The End of Management?

endIt’s not very often that you realise that a startling change is underway in the world. But I had one of those epiphanic moments at the recently concluded TCS European Summit at Budapest.

At the Summit, Ruud Gullit, who has the personality to take over the room when he speaks, spoke about his key frustration with today’s footballers. His key complaint was that today’s players have lost the element of personality and opinion and have become followers of instructions. They no longer have their own views about how they should play, or where they should position themselves. They leave that entirely to the manager. This seems to mirror how American football is played, where the coach does all the thinking and the players are effectively automatons on the pitch, following instructions. This is an armchair debate we’ve all had about our favourite sport — what’s the role of the captain on the pitch? How much does the coach or manager influence or control the decision making? For coaches like Pep Guardiola, it seems that there is a very high level of control and specificity about his instructions and players are, in fact, urged to simply follow the plan. For others, the level of separation between management and execution may vary. But I was intrigued by Gullit’s idea that football is being taken over by management.

Management as we know it is, of course, a product of the industrial revolution. Mass production and factories created production lines and turned people into automatons, where they were required to do, not think. And the control, evaluation, governance and thinking was all abstracted to a supervisory layer of people whose job was to think and not do. And thus modern management was born. While this has always existed in the historical context, in armies and in the construction of castles, pyramids and fortresses from the earliest days, the emergence of a global, scaled middle class came only with the dramatic rise in mass production of goods and services. One of the most dramatic changes in production philosophies — lean manufacturing — actually involves getting factory production workers to think. But conversely, even in white-collar work in a lot of businesses, there is an abundance of people doing apparently cerebral jobs like talking to customers, who have been turned into automatons who have to follow a script. They’ve been reduced to doing, rather than thinking.

Today the world over, Management has been enshrined as the backbone of how every organisation works. It is seen as the difference between success and failure. Managers are rewarded many times more handsomely than the most productive worker. Decision making is seen as the preserve of the manager. To serve this need, business schools have long produced people trained to be managers. It is both an art and a science. It involves people skills, domain knowledge, technical skills, risk-taking ability, leadership, problem-solving and many others. So much so that management is synonymous with leadership today.

So when Michael Clijdsdale from ING suggested that we should basically get rid of management, a ripple of laughter went around the room. It was one of those jokes speakers make. But it became obvious in his next few sentences that he was in fact, serious. And over the following days, having spoken with other leaders, and correlating with what I’ve been reading and reflecting on, I’ve come to realise that this wasn’t just a stray opinion and that there is indeed a big change underway. This is based on 3 pillars:

The first is the now universally accepted idea that software is, in fact, eating the world. More and more of the value of business and commercial activities are tied to the software rather than the hardware or human activity involved. In the book Breaking Smart, the author Venkatesh Rao articulates this very well. He talks about how software helps industries ‘break smart’ by moving into a world of software-driven value which allows them to change the business models, and reorganise themselves around a new set of economic and externalities. This reorganisation often requires rethinking organisational structures and processes, often increasing technology infrastructure and software and cognitive processes, rather than the industrial and management processes.

The second is the evolution of how software is actually created. The Agile Manifesto was created in 2000 by a group of seasoned developers who believed that better software could be developed, more efficiently, if they reduced the separation between management (thinking, planning) and execution (coding). Agile has come a long way since then and is now at the heart of a lot of significant enterprise platforms. Agile puts the focus and the responsibility back into the self-managed team and reduces management overload. Agile teams today set their own pace, course-correct as necessary, think and act in equal measure and end up delivering more than teams that are externally managed.

The third is the VUCA world we find ourselves in. The term was created by the US military but increasingly has become more mainstream. It stands for volatile, uncertain, complex and ambiguous — an apt description for the world most businesses find themselves in. From uncertainty about the outcome of political negotiations such as Brexit, or trade wars, or the volatility of extreme weather conditions, the complexity of interlinked economic events, or the uncertainty of disruptive technologies. VUCA conditions reduce the impact of planning and control and instead reward agility, adaptability and empowered choices.

When you add these 3 up, you start to understand why this might be the end of management as we know it — a layer of people whose only job is to manage other people and own the thinking and planning role without actually being able to execute the tasks themselves. This is not to say we won’t need management, thinking, and planning — we will. It’s just that these tasks will also be done by those actually doing the work. And conversely, we may see the end of the pure execution role. Those tasks that are performed by people acting as automatons, will, in fact, be done by machines in the future. What we’ll get is a new category of empowered workers. Collaborative teams that can plan, think, build and run. To give you a military analogy, a general who plans a battle but sits in headquarters is of very limited use when the battle conditions change frequently and strategy and tactics need to be adapted in near real time. Conversely, soldiers who can’t (or aren’t allowed to) adapt to the changing needs are also likely to be doomed to failure.

The first place we will see this is in software and technology development and operations. Software development is the sharp end of this change, and some of the newest thinking about work and its organisation in the recent past has come from the software and technology world. Open source, collaborative working, mobility, network effects and platformisation, and the gig economy, just to name a few. It is likely that as software continues to eat the world, and our organisations, we will see it swallow the traditional (industrial era) style of management, in favour of empowered doers. Ironically, therefore, even as technology replaces some jobs, in the longer term, it may be the best thing that happened to workers.

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6 Business Lessons from the World Cup 2018

world cup russia
I spent 12 days in Russia, watching some football games and soaking up the carnival atmosphere of the World Cup. While there are many personal moments I will cherish, I’m also reflecting back on some professional and business lessons I took away from this once-in-a-lifetime experience.

(1) Clockwork Efficiency

Some 20 minutes before the World Cup final was due to start, Ronaldinho had just exited, there were performers on the pitch, the flags had to be unfurled, the anthems still had to be played, and there was mayhem all around. Yet the game started on the dot at 6 PM local time. This was true of almost every game – the last 30 minutes leading up to kick off looked chaotic, but on reflection were running like clockwork. Presumably because all the hundreds of people involved knew their cues, somebody was watching the clock, and they had practiced this to death. Kudos to FIFA and the organisers for this display of causal efficiency. Bottom line, you don’t have to go all military to retain operational efficiency. Most large events or businesses have a similar veneer of chaos, but you can always tell the ones that are operationally tightly run by the extent to which they keep to time.

Even getting close to a hundred thousand people in and out of stadiums, from and to metro stations was managed quite effectively with the help of hundreds of volunteers. Interestingly, there were a lot of police and uniformed, military or paramilitary personnel but the primary interaction with the spectators was through the volunteers. So people had a pleasant interaction with a bunch of young people, while being clear that any bad behaviour would be dealt with. The additional lesson here is to distinguish between the experience and the governance.

(2) The Impact of Willful Internal Strife

One of the stories of the world cup was the extent to which internal strife impacted the performance of the tournament favourites. The two examples here are the last two World Cup winners – Germany and Spain – and both provide interesting insights. On the eve of the World Cup, Spain hit a snag. Real Madrid announced Lopetegui, the Spain coach, as their next manager. The timing could not have been worse, and Madrid probably had their internal political issues to address. But it was done unilaterally and without consulting the Spanish FA. Consequently, the president of the FA removed Lopetegui from his post and appointed Fernando Hierro in his place. Hierro was already a part of the coaching set-up and no outsider, but it was a big ask at a critical juncture. When Spain played against Russia and passed the ball aimlessly for 120 minutes, you could question why he didn’t come up with a different tactical model or instructions. Why did he play with Koke, a defensive midfielder who was singularly unadventurous, rather than add to his goalscoring firepower? Perhaps he was adopting a safety-first approach, being a new manager? Perhaps he was out of his depth? Or the fact that Hierro was himself a defensive midfielder may have had something to do with his approach? You can question the set of decisions leading up to Hierro’s appointment, but it was made on principle, and Spain suffered.

Germany had the same manager – Loew – who has been at the helm for one of the most successful periods in their footballing history. But their split was more internal. Following an appearance with Erdogan, the Turkish president, there was a backlash against 2 players of Turkish descent, namely Ozil and Gundogan, and reports emerged of a split between the Bavarians and the others in the team. Again, Germany’s disjointed performance was obvious for people to see and we are still seeing the aftermath of this with the German football fraternity split over Ozil’s decision to leave the national football team.

The obvious lesson is that no organisation can perform if there are significant internal schisms or disruptions to the operating model at critical junctures. If either of these incidents had happened 6 months ago, you could argue that both these teams would have gotten over them by the time the cup came around. But the more interesting question here is about principles. The Spanish FA took a stand that on the surface was a principled one (though some called it an ego issue), and paid the price. The German players of immigrant origins were dealing with their own identity issues. The bigger lesson to be learnt here is how to deal with these kinds of issues without letting it impact performance. Any answers?

(3) Leadership Lessons

Ms. Kolinda Grabar-Kitarovic was a one-person leadership lesson through the late stages of the tournament. She stood with the fans in her team jersey, as one of the people. She visited the Croatian team after the games in their dressing room after their game with Denmark, with no pomp and ceremony, to congratulate them. And on the final, as the rain came down she stood in the rain hugging her own players, and the winning French team members in a heartwarming act of humanism. Vladimir Putin deserves credit for Russia delivering a wonderful World Cup, but on that stage, he was overshadowed significantly as he stood under the umbrella, while Macron and Grabar-Kitarovic shared the rain with their compatriots.

In a sporting event so dominated by men that no other woman has been anywhere close to centre-stage, it was also quite dramatic to see Grabar-Kitarovic be effectively the only woman who was celebrated over the course of the 5 weeks of the World Cup. It was a gilt-edged opportunity to stand out and Ms. Grabar-Kitarovic rose to the occasion in all her red-and-white-chequered glory.

(4) Smart Branding

As a fan, it has always bothered me that FIFA so tightly control the branding and use of the World Cup, in connection with their Corporate Sponsors. This time, being there, I felt the benefit of the strong branding. Right from after the 2014 event the branding for the 2018 event has been in force. The Dusha font was specifically developed to work across English and Cyrillic scripts, with Asian overtones, and has been consistently used everywhere. You may have seen this in all the FIFA World Cup images and events. And the control of the branding means that the colours, fonts, and logos are instantly recognisable as official. Whether they are signs painted on the floor of the metro stations, or they are on Television, the strong branding has played a role in the instant recognition of the World Cup event, locations, merchandise, and signage. I have to say that this significantly helped to identify signage and directions around the World Cup cities as well.

(5) Harnessing the Collective

The World Cup on the pitch was clearly a victory for the collective. The GOAT (Greatest of All Time) aspirants all went home early. Messi, Ronaldo, Neymar et all were back home before the semifinals. Croatia was the very embodiment of the collective over the individual. France too were a team where individuals were all able to put the team above themselves. Whether it was Mbappe, or Pogba, or Griezman, you could see they were all playing for the team, not for personal glory. While it was reassuring to note that teams still win over individuals, it begs the question of what great teams and managers do to instill the right culture. The France manager, Deschamps was himself a World Cup-winning player, but played in an unglamorous role, which was derided by Eric Cantona as a ‘water carrier’. The Croatia manager was largely unknown before the World Cup. If anything, this eschewing of the star culture was a real reassurance for those who have always believed in the team over the individual.

This isn’t a call for removing individualism. Great performers will always shine – as Modric, Mbappe, and Pogba all did, but great teams are built around overarching team ethics that both elevate and subsume the stars. That is the mark of great management.

(6) Technology Wins

One of the biggest talking points in the World Cup was the use of VAR. FIFA made a bold move to implement VAR (Video Analysis Replays) in the World Cup which allowed referees to use video replays where there was a reasonable doubt about the decision. A bank of assistant referees was continuously watching the replays and signaling to the on-field referee where there was a potential need for a replay.

In the main, the technology was a success. A number of decisions were given or reversed based on the video evidence. By and large, the right result was achieved. You would have to say this was a big step forward for football. The one notable case where people had doubts was the call in the final to award France a penalty. Still, you can say that the referee looked at this closely and from a number of angles, and made his best call. The rules in football still leave room for subjectivity and judgment, so this allows the referee to base that judgment on more data than ever before.

If anything, FIFA have erred in setting the expectations of this technology by announcing that only clear and obvious cases will be reviewed. Why? The only driver for the technology should be that the right decisions are reached every time, as best as possible. This idea of ‘clear and obvious’ has led to a lot of quibbling between pundits on panels. Whereas really, all we should be concerned with is that the technology enabled the right decisions to be achieved, and in 9 out of 10 cases was an unmitigated success.

One area where more work needs to be done, and a point I’ve argued before, is the ability of VAR to engage the audience, especially in the ground. The tv audience gets to see the replays, as do the referees. There’s no reason why the audience at the ground shouldn’t see the same replays in the way tennis and cricket audiences do. If anything this reflects on the organisers’ view of the maturity of the spectators to behave with decorum no matter what the final decision of the referee is.

Introducing technology is always fraught with risk, as most businesses know. The lesson here is to be clear about the value of technology and data on better decisions, and also focusing on the user experience of customers.

On a side note, the other big technology winner at the World Cup was Google Translate. A point already highlighted in the Guardian newspaper. Google translate was the saviour for the hundreds of thousands of people coming to Russia from across the world. A quarter of a million people just from South America alone, for example. Most of these people were communicating with shopkeepers, taxi drivers, and restaurant staff via Google Translate and doing so quite effectively. Our taxi driver from the airport to our apartment in Moscow had google translate set up so he would talk into it in Russian while driving and it would repeat the words in English. He even managed to point out some sights and tell us a couple of jokes, over the course of our half hour journey.

There are probably many more if you look hard enough, but these are the ones that stand out for me. What are yours?

20 Takeaways From The CogX Event, London

IMG_5043First, a clarification. I visit events such as the CogX events to be stimulated, to have new thoughts and to have the neurons in my brain fired in new ways. I go to learn, not to network. So I agonise over sessions to attend, and importantly the sessions I miss. Of which there is an overwhelming majority, as this event was running 5–7 conference tracks at any time, as well as a lot of other small stage events. By and large though it’s a weirdly monastic experience, surrounded by people, but very much alone in my head, to the point where I’m actually a little bit annoyed when somebody wants to talk to me! This then is the list of things that made me think.

  1. If there was one session that made attending the event worthwhile for me, it was Zavain Dar’s session on the New Radical Empiricism (NRE). His argument is that the traditional scientific method is based on certain rational assumptions — which are now challenged. In the classic method, you would hypothesise that the earth was round, find the right experiments to run, collect data and prove/ disprove your hypothesis. This runs into trouble when the computational models are too complex and / or changing too often — such as gene sequencing or macroeconomic data. Also this is not efficient when the range of options is vast and we don’t know what data might be relevant — e.g. curing cancer. The traditional methods may yield results, but it might take a lifetime of research and work to get there. What Dar calls the NRE is the opposite — a data driven view which allows machine learning to build hypotheses based on patterns it finds in the data. So in the NRE world, rather than starting with whether the earth is round, you would share a lot of relevant astronomical data and ask the machine to discover the shape of the world. This approach works best in areas where we have a data explosion such as genomics and computational biology. Or where there is plenty of data but is shackled by traditional hypotheses based methods, such as macroeconomics. An additional problem that NRE solves is where the problem space is simply to complex for human minds to compute — both the examples above are instances of this complexity. You may know that Radical Empiricism is by itself a construct from the late 19th century by William James — which eschews intuition and insists on physical experiences and concrete evidence to support cause and effect. Its worth noting that there are plenty of examples of environments where quantifiable data is not yet abundant, where experts still follow the traditional method driven by hypotheses. VC investing, ironically, is such an area!
  2. This also led to a discussion on Deeptech led by Azeem Azhar of Exponential View and panelists from Lux, Kindred Capital and Episode1 Ventures. Deeptech is defined from an investment perspective as companies and start ups who are building products which involve technical risk. Not using existing tech to solve new problems. Usually involving products and ideas which a few years ago would have to subsist on research grants and be housed by academic institutions.
  3. Jurgen Schmidhuber’s session on LSTM was another highlight. Schmidhuber’s PhD thesis on LSTM (Long Short Term Memory), in 1997 was a foundation of the AI advancement which was used by a number of technology products and subsequent development. Schmidhuber presented an excellent timeline of the evolution of AI in the past 20 years and ended with a long view where he explored the role of AI and ML in helping us reach resources that were not on earth but scattered across the solar system, the galaxy and beyond. And how we might perceive today’s technology and advancement in a few thousand years.
  4. One of Schmidhuber’s other points was around curiosity driven learning. Mimicking the way an infant learns, by exploring his or her universe. This is the idea that a machine can learn through observation and curiosity, about it’s environments.
  5. Joshua Gans, the author of Prediction Machines, and professor of Economics and Tech Innovation, talked about AI doing to prediction what computers did to arithmetic. Essentially they dramatically reduced the cost of complex arithmetical operations. AI does the same for prediction or inference. Which is essentially making deductions about the unknown based on the known. And bringing down the cost of prediction has a massive impact on decision making because that’s what we’re doing 80% of the time, at work, as managers.
  6. Moya Green, the CEO of Royal Mail talked about the transformation that Royal Mail went through — including an increase in technology team size from 60 to over 550 people. She also made the comment that most managers still under-appreciate the value of tech, and overestimate their organisations capability to change, and absorb new tech.
  7. Deep Nishar of Softbank used an excellent illustrative example of how AI is being used to provide personalised cover art for albums by digital streaming and media providers, based on users choices and preferences.
  8. Jim Mellon, long time investor and current proselytiser of life-extending tech suggested that Genomics would be a bigger breakthrough than semiconductors. He was joined by the chief data officer for Zymergen, which works on bio-manufactured products, based on platforms which work with microbial and genetic information.
  9. A very good data ethics panel pondered the appropriate metaphors for data. We’ve all heard the phrase data is the new oil. Yet that may be an inadequate descriptor. Experts on the panel posited metaphors such as ‘hazardous material’, ‘environment’, ’social good’ etc. because each of these definitions are useful in understanding how we should treat data. Traditional property based definitions are limited and it was mentioned that US history has plenty of examples of trying to correct social injustice via the property route (reservations for native Americans), which have not worked out. Hence we need these alternative metaphors. For example, the after-effects of data use is often misunderstood, and sometimes it needs to be quarantined or even destroyed, like hazardous material, according to Ravi Naik of ITN Solicitors.
  10. Michael Veale of the UCL suggested that ancient Greeks used to make engineers sleep under the bridges they built. This principle of responsibility for data products needs to be adopted for some of the complex products being built today by data engineers. Data use is very hard to control today, so rather than try and control it’s capture and exploitation, the focus perhaps should be on accountability and responsibility.
  11. Stephanie Hare made the excellent point that biometric data can’t be reset. You can reset your password or change your email, phone number, or even get a completely new ID. But you can’t get new biometrics (yet). This apparent permanence of of biometrics should give us pause to think even harder about how we collect and use it for identification, for example in the Aadhaar cards in India.
  12. Because of the inherently global flows of data and the internet, the environmental model is a good metaphor as well. Data is a shared resource. The lines of ownership are not always clear. Who owns the data generated by you driving a hired car on a work trip? You? Your employer? The car company? The transport system? Clearly a more collective approach is needed and much like social goods, such as the environment, these models need to validate the shared ownership of data and it’s joint stewardship by all players in the ecosystem.
  13. Stephanie Hare, who is French Historian by education provided the chilling example of how the original use vs ultimate use of data can have disastrous consequences. France had a very sophisticated census system and for reasons to do with it’s muslim immigrants from North Africa captured the religion of census correspondents. Yet, this information was used to round up all the jewish population and hand them over to the Nazis because that’s what the regime at the time felt justified in doing.
  14. On a much more current and hopeful note, I saw some great presentations by companies like Mapillary and SenSat, and Teralytics which focus on mapping cities with new cognitive tools. Especially for cities which are of less interest to tech giants, and using crowdsourced information and data, which may include mobile phone and wifi usage, or street level photographs all used with permission, for example.
  15. At a broader level, the smart cities discussions, strongly represented by London (Theo Blackwell) and TFL (Lauren Sager Weinstein) shows the transition from connected to smart is an important one. Very good examples by TFL on using permission based wifi tracking at platforms to give Line Managers for each of the tube lines much more sophisticated data on the movement of people, to make decisions about trains, schedules and crowd management, over and above the traditional ways which include CCTV footage or human observation on platforms.
  16. At a policy level, a point made by Rajiv Misra, CEO of Softbank Investment Advisors (aka the Vision Fund) is that while Europe leads in a lot of the academic and scientific work being done in AI, it lags in the commercial value derived by AI, notably to China and the US. A point echoed by the House of Lords report on AI which talks about the investments and commitment needed to sustain the lead the UK enjoys in AI, currently. Schmidhuber’s very specific solution was to mimic the Chinese model — i.e. identify a city and create an investment fund of $2bn to put into AI.
  17. I also sat through a few sessions on Chatbots and my takeaway is that chatbots are largely very much in the world of hype machines. There is very little ‘intelligence’ that it currently delivers. Most platforms rely on capturing all possible utterances and coding them into the responses. Even NLP is still at a very basic stage. This makes chatbots basically a design innovation — where instead of finding information yourself, you have a ‘single window’ through which to request all sorts of information. Perhaps its a good thing that the design challenges are getting fixed early, so that when intelligence does arrive, we won’t stumble around trying to design it.
  18. Within the current bot landscape, one useful model that I heard is ‘Treat a bot like a new intern that doesn’t know much’ and let it have a similar personality so that it provides responses that are appropriate and also sets expectations accordingly. It might just start with a ‘hello, I’m new so bear with me if I don’t have all the answers’, for example.
  19. Dr Julia Short, who has built Spot — a chatbot to handle workplace harassment provided a very interesting insight about the style of questions such a bot might ask. A police person’s questions on the one hand are all about capturing in detail exactly what happened and making sure that the respondent is clear and lucid about events, incidents, and the detail. A therapists questions and line of discussion on the other hand is all about helping a victim get over some the details and get on with their lives. This suggests that you need to be clear whether your bot is an extension of the law enforcement or a counselling body. It also suggests that you might want to do the former before the latter.
  20. A really important question that will not leave us is: what do we do if the data is biased? If we are conscious of certain biases which are to do with gender, race or age, then we can guard against them either at the data level or at the algorithmic level, but we also need to be able to detect biases. For example, the example which I’ve now read in a few places of how the leniency of sentences handed out by judges in juvenile courts in the US vary inversely with the time since the last meal of the judge.

Clearly all of this really represents under 20% of the great discussions over the 2 days. Please do add your own comments, takeaways and thoughts.

Hail Mary (Meeker)!

Hail Mary (Meeker)

Internet

 

On the 29th of May, Mary Meeker released her annual compendium of the digital state of the world – the KPCB Internet Trends. For those who may not remember, Mary Meeker was a veteran who survived the dot.com crash and also the financial crisis of 2008, as the head of tech research for Morgan Stanley. She was named as among the 10 smartest people in Tech. She now serves as a partner at KPCB (Kleiner Perkins Caufield and Byers) and has been publishing her annual opus for a few years now.

The problem is that when you’re Mary Meeker, you can get away by putting out a deck with 294 slides. For us mere mortals, reading and absorbing this encyclopaedia of information is a challenge by itself. Every year I get this and carefully save the deck to read in detail and of course, it never happens. So this year, with the benefit of a relatively free weekend, I thought I would do a first pass and pull out some of the most interesting things that I found in the report. So here are my top 10 interesting things to take away from the Mary Meeker report – some of them confirm what we know, while others are what we didn’t know, or are truly counter-intuitive.

What I knew or suspected.

1. The devices story mobile device shipments growth has shrunk to zero. This confirms what we’ve known for a while – device evolution has stalled since Steve Jobs. And since Samsung, the largest manufacturer has a ‘follow Apple’ strategy. Will we see a new device redefine growth or will the we see a decline in shipment numbers next year? HMD – are you watching? (Slide 6)

2. The decline in desktop use despite overall growth. While mobile internet growth is expected, it’s the ‘other devices’ that is interesting. This will presumably include netbooks, etc. but also smart things. I expect in future this category will be broken out to reflect the detail on Internet of Things. (Slide 11)

3. The privacy paradox will be one to watch – after all data is how every single provider improves their services, while keeping prices low, which leads to user spending more time and sharing more data. Versus the regulators needs to protect consumers and protect data use. This will be a key axis of debate going forward and will determine the balance between innovation and protection. Unfortunately Meeker’s slides don’t carry too much insight on this by way of data. (Slides 31-36)

What I didn’t know (I’m intentionally using the singular, as you may well be aware of this)

1. While we’re aware that big tech now dominates the market cap list, what should worry the rest of the pack is how they dominate the R&D spending list, which points to a continuation of their dominance at the top. The top 15 R&D investors list is dominated by 6 technology firms, with 2 each from automotive, petroleum, telcos, Pharma), with GE as the only conglomerate. The top 5 in the list are Amazon, Alphabet, Intel, Apple, and Microsoft. Also, tech firms report the highest growth in R&D, with 9% CAGR and 18% YoY growth. (Slides 40-41)

2. We know that image recognition is an area where AI has now passed the human levels of accuracy leading to all kind of applications across scan analysis in healthcare, and more controversial applications such as face recognition. Now, voice-based natural language recognition is another areas as also demonstrated recently by Google. This should drive a revolution in customer contact centres and in human-computer interfaces in general. (Slide 25)

3. The extent to whichAmazon & Google are getting to dominate the enterprise AI race. To be honest, we know instinctively that the AI race will be one by players with the largest data stockpile. But the range of services being offered for enterprise customers is still an eye-opener. We’ve just started playing around with Google’s Dialogflow, but they also have Tensor (cloud-based H/w), the recently announced AutoML (machine learning), and Vision API (Image recognition), while Amazon has AWS based tools such as Rekognition (image recognition), Comprehend (NLP), Sagemaker (ML framework), and of course their AWS GPU clusters. (Slide 198 – 200)

4. The growth of Fortnight and Twitch on the gaming front – pushes forward what we saw with Pokemon Go. The sweet spot between the hardcore platform based gamers and the casual gamers and kids where millions of people get just a little bit more involved about game, that does not need a special platform – is the story behind Fortnight (Slide 24)

What I didn’t expect

1. The highest increase in spending in enterprise IT is in networking equipment. This is a surprise. I haven’t found the data on this yet, and while the 2nd and 3rd place results don’t surprise me, (AI and hyper-converged infrastructure), my curiosity is definitely piqued by why companies are spending more on networking equipment – connecting to cloud environments from the enterprise perhaps? More connected devices and environments?

2. I’m seeing a lot more confirmation of the models of lifelong learning. This is repeated by Meeker, but her really interesting insight is around how much more learning freelancers invest in compared to their presumably complacent employee counterparts. Perhaps unsurprisingly the top courses sought include AI & related subjects, cryptocurrency, maths and English. (Slides 236 and 233)

3. Meeker makes a great point but Slack and dropbox and I wouldn’t have picked these 2 companies as the flagbearers of consumer-grade technology in the enterprise. But clearly, they are among the most penetrated consumer style tools in the corporate environment. (Slides 264-268)

Meeker has a big section on the Job market, on-demand jobs and future jobs. She also makes the same point others have made about how all technologies so far has created net new jobs. While I agree with this backlog, history is not always the best predictor of the future. And the fact that there will be net new jobs tends to gloss over the significant short-term and geographical disruption in livelihoods that is likely to occur. Think Detroit or Sheffield. There may be more automotive and steel manufacturing jobs today than in 1980 but they are in China, not in Detroit or Sheffield. And so of not much solace to the unemployed factory worker and his / her family in these towns. This may well be the story of AI – but potentially at a larger scale and possibly in a shorter time frame. (See slides 147-163).

There are also useful slides on the gig economy and on-demand jobs now being a scaled phenomenon. (Slides 164-175)

There are also entire sections on China, Immigration and Advertising – which I’ve not delved into as they are currently of less interest to me personally. The E-commerce section also didn’t have anything that jumped out at me as noteworthy. Happy to be corrected!

Seven in 7: Amazon’s Infinite Monkey Theorem Defence, GDPR Impact on Innovation, Ocado’s Successful Transformation, and More…

Seven for 7: Alexa sends the wrong message; does GDPR take us backwards? Uber crash – design flaw; future gazing with Michio Kaku; AI Winners; Ocado transformation and Energy Industry Updates.

(1) Amazon Echo: message in a bottle

The technology story of the week is undoubtedly the one about Amazon Echo and the message it inadvertently sent. ICMYI, a couple in Oregon had a call from an acquaintance to say that Alexa had sent them a recording of a private conversation of the couple, without their permission, or even knowledge. Amazon’s explanation is that this is the rare combination scenarios where a normal conversation between the couple somehow triggered all the keywords and responses that made Alexa record, validate and send the conversation to the acquaintance. This feels like the equivalent of the money, typewriter and Shakespeare problem, only, it’s not an infinite amount of time.

Here’s Amazon’s explanation: https://www.recode.net/2018/5/24/17391480/amazon-alexa-woman-secret-recording-echo-explanation

(2) GDPR  – impact on marketing and innovation.

I’m sure you’ve all received hundreds of emails in the past week exhorting you to stay in touch and re-sign up for all the emails you’ve been getting from people you didn’t know were sending you emails. But now that the moment has come, how will marketing work in a GDPR world? In one way this will take marketing backwards – as there is now a ban on algorithmic decision making based on behavioural data. It’s a moot point whether advertising falls into this category but companies may want to play it safe and in any case, the confusion will create a speed breaker in the short term. We may now be back in the world where if you’re watching or reading about champions league football you will see a beer ad irrespective of who you are. Not just marketing – a lot of innovation will also come under fire – both because of safety first practices, but also because some organisations will use GDPR as a shield for enabling innovation stifling practices, as highlighted by John Battelle of NewCo Shift. He argues that the regulation favours ‘at scale first parties’ – large tech platforms that provide you with a direct service such as Netflix, Facebook, or Uber – where users are likely to still give consent for data use more readily than to smaller, upcoming or relatively new and unproven services.

Dipayan Ghosh in the HBR – GDPR & advertising: https://hbr.org/2018/05/how-gdpr-will-transform-digital-marketing

John Battelle on GDPR & Innovation: https://shift.newco.co/how-gdpr-kills-the-innovation-economy-844570b70a7a

(3) Driverless / Uber/ Analysis

The analysis of Uber’s recent driverless crash has now thrown some light on what went wrong. And the answers aren’t great for Uber. In a nutshell, the problem is design and not malfunction. Which means that all the components did exactly what they were designed to do. Nothing performed differently and no components were at fault for failing to do their job. But as a collective, the design itself was flawed. The car had 6 seconds and 378 feet of distance to do something about the pedestrian crossing the street with her cycle. But it was confused about what the object was. The human in the car only engaged the steering 1 second before the crash and started breaking 1 second after the collision. The car was not designed to warn the human driver about any possible threats. A lot of the inbuilt safety systems in the Volvo vehicle including tracking driver alertness, emergency braking and collision avoidance, were disabled in the autonomous mode. In a nutshell, the responsibility lies with Uber’s design of autonomous cars. Uber has stopped testing in Arizona but has now started exploring flying taxis. Not a project that might fill you with confidence!

Uber crash analysis: https://sf.curbed.com/2018/5/25/17395196/uber-report-preliminary-arizona-crash-fatal

(4) A glimpse of the future: Michio Kaku & Jack Ma

The robotics industry will take over the automobile industry. Your car will become a robot – you will argue with it. Then your brain will also be robotised and brain net will allow emotions and feelings to be uploaded. You will be able to mentally communicate with things around you. Biotech allows us to create skin, bone, cartilage and organs. Alcoholics may be able to replace their livers with artificial ones. You may be able to scan store goods with a contact lens and see the profit margin on goods. The first 7 mins of this video tells you all of this through the eyes and experience of futurist Michio Kaku. Jack Ma (14 mins in) also talks about trusting the next generation. And how we are transitioning from the industrial era where we made people behave like machines, to a world where we are making machines behave like people. Believe the future before you see it, to be a leader, according to Ma.

https://www.youtube.com/watch?v=K1EZWYqm-5E&feature=youtu.be

(5) Who’s Winning The AI Game?

With the whole world hurtling towards an AI future, this piece looks at who exactly wins the AI game – across 7 different layers. It won’t surprise you to know that China is making amazing gains as a nation – their face recognition can pick out a wanted man in a crowd of 50,000. But it might surprise you to note that Nvidia’s stock is up 1500% in the past 2 years on the back of the success of their GPU chips. Meanwhile, Google is giving away Tensorflow free. All this points to a $3.9 tn market for enterprise AI in 2022. Are you ready for the challenge?

Who wins AI, across 7 layers
https://towardsdatascience.com/who-is-going-to-make-money-in-ai-part-i-77a2f30b8cef

(6) Ocado – digitally transformed.

When Ocado launched in 2000, it was on the heels of Webvan, a category of providers who set up to focus on eCommerce fulfilment, as an arm of Waitrose. Cut to 2018, and Ocado is a story of successful digital transformation. Ocado is today a provider of robotic technology for warehouse automation. Having become profitable in 2014, it now has a valuation of $5.3 bn and is set to become a part of the FTSE 100.

https://www.ajbell.co.uk/news/index-reshuffle-looks-set-deliver-ocado-ftse-100
https://www.theguardian.com/business/2018/may/17/us-deal-boosts-ocados-stock-market-value-above-5bn

(7) Understanding statistics: What medical research reports miss

When a drug is tested and the outcome suggests a 5% chance of a possible side effect, this does not mean that you have a 5% chance of being impacted if the drug is administered to you. It means there is a 5% chance that you will have the condition which leads to you having a 100% likelihood of being impacted. This is a subtle but very important distinction in how we interpret the data. But continuing down this stream of thought, it points to the lack of personalisation of medicine, not just the misinterpretation of data.
https://medium.com/@BlakeGossard/the-underrepresentation-of-you-in-medical-research-85289b591ba

 

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.