Regulating Digital Businesses – Like Chasing Trains

chasing train

I don’t know if you’ve ever had the experience of running for a train that’s just started to move? I’ve had to do it a few times. Yes, I was younger and more foolish then. But it was usually within seconds of the train moving that I was on it. It’s only in old movies that you see the protagonists dashing down the platform as the train picks up speed. Usually, you just have the platform length and the problem is that the train is accelerating. There is a finite window of opportunity after which you’re just going to be left on the platform. This is my very long-winded analogy for regulators and technology. As technology accelerates – it’s getting harder for regulators to keep pace and in fact, in many areas they are just like the proverbial train chasers, running desperately after an accelerating train – often in a futile bid to control a business or industry that is on the verge of leaving the station of regulatory comfort. You can pick from a range of visual metaphors – a man trying to control seven unruly horses, or grabbing a tiger by the tail, but you get the idea. Regulators are in a fix.

The sight (and sounds) of the congressional hearing of Mark Zuckerberg did not bode well for regulators. They should have had Zuckerberg dead to rights over it’s (willing or unwilling) culpability in the Cambridge Analytica imbroglio. Yet he came out with barely a scar to show for 2 days of grilling. Many of the people asking him questions came across as the stereotypical grandparent trying to figure out the internet from their grandchild, even if these are very exaggerated caricatures. There is arguably a 40 year age gap between the average lawmaker and the average entrepreneur. But the age challenge is just a minor problem. Here are some bigger ones.

Technology businesses are shape-shifting enterprises invariably redefining industries. Platforms cannot be regulated like their industrial counterparts. Uber is not a taxi company. Facebook is not a media business. Airbnb is not a hotel. No matter how convenient it might be to classify and govern, or how often someone points out that the world’s biggest taxi company doesn’t have taxis. No, these are data and services platforms, and they need an entirely new definition. You could argue that the trouble with Facebook has come about because they were being treated like a media organisation, rather than a data platform. And let’s not forget that the only reason Facebook was in the dock is because of the success of Cambridge Analytica in actually influencing an election. Not for the misuse of customer data on a daily basis which may have gone on for months and years by Cambridge Analytica as well as other similar firms. While governments’ focus on Uber stems largely from incumbent and licensed taxi services, nobody seems to be worried that Uber knows the names, credit card details and the home and office residences of a majority of its users.

Tech businesses, even startups, are globally amorphous from a very early age. Even a 20 person startup barely out of its garage can be founded in California, have it’s key customers in Britain, its servers in Russia, its developers in Estonia and pay taxes in Ireland. Laws and governments are intrinsically country bound and struggle to keep up with this spread of jurisdiction. Just think of the number of torrent services that have survived by being beyond the reach of regulation.

These are known problems and have existed for a while. Here’s the next challenge which is a more fundamental and even an existential one for lawmakers. With the emergence of machine learning and AI, the speed of technology change is increasing. Metaphorically speaking, the train is about to leave the station. If regulators struggle with the speed and agility of technology companies today, imagine their challenge in dealing with the fast-evolving and non-determinate outcomes engendered by AI! And as technology accelerates, so do business models, and this impacts people, taxes, assets, and infrastructure. Imagine that a gig-economy firm that delivers food home builds an AI engine that routes its drivers and finds a routing mechanism that is faster but established as being riskier for the driver. Is there a framework under which this company would make this decision? How transparent would it need to be about the guidance it provides to its algorithms?

I read somewhere this wonderful and pithy expression for the challenge of regulation. A law is made only when it’s being broken. You make a law to officially outlaw a specific act or behaviour. Therefore the law can only follow the behaviour. Moreover, for most countries with a democratic process, a new law involves initial discussion with the public and with experts, crafting of terms, due debate across a number of forums and ultimately a voting process. This means we’re talking in months, not days and weeks. And if technology is to be effectively regulated and governed, a key challenge to address is the speed of law-making. Is it possible to create an ‘agile’ regulatory process? How much of the delay in regulation is because the key people are also involved with hundreds of other discussions. Would lawmaking work if a small group of people was tasked to focus on just one area and be empowered to move the process faster in an ‘agile’ manner? We are not talking about bypassing democratic processes, just moving through the steps as quickly as possible. A number of options are outlined in this piece from Nesta website – including anticipatory regulation (in direct contravention of the starting point of this paragraph), or iterative rather than definitive regulation. All of these have unintended consequences so we need to tread cautiously. But as with most businesses, continuing as present is not an option.

Then there’s the data challenge. The big technology platforms have endless access to data which allows them to analyse them and make smarter decisions. Why isn’t the same true of regulators and governments? What would true data-driven regulation look like? We currently have a commitment to evidence-driven policymaking in the UK (which has sometimes been unkindly called policy driven evidence making!) but it involves a manual hunt for supporting or contradicting data, which is again time-consuming. What if a government could analyse data at the speed of Facebook, and then present that to the experts, the public, and legislators in a transparent manner? The airline industry shares all the data about every incident, accident and near miss, across its ecosystem, competitors, and regulators, and this is a significant contributor to overall airline safety. (Outlined in the book Black Box Thinking, by Matthew Syed.) Why isn’t the same true for cybersecurity? Why isn’t there a common repository for all the significant cyber attacks, which can be accessed by regulators armed with data science tools and skills, so that they can spot trends, model the impact of initiatives and move faster to counter cyber attacks? If attacks seem to originate from a specific territory or impact a specific vulnerability of a product, pressure can be brought to bear on the relevant authorities to address those.

running after train
These are non-trivial challenges and we need to be aware of risks and unintended consequences. But there is no doubt that the time has come for us to think of regulation that can keep pace with the accelerating pace of change, or governments and regulators will start to feel like the protagonists of movies where people run after trains.

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Seven in 7 – Agile @ Scale, Maturing AI, The Ring of Success, Defending Democracy and More…

Agile at scale
As we head into the TCS UK Innovation Forum this week, I’m preparing myself to discuss big ideas and disruptive changes. With that in mind, this week’s Seven in 7 looks at scaling AI, a startup that was bought for a billion dollars, and hacking democracy. But also, as we’re committed to becoming agile as an organisation, where better to start, than at the great article in the new HBR about how to drive agile at scale!

(1) Doing Agile at Scale

This is a very timely look at Agile adoption at scale in the enterprise. It starts with enshrining Agile values in leadership roles, which requires a continuous approach to strategy. The next key thing is a clear taxonomy of initiatives which may be classified into 3 categories: customer experiences, business processes, and IT systems. The next step is sequencing the initiatives, with a clear understanding of timelines. It can take 5-7 years for real business impact, but there should be immediate customer value. Enterprise systems such as SAP can be delivered using agile as well. But it needs the organisation to create and move with a common rhythm. There are businesses working in agile who use hundreds of teams, solve large problems, and build sophisticated products. This can be made easier with modular products and operating architectures – which essentially mean the plug and play capabilities of individual components. It’s important to have shared priorities and financial empowerment of teams. Talent acquisition management needs to be reshaped to meet the new needs. And funding of projects and initiatives needs to be seen as ‘options for further discovery’. After all, at the heart of agile is the ability to proceed with a clear vision but without necessarily knowing all the steps to get there.

(2) Artificial Intelligence At Scale – for non-technology firms

It’s clear that Tech firms from Google, to Amazon and Twitter, have all been able to deploy AI at scale – in enabling recommendations, analysis and predictive behaviour. For non-tech firms too, the time may have come for delivering scaled AI. One of the key areas where AI seems to be ready to scale is around computer vision (image and video analysis) – relevant to insurance, security, or agribusinesses. The article below from the Economist also quotes TCS’s Gautam Shroff, who runs the NADIA chatbot project. A critical assertion the article makes is that implementing AI is not the same as installing a Microsoft program. This might be obvious, but what is less so, is that AI programs by design get better with age, and may be quite rudimentary at launch. Businesses looking to implement AI may need to play across multiple time horizons. And while the short-term opportunity and temptation is to focus on costs, there role of AI in creating new value is clearly much bigger.


(3) The Ring of Success:

What makes a new product successful? I met Jamie, the founder of Ring a couple of years ago in London and was struck by his directness and commitment. He even appears in his company’s ads. Ring.com was recently acquired by Amazon for $1bn. Here one of the backers of Ring talks about the factors which made Ring a success. In a nutshell, the list includes (1) the qualities of the founder, (2) execution focus and excellence, (3) continuous improvements, (4) having a single purpose, (5) pricing and customer value. (6) integration of hardware and software. (7) clarity about the role of the brand.


(4) Blockchain and ICO redux:

Do you know your Ethereum from your Eos or your MIATA from your Monero? This piece from the MIT Tech Review will sort you out. And for those of you who are still struggling to understand what exactly blockchain is, here’s a good primer. Of course, you could always go look at my earlier blog post on everything blockchain.

Links:


(5) X and Z – The Millennial Sandwich

X & Z: Or the millennial sandwich. All the talk in the digital revolves around millennial, but there is a generation on either side. The generation X – followed the baby boomers, and it turns out they have a better handle on traditional leadership values than millennial. This article talks about Generation X at work.

On the other side, there’s a generation after the millennials – the generation Z. They’re the ones who don’t have TV’s, don’t do facebook, and live their lives on mobile phones. This article talks about how Financial services are being shaped by Gen Z.


(6) Big tech validates Industry 4.0

This week, the large tech players disclosed significant earnings, beating expectations and seeing share prices surge. In a way it’s a validation of the industry 4.0 model – the abundance of capital, data, and infrastructure will enable businesses to create exponential value, despite the challenges of regulation, data stewardship issues and other problems.  Amazon still has headroom because when push comes to shove, Amazon Prime, which includes all you can consume music and movies can probably increase prices still more.


(7) Defending Democracy

The US elections meets the technology arms race – this article presents experiences from a hacking bootcamp., run for the teams who manage elections. While the details are interesting, there is a larger story here – more than influencing the elections either way, the greater harm this kind of election hacking wreaks is in its ability to shake people’s faith in democracy. As always, there’s no other answer than being prepared, but that’s easier said than done!

Reading List: 7 for 7 – April 23: Palantir > Facebook, Generative Design, Alexa With Eyes, and More…

The 7 most interesting things I’ve read over the past 7 days.
 future doors
(image credit: Pixabay)

(1) The One Thing You Should Read about AI this week: 
In March, we ran a TCS DEX event where we posed the question to our partners and clients, around whether every company should have an AI strategy. While there was general agreement about the need for an AI strategy, there was no clear starting point. This may be the challenge for most companies. And perhaps the first steps towards a strategy are gathering information and running experiments.
If you read one thing this week, read this AI paper by the McKinsey Global institute – they publish results from a comprehensive survey and analysis of AI across industries, functions, and use cases and by the relevance of the different techniques, such as Transfer Learning, Reinforcement Learning, and Deep Learning Neural Networks. If some of that sounds obscure to you, I suggest reading up a little bit as these will become common business parlance in the not-too-distant future, and clients will be asking about them. In any case, succinct explanations are provided in the paper. It will probably take you a couple of hours to read (not skim) the 40 odd pages. Here are some of the very high-level takeaways:
  1. Industries where the number of use cases are the highest, include (1) Insurance, (2) Banking, (3) Retail, and (4) Automotive & Assembly.
  2. Functions with the highest number of use cases include (1) Supply Chain management and manufacturing, and (2) Marketing and Sales
  3. Specific domains where the impact might be the highest include (1) customer service & management (2) risk modelling (3) predictive service / intervention (4) workforce productivity and efficiency (5) analytics-driven hiring and retention, and (6) yield optimisation.
Some other takeaways:
  • The highest absolute impact of AI is to be found in Retail, but Travel and transport & logistics can extract the highest incremental value over other analytics techniques.
  • Image data is the highest value, after structured and time series data, and ahead of text.
  • Challenges and limitations: (1) labelling training data (2) obtaining large enough data sets (3) explaining the outcomes and decisions in clear enough terms – e.g. for product classification or regulatory (4) transferring of findings to adjacent use cases, and (5) risk of bias in data/ algorithms

(2) Data: Facebook is a misguided amateur compared to Palantir 
Palantir is much more dangerous than FB. Why? (1) Because Peter Thiel, the founder is a man of metamorphosis – he has quixotic views of the world – such as ‘freedom is not compatible with democracy’; (2) because Palantir is a much more shadowy and secretive organisation but built specifically for next-generation analytics for powerful clients. (3) Because this kind of analytics power can be destructive if individuals go rogue – the article talks about Peter Caviccia who ended up running his own spying operation within JPMorgan in what is described in the article as Wall Street meets Apocalypse Now, and (4) because tools like this are being used by police forces such as LAPD to predict crime – but also to do that to build deep and intricate views of a lot of individuals and their lives. The article also provides a very good visual model of Peter Thiel’s incredible original Paypal team and network which includes Elon Musk, Reid Hoffman (LinkedIn), Steve Chen (Youtube) and many others.

(3) Design: Welcome to Generative 3D Design 
What do you do when you need to design and build a spinal implant that needs to be appropriately strong, light and pliant? You use an algorithm-driven design process called generative design with 3D printing. Algorithmic design takes in your specifications or requirements and generates a number of options, which are developed faster than humans and enables a lot more personalisation of complex materials. In future, these will probably be custom built to specs in a way that humans simply can’t. It also uses the least amount of material possible (it’s one of the constraints/ objectives). This story in the Wired magazine talks about how Nuvasive does this using AI and 3D.

(4) eCommerce and Retail – change of guard, and disruption for the economy
This week we had a direct comparison between M&S vs ASOS: M&S is a struggling brand – losing share in apparel, and under pressure on foods. Other brick and mortar retailers like New Look are also in trouble. ASOS sales, on the other hand, hit £1.9 bn 2017 which amounted to a 33% increase. It’s also instructive to note that eCommerce contributes some 25% of British clothes retail numbers. In fact, the UK has the highest amount of online commerce (as a % of overall retail numbers – almost 18%), but the retail industry also accounts for 10% people and 10% of the economy – so significant disruptions lie ahead.

(5) Asset-light business models 
We’ve seen them in telecoms (MVNOs), in retail, and also in utilities. Lightweight, direct to consumer competitors who don’t carry the baggage of their larger competitors. They have no legacy IT and are built ground up on digital platforms, for a start, and also have a much more nimble operating model. Companies like Asos and Ovo energy are successful because they attract a particular consumer niche, operate in an agile way and are not weighed down by the legacy business and IT challenges of their larger peers (zero inventory, for example). This trend goes all the way down to micro brands in the consumer goods space. Many of these businesses will die or stay micro, but once in a generation, they will lead to the next FB or Amazon.

(6) Alexa Fashion – a glimpse of the future 
What’s Alexa’s next trick? How about a camera that can give you fashion feedback? Amazon’s Echo Look (not yet launched to the public, but on invitation only basis) has a camera and lets you take selfies and gives you feedback on what you’re wearing. For those worried about whether Amazon was listening to all your conversations, this will definitely be a step too far! This piece is a good take on the social and psychological implication of a tool like this. Of course, if you want algorithmic advice but don’t want something that invasive, you can always turn to Miquela

(7) Battery Wars 
We all know that a move to electric cars is a ‘when’ and not an ‘if’ question by now. What that means, however, is a near insatiable demand for batteries and a huge spotlight on battery technology. Currently, the minerals that go into batteries such as Lithium and Magnesium are seeing a huge spurt in demand. It turns out that DR Congo is the worlds dominant source of Magnesium. In all of this, the UK is seeking to play a leadership role in battery technology. But is it either feasible or desirable? On the other hand, Williams has been working on safer batteries which are tough-tested in the Formula E competition – where electric-only cars race, collide and crash.

When Technology Talks

Conversational Systems aka chatbots are starting to become mainstream – here’s why you should stay ahead of the game:

chatbots

The shape-shifting of the yin-yang between humans and technology is one of the hallmarks of digital technologies, but it is perhaps most pronounced and exploit in the area of Conversational Systems. But to truly appreciate conversational systems, we need to go back a few steps.

For the longest part of the evolution of information technology, the technology has been the unwieldy and intransigent partner requiring humans to contort in order to fit. Mainframe and ERP system were largely built to defend the single version of truth and cared little for the experience. Cue hours of training, anti-intuitive interfaces, clunky experiences, and flows designed by analysts, not designers. Most of us have lived through many ages of this type of IT will have experienced this first hand. If these systems were buildings they would be warehouses and fortresses, not homes or palaces. Too bad if you didn’t like it. What’s ‘like’ got to do with it! (As Tina Turner might have sung!)

Digital technology started to change this model. Because of its roots in consumer technology rather than enterprise, design and adoption were very much the problem of the providers. This story weaves it’s way through the emergence of web, social media and culminates with the launch of the iPhone. There is no doubt – the iPhone made technology sexy. To extend the oft-quoted NASA analogy, it was the rocket in your pocket! With the emergence of the app environment and broadband internet, which was key to Web 2.0, it suddenly introduced a whole new ingredient into the technology cookbook – emotion! Steve Jobs didn’t just want technology to be likable, he wanted it to be lickable.

The balance between humans and technology has since been redressed significantly – apps and websites focus on intuitiveness, and molding the process around the user. It means that to deal with a bank, you don’t have to follow the banks’ convenience, for time and place, and follow their processes of filling a lifetime’s worth of forms. Instead, banks work hard to make it work for you. And you want it 24/7, on the train, at bus stops, in the elevator and before you get out from under your blanket in the morning. And the banks have to make that happen. The mouse has given way to the finger. Humans and technology are ever closer. This was almost a meeting of equals.

But now the pendulum is swinging the other way. Technology wants to make it even easier for humans. Why should you learn to use an iPhone or figure out how to install and manage an app? You should just ask for it the way you would, in any other situation, and technology should do your bidding. Instead of downloading, installing and launching an app, you should simply ask the question in plain English (or a language of your choice) and the bank should respond. Welcome to the world of Conversational Systems. Ask Siri, ask Alexa, or Cortana, or Google or Bixby. But wait, we’ve gotten ahead of ourselves again.

The starting point for conversational systems is a chatbot. And a chatbot is an intelligent tool. Yes, we’re talking about AI and machine learning. Conversational systems are one of the early and universal applications of artificial intelligence. But it’s not so simple as just calling it AI. There are actually multiple points of intelligence in a conversational system. How does a chatbot work? Well for a user, you just type as though you were chatting with a human and you get human-like responses back in spoken language. Your experience is no different from talking on WhatsApp or Facebook Messenger for example, with another person. The point here is that you are able to ‘speak’ in a way that you are used to and the technology bend itself around you – your words, expressions, context, dialect, questions and even your mistakes.

Let’s look at that in a little more detail. This picture from Gartner does an excellent job of describing what goes into a chatbot:

The user interface is supported by a language processing and response generation engine. This means that the system needs to understand the users’ language. And it needs to generate responses that linguistically match the language of the user, and often the be cognizant of the mood. There are language engines like Microsoft’s LUIS, or Google’s language processing tool.

Behind this, the system needs to understand the user’s intent. Is this person trying to pay a bill? Change a password? Make a complaint? Ask a question? And to be able to qualify the question or issue, understand the urgency, etc. The third key area of intelligence is the contextual awareness. A customer talking to an insurance company in a flood-hit area has a fundamentally different context from a new prospect, though they may be asking the same question ‘does this policy cover xxx’. And of course, the context needs to be maintained through the conversation. An area which Amazon Alexa is just about fixing now. So when you say ‘Alexa who was the last president of the US’ and Alexa says ‘Barack Obama’ and you say ‘how tall is he?’ – Alexa doesn’t understand who ‘he’ is, because it hasn’t retained the context of the conversation.

And finally, the system needs to connect to a load of other systems to extract or enter data. And needless to say, when something goes wrong, it needs to ‘fail gracefully’: such as “Hmm… I don’t seem to know the answer to that. Let me check…” rather than “incorrect command” or “error, file not found”. These components are the building blocks of any conversational system. Just as with any AI application, we also need the data to train the chatbot, or allow it to learn ‘on the job’. One of the challenges in the latter approach is that the chatbot is prone to the biases of the data and real-time data may well have biases, as Microsoft discovered, with a Twitter-based chatbot.

We believe that chatbots are individually modular and very narrow in scope. You need to think of a network of chatbots, each doing a very small and focused task. One chatbot may just focus on verifying the customer’s information and authenticating her. Another may just do password changes. Although as far as the user is concerned, they may not know they’re communicating with many bots. The network of bots, therefore, acts as a single entity. We can even have humans and bots working in the same network with customers moving seamlessly between bots and human interactions depending on the state of the conversation. In fact, triaging the initial conversation and deciding whether a human or a bot needs to address the issue is also something a bot can be trained to do. My colleagues have built demos for bots which can walk a utility customer through a meter reading submission, for example, and also generate a bill for the customer.

Bots are by themselves individual micro-apps which are trained to perform certain tasks. You can have a meeting room bot which just helps you find and book the best available meeting room for your next meeting. Or a personal assistant bot that just manages your calendar, such as x.ai. We are building a number of these for our clients. Bots are excellent at handling multi-modal complexity – for example when the source of complexity is that there are many sources of information. The most classic case is 5 people trying to figure out the best time to meet, based on their calendars. As you well know, this is a repetitive, cyclical, time-consuming and often frustrating exercise, with dozens of emails and messages being exchanged. This is the kind of thing a bot can do very well, i.e. identify (say) the 3 best slots that fit everybody’s criteria on their calendars, keeping in mind travel and distances. Chatbots are just a special kind of bot that can also accept commands, and generate responses in natural language. Another kind of bot is a mailbot which can read an inbound email, contextualise it, and generate a response while capturing the relevant information in a data store. In our labs we have examples of mailbots which can respond to customers looking to change their address, for example.

Coming back to chatbots, if you also add a voice i.e. a speech to text engine to the interface, you get an Alexa or Siri kind of experience. Note that now we’re adding yet more intelligence that needs to recognise spoken words, often against background noises, and with a range of accents (yes, including Scottish ones). Of course, when it’s on the phone, there are many additional cues to the context of the user. The golden mean is in the space between recognising context and making appropriate suggestions, without making the user feel that their privacy is being compromised. Quite apart from the intelligence, one of the real benefits for users is often the design of the guided interface that allows a user to be walked step by step through what might be a daunting set of instructions or forms or a complex transaction – such as an insurance claim or a mortgage quote.

Gartner suggest that organisations will spend more on conversational systems in the next 3 years than they do on mobile applications. This would suggest a shift to a ‘conversation first’ interface model. There are already some excellent examples of early movers here. Babylon offers a conversational interface for providing initial medical inputs and is approved by the NHS. Quartz delivers news using a conversational model. You can also build conversational applications on Facebook to connect with customers and users. Chatbots are also being used to target online human trafficking. Needless to say, all those clunky corporate systems could well do with more conversational interfaces. Imagine just typing in “TravelBot – I need a ticket to Glasgow on Friday the 9th of February. Get me the first flight out from Heathrow and the last flight back to either Heathrow or Gatwick. The project code is 100153.” And sit back while the bot pulls up options for you, and also asks you whether you need to book conveyance.

Conversational systems will certainly make technology friendlier. It will humanise them in ways we have never experienced before. I often find myself saying please and thank you to Alexa and we will increasingly anthropomorphise technology via the nicknames we give these assistants. You may already have seen the movie “Her”. We should expect that this will bring many new great ideas, brilliant solutions and equally pose new social and psychological questions. Consider for example the chatbot that is desi§gned just for conversation – somebody to talk to when we need it. We often talk about how AI may take over the world and destroy us. But what if AI just wants to be our best friend?

My thanks to my colleagues and all the discussions which have sharpened my thinking about this – especially Anantha Sekar – who is my go-to person for all things Chatbots.

My book: Doing Digital – Connect, Quantify, Optimise – is available here, for the price of a coffee!

As with all my posts, all opinions here are my own – and not reflective of or necessarily shared by my employers.

Why Are We Suddenly So Bad At Predicting the Future?

Imagine that a monkey got into the control room of the universe and spent the year clicking random buttons. Imagine him hopping about on the ‘one musician less’ button, stomping on the ‘auto-destruct’ lever and gurgling while he thumped repeatedly on the ‘introduce chaos’ switch. Close your eyes and picture him dropping a giant poo on the bright red panel marked ‘do not touch under any circumstances’. That my friends is the only way to think about 2016 – after all, it was the year of the monkey in the Chinese zodiac. It was the year when rational thinking took a beating, when meritocracy became a bad word, when liberalism escaped from the battlefield to a cave in the mountains to lick its wounds. And not surprisingly, a year when projections, predictions and polls made as much sense in the real world as an episode of Game of Thrones on steroids.
monkey-prediction
Given much of our lives are spent in productively engaging with the future and making decisions based on big and small decisions about the possible future, this last point is more important than just the schadenfreude of laughing at pollsters and would be intellectuals. The present passes too quickly, so really every decision you’ve ever made in your life is counting on future events to turn out in ways that are favourable. Getting this wrong is therefore injurious to health, to put it mildly. And yet our ability to predict the future has never been under such a cloud in living memory. Why is this so?

Fundamentally, we’re wired to think linearly in time, space and even line of sight. We are taught compound interest but we get it intellectually rather than viscerally. When you first encounter the classic rice grains and chessboard problem, as a smart person, you know that it’ll be a big number, but hand on heart, can you say you got the order of magnitude right? i.e. the total amount of rice on the chessboard would be 10x the world’s rice production of 2010? Approximately 461,168,602,000 metric tons? This problem of compounding of effects is incredibly hard to truly appreciate, even before you start to factor in all the myriad issues that will bump the rate of change up or down, or when the curve hits a point of inflexion. The Bill Gates quote  – ‘we over-estimate the impact of technology in 2 years, and under-estimate the impact over 10’ – is a direct reframing of this inability to think in a compound manner.

Then there’s the matter of space and line of sight. The way the future unfolds is dramatically shaped by network effects. The progress of an idea depends on it’s cross fertilisation across fields, geographies and disciplines, across any number of people, networks and collaborations. These collaborations can be engineered to a point or are the result of fortuitous clustering of minds. In his book ‘Linked’ – Ablert-Lazlo Barabasi talks about the mathematician Erdos who spent his life nomadically, travelling from one associates’ home to another discussing mathematics and ironically, network theory. Not surprisingly, a lifestyle also practiced for many years by a young Bob Dylan, if you substitute mathematics for music. Or consider the story of the serial entrepreneur in Rhineland in the 1400s, as told by Steven Johnson, in ‘Where Good Ideas Come From’. Having failed with a business in mirrors, he was working in the wine industry, where the mechanical pressing of grapes had transformed the economics of winemaking. He took the wine press, and married it with a Chinese invention – movable type, to create the worlds first printing press. His name of course, was Johannes Gutenberg. This kind of leap is not easy to predict, not just for the kind of discontinuity they represent (more on that later), but also because of these networked effects. Our education system blinkers us into compartmentalised thinking which stays with us through our lives. Long ago, a student of my mothers once answered a question about the boiling point of water by saying “in Chemistry, it’s a 100 degrees Centigrade, but in Physics, I’m not sure”. We are trained to be specialists, becoming more and more narrow as we progress through our academic career, ending up more or less as stereotypes of our profession. Yet human progress is driven by thousands of these networked, collaborative, and often serendipitous examples. And we live in a world today with ever expanding connections, so it’s not surprising that we have fallen behind significantly in our ability to understand how the network effects play out.

If you want to study the way we typically make predictions, you should look no further than sport. In the UK, football is a year round sport, so there are games every weekend for 9 months and also mid week for half the year. And with gambling being legal, there is an entire industry around football gambling. Yet, the average punter, fan or journalist makes predictions which are at best wilfully lazy. There is an apocryphal story about our two favourite fictitious sardars – Santa Singh and Banta Singh, who decide to fly a plane. Santa, the pilot, asks Banta, the co-pilot to check if the indicators are working. Banta looks out over the wing and says “yes they are, no they aren’t, yes they are, no they aren’t…” – this is how a lot of predictions are made in the world of premier league football today. Any team that loses 3 games is immediately in a ‘crisis’ while a team that wins a couple of games are deemed to be on their way to glory. Alan Hansen, an otherwise insightful pundit and former great player, will always be remembered for his one comment “You can’t win anything with Kids” – which he made after watching a young Manchester United side lose to Aston Villa in the 1995-96 season. Manchester United of course went on to win the season and dominate the league for the next decade and a half. Nobody predicted a Leicester City win in 2016 of course, but win they did. The continuous and vertiginous increase in TV income for football clubs has led to a relatively more equal playing field when it comes to global scouting networks, so a great player can pop up in any team and surprise the league. Yet we find it hard to ignore all the underlying trends and often find ourselves guilty of treating incidents as trends.

The opposite, is amazingly, also true. We are so caught up with trends that we don’t factor in the kinks in the curve. Or to use Steve Jobs’ phrase – the ding in the universe. You can say that an iPhone like device was sure to come along sooner or later. But given the state of the market – with Nokia’s dominance and 40% global market share, you would have bet your house on Nokia producing the next breakthrough device eventually. Nobody saw the iPhone coming, but when it did it created a discontinuous change that rippled across almost every industry over the next decade. The thing is, we like trends. Trends are rational and they form a kind of reassuring continuity so that events can fit our narratives, which in turn reaffirm our world view. And unless we’re close to the event, or perennial change seekers and nomads ourselves, it’s hard to think of countercyclical events. It’s now easy to see how in 2016 we were so caught up in the narrative of progressive liberalisation and unstoppable path to globalisation, we failed to spot those counter-cyclical events and cues that were right there in our path.

In fact there are any number of cognitive biases we are guilty of – on an everyday basis. This article just lists a dozen of them. My favourites in this list are the confirmation bias and the negativity bias. Both of these are exacerbated by social media and digital media. While social media has led us to the echo-chambers – the hallmarks of 2016, our projection bias is also accentuated by our ability to choose any media we want to consume, in the digital world, where access is the easy part. Similarly, bad news spreads faster on social networks and digital media today than at any time before in history. Is it possible that despite knowing and guarding against these biases in the past, we’ve been caught out by the spikes in the impact and incidence of a couple of these, in the digital environment we live in today?
To be fair, not everybody got everything wrong. Plenty of people I know called the Donald Trump victory early in the game. And amongst others, John Batelle got more than his share of predictions right. There is no reason to believe that 2017 will be any less volatile or unpredictable than 2016, but will our ability to deal with that volatility improve? One of the more cynical tricks of the prediction game is to make lots of predictions at many different occasions. People won’t remember all your bad calls, but you can pick out the ones you got right, at leisure! This is your chance, then, to make your predictions for 2017. Be bold, be counter-cyclical. And shout it out! Don’t be demure. The monkey is history, after all. This is the year of the rooster!

2016/2017 Shifting Battlegrounds and Cautious Predictions for Digital

Innovation slows down in mobile devices but ramps up in bio-engineering. Voice goes mainstream as an interface. Smart environments and under the hood network and toolkit evolution continues apace.

For most people I know, 2016 has ranged between weird and disastrous. But how was it for the evolution of the digital market?

The iPhone lifecycle has arguably defined the current hypergrowth phase of the digital market. So it’s probably a good place to start. In the post Steve Jobs world, it was always going to be a question about how innovative and forward thinking Apple would be. So far, the answer is not very. 2016 was an underwhelming world for iPhone hardware (though Apple has tried harder with MacBooks). Meanwhile, Samsung which you suspect has flourished so far by steadfastly aping Apple, ironically finds itself rudderless after the passing of Steve Jobs. It’s initial attempts at leapfrogging Apple have been nothing short of disastrous with the catastrophic performance of the new inflammable Note phones/ batteries. Google’s Pixel Phone could hardly have been timed better. By all initial accounts (I’m yet to see the phone myself) it’s comparable but not superior to an iPhone 7, Google’s wider range of services and software could help it make inroads into the Apple market. Especially given the overwhelming dominance of Android in the global OS market. The market has also opened up for One Plus, Xaomi and others to challenge for market share even in the west. Overall, I expect the innovation battleground to move away from mobile devices in 2017.

While on digital devices, things have been quite on the Internet of things front. There have been no major IOT consumer grade apps which have taken the world by storm. There have been a few smart home products, but no individual app or product stands out for me. As you’ll see from this list – plenty if ‘interesting…’ but not enough ‘wow’. I was personally impressed by the platform capabilities of enabling IOT applications, form companies such as Salesforce, which allow easy stringing together of logic and events to create IOT experiences, using a low code environment.

AR and VR have collectively been in the news a lot, without actually having breakthrough moment. Thanks to the increasing sophistication of VR apps and interfaces, with Google Cardboard and the steady maturing of the space. But the most exciting and emotive part of AR / VR has been the hololens and holoportation concepts from Microsoft – these are potentially game changing applications if they can be provided at mass scale, at an affordable cost point and if they an enable open standards for 3rd parties to build on and integrate.

Wearables have had a quiet-ish year. Google Glass has been on a hiatus. The Apple Watch is very prominent at Apple stores but not ubiquitous yet. It’s key competitor – Pebble – shut shop this year. Fitbits are now commonplace but hardly revolutionary beyond the increasing levels of fitness consciousness in the world today. There are still no amazing smart t-shirts or trainers.

The most interesting digital device of 2016 though, has been the Amazon Echo. First, it’s a whole new category. It isn’t an adaptation or a next generation of an existing product. It’s a standalone device (or a set of them) that can perform a number of tasks. Second, it’s powered almost entirely by voice commands “Alexa, can you play Winter Wonderland by Bob Dylan?”, third, and interestingly it comes from Amazon, for whom this represents a new foray beyond commerce and content. Echo has the potential to become a very powerful platform for apps that power our lives, and voice may well be the interface of the future. I can see a time the voice recognition platform of Echo (or other similar devices) may be used for identity and security, replace phone conversations, or also become a powerful tool for healthcare and providing support for the elderly.

Behind the scenes through there have been plenty of action over the year. AI has been a steady winner in 2016. IBM’s Watson added a feather to it’s cap by creating a movie trailer. But away from the spotlight, it has been working on gene research, making cars safer, and even helping fight cancer. But equally, open source software and the stuff that goes behind the websites and services we use every day have grown in leaps and bounds. Containerisation and Docker may not be everybody’s cup of tea but ask any developer about Docker and watch them go misty eyed. The evolution of micro services architecture and the maturing of APIs are also contributing to the seamless service delivery that we take for granted when we connect disparate services and providers together to order Uber cabs via the Amazon Echo, or use clever service integrators like Zapier

All of this is held together by increasing focus on design thinking which ensures that technology for the sake of tech does not lead us down blind alleys. Design thinking is definitely enjoying its moment in the sun. But I was also impressed by this video by Erika Hall that urges us to go beyond just asking users or observing them, and being additionally driven by a goal and philosophy.

2016 has also seen the fall of a few icons. Marisa Meyers has had a year to forget, at Yahoo. Others who we wanted to succeed but who turned out to have feet of clay, included Elizabeth Holmes at Theranos, and the continued signs of systemic ethical failure at Volkswagen. I further see 2016 as the year when external hard drives will become pointless. As wifi gets better, and cloud services get more reliable, our need to have a local back up will vanish. Especially as most external drives tend to underperform over a 3-5 year period. Of course, 2016 was the year of the echo-chamber – a reminder that social media left to itself insulates us from reality. It was a year when we were our worst enemies. Even through it was the Russians who ‘Hacked’ the US elections and the encryption debate raged on.

One of the most interesting talks I attended this year was as the IIM Alumnus meeting in London, where a senior scientist from GSK talked about their alternative approach to tackling long term conditions. This research initiative is eschewing the traditional ‘chemical’ based approach which works on the basis that the whole body gets exposed to the medication but only the targeted organ responds. This is a ‘blunt instrument’. Instead, the new approach takes an ‘bio-electronic’ approach. Galvani Bioelectronics, set up in partnership with Alphabet will use an electronic approach to target individual nerves and control the impulses they send to the affected organ, say the pancreas, for diabetes patients. This will be done through nanotechnology and by inserting a ‘rice grain’ sized chip via keyhole surgery. A successful administration of this medicine will ensure that the patient no longer has to worry about taking pills on time, or even monitoring the insulin levels, as the nano-device will do both and send results to an external database.

Biotech apart, it was a year when Google continued to reorganise itself around Alphabet. When Twitter found itself with it’s back to the wall. When Apple pondered about life beyond Jobs. Microsoft emerged from it’s ashes, and when Amazon grew ever stronger. As we step into 2017, I find it amazing that there are driverless cars now driving about on the roads, in at least one city, albeit still in testing. That we are on the verge of re-engineering the human body and brain. I have been to any number of awesome conferences and the question that always strikes me is, why aren’t we focusing our best brains and keenest technology on the worlds greatest problems. And I’m hopeful that 2017 will see this come to fruition in ways we can’t even imagine yet.

Here are 5 predictions for 2017. (Or around this time next year, more egg on my face!)

  • Apple needs some magic – where will they find it from? They haven’t set the world alight with the watch or the phone in 2016. The new MacBook Pro has some interesting features, but not world beaters yet. There are rumblings about cars, but it feels like Apple’s innovation now comes from software rather than hardware. I’m not expecting a path breaking new product from Apple but I’m expecting them to become stronger on platforms – including HomeKit, HealthKit and to seeing much more of Apple in the workplace.
  • Microsoft has a potential diamond in LinkedIn, if it can get the platform reorganised to drive more value for its, beyond job searches. Multi-layered network management, publishing sophistication, and tighter integration with the digital workplace is an obvious starting point. Microsoft has a spotted history of acquisitions, but there’s real value here, and I’m hoping Microsoft can get this right. Talking about Microsoft, I expect more excitement around Hololens and VR based communication.
  • I definitely expect more from Amazon and for the industry to collectively start recognising Amazon as an Innovation leader and held in the same esteem as Apple and Google. Although, like Apple, Amazon will at some point need stars beyond Bezos and a succession plan.
  • Healthcare, biotechnology, genetics – I expect this broad area of human-technology to get a lot of focus in 2017 and I’m hoping to see a lot more news and breakthroughs in how we engineer ourselves.
  • As a recent convert, I’m probably guilty of a lot of bias when I pump for voice. Recency effect, self referencing, emotional response over rational – yes all of the above. Voice is definitely going to be a big part of the interface mix going forward. In 2017, I see voice becoming much more central to the interface and apps planning. How long before we can bank via Amazon Echo?

Happy 2017!

The Future of Retail: How Will You Fight Amazon?

What to do when the elephant in the room is a 600-pound gorilla?

digital-retail

Once upon a time, there were 4 high street electronic retailers. Now, they are one. Dixons Carphone, which also includes PCWorld and Currys, now employs some 42,000 people and manages 17 brands across Europe. Yet, while the company continues to innovate and do a lot of the things you would expect from a leading retailer, they are fighting a very different kind of opponent. Like the movie Predator, this is an almost invisible creature, capable of superhuman strength, focus, and accuracy. This is Amazon.

It’s not just retail, the story is repeating itself in other segments too. In some cases, the commercial model has changed as well – for high street music retailers, see Apple and Spotify. For Blockbusters, it’s Netflix. But for most categories, such as for Book chains like Borders, its still Amazon. And given Amazon’s relentless strategy of growth and customer intimacy before profits, its the question every retailer must ask – how to compete with Amazon?

Everyone knows a few legends about Amazon. Many are about the maniacal customer focus – how Jeff Bezos and his family spent Christmas packing gifts by hand. Or how, when asked about why Analysts weren’t buying his stock, he said that as long as customers were buying his products, he didn’t care if analysts bought the stock. The fact is that Amazon is the 600-pound gorilla in the retail business. In 2015. 50% of all e-commerce growth in the US went to Amazon.

What lies behind Amazon’s relentless growth? A combination of the obvious and perhaps less obvious. Global distribution centres, world leading warehouse automation, customer experience par excellence, recommendation engines, one-click purchasing. Kindle readers, prime membership, all you can eat subscriptions. All of this is known and well documented. But there are three key areas where perhaps less attention is paid.

First, Amazon is arguably the worlds most effective innovation company. Its string of relevant and successful innovations from automated warehouses to Amazon Echo, speak for themselves. Second: Amazon deeply understands what it means to be truly committed to an excellent customer experience – and they execute this across payments, site design, offers, delivery, and returns. Third, and most importantly it’s a digital native company. This means that all its core processes are run by software and algorithms, rather than people. Software behaves more consistently, doesn’t suffer fatigue or human errors, and can be improved relatively easily, compared to upskilling humans. Amazon can decide where to introduce human intervention rather than worry about where to automate.

Quite a few brick and mortar businesses have enjoyed success in the past decade, in the UK, through differing strategies. Tesco’s rise and fall with the Dunnhumby data business have been well documented. John Lewis continues to focus on customer service delivered via its partnership model. Halfords focuses on the cycling and travel niche. Each of these businesses will face the same Amazon question and have to figure out how to compete, especially if Amazon decides to open physical stores in future.

So How Should Brick & Mortar Stores Fight Amazon? Here’s a starting 5 point list:

  1. Dominate your segment – make sure that you define a sustainable market (e.g. kitchenware in the UK) and can be the dominant brick and mortar store in that segment, or as consolidation sets in, the last one standing.
  2. Build a strong digital proposition – one that spans the web and mobile, both deeply integrated into your business model. make sure you invest both in digital marketing and in your e-commerce platform. Exploit online communities and design around customer needs.
  3. Build powerful experiences which cannot be created online. Tactile, immersive and human experiences, which can exploit your physical store. You may redesign significant parts of your physical store and even allow customers to comparison shop and complete the purchase online, in some cases.
  4. Bring your physical and digital retail universes together – and ensure that this omnichannel experience becomes a source of data for sharpening your customer experience, in addition to contributing to your sales and profits.
  5. Automate your core processes – from merchandising, offers, check out, payment, delivery and returns, and then focus specifically on where human inputs will improve the process. Invest in developing algorithms that are valuable to your business.

Of course, this is only a beginning and you’ll need to keep investing and building competence in any number of new areas. Some that spring to mind include: trust models, building strong data stewardship, creating a lifetime value of customers, providing technical support for the increasingly smart products you’re likely to be stocking, creating new commercial models – perhaps around the idea of leasing or renting rather than outright purchase, understanding immediacy and real-time business models, advanced security modelling, designing of smart experiences, and deep supply chain visibility – these are just some of the areas you will want to ensure you understand well.

We should also expect to see other market patterns emerge – for example, corner shops/ convenience stores could be pulled together with a common platform which allows them to run independently but provide a shared platform for online & mobile ordering, stocking, supply chain and even leasing drones for delivery. After all, you would go to your corner shop when you need something quickly – when your sugar runs out, in the middle of making tea, for example. What better way for them to deliver to these urgent needs by having drones drop a packet of sugar to your doorstep even before you finish making the tea?

Because of course, if corner shops won’t do it, and the high street groceries dilly dally, this is something Amazon are already planning to do. And frankly, as a consumer, I’ll go to whoever meets my needs in the most painless way.