Why Are We Suddenly So Bad At Predicting the Future?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Happy 2017!

The Future of Retail: How Will You Fight Amazon?

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

digital-retail

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

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

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

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

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

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

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

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

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

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

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

The Missing Pieces of Innovation

I realised something very important while I was a the innovation event organised by EditorEye at the General Assembly recently. It became clearer to me why, despite spending a lot of time and effort on innovation and hiring some excellent people, organisations are still struggling to get the results out of innovation. The speakers at the event, by the way, were all very good and were all covered the topic extensively. But there are some aspects of innovation which are simply not talked about, while others get a lot of focus, as I’ll show you later in this piece.

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But the problem, I believe starts at the root. What is innovation? You can get as many definitions as there are people in the room. Is it new product development? Is it new ideas? Is it creativity? Is it bringing ideas to market? My definition is simple – it’s doing more with less. If it takes 100 units of resources to solve a problem, and you figure out a way of doing it with 80, that’s innovation. We can debate this later, but let me ask you a different question. Is all product development innovation?

Let’s suppose you are an insurance company and you figure out that increasingly there are older people whose lives depend extensively on technology. You survey the market, and you create a new product which looks at a comprehensive technology devices cover for this audience. You create an app and website for it, which is designed to be used by older people – simpler interfaces, larger fonts, etc. You spend time with prospective customers to understand their specific behaviour and problems and design your product to deliver their key unarticulated needs. Your product is a success. Was this an example of innovation? Let’s assume this product was prototyped in your ‘innovation lab’ which has been set up for bringing such new products to market. Still, is it innovation? Which part of this is innovative, given that all these processes are now standard practice for product development? It may be an excellent example of product development, but I repeat, is it really innovation?

Similarly, you might argue, is every successful advertising campaign ‘innovative’? Is every market research that delivers customer insight an act of innovation? What about a new business, or start up? The reality is that you’re perfectly justified in saying yes to all of these questions. And if your innovation lab delivers successful products, then surely it’s justified, irrespective of what you call it. And I’m not disagreeing with that.

However, to miss the point of innovation is also to walk away from a lot of value. Let’s suppose the typical new insurance product costs £2m to develop and test and £10m to market, hypothetically speaking. Let’s say that the numbers here represent the comprehensive resource cost, and not just actual cash outflows. Now if your new product took about the same, and was averagely successful, you’re at par. But what if you could deliver similar success with 20% investment? Or much better returns? And what if you could build a methodology for doing this consistently? That’s innovation! That means you can deliver more for less and on a consistent basis.

Most organisations look at innovation as a way of delivering new products, or new businesses. It’s also common to look at it as a process that follows a standard path: brainstorm ideas, prototype in a lab and then scale through the organisation. I challenge both of these premises. First, looking at innovation as an idea to market process limits our thinking. Innovation needs to be seen as a problem solving methodology. And specifically, a methodology that looks to improve on the expected resource requirement for solving the problem. And to the second, if everybody is replicating this model, then it stops being innovative. Not that it becomes less valuable if the new products work. But the reality is that most innovation initiatives in most companies don’t lead to success. And wouldn’t it be great if we could increase the success rate?

My model of innovation therefore starts earlier, with Problem Definition. If you’re thinking of a new product, why? Is it to ensure coverage of the market? is it opportunistic? Do you believe that there will decline in current products & revenues? Is it a strategic response to competition? Do you feel you underserve the market? Is the problem financial – your return on capital is too low and you’d like to improve this? Are you missing out on more profitable customers or a growing segment? As you can see there are many, many ways of framing your new product development effort and the problem you’re trying to solve may vary significantly. 3M for example, has a commitment to drive 30% of revenues from ‘new products’ – i.e. those built in the last 4 years. For a pharmaceutical company a new product that better addresses a disease or a family of problems, is a protectable revenue stream that can run for over a decade, even as older revenues decline through patent expiration. Whereas Google (Alphabet) just wants to solve bigger problems. That allows it to be mission driven but even there, for example there are specific problems. Arthur Levinson, ex CEO of Genentech leads a platform in Alphabet to combat ageing. Whereas in the new famous example of British Cycling, the marginal innovations are aimed at driving higher performance, and nothing to do with a product at all. This is where we step away from product development and recognise that innovation is a methodology for solving any problem in a ‘do more for less’ way, not just product development. To do this well, try approaching this problem as at least two out of a CFO, a CEO and a head of Operations, or Marketing. Or apply design thinking principles to see how the people impacted by this problem think about it. This award winning redesign of the ambulance started by looking at the ambulance as an extension of the hospital, and the start of the medical process, rather than just a form of transportation to the hospital. Kees Dorst’s book ‘Frame Innovation’ is a good starting point for thinking about problem framing.

The next step is the Research and Baselining phase, so we know what benchmark we’re trying to beat. It is likely that your ambitions are not at the same scale as Google’s (or Alphabet’s), Amazon or Elon Musk’s. In fact you may just be looking to solve an punctuality problem in your department in a way that nobody in your company has done yet. If you define your context as your company, department, industry or the world, you can accordingly set your benchmarks. This is critical because what’s innovative for a government agency (say agile development) could be very old hat for a Silicon Valley company. But this my second key point. Innovation is surely about being different, and not replicating a tried and tested method. So you need to clearly set out what you’re going to do differently (better!) from other similar efforts, before you start. It’s worth noting that of the 5 stages, this gets the least amount of attention because it’s probably the least sexy part of innovation. But it could save you a lot of effort and also dramatically sharpen the subsequent phases. In fact, often, you will be able to find a lot of examples workable ideas in other industries and organisations. No better example of this than the Great Ormond Street Hospital for children learning from Formula 1 pit stops, about swift handovers from surgery to intensive care. This represents a huge reduction of risk, in the innovation journey.

It’s only once you’ve done stages 1 and 2 that you should then get to the Creative Spark stage. For most organisations, this equates to a brainstorming exercise. One of the biggest mistakes in the area of innovation is that people jump into brainstorming with a loosely defined problem and no benchmark. To make it worse, you then get a lot of people with very little knowledge of the context of the problem state coming up with ideas many of which clearly won’t work. I know of a company which was keen to ‘pitch’ ideas to Transport for London. They ran a competition internally, generated hundreds of ideas, evaluated them and drew up a short list of 10. But the brainstorm was run with a global team, not based in London, and consequently many of the ideas, such as mobile app based solutions for contactless ticketing did not factor in the actual challenges of rush hour volumes, or the speed of response required. Besides, many of the ideas were already at play at TFL, which hadn’t been researched well enough. Running pure-play brainstorms is also of limited use if the team doesn’t have enough context of the problem. You can’t brainstorm ways of improving care pathways in the NHS, or supply chains for broadcast equipment, if you don’t know much about them, or the problems they face. There are, however, plenty of techniques for running more effective brainstorms and idea sessions. Additionally, there are other ways apart from brainstorming for the creative spark phase. Best results are often achieved through having creative people in the mix along with experts, or building unpredictability into the process. Tim Harford’s book ‘Messy’ suggests some excellent ways in which this happens.

Once you have ideas you want to take forward, you can then push them through the Innovation Lab stage. Of the 5 phases of this methodology, this is the one most organisations have invested in already and are doing with a lot of focus. Setting up a lab environment, running ‘Google sprints’, ensuring that small teams turn around quick prototypes, building design thinking into the mix and fusing the efforts of creative technologists with deep experts, a lot of companies are able to do a reasonably good job of taking new ideas through a laboratory process to an MVP stage. When I was working at Cognizant, in 2015 we conducted a quick research of ‘innovation labs’ and were not surprised to find that an overwhelming majority of leading banks and retailers already had an innovation lab of some kind. If you haven’t yet been exposed to or been a part of an exercise like this, grab hold of ‘Sprint’ by Jake Knap et al.

But even that is not enough because a lot of initiatives can fail even after lab success. Be they new products or internally facing solutions. Scaling innovation is fraught with risk, and even Google is famous for the number of initiative it has killed after promising starts. This is the key reason that many organisations prefer to buy in the finished product rather than try to build it in house. What the newly created and lab-tested idea needs is not just organisational support, but often a network in which to flourish. The best results are created when the new idea has a life of it’s own and is allowed to grow and morph independently, not simply scale to a larger replica of it’s initial form. The perfect baby needs to grow into a healthy human adult, not a full sized replica of the baby. Most businesses are unable to provide this kind of sustaining network. Steven Johnson’s excellent book ‘Where Good Ideas Come From’ beautifully elucidates this idea of a sustaining networks. When GE set up it’s fledgling IOT business in Silicon Valley, it was not just allowing it to flourish outside of the corporate headquarters, but also allowing it to sustain and nourish itself in a high tech network. In organisations such as Google and 3M, there isn’t a small and tightly defined number of ideas being pipelined to the market, there is a huge internal innovation network, where dozens or even hundreds of ideas feed off each other, combine and morph on their way to a handful becoming successful products. If it’s new product and new business development that defines innovation for you, then you could do well to keep at hand the Innovators Solution, by Clayton Christiansen.

innovation-methodology

This is just the tip of the iceberg, in a way. Innovation is hard work, and much of it is done away from the public eye, and the adulation of success. But more importantly, innovation is a methodology, which when applied, dramatically improves your ability to problem solve in a way that is ahead of the competition.