that data into a spreadsheet and deciding what was selling well) and act upon it (telling staff to order more of those things).

Today however data is everywhere and is usually created automatically (for example by shops Point of Sale (tills to you and me), supply chain management systems, and stock-tagging). Staff might walk around, but they would carry a computer and scanner and input this data directly into a huge database at head-office. But because computing storage and processing is extremely cheap today companies can go further to include other sources of information in making their decisions –  for example they can process internet sourced information (Facebook, Google, Twitter) to understand how people perceive a product or brand; they can process weather information to understand likely consumption patterns, they can process  all their past sales information, and they can even process data on customers – their buying patterns, habits, census data about where they live  – even how they walk around a store (using their smartphone wifi or CCTV).  Decision making has thus shifted from a scarcity of information to an abundance.

2) Connections and coordination

Management decision making is thus about dealing with this profusion of data and trying to identify causalities and inferences from the connections within it – and yet we are beginning to learn that this is often beyond the comprehension of humans. For example in some supermarkets Nappies and Beer are advertised together on Friday evenings.  Why? Because an information system with access to all sales data noted a direct correlation between the sale of nappies and beer on Friday evenings – and from this the head-office manager can infer that Men (mostly) are asked to collect nappies on the way home from work, and if prompted will probably buy beer at the same time. This decision however would probably only have been made through an information system analysing past sales data automatically.

But connection is also about extending information systems beyond a single enterprise. A new innovation termed “Cloud Computing” has shifted the IT used by many organisations outside their organisation and onto the Internet.  This has increasingly allowed them to create connections and networks between organisations, and build ever more profitable, and ever more complex, Information systems.

In a book I recently co-authored with two colleagues here at the LSE we termed this “moving to the cloud corporation” and describe a company called NewGrove who specialise in helping businesses by connecting and collating huge quantities of data from both within the organisation and from outside it and displaying this data as google-maps with colours, images and graphs showing how the business operated on a street by street basis. Managers with NewGrove’s information system can, arguably, make more informed decisions than competitors without such systems.

Connections and coordination are perhaps most evident at an airport – who flew here today – yesterday… How many of you didn’t really interact with a human being except at the departure gate (and perhaps the coffee stand)? Instead your passage through the airport was coordinated by a plethora of systems interacting and communicating together…  in the USA you put your frequent-flier or credit card into a machine and in those 3-4 seconds a conversation occurs between various machines – flight-status systems, Airlines systems, past-travel history, name with Transportation Security Administration, NSA (perhaps) , food providers, computers in the destination, connecting flights, weight distribution on the plane – what Brian Arthur (Arthur 2011) described as “ a second economy” alongside the visible economy. Throughout complex decisions and inferences are made by software that were previously made by human beings.

However as the software and computing power improves, as more IT is available through “cloud computing” shifts, and as more data is capable of processing, so the decisions and inferences can become more complex – and appear more like Peter Drucker’s “Knowledge Work”.

In 2010 IBM built a computer system called Watson capable of winning the complex gameshow “Jeopardy” against the shows two best ever players by keeping a gigantic database of information (taken from the internet) available in its memory- 200million pages of data.  Today Watson is being developed for sale as a management-support systems to work alongside physicians, lawyers, financial services and government. Note that since 2010 Watson improved its performance by 2300 percent and shrunk to the size of three pizza boxes[1]

However more importantly cloud computing allows large scale computing power to be available to any business paid-for with a credit card. The key lesson here then is not that these things are going to be amazing, but that they may become integrated into the mundane business systems central to every business today.

Finally these connections and their coordination change businesses and business models. Obviously music, TV, newspapers and photography have been fundamentally reconfigured by information technology. For example financial markets have been changed as companies harness similar data-abundance to produce computer systems that trade automatically… making complex financial decisions at huge speed through complex information systems which connect data from across the world.  This was made obvious on the 5th June 2010 at 2:45pm when 9% was wiped off the Dow Jones in about 5 minutes…. Within this time it was not human traders who were leading the volume of trades, but algorithmic trading systems created to play the market by making decisions based on vast quantities of data at speeds no human can match.

Now obviously the examples I have used in this talk so far might be classed as extreme – but they hopefully paint a picture. Information Technology are no longer simply tools used by managers – Information Systems are integrated, connected, coordinated systems with an abundance of data at their disposal which are making complex management decisions already.

Knowledge-Work is not longer, Brynjolfsson  and McAffee would argue, the preserve of the Human –and so we are entering a second machine age.

So what in conclusion I want to take away that:

1) Even if you don’t think you want to study computers you should ensure that you understand information systems at a basic level because they are as integral, pervasive and important as the people in a business – though often less visible. If we want to understand how businesses work from a financial or human resource perspective – we should probably do the same from an Information Systems perspectives.

2) Information Systems are part of what an organisation is and shape the way organisations evolve and change. We need to understand how to better design and build such systems, and how such systems constrain and shape businesses.

3) We must understand how we can prepare for the information systems created by others (be they competitors, partners or government) –Uber’s[2] impact on taxi-firms, TripAdvisor[3] on Hotel chains, Flikr and Facebook’s on Kodak, Amazon on retail and publishing, Google on mapping and search. When managers ignore the impact of information systems on their business, they are taking fundamental risks with their organisations future.

Thank you,

Will Venters.

I’m presenting at “The Exchange 2013 – Knowledge Peers”


I’m excited to be presenting at “The Exchange 2013 – Knowledge Peers” on the 28th November. Not only is it at the Kia Oval (which I drive past regularly so am looking forward to getting the tour inside), but also because their focus is on networking with smaller and medium sized organisations. I am of the opinion that cloud computing will offer more valuable and exciting opportunities for SMEs than large organisations so I am looking forward to connecting with many more small organisations at the event.

I hope you can join me there!


Our 2nd Report: Meeting the challenges of cloud computing – Accenture Outlook

Our second Accenture report on Cloud Computing is about to be published!  As a taster the above link takes you to a short synopsis (Published in the Accenture Outlook Points of View series). I will post a link to the full report when it is out.

While in danger of providing a summary on a summary, this second report builds on our first “Promise of Cloud Computing”  report to analyse the challenges faced by a move to cloud. We identify the following key challenges:

Challenge #1: Safeguarding data security

Challenge #2: Managing the contractual relationship

Challenge #3: Dealing with lock-in

Challenge #4: Managing the cloud

Once you read the paper I would love to hear your views – please use the add comments link at the bottom of this section (its quite small!) or email me directly on

I would also suggest you also review the whole report when it is out – much of the important detail is missing from this shorter synopses.

Why you can’t move a mainframe with a cloud • The Register

Why you can’t move a mainframe with a cloud • The Register.


This is a detailed technical analysis of the market for mainframes – discussing the infrastructure issues of moving Mainframes to cloud, or cloud to mainframes. The issues discussed are somewhat perennial – “greying workforce” shift to cheaper platforms of linux and java. But as the article attests it is the shear reliability and stability of mainframes which keeps them going – something those who proclaim the cloud will prevail must understand and respond to. With such guaranteed uptime of years  for transaction processing we cannot really envisage the Cloud for the core applications which run our information economy.