Your desk is a guide to the future of work

The future of work is the topic on everyone’s lips. The talk of automation and artificial intelligence can seem really abstract and alien, making the future seem scarier than really needs to be the case. A good way for white collar workers to think about what lies ahead is by looking at their office surroundings and how those might change in the coming years.

Anyone worried about what work might look like in the coming decades should remember a worker of the 1970s would feel like a fish out of water if they were magically transported to today. The desk of a white collar, clerical, worker would likely have had “in” and “out” trays, stationary and a phone (an extension of a central number). By the 1980s, they may have had their own direct phone number and possibly, depending on their job, access to a computer terminal for very basic tasks. Reports were manually typed, with large offices and senior staff having access to typists who would convert their handwritten notes into neat reports. Rows of those typists were exacting workers who had to mix speed with accuracy and were the ultimate slaves to their desks.

The desk of the turn of the century was very different. The trays were rapidly disappearing. Every desk had a personal computer with a graphical interface and the tethered phone was giving way to IP telephony allowing hot desking to make an appearance. The basic mobile phones and dial-up networking that most workers had at home meant that working remotely was becoming possible, if not practical, for many tasks. Working from home was still called “teleworking” referring to the use of the telephone as the predominant infrastructure.

Ten years later, the desk didn’t look very different, just a little more efficient. Internet connections were faster but the equipment was fundamentally the same. Although smart phones were starting to appear, they weren’t ubiquitous and the functions to which they were applied were basic.

Today the desk is starting to change in much more fundamental ways, but the transformation is no more dramatic than the changes that we’ve gone through in the relatively recent past.

Our desk is finally less bound by paper with some evidence that the US, at least, is seeing a reduction in its use as the form factor of tablets is encouraging less paper (having said that, at about 10,000 sheets of paper per worker per year it is still very high). Our technology is also moving from being a passive tool of efficiency to an active driver of activity.

The balance of power may also have turned. Equipment on our desks are starting to monitor the work being done and the worker themselves. Testing whether the white-collar worker is being productive or, when working remotely, whether they are being active at their desk. This is a form of working for the machine rather than the machines working for us and is likely to be the subject of debate in years to come.

Even more dramatic than simply detecting whether the worker is active, the equipment of our desk is increasingly able to allocate tasks between workers. While every good leader argues that we should measure outcomes and outputs rather than effort, a world where workers can be remote and be paid by output can lead to problems.

When there is a break between the supervisor and the supervised, the nature of competition means the rate individuals get paid often gets pushed down. We are already seeing this effect in the so-called “gig economy”. It is likely that governments will need to step in to protect workers, particularly in fields where there is substantial competition.

If understanding the desk of the future is important, what will be surrounding our workspace in the coming decade? For the first time in a long time, the form factor of the devices on that desk are far from “one size fits all”. The single hinge notebook has given way to all forms of tablets. The desk phone is gone and the fax is long gone. It even seems that voice control is finally finding form in technology dubbed “beyond glass” but also challenging the fully open plan office.

Electronic communications are also rapidly changing. Social networks are merging with messaging services and we are just starting to move past email. It is very likely that more structure will be added to the interactions we have through our work activities, probably driven by our artificial intelligence co-workers.

The future of work is uncertain, but not such a radical shift from what we do today. There are risks that we need to navigate but using history as a guide we should be able to manage the transition without a single change on its own tipping over too many desks at once.

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Defence as the best form of attack

The global economy is powered by business innovation with small and large organisations alike inventing the future for us all. The rapid rate of change brings both opportunities and threats with recent cyber events acting as a wake-up call. Far from being afraid, we should be reminded that we need to design businesses to operate and even thrive in unexpected circumstances.

In the 1970s, companies like Toyota revolutionised manufacturing with “just in time” supply chains. Nothing ever comes for free, for every dollar of stock taken out of the system there is a dollar of contingency and slack also removed. When everything works well, there isn’t a problem, when it doesn’t the flow-on effects can create a supply chain whiplash or bullwhip effect. The best manufacturers in the world solved this by putting enormous pressure on quality to avoid exactly these sort of disruptions.

These are lessons we need to learn as we look to roll out more sophisticated systems in our society such as connected infrastructure and transport and even make the move to autonomous systems. Our society in a few short years is likely to be orders of magnitude more connected through complex networks and supply chains.

Computing generally follows real world models in its first iterations, and its mirroring of best practice supply chains is no exception. Moving from a world where each system was independent to one where they are tightly coupled across corporate boundaries has seen a data supply chain that lends heavily from manufacturers. The addition of cloud computing means that almost every process involves at least two and often more players linked together through a multitude of interdependencies.

This trend is as prevalent in our digitally enabled infrastructure (such as support for our rail networks and energy grids) as it is in digital-only systems (such as banking, telecommunications and government systems). The tighter those linkages the more functions that can be added and the lower the overall cost.

As amazing as the capabilities of our world of technology is, the integration leaves us with almost no room for error or ability to flex in an environment of disruption. For example, our energy grids seem to be becoming more brittle with the rise of interconnections and regular travellers know the impact of airlines operating without slack when something goes wrong.

Like the manufacturing supply chains of the last half century, the key to keeping this technology running is quality with CIOs aiming to keep systems up 24/7. Even small outages, though, have a flow-on effect that is harder to predict and further reaching than the equivalent disruption in a manufacturing process. That’s because the complexity of these system interdependencies has grown exponentially.

The brittle and inflexible nature of complex systems have been one of the reasons that retail has struggled to adjust to the juggernaut of online shopping and manufacturers are still trying to get control of their supply chains back. Recent cyber-attacks, leaving major companies offline, have brought this into stark focus. The attacks have typically encrypted or hijacked one or two systems in the network and brought a brittle environment to breaking point.

The architects of systems and processes tend to design for today’s business. Defensive computing is a paradigm for boxing components in such a way that they work regardless of what happens. This is a mindset that goes beyond testing for the scenarios outlined by stakeholders and moves to safe failovers in the event of anything unexpected.

Defensive measures include having systems work while offline or while counterpart systems are unavailable and when reference data is corrupted or hijacked. If technologists adopt a more defensive mindset, the testing burden is dramatically reduced and the uses of their systems can be extended far beyond the context of their initial design.

Where tightly-coupled systems are brittle, those that have been defensively architected are like flexible buildings that can withstand the buffeting winds of cyber-attacks and the shifting sands of changing business models.

Defensive design requires more expansive thinking about the worst-case scenario for every module. Data should backed-up incrementally and then be thoroughly validated. Connected systems should be assumed to provide completely unexpected and illegitimate responses. Users can be expected to approach every interaction with an almost destructive mindset.

Every part of a system should be independently robust and proactively test that every interaction is valid, rather than only checking for known invalid responses. The more modular and API-driven such a solution is, the more likely it is to be flexible and robust enough to survive cyber-attack as well as business disruption through combination with new applications.

Our infrastructure is never going to be impregnable. Even the strongest perimeter barriers can be breached by one innocent user clicking on the wrong link. Similarly, our business models aren’t invulnerable. The answer is to have each component of the information supply chain designed in a defensive way such that it assumes the worst of even trusted systems, users and competitors.

Businesses building for the worst case, planning to run even when seriously compromised, will find that they more easily weather cyber issues and competitive disruption. Neither should ever come as a surprise.

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More but not better jobs

The future of work is the topic on everyone’s lips. We’ve gone from worrying about whether our children will have jobs to worrying about our own place in the workforce. The rise of artificial intelligence and robotics has been at the upper end of twenty first century predictions.

Everyone wants to know whether automation will trigger a massive cut in jobs. This is not academic. Previous predictions that automation would hit employment in the 1970s and 1980s led some countries such as France to move to shorter working weeks and effectively ration available work. The consequences have been large with lost productivity and languishing economic performance which, in turn, has created the very unemployment they were trying to avoid.

The automation of human labour is not new. The development of cotton mills in the industrial revolution and Henry Ford’s production line of the early twentieth century are perfect examples. Both reduced the labour required per unit of production but increased demand caused a net increase in jobs.

The Luddites in the early days of the nineteenth century worried about the impact of automaton and showed their opposition by smashing machines. No lesser technologist than Bill Gates recently said “Right now if a human worker does, you know, $50,000 worth of work in a factory, that income is taxed. If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level”. We can interpret his comments as a proposed brake on machines through taxation, a modern equivalent to the machine smashing of the Luddites.

The rise of artificial intelligence is just a continuation of computer-driven automation since the 1970s. We have seen many jobs displaced in that time, yet more work has been created. Word processing has displaced the typing pool. Workflow processing has all but eliminated traditional clerical roles. Yet there are more jobs today than ever before. At about this point many people say “more and better” jobs. It is the latter half, “better” that needs closer examination.

Our jobs can increasingly be described in terms of whether we’re working “on”, “with” or “for” machines.

Those of us who work “on” machines are shaping what they do, we are defining the problems they solve and identifying the questions that need answers. Examples of working “on” the machines include programming, design and data science. These are activities that require an insight that is not within the scope of this second generation of artificial intelligence (see Your insight might protect your job). I would argue that there are the same or slightly more of these jobs than in the past and that the jobs are, largely, as good as ever.

The jobs that work “with” the machines are giving many of their day-to-day repetitive activities to artificial intelligence and traditional technology. Teachers are increasingly handing over much of the content creation and learning interaction to technology and students are largely responding well. This is true at all levels of schooling, from pre-school to university. Teachers are, though, more important than ever, for example see Universities disrupted but not displaced.

Finally, the largest pool of increased employment is working “for” the machines. These are the jobs that are scheduled and managed by technology. At the extreme are the “Mechanical Turks” and other crowd workers who do piecework for a few cents a job. Also in this category are rideshare drivers, online retailing pickers and increasingly some of the more manual health roles. Done well, these jobs can fit into a flexible working arrangement that suits many lifestyles.

To put this third category of jobs into perspective, consider what actually happened with the early nineteenth century cotton mills and then again with the early twentieth century production lines. Far from destroying jobs, more labour than ever before was needed. But as anyone who has watched a period drama or read a Dickensian novel knows, these were not pleasant places to work. Workers were regularly injured or killed, rights were almost non-existent and worker was played-off against worker.

The future of work could see more of these jobs that work “for” machines created with the emphasis on dehumanising and optimising scheduling to suit the needs of technology and employers. Working remotely, or largely instructed by computer, it’s possible that we’ll repeat the mistakes of the past.

But history also gives us reason for optimism. Within a few decades the cotton mills and production lines became much more desirable places to spend a working day. In our far more competitive world, many companies are realising that there is a commercial advantage to eliminating the several decades gap between creating new job and making them desirable. Those companies are winning the war for talent.

At the end of the day, business production of goods and services is for the consumption of humans. The modern services economy means customers are interacting with workers more than ever, and want it to be a social and positive experience. Even the production of goods is increasingly social with the rise of shorter supply chains and a booming “craft” movement of artisan products ranging from food to furniture. Businesses that want to win in this world need employees who are going to portray their brand in a good light and for that their day-to-day work needs to be life affirming.

With a focus on the right things, there is an opportunity for the automation of the coming years to lead to both more and better jobs.

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Information age delivers new space race

At the height of the race for the moon, everyone imagined that by now we would be living in a space age. Instead we got the information age which has given us access to unparalleled global connections and almost the sum of human knowledge at our fingertips. Stanley Kubrick’s “2001: A Space Odyssey” assumed the information revolution would happen alongside the move into space, in fact it seems they had to be sequential. Despite a many decade hiatus, space is back in vogue and the connections to the technology industry are intimate.

In the 1960s, the Cold War spurred the US to race to the moon in the largest peacetime project in history. The hangover from the massive drain on the budget (where up to 400,000 Americans were employed by the programme through NASA or its contractors and many times that indirectly) actually delayed space development through the next fifty years. Today, it is well recognised that the massive investment in technology had huge spinoff benefits, and being a peacetime project it didn’t come with the truly horrible consequences of wartime investments in technology.

As exciting as the robot explorers of the solar system are, the future of our species is inspired by our own travels. With the aging Russian Soyuz capsules as the main working space vehicles for human travel it is time for a new generation to enter the race. The increasingly sophisticated, autonomous, systems that have powered the exploration of the solar system have, however, been both beneficiaries of and contributors to our Earth-bound robotic technologies. Far from relying on manual intervention, it is now possible for unbelievably complex decisions to be made without human intervention by machines that are too far from home to get human help.

It isn’t a coincidence that big names such as Elon Musk and Jeff Bezos are behind the new space age. There are still quick riches to be made from connecting people on the Internet in new ways, but real wealth is created from fundamental shifts. Musk is betting on infrastructure and Bezos on supply chains. Both need new innovations and inventions to achieve their commercial aims and support Tesla and Amazon respectively. Both companies are priced by investors on potential rather than profit and risk a collapse of their value if future inventions don’t continue their apparently endless expansions.

In an era of digitisation of the physical world, it’s hard to say “it’s not rocket science”, because it is! To make our world work better, millions (and eventually billions) of devices have to be integrated. The technology required to safely launch humans and successfully reuse some of the equipment takes this requirement to extremes. Batteries, fuels, guidance systems and sensors are just the start of a tightly integrated network which has to operate together to take a vehicle into space and return it safely to Earth. With Earth’s orbit becoming increasingly crowded with active equipment, and inactive junk, smart traffic management is going to make the race for autonomous vehicles look like child’s play!

The beneficiaries of the technologies of the new space industry will include the energy grid, electric vehicles, autonomous transport, medical life support and propulsion for air travel, to name just a few that we can imagine today. The technologies that will spin out of investment in a new generation of space activity will be just as disruptive to today’s business models as the recent explosion of new, technology-fuelled, platforms.

The information age has removed many inefficiencies in the distribution of existing resources. The shift of many economies from production to services reflects the huge waste that was occurring in the use of infrastructure and goods. The ongoing focus on financial services, telecommunications and other services sectors, leveraging disruptive information technologies, suggests that there is still much further to go. However, no matter how you look at it, the information age is not adding to the sum of resources for humanity to draw on.

Turning our information age into a space revolution is different. Far from just reallocating what we have today, it promises access to minerals from extra-terrestrial ore bodies and super-efficient farm production to name just a couple of opportunities to add to the sum of resources available to humanity.

Just as investors have gotten their heads around the transformation of existing business models to disruptive platforms, we are all going to have to think about business and government differently all over again. The ability to think about every aspect of what we do through the use of disruptive technology will serve us well in the years ahead. Just when we thought we might see some stability in how we do business, it is clear that the changes have barely begun.

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Machine rights

When an employee leaves one organisation and moves to another, they are not allowed to take the property of their first employer with them. That includes lists of customers, algorithms or other intellectual property. It doesn’t, however, stop employees from taking what they’ve learnt and applying it in their new role. The rules around what is fair (and legal) have developed over many years. We are just starting to explore the same questions now with robots powered by machine learning.

It is worth a reminder on the two main types of robot. The first, and the origin of the term, are those that manipulate the world around them supporting tasks like manufacturing, cleaning and an increasingly wide range of other real world physical tasks. The second are virtual agents that mimic real world user activity online, such as filling in forms, responding to emails and conversing on chat tools. Although the conventions are still forming, online agents are generally referred to as “bots” (derived as a shortened form of robot).

A debate on the role of bots (and robots more generally) moving between organisations isn’t academic, as most robotic process automation (bots replacing people in routine, often “cut and paste”, processes) are provided by third parties through the cloud. When a bot finishes with one organisation, what does it need to leave behind and what can it take with it?

There is no doubt that the data a bot deals with belongs to the company that created it. However, bots use artificial intelligence (AI) to get constantly smarter. The question is whether this AI-powered machine learning is deemed to be a form of data that is derived from the data that supported its learning.

It would be very easy to descend into a legal debate. My intention is to focus on what the right answer is to these important questions. Lawyers, guided by business, can then direct the development of contracts that support these positions.

If a business wanted to play to its own maximum advantage, it could insist that any machine learning done on their data was only to be used for their advantage. Taken to its logical conclusion, the consequences of such an approach would extend beyond bots to learning algorithms such as search engines. Search providers would actively resist attempts to isolate the activities of individuals in particular organisations from the constantly improving results they deliver for all their users.

Even if this position were possible to enforce, it would not be in any organisation’s favour unless they were the only ones that were applying such a rule. Any economy that allows the free flow of capability is better and more productive as a result. We all benefit by sharing as the machines we deal with get smarter.

However, taken to the other extreme, a robot that learns the secret algorithm behind the pricing or apportionment of a business should not be taking that knowledge to another organisation.

The difference, of course, between machine and human learning is the recall of the former. When a machine encodes something, it has total recall. By comparison, if a human sees a list of customers and their phone numbers their accurate recall would be close to zero!

The argument in favour of limiting machine learning derived from an “employer” would be that learning is at best an analogy rather than an exact analogue. The argument against is that everyone benefits as the pool of machine “employees” improves, a little like competing employers actively working together to improve the quality of professional education.

In my view, organisations overestimate the exclusivity or differentiation of their intellectual property. I also believe that they underestimate the power of working as part of a community that grows the whole economy. The most successful organisations grow the size of their market rather than treat it as a zero sum game. That doesn’t mean that businesses don’t have secrets that provide them with unique advantages, but rather that there are few that are genuinely valuable, they expire quickly and they are generally less valuable than having access to more capable people and machines.

Bots that learn across a community of businesses can actually make the whole economy stronger, no-one needs to lose in that equation!

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Post-truth surprises

Unexpected election results around the world have given the media the chance to talk about their favourite topic: themselves! With their experience running polls, the media are very good at predicting the winner out of two established parties or candidates but are periodically blindsided by outsiders or choices that break with convention. In most cases, there were plenty of warnings but it takes hindsight to make experts of us all.

Surprises are coming as thick and fast in business as they are in politics and similarly there are just as many who get them right with perfect hindsight! The same polling and data issues apply to navigating the economy as they do to predicting electoral trends.

The Oxford Dictionary picked “post-truth” as their 2016 word of the year. The term refers to the selective use of facts to support a particular view of the world or narrative. Many are arguing that the surprises we are seeing today are unique to the era we live in. The reality is that the selective use of data has long been a problem, but the information age makes it more common than ever before.

For evidence that poor use of data has led to past surprises, it worth going way back to 1936 when a prominent US publication called The Literary Digest invested in arguably the largest poll of the time. The Literary Digest used their huge sample of more than two million voters to predict the Republican challenger would easily beat the incumbent, President Roosevelt. After Roosevelt won convincingly, The Literary Digest’s demise came shortly thereafter.

As humans, we look for patterns, but are guilty of spotting patterns first in data that validates what we already know. This is “confirmation bias” where we overemphasise a select few facts. In the case of political polls, the individuals or questions picked often reinforces a set of assumptions by those who are doing the polling.

This is as true within organisations as it is in the public arena. Information overload means that we have to filter much more than ever before. With Big Data, we are filtering using algorithms that increasingly depend on Artificial Intelligence (AI).

AI needs to be trained (another word for programming without programmers) on datasets that are chosen by us, leaving open exactly the same confirmation bias issues that have led the media astray. AI can’t make a “cognitive leap” to look beyond the world that the data it was trained on describes (see Your insight might protect your job).

This is a huge business opportunity. Far from seeing an explosion of “earn while you sleep” business models, there is more demand than ever for services that include more human intervention. Amazon Mechanical Turk is one such example where tasks such as categorising photos are farmed out to an army of contractors. Of course, working for the machines in this sort of model is also a path to low paid work, hardly the future that we would hope for the next generation.

The real opportunity in Big Data, even with its automated filtering, is the training and development of a new breed of professionals who will curate the data used to train the AI. Only humans can identify the surprises as they emerge and challenge the choice of data used for analysis.

Information overload is tempting organisations to filter available data, only to be blindsided by sudden moves in sales, inventory or costs. With hindsight, most of these surprises should have been predicted. More and more organisations are challenging the post-truth habits that many professionals have fallen into, broadening the data they look at, changing the business narratives and creating new opportunities as a result.

At the time of writing, automated search engines are under threat of a ban by advertisers sick of their promotions sitting alongside objectionable content. At the turn of the century human curated search lost out in the battle with automation, but the war may not be over yet. As the might of advertising revenue finds voice, demanding something better than automated algorithms can provide, it may be that earlier models may emerge again.

It is possible that the future is more human curation and less automation.

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Moonshots

In business, we tend to focus on the incremental changes we are dealing with every day. The big opportunities always seem too far away to build into our monthly, quarterly or even annual plans. These opportunities, though, are the “moonshots” that completely change the world and generate growth for years to come.

The big changes can come from unexpected places. There are, however, themes that help all of us to be ready to jump-to and lend a hand when we see answers to the biggest questions our generation needs to answer. At the right time, these changes are opportunities we need to be ready to grab.

Google coined the term “moonshots” to describe big, imaginative, investments that were of significant scale and potential impact. Most famously, perhaps, they took on autonomous vehicles before they were popularly regarded as seriously disruptive. I really like the term because it describes both ambition at a global level and tackling things at the edge of our abilities.

I would argue that the moonshots of our time tackle at least one, and often more, of the major challenges of the twenty-first century: environment, living space, energy, resources and health. The danger of the current focus on the information and digital economy is the tendency towards small incremental gains which just aren’t going to cut it in a world that is going to be dramatically different in forty years. That doesn’t mean that our view of innovation today is bad, but there is not enough focus on these major challenges as opposed to incremental gains on what we already do well today (see Where is the digital-fuelled growth?).

The twentieth century’s growth in population (nearly quadrupling to more than 6 billion) and industrialisation of the twentieth century has been both caused by and a cause of our huge lift in economic growth and living standards. At the same time, it has created an undeniable strain on our environment for which transformative technologies can make a huge difference. Technologies scrubbing carbon, cleaning particle pollution, protecting species are almost guaranteed to be developed but they won’t get mass appeal unless the information economy takes the lead to find pathways to market, profitable funding models and integration of what are likely to be disparate solutions.

The same growth in population and urbanisation has put an enormous strain on our cities. As governments struggle with affordable housing, there are few miraculous ways of creating more land near the city centres where a large portion of the population works. While working from home is now a viable part of many a commuter’s week, it is only a stop gap for the social activity of work. The real moonshot here will be to make commuting from a much wider geographic area possible through revolutionary transport technology. As Uber has shown, joining the dots on transport can accelerate the viability of different vehicle options.

Energy security and cost is at the forefront of the minds of many as the race for a low carbon future collides with disasters like Fukushima, gaps in renewable technologies and monumental spending requirements on grid infrastructure. Where there is a great need (cheap energy is a economic growth opportunity) and material friction (unaffordable and inadequate technology) there is a moonshot opportunity.

Even solving the energy gap will not solve the inevitable crunch on many of our planet’s resources. Global supply chains have enabled tremendous gains in economic efficiency, but at the cost of resources (with each stage often adding a layer of wastage). Advanced manufacturing, urban food production and other technologies that shorten supply chains are likely to be in high demand. While many of us who grew-up with the promise of space travel would love it to be a solution to living space constraints, it is far more likely that our century’s space moonshots will be geared towards mineral riches from our near solar neighbours.

Finally, most health moonshots concentrate on new technologies to solve the remaining killers. The opportunity that is often missed is to dramatically reduce the cost of maintaining our overall health. Societies around the world are dealing with healthcare costs that are blowing-out, while recognising the inherent inefficiencies of our current health bureaucracies. Digital solutions that turn the problem on its head could potentially save more lives worldwide than almost any new drug.

We’ve seen moonshots in past centuries bring us efficient transport, industrialisation, modern medicine and, of course, the first footsteps on the moon! While we face many challenges in this century, I believe there are more reasons to be an optimist than a pessimist as long as we are prepared to take-on exciting new moonshot opportunities.

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