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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Measuring transformation

We live in times of rapid change when businesses that assume they have a secure market are suddenly having their world turned upside down. With the most substantive impact coming from technology, many have assumed that large investments in IT and digital would act as a protection. In fact, many of the businesses who have made the largest investments, such as some retailers, are actually the ones experiencing the greatest disruption to their operations.

It is hard to describe disruption in a meaningful way, but I like Jack Welch’s famous quote “if the rate of change on the outside exceeds the rate of change on the inside, the end is near”. A disruption index can be described in terms of the ratio of the external and internal rates of change. But, how do you measure change and the transformation within your organisation (the numerator and denominator of this ratio)?

When I was putting the finishing touches on Information-Driven Business, I had the opportunity to share an editor with Douglas W. Hubbard who wrote How to measure anything. This book is a wonderful reminder that the only limit to putting a numerical value on any business problem is our imagination! Whenever someone argues that their change, driven by transformation, is too hard to measure, I’m reminded of this book.

Not only do I think that the change associated with any transformation can be measured, I also think that the first measure you think of is unlikely to be the best. For example, customer-service focused transformations often default to net promoter score as the main measure while overhead-driven transformations frequently rely on measuring the cost or headcount taken out of the business.

These are good measures, and should play a role, but they aren’t great denominators for the disruption ratio. What we really need to measure is sustainable strategic change in an environment where the very nature of corporate strategy is changing. On the one hand, top-down one-off strategy work is making way for ongoing experimentation combined with a small number of “crossing the Rubicon” moments. On the other hand, too little focus on the Rubicon leads to worrying about horse carcasses in growing cities, something I discussed when I wrote about the difficulty of seeing past today’s problems.

Customer transformations that rely too heavily, for example, on net promoter score, lend themselves to disruption by a better offer. I’ve seen numerous organisations get customer feedback after each interaction only to find it a poor correlation to customer churn. The issues are many, but can include a metric-driven incentive for customer service agents to provide exactly what the customer wants to hear but without any realism that it can actually be delivered.

When we talk about customer loyalty, that really means a build-up of value. Really thinking about this could result in some form of balance sheet recognition. Each time there is a genuine discount to the market, a real solution to a meaningful problem or a deeply insightful interaction there is value. Similarly, the balance sheet value of employee-generated IP is as much a meaningful measure of employee satisfaction and inventiveness as any engagement score or innovation survey.

A great resource which combines employee and customer engagement is Zeynep Ton’s work on The good jobs strategy. Ton’s research very nicely identifies the relationship between the cost of staff, investment in their capability and the loyalty of customers. From here can come an approach to measuring a sustainable transformation.

Like many researchers, Ton has identified that transformation is as much about what you take away as what you add. Simply targeting the creation and launch of new products ultimately destroys organisational agility and adds complexity which stymies both customer service and future innovation. Radical decommissioning is one approach, but another is to measure complexity and target its gradual reduction as I’ve previously suggested by Trading your way to IT simplicity.

Regardless of whether it is customer service, supply chain, human resources, costs or products that you are trying to transform, the challenges are similar. While the strategic goals might be easy to describe, the real work happens when you try to design measures. Rather than setting once and assuming the measure is right, constant experimentation and confirmation is essential.

The attribute of a great transformation measure is that it doesn’t just correlate with the outcome you want, it is intrinsic to it. Given the complexity of changing a business, it is very likely that these outcomes will be complex and the measures you need equally sophisticated.

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Who do you love?

An alien relying on TV for their knowledge of humanity might watch a few ads and assume our closest emotional relationships are with banks, utilities and retailers. After all, they all claim to be your best friend, look how many ads talk about “falling in love” with your service provider!

It is popular to talk about the relationship between customers and the businesses that serve them. Banks, airlines and utilities all seek to be best friends with their customers. This is probably understandable given that most of us are passionate about the businesses we work for and we want our customers to be as well.

In building such a relationship, marketers can point to great examples such as airline loyalty schemes, social media and even the account balance page of internet banking sites,. In each case, there are individuals who interact daily, even hourly, with these services and look forward to each touchpoint.

Such a strong relationship is hard for most businesses to maintain with the majority of their customers. After all, most people don’t get excited looking-up their electricity prices, mortgage rate or recent phone numbers they’ve called.

The common attribute of the businesses we care about seems to be the information they provide. Many people can’t imagine why they would care deeply about a bank, yet a small number of people check their bank account balances multiple times in a day. Anecdotally, those repeat checkers are dreaming of a saving goal which provides a halo effect for the bank.

Similarly, many travellers love to track their frequent flyer status which they see as a reward in its own right. The airlines create portals that engage their premium passengers and offer a regular sense of progress and engagement.

Uber has a fascinating screen on its app showing all the cars circling locally while eBay has nailed the search for a bargain. Some fintechs are attracting customers by creating a “fiddle factor”, letting them earn small rewards in different ways.

At the same time, it doesn’t seem that people care too much whether they love their basic services. Most people just want their savings to be safe, their lights to stay on and their phones to ring. The only problem is that in an environment where they can change providers easily, this lack of loyalty means that they are more likely to make a switch.

How can a brand that provides a capability that people need, but lacks passion, align with a brand that everyone cares about? This is the power of the API economy where it is easy for businesses to partner seamlessly.

Banks and airlines were pioneers in partnering, bringing together credit cards and air miles. Similarly, phone companies are partnering with music and movie streamers to dramatically increase engagement with their services. In coming years we can expect to see social media, fashion brands and travel businesses join with the everyday services that meet our basic needs.

To be successful, partners need to make sure they understand what elicits a strong affinity. To-date, brands have largely taken the same approach for all customers. For example, “daily-deal” style retailers are highly attractive to some customers and highly annoying to others. Basic services, such as insurance, who choose to partner with businesses like these need to be very targeted, otherwise they risk alienating as many customers as they delight. Too many marketers have made this mistake and have potentially damaged their brands.

The key to a meaningful relationship is tailoring the partnerships to offer the customer something they genuinely want to engage with. Talking to their customer community and offering them choice is a very good start, giving the winners in the race to pair more opportunities to generate genuine friendship, if not love!

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