Thinking Beyond Design Thinking

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You’ve probably experienced design thinking in any digital project you’ve started or been a part of in the past few months. The tools of design thinking are highly popular in the enterprise today – from ethnographic research, contextual inquiry, and shadowing, as well as delving beneath the surface of user stories for real insight.

For enterprise long used to technology solutions that take aeons to deliver and still turn out clunky and unusable, it’s easy to understand why design thinking is so ubiquitously popular. And I’ve seen it at work in any number of client and internal projects. And I think there’s a danger that design thinking might crumple under the weight of its own expectations. This article is about why we need to use design thinking carefully.

But let’s start by reminding ourselves about why it works? The reasons are fairly obvious design thinking starts from a position of empathy. This by itself should be an obvious thing, but clearly it’s been lost in the ossification of corporate processes and taken over by the lure of experience. So people have been building solutions based on what they ‘think’ they know about what users want and how, rather than asking them. As this piece from the HBR, by Jeanne Liedtka, highlights, design thinking works because it forces us to challenge our assumptions. The more interesting questions we ask, the better the answers are.

Also, design thinking gets past 2 of the biggest barriers in designing solutions. The first is assumptions. We typically have truckloads of assumptions about our users, and design thinking forces us to validate and often counter them. The second is the false comfort and lure of experience. A marketing manager with 20 years of experience thinks she understands her customers really well. Or perhaps we self reference, after all we’re all experts in retail because we shop every week.

So design thinking works, and it delivers by addressing all of these challenges and building a solution around the users needs. But while it is the mythology du jour of defining digital product experiences, it isn’t exactly new. Any product developer will tell you that this is exactly what product designers have done throughout history. My favourite example is that of the Honda engineering and design team that spent a week at Disneyland in the US just observing what families took out and put into the boot of the car.

Steve Jobs was apparently not a big fan of asking his customers what they wanted. Nor according to received wisdom was Henry Ford. But that doesn’t mean they weren’t keen observers of human behaviour and needs. The latter also suffered because he didn’t keep pace with the changes in the needs and expectations of his customers.

My current concerns, working with a number of design thinking based projects and scenarios, stems from 3 key challenges. The first is the dilution of the idea of design thinking. This is when we jump to the rituals of design thinking without actually getting to its key principles. Plenty of so called design thinking workshops are just brainstorming sessions under a different name. If the right questions aren’t asked and the right people aren’t responding, then this is just another workshop. The second is the danger of reinventing the wheel. Design thinking, when applied to a known problem space, should follow after enough secondary research has been done, else you will simply be learning the hard way what others already know. For example, while designing a shopping cart for a new ecommerce business should not be design thinking based from scratch, given that there are hundreds of examples of working shopping cards online to compare and evaluate. The third, follows from the second therefore in knowing what kind of problems to apply design thinking to. And this is typically new areas, and new products, or where there is a belief that current solutions don’t do enough. And you can’t know that unless you’ve done the research.

This article, also from the HBR, by Natasha Iskander, provides a more structured critique of design thinking – as it highlights the gatekeeping role of the designer, the inevitable subjectivity of the process and the problem of a finite and book-ended process of design that ends up preserving status quo, rather than a continuous evolution. Those of you who have been involved in agile projects will know the difficulty of fitting the commonly accepted double diamond design approach into an ongoing agile process. Usually it ends up as an early stage activity that has an end point, while product development becomes an ongoing and open ended activity. One of the most insightful points Iskander makes is the challenges faced by the prescriptive nature of the design thinking process in a world defined by continuous and evolving uncertainty. In other words, a VUCA world.

So the next time you go into a design thinking exercise, ask yourself (1) is this a problem that has been solved before? And by whom? (2) have we first made sure we’ve looked at what’s already been learnt before we go discovering for ourselves? (3) are we really following the design thinking principles, or are we going about gathering requirements in a traditional way, but just calling it something more cool and interesting? And (4) are we keeping the design and ideation process alive, or is it seeking an artificially finite solution? I have a hunch that not all your design thinking projects will pass these tests.
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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!

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!

FT Innovate Conference, Day 2

The Government seems to be thinking the right way. Phrases like ‘strategy is showing/ delivering’ don’t normally roll off the tongues of digital tsars. Also creating a data and API repository which other public sector units can use is quite a forward step. @MTBracken

In general the UK is now emerging as a strong innovation hub. The narrative of 3-5 years ago, which was all about ‘needing to move to Silicon Valley’ has gone away, as local and US investors have stepped up to build businesses here. As would be expected, the UK leads in areas such as FinTech. With a recent track record of successful European start ups, from Spotify, to Skype, to Raspberry Pi, there is also a ripple effect as senior members from successful start ups step out to create their own businesses. There are now some nine clusters of tech across the UK, including Cambridge, Portsmouth and Birmingham, driven by talent, cost of living, and telecoms infrastructure, amongst others. @ashleyhi

At the other end of the scale, GE are positioning Predix as the OS of the industrial internet. And Marco Innunziata, Chief Economist pointed out that allowing windmills in windfarms to ‘talk to each other’, allow wind farms to reduce the costs by a factor of 6. @marcoannunziata.

One of the interesting challenges of the IOT is the blurring of blue-collar and white-collar work. This is another potential disruption and a cultural challenge. In fact Innunziata describes GE as a ‘part industrial’ and ‘part software’ company, with significant presence in the Silicon Valley.

There is still a big question mark around reaching millennials and the next generation of employees/ customers. On the one hand this is a natural order. One way or the other you will be hiring the next generation over the next 5 years. But to consider this the panacea for changing your corporate culture is misleading. How quickly will the 21 year old you hire become a part of the orthodoxy? Will he or she really understand the subsequent generation – somebody who might be 15 today? Creating an open culture in the longer term needs more than hiring a few young people. @jooteoh.

In a fast changing and unpredictable world, the value of simulations rises, and with the computing power and data at our disposal, it is now much more feasible to run simulations for all the situations which may not be easy to replicate in real life (plane evacuation on water, for example), or logistically impossible (a thousand repetitions of the plane evacuating in water with varying conditions). In the language of innovation, simulations is a very useful way of creating fast-fail models without having to repeat all of them in reality. @simudyne

A new and welcome way of thinking about diversity emerged when Belinda Parmar played up (rather than down) gender stereotypes. This goes with my personal belief that we need to celebrate inequality and use it, rather than trying to create a synthetic equality. Men and women are differently wired. People from different continents and cultures think differently. This is why diversity adds value. If we were all the same, then an all male all asian team would be no different from an ethnically diverse, gender balanced one. So the argument about women’s rights – for equal opportunities – starts to diverge from the argument about the need for gender diversity, in some ways. Belinda’s axis of empathisers vs systemisers, and building more empathy in the workplace, was an interesting one and worth thinking about. @belindaparmar.

3D Printing and prototyping is ready for primetime. With the price of 3-d printers falling to £1000-£2000, and the consumables – a roll of low cost filament which allows you print small components at under a £1 running cost, it will be increasingly possible for hardware and product prototyping to become faster, cheaper and more diy, thereby speeding up the pace of innovation. With more companies getting into specific usages around 3D scanning, this will also open up new opportunities for modelling of people, places, and things. It took about a minute for me to get a 3D scan of myself, standing on a rotating base. @3dify @ultimaker

Christie’s is a classic old world business but Steven Murphy’s session was a wonderful illustration of how businesses can evolve quite smoothly into a digital culture without having to rip the guts out of the operations. I also felt they did a great job of making the brand younger and more accessible. The results were clear – art is now democratic, global, collaborative and digital, and Christies is still at the centre of it. @christiesinc

Also fascinating was the Honest By session with Bruno Pieters. It may be a glimpse of the future, but to build a business that does not start with the objective of profit maximisation takes a very specific type of person. If trust is the new currency of the digital age, then Honest By will never fall short of working capital. #brunopieters

The PerfectPitch session was very instructive, not least, in the way crowds think. 3 Startups pitched their business, not just to the on-stage dragons, but to the audience, in a live crowdfunding model. The audience used an app to commit notional sums in real time, for each of the businesses, based on their funding need, business idea, model, and overall story. This may be a London bias, given the high leaning towards media and media studies, but I found it amazing that a room full of innovation people, were more interested in funding a magazine subscription model (Readbug) than a healthcare innovation (Pocket Anatomy). The magazine subscription model – similar to Spotify, where you pay a fixed subscription per month for unlimited access to a number of magazines is one that I personally would all but opt out of immediately. In the world of Flipboard and Zite, and about a zillion others, and trying to build a model around paid content (talk to Newscorp about this one), you would have to work very hard to convince me. On the other hand as the healthcare space opens up, an app that captures the human anatomy and allows doctors to give patients a much more visual and recordable explanation of their problem, one that can be saved for later is such a good idea. Even if the initial idea doesn’t work, there is a lot of room for flipping this business to something that does work. I would be in there in a flash. The third business was Podpoint, who do the charging stations for electric vehicles, once again, a clear growth area if it can be done well. I had to leave before this one finished.

It would be remiss of me not to mention Awabot – the little robots ambling around the rooms, talking to people. Awabots are operated by humans using controls, but provide an eye level screen for conversation. So you can actually see & talk to the other person behind the robot. Interesting idea, though in it’s infancy and a lot will depend on the dexterity of mechanical operations, and hopefully the addition of more AI into the interface. Meanwhile this little French start up is looking to make friends with you. @awabot

Overall, 2 great days spent and lot’s of ideas sparked. Got to sit in a Tesla and meet some likeminded people. I missed a couple of good sessions – half of Alberto Prado’s (Philips) session and Ron Williams (Simplest). Some of the things I didn’t see were mobile payments – spreading like wildfire as we speak, true healthcare service innovation, and perhaps the kind of 10X thinking that Larry Page keeps talking about. Something for the next year perhaps @ftlivedigital?

As always for me it’s not just about what I hear from the speakers, or speak with fellow attendees, it’s the thoughts and sparks it creates in my head that is the real takeaway of the event. After all, innovation can’t stop after the conference is over – the real work starts now!

Thoughts & Lessons – FT Innovate Conf Day 1

The UK is the most digital economy in the G20, measured as contribution of digital industries to GDP, as per Baroness Shields. But it faces a deficit of 750,000 digitally trained people. There are now 18 tech clusters in the UK – including Cambridge and Edinburgh for example.

Yet, according to Marianna Mazzucato, the UK also has below average R&D spends. Gross & Industrial R&D.

I’m sure that Marianna’s book sales spiked during her great talk, since at least 4 people I spoke with bought her book during her talk. I did too!

I heard the phrase “scale up” as distinct from ‘start up’ – to describe companies looking for global scale from a successful start. I think it’s a really useful phrase to keep in mind.

What is the role of the state? To create the conditions under which new businesses can thrive. What’s the balance between nation state thinking and global, post-national mindsets? As always the answer lies in between. You still need national policies to execute in specific areas such as education and infrastructure. (I’m still thinking about this one).

GPs are supposed to be able to give patients their medical data by 2015, according to PWC, and only about 1% are ready yet. I also think that about 1% of patients are actually ready to receive, and take stewardship of their medical data. 

Who owns data? Such as from Nest, or from your smart fridge? Well, you own the raw data as a user, but I would think if the data is used to create specific insights by combining it with other data and/or analysing using somebody else’s models, then the new ‘cooked’ data generated, also known as the insights or the meaning, should be the property of the provider of that insight. It’s a bit like supplying components to a manufacturer.

I learnt about the fascinating world of the slime mould. This is a one celled organism that can concatenate across multiple cells to create a single large cell like structure, with multiple nucleii. It has no brain, but is able to communicate, self organise and optimise. Given a bunch of oats laid out, the slime mould will go after the food, and then build optimal lines between the food points. This has been used to effectively create transport maps for cities.

Retailers such as the newly merged Dixons Carphone are working at a number of levels to combat the threat of Amazon. This includes creating strong digital propositions, but also improving the existing physical store operations, such as linking bonuses of store staff to customer satisfaction, and rewarding stores for digital sales from their catchment area.

To truly create an innovative culture, the leadership has to demonstrate it themselves. This includes being in the frontline, taking risks, demonstrating fast fail, and taking the responsibility for the fail, if and when it happens, as is demonstrated by Nike’s Mark Parker . This sends out the real message to employees – that it’s okay to take risks and fail occasionally. But this need to be backed by celebrating success and even rewarding failure if it’s really a fast fail – where the clear lessons are learnt and new hypotheses are set up.

Can IOT Revive The Connected Homes Opportunity?

In 2011, I authored the Intellect (now TechUK) report on Connected Homes, for the UK. Among the key findings were (1) that while this is a massive opportunity, the inherent cross-industry environment creates a number of challenges, from standards, to service optimisation, to ownership; (2) that the infrastructure in most homes will need to be upgraded – with challenges to networks, physical infrastructure, and home equipment; and finally (3) a more pervasive level of connectivity may be required for essential services such as healthcare and education, so as not to exacerbate the digital divide. 

What did surprise me during the course of that research was the complete absence of any kind of linkage between property prices / value and home technology or connected services. Whether it was real estate brokers, property portals, or architects and developers, there were no real incentive to put in better infrastructure or technology, as there was no perceived value (i.e. reflected in a correspondingly higher price). 

As the population ages, and with a bigger challenge of care for the elderly, I fully expect this link to get established in future, and was happy to see at least one article commenting on the lack of connectivity in high value properties. Arguably, this is just anecdotal, and a one-off, but it’s a start! 

More excitingly, we are seeing a re-emergence of connected services with the rapid evolution of the sensor economy and the Internet of Things. 

At the Mobile World Congress in Barcelona, in February, it was noteworthy that Telcos, especially the Asian ones, were deeply committed to the sensor economy. Having lost out on OTT services in the last spurt of innovation, Telcos seem to have recognised that expecting to get paid because of their structural role in the ecosystem is a bad strategy (notwithstanding the recent Netflix deal). This time around, elcos are participating more wholeheartedly in the service delivery. From smart t-shirts to track your heartbeat to birthing systems for farm animals, and from home-automation to education, a slew of services are now being provided from telcos which put the user at the core and keep the technology under the hood. NTT Docomo reported that they are now making over $10bn per year from non-traditional (Voice/ Text) services.

Cow BirthingIt’s not just telecoms, but a number of other businesses are now eyeing the home for connected services. Insurance companies, utility firms and technology majors such as Google (Nest, TV), Apple (TV) and others have their eye on your home. The Internet of Things has the potential to democratise a lot of these services, so that small, 3rd party companies can build simple and innovative solutions with access to devices and data. 

Personal and home technology will be the next battleground, therefore, and may be the connected home will finally become a reality.