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!
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.
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.
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.
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!
Information overload is as much an overwhelming feeling as it is a measurable reality. We often feel an impossible obligation to be across everything, which leaves us wanting to give up and absorb nothing that hits our various screens. Despite all this, the good news is that the majority of the information we need seems to appear just in time.
Where does that leave those of us who are control freaks? I am not comfortable to know that the right information will find me the majority of the time. I want to know that the information I need is guaranteed to find me every time!
The trouble is, guarantees are expensive. This is related to the debate between search based big data solutions and enterprise data warehouses. Google provides a “near enough” search solution that, given the massive amount of data it trawls through, usually seems to find what we need. Knowledge and business intelligence solutions provide the predictable information flows but come at a huge cost.
Of course, the real sense of serendipity comes when information arrives unsought just when we need it. It can come through the right article being highlighted in a social media feed, a corporate policy being forwarded or the right coffee conversation with a colleague. Of course, serendipity isn’t random coincidence and there is much we can do to improve the odds of it happening when we need it most.
Before doing so, it is important to know what things have to be predictable and reliable. A list is likely to include financial reports, approvals and other controls. What’s more, a scan of any email inbox is likely to show a significant number of messages that need to be read and often actioned. Despite its tyranny on our working lives, email works too well!
Serendipity depends on the quality of our networks, both in terms of who we know and the amount of activity the passes between the nodes. A good way to understand the power of relationships in an information or social network is through the theory of “small worlds” (see chapter 5 of my book Information-Driven Business).
Ironically, in an era when people talk about electronic isolation, social networks, that is who we know, are more important than ever. Serendipity relies on people who we know, at least vaguely, promoting content in a way that we are likely to see.
Just as control freaks worry about relying on serendipity, those that are more relaxed run the risk of relying too much on information finding its way mysteriously to them at the right time. Those that don’t understand why it works, won’t understand when it won’t work.
Far from making experts and consultants redundant, this increasing trend towards having the right information available when it’s needed is making them more necessary than ever before. The skill experts bring is more than information synthesis, something that artificial intelligence is increasingly good at doing and will become even better at in the near future. The job of experts is to find connections that don’t exist on paper, the cognitive leaps that artificial intelligence can’t achieve (see Your insight might just save your job).
The first thing is to be active posting updates. Networks operate through quid quo pro, in the long-term we get back as much as we give. In the office, we call this gossip. Too much gossip and it just becomes noise but the right amount and you have an effective social network. Those people who only ever silently absorb information from their colleagues quickly become irrelevant to their social circle and gradually get excluded.
The second is to be constantly curious, like a bowerbird searching and collecting shiny pieces of information, without necessarily knowing how they will all fit together. The great thing about our modern systems is that massive amounts of tagged content is easy to search in weeks, months and years to come.
Finally, have some sort of framework or process for handling information exchange and picking a channel based on: criticality (in which case email is still likely to be the best medium), urgency (which favours various forms of messaging for brief exchanges), targeted broadcast (which favours posts explicitly highlighted/copied to individuals) or general information exchange (which favours general posts with curated social networks). Today, this is very much up to each individual to develop for themselves, but we can expect it to be part of the curriculum of future generations of children.
No matter how often it seems to happen, almost by magic, information serendipity is no accident and shouldn’t be left to chance.
It often feels like every idea has its time. Be it workplace trends, new technologies or social changes. With the benefit of hindsight, we can’t imagine why these things took so long. Yet, when a good idea’s time hasn’t yet come, it is incredibly hard to get innovations off the ground in business or government anywhere in the world.
Equality for everyone in the workplace seems so obvious it is outrageous that it took so long (and unacceptable that it is still an issue in so many areas, but that is a separate topic). Workplace safety was once almost ignored but is now simply the norm. The idea of telecommuting was controversial but now most professionals take it granted for at least some of their job. Yet before all three of these changes became mainstream they seemed just as compelling but too hard to make into reality.
Humans are social animals and we like to move in a herd. As much as we laud pioneers, we are seldom comfortable being too far out in front. So where does that leave all of us who are trying to create change and enable transformation in business?
That change is necessary must be obvious to anyone who looks at the poor rate of productivity growth over many years and the graveyard of businesses that just haven’t spotted disruption in their sectors. How do we bring forward the “time” for every idea rather than wait?
New ideas don’t just happen because they are logical. The herd has to provide leaders with a sense of protection and the people who follow have to be more than vocally supportive, they have to move with them. Leveraging our social instincts we can give people confidence to try new things, such as new technologies, by either moving the herd or creating a paddock where it feels like the herd is already headed.
The latter requires a level of creativity. The sense that the herd is moving when they may only be just starting to break. This requires those who seek to introduce something new to “frame the market”. It creates certainty (continuing the paddock metaphor, it is describing to the herd where the fences are).
Many proponents of new technologies and solutions are amazed that their proposals aren’t immediately adopted. This is as true today with technologies such as robotic process automation and social media at work as it was with email and graphical user interfaces in the 1980s and early 1990s. I remember having a debate in one organisation I worked with about whether staff should have access to the internet at all, a laughable proposition now but a very real concern to the leaders at the time. What swayed them was when their peers in comparable organisations allowed their staff access (they could take comfort in being part of the herd).
Business transformation, combining technology and organisational change is about as big as any change that we need to tackle and brings in new organisational models. Anyone seeking to introduce automation, streamlining and simplification needs to make the case that these are mainstream concepts. Vendors looking to sell these capabilities should consider moving away from claiming their wares are unique to putting them in the context of a wider market that is already there.
It is the dream of everyone who operates in a competitive marketplace to be the owner of a monopoly franchise. Putting aside the fact that a lack of competition makes us lazy, the evidence is that buyers avoid buying at all if they don’t have some sort of choice.
Even the solutions who appear for a time to be in a category of one (such as Windows or Facebook) have grown as a result of being part of a market. Uber needs Lyft, Windows needed the Mac and Facebook was possible because of Myspace.
We see this today in a range of technologies that could accelerate productivity but are struggling to gain traction. Each has vendors who claim to have a monopoly on a specific component and a market that is very hard to frame. Gartner talk in terms of their “hype cycle” where, at the peak of the hype, the innovations has been shown to have some sort of value. However, at that peak it is hard for users to make a comparison and the market is left to a few early adopters.
What this means for those of us trying to create change is that if we want organisations to adopt new ways of working, new technologies and new approaches to skills we need to frame the market and collaborate with our competitors to create a larger opportunity.
Collaboration between competitors can take the form of common standards, methods or even just the adoption of similar language to enable organisations to make meaningful comparisons. After all, we now expect to go into supermarkets and see the price per unit of weight and nutritional information in a form where we can make a reasonable comparison between competing products. The same should be true of more complex products and services used by organisations.
When ideas are new, collaboration can create larger and more competitive markets which benefits everyone.
Negotiation is one of the oldest human activities and is an important part of our economy. It is essential for sharing resources between people and groups. However, as our organisations have become more complex, the outcomes that we are achieving are getting worse not better.
The usual objective of negotiation is to match the needs of two parties. In business, this is often bringing together the provider of goods or services with someone who is in need of the resource or to get the holder of budget to release it for a given initiative.
Negotiations have three potential outcomes: win-win, win-lose or lose-lose. Amazingly, talking to many businesses over many years, I’ve come to the conclusion that lose-lose is increasingly the most common outcome. That is because in the absence of confidence, people opt to avoid loss and choose not to act. But in choosing not to act, they are adding friction into their businesses and missing out on the return on risk they should be taking.
As business has become more complex, so it has become much harder for buyers to know what a good outcome is. Worse, this is becoming a major cause of stress in business as both buyers and sellers have a huge amount at stake and lessening ability to navigate to a good result.
To illustrate the problem, consider this case we can all relate to. You are in the market for a car, but have limited time and need to buy now. You know the car make and model you want and know the recommended retail price. You walk into a dealer who makes an on-the-spot offer for the car that matches your requirement at 10% less than the listed price. Do you sign?
The dealer could be offering you a great deal, or they could be holding out and most customers could be getting 15% off. You just don’t know. Meanwhile, if it is the former the dealer is frustrated and has less incentive to offer a good starting price for future customers.
Independent brokers are sometimes a solution for this dilemma, having knowledge of the market and knowing which price is actually a fair one.
Few executives have any more time available than our hypothetical car buyer. Also like the car buyer, most things they are seeking to acquire are outside of their day-to-day experience.
It’s relatively easy when commodity items (like replacement parts for machinery) are needed, procurement experts can negotiate against a pricelist. However, when it’s a complex product, such as a computer system or an internal budget allocation for a new service, then there are few points of reference within the organisation.
I lamented in a past post about the lack of productivity growth resulting from our transition to a digital economy (see Where is the digital-fuelled growth?). Where new approaches to sharing knowledge takes friction out of the systems, there is a boost to productivity and this is where much of our effort should go. Negotiations are a prime candidate for this focus.
Increasingly digital solutions are allowing for the creation of anonymous or semi-anonymous benchmarks. But complex procurement and negotiations require more than simply finding a fair price. Factors at play often include risk, time, quality, the competitive landscape and so many more.
Artificial Intelligence and Robotics
The two technologies that could make a material change here are Artificial Intelligence and Robotic Process Automation.
Artificial Intelligence, or Cognitive Computing, is a form of very advanced analytics. In negotiations between parties, the primary objective is for the person who wants to acquire the resource to work out what a fair trade would look like. Even when it is just the allocation of internal budget for a new capability, there is still a need to know whether the return is commensurate.
Where these technologies come in is their capability to find things that are similar based on a wide range of criteria. For example, anyone who has seen how well search engines can group similar questions, worded completely differently, has some idea of how Cognitive Computing can bring together the right answers from disparate sources.
Where Robotic Process Automation (RPA) can be most effective is in taking the emotion out of negotiation and pushing to get the best outcome based on relative criteria. There is an argument that the political and emotional process is an important part of getting to the best possible outcome. The problem with this is that as the environment has become more complex, negotiations have become simplified on a subset of dimensions meaning that it isn’t the best argument that wins but, all too often, no argument that wins.
Most negotiations are a manual process with lots of spreadsheets and lists of points to push on and others to give on when pushed. This is exactly the sort of complex process that RPA is ideal at and makes it an ideal target for the technology.
Our robots can be programmed to provide a win for us all. While some negotiators are happiest with a win-lose, win-win puts the right incentives in the system for the long-term. If we program our robots that way we can be freed-up for the creative task of getting the right job done with the right resources.
In a future economy, where robots act on our behalf to find winning combinations, everyone could win.
Business is both complicated and structured. Our education, training and professional lives all teach us to think inside the box. Before rampant automation, and when problems sat inside the same box, this was ideal. The business world we are dealing with today needs a new approach.
It is increasingly popular to approach strategic questions using the power of games which encourage people to leave their assumptions behind. I’ve talked before about the role of games more broadly (see Turning decision making into a game).
While games are great, they still keep decision making within a frame. Games are a competitive activity within the confines of a set of rules. Every lunchtime kids launch into all manner of ball games in schoolyards around the world. Most games follow structure, build teamwork and have a win/lose outcome.
Sometimes, though, rather than play a defined game, children feel free to make up their own rules and migrate to free play.
To test this, give a group of kids a ball of any shape or size and tell them to make up a game. Watch what happens as they play and explore different approaches. Free play is really important for children to learn about the world around them. For children the world is far more full of mystery than known boundaries and rules-based learning doesn’t work until they have a better handle on their surroundings.
Many parents would know about the Reggio Emilia approach to preschool teaching. The idea of learning through exploring the world around you. Watch a child and they explore everything with an open mind.
It is interesting that modern sports went through an intense period of development in the eighteenth and nineteenth centuries with free play exploring different sets of rules that might make for great games. Largely (and there are, of course, exceptions) today’s most popular games have had stable rules for many decades.
In our world of disruption, we can argue that in many business settings the world around us is full of more mystery than known parameters. The sport of business that seemed so well defined is now up for grabs. No wonder a structured approach seems to limit us to thinking inside the box.
It is hard to find a consistent definition of play, but it does seem to be an activity conducted for pleasure, with the journey being the goal rather than any end and it is self-directed with minimal rules. It seems that play is far more important to our wellbeing than we ever realised, as described by psychiatrist Dr Stuart Brown in his TED talk.
When we’re looking for new employees, it makes sense to interview for the skills of the job they will tasked with. The trouble is that the return on the investment is unlikely to come with the first task that they complete but rather the job they will do over a number of years. Increasingly, that job hasn’t even been invented yet!
My personal view is that the characteristic that really matters in future employees is a curiosity about the world around them and a willingness to play for its own sake. In my own field of management consulting, I regard this as the renaissance consultant.
Elon Musk gets a lot of press around his intensity, but he does embody the idea of the renaissance with his wide range of interests (rockets, electric vehicles, batteries et cetera). Like Leonardo da Vinci, the best of our next generation will be interested in everything from science to music and much that goes in between.
There is a lot of discussion at the moment on the role of STEM (Science, Technology, Engineering and Maths) in education and our future workforce. Some are arguing that many STEM graduates are struggling to find work, while the reality remains that there are hundreds of thousands of jobs that can’t be filled that require these skills.
The problem isn’t with STEM, rather it is that not all STEM pathways are equal. It isn’t any one skill that is needed, but rather it is a flexibility and willingness to learn. Even more important, it is the combination of STEM foundational skills with a natural curiosity and willingness to explore.
In a world that is changing fast, none of us can assume any existing approach to our work will serve us well even into the near future. We need to be willing to play in order to find the new rules that are going to define the business answers for the coming years.
The great news is that there is a child in all of us!
We’ve had about 50 years of computing in business and about 20 years of the digital revolution. How are we faring on the question of digital fuelled growth and productivity? Many economists are coming to the surprising conclusion that technology may not be providing the boost we had expected.
This question really matters as politicians around the world are grappling with a voter backlash at disruption to industries and the promise of growth providing new jobs seems to be wearing thin. The population wants jobs but many fear that the new employment, relying on technology, are not going to be relevant to their individual skills or geography. New tech jobs are ending-up being concentrated in a few locations and requiring skills that are out-of-reach to those that have been displaced by global trends driven by digital channels.
Robert Gordon (author of The rise and fall of American Growth) splits productivity into three industrial revolutions: 1770-1840 (steam and transport), 1870-1920 (electricity, cars, city infrastructure, chemicals and working conditions) and 1970- (ICT). He argues that the second revolution provided about three times as much productivity growth as the other two. Worse, when he breaks-down the third revolution he argues that productivity growth has stagnated since early in this century.
The last part of the 20th century saw almost universal growth driven (arguably) by mass liberalisation of trade and the opening of new markets. Many assumed that technology was providing a virtuous boost. It seems that the rise of the web, digital technology and the smartphone have driven consumer demand but more economists like Gordon are questioning whether it has made the supply of that demand any more efficient.
So where has the productivity gone? I’ve argued before the IT has become too complex and expensive. In addition, we’ve lost some of the traditional ways of encouraging organisations to leverage their investments. Many of the online tools that we all use (such as search, collaboration and workflow) are fantastic but they don’t cost very much (and are often free) resulting in little governance to make sure that the benefits are realised.
Without a clear focus on realising productivity as the main aim of technology, many benefits are pleasing but of little benefit to the economy. For example, is there a real gain for the economy being able to check-in to your aircraft in half a dozen different ways? What about buying soap with a QR reader?
Ergonomics matter but much of what we implement is about gimmicks that are pleasing but don’t improve society.
That doesn’t mean that productivity growth for our economy isn’t coming, rather just that it may not be as easy or clear cut as we had expected. As we approach a new generation of robotics and artificial intelligence what do we learn? The problem is that the combination of genuine displacement of people without economic benefits mean there aren’t resources available to grow the job pool in other ways.
There have been thousands of words written about the threat of automation and I’ve previously given my view that our machines won’t outsmart us. I’ve also written about why we haven’t lost jobs yet.
We need to pivot our focus from whether jobs will be lost (they will, but new ones can be created) or whether machines will lead us into a terminator style future (they won’t), but rather how we change the trend on productivity.
The last 200 years have been amazing. Angus Maddison was an eminent economist who estimated the world’s long-term economic growth to be surprisingly small. According to Maddison’s work, from the Middle Ages through to the Industrial Revolution, the normal annual growth was less than 0.07%, far less than the numbers we assume today.
Without a change to the status quo, including new approaches to technology which unlock productivity growth, it could be that we are heading back to a world where growth is near zero. By the middle of the century, even population growth won’t help the world economy.
This is so important that it may be that there is a role for government regulation to ensure investment in technology results in productivity that is seen in the economy. It is in all of our interests to change the equation and find a way to turn our digital revolution into a new wave of productivity and wealth that everyone can share in.
TODAY: Sun, April 23, 2017April2017