MAITRE D: And finally, monsieur, a wafer-thin mint.

Mr. CREOSOTE: Nah.

:

MAITRE D: It’s only wafer thin.

Mr. CREOSOTE: Look. I couldn’t eat another thing. I’m absolutely stuffed. Bugger off.

MAITRE D: Oh, sir, just– just one.

Mr. CREOSOTE: [groaning] All right. Just one.

Rule #1 with my friend Ancient Steve is don’t phone him at home early evening during the week. I’m not sure he knows about this rule, but I think all of his other friends have worked it out too. The rule, so far as I could establish it was an actual rule, started about a year ago. The change happened suddenly, though, and that’s the point of this post. How small changes can create non-linear change. How one last straw comes to break camel’s back.

Phone Ancient Steve up on a Wednesday evening now and, if he bothers to answer the phone at all, what you’ll hear on the other end of the phone is a gruff, curt, borderline offensive ‘what do you want this time?’ kind of hello. The same call a year ago would have been answered by normal Ancient Steve. Still a bit gruff, but gruff in a pleasant enough tone that you knew he wasn’t going to snap your head off.

Somewhere between the two, something happened.

What it turned out happened is that, because Ancient Steve doesn’t believe in things like phones with caller id on them or going ex-directory, when someone calls him, he doesn’t know who it is on the other end of the line until he picks the phone up.

Now the brain is really just a big old prediction engine as far as most of our lives are concerned. All those neurons are there to anticipate what’s going to happen in the next half second. This is a good thing to be able to do from an evolutionary survival perspective. It also means that, when the phone rings, our brain immediately gets busy anticipating who’s going to be on the other end of the line.

Prior to tweleve months agao, when Ancient Steve’s brain was making that prediction, the weight of probability was that the person on the other end of the line was going to be friendly, and so Ancient Steve was able to flood his response system with ‘be-polite’ chemicals and respond accordingly.

Around this time, Ancient Steve was also, it turns out, receiving a growing number of cold-calls from feckless salespeople. Early evenings during the week, the whole world of double-glazing sales personnel around the country gradually came to realise that Ancient Steve might be interested in buying replacement windows. Or a free life insurance assessment. Or a better deal on his utilities.

This was sort of okay until the fatal day when the balance of probabilities shifted past the fifty percent mark. When our brain does its prediction thing and tries to establish whether the incoming call is from a friend or double-glazing vending foe, the calculation is an essentially binary one. If 50.1% of past calls have been a friend, then the prediction that gets made is ‘the caller is friendly, be polite’. Ditto if the percentage is 50.01%. But, come the day that the balance of experience tips the probability the other side of the 50% mark, to 49.99%, then the default prediction becomes ‘the caller is an enemy, be gruff, surly and borderline offensive’. One call from the wrong person, one more piece of straw, tipped Ancient Steve’s behavioural balance.

And then, soon enough, they also affected mine. My prediction engine when I called Ancient Steve used to tell me, ‘here comes good old Ancient Steve’, so that I could pre-fire my own version of ‘be-vaguely-polite’ chemicals. But the moment the balance of probability in my own head about whether Ancient Steve was going to be gruff in a good way or a bad way shifted to the point where it was more likely I was going to hear the Bad version, then that’s the prediction I made.

Again, all it took was one too many calls to Bad Ancient Steve for me to change my whole view of what to expect when I phoned him.

That’s how it works. And until such times as Good Ancient Steve comes on the phone more than Bad Ancient Steve, that will remain my default expectation. Its the same thing with every other prediction calculation we make.

When I’m driving, and I see a car coming towards me flashing its headlights, my default reaction used to be to check that I hadn’t inadvertently left mine on full-beam. Then, when I realized I almost never had my lights on full-beam, my automatic, balance of probability prediction tipped to flashing my lights back at them to demonstrate that, look you idiot, I didn’t leave my full-beam on. Now I’ve lived in rural Devon for a year, the balance of probability has tipped again. When someone driving towards me flashes their lights at me now, the likelihood is there’s a silage-toting farm vehicle around the corner.

Strange that one wafer thin mint too many, as with Mr Creosote, can have such a non-linear results.

And, stranger still, even knowing all of this, is wondering how come after nearly a year of trying I haven’t managed to sell Ancient Steve any double-glazing yet.

One old joke goes that the opposite of ‘innovation’ is ‘innovation consultant’. I can see a fair amount of truth in this assertion. Especially if we qualify the statement as ‘innovation consultant who thinks the problem to be solved is lack of creativity’.

More pragmatically, it seems there is no antonym for the word. A bit like when Nassim Nicholas Taleb struggled to find an opposite for fragile. He ended up with ‘anti-fragile’, but I’m not sure the same prefix does the job in innovation-land. ‘Anti-innovation’ somehow sounds more like a protest than the proactive act of not innovating.

In any event, I’ve always kind of struggled with these kinds of opposite ends of a spectrum question anyway. Mainly because I think, rather than forming the two ends of a straight line, the two ends somehow bend themselves around to form an almost complete circle. The two ends, in other words, become effectively the same thing.

Love and hate for example. It’s a thin line between love and hate, so says the song, the implication being that you can love someone or something so much, you’re a tiny mis-step away from falling off a cliff and hating them or it. Or vice-versa. Which is pretty much the premise of nearly every Hollywood romcom ever made come to think of it: Boy meets girl; boy loses girl; boy gets girl back again.

Put love and hate at two ends of a spectrum, curve the line into a circle and the actual opposite of love becomes the halfway point along the spectrum. The opposite of love, in other words, is indifference.

The other classic two-ends-of-a-spectrum-are-the-same can be seen in left versus right wing politics. Go far enough in either direction and the end result looks pretty much the same as far as citizens are concerned. The political opposite of left or right is thus in reality something along the lines of laissez-faire liberalism.

For a long time, I’ve assumed that love/hate and left/right are the only cases where the two ends of a spectrum are to all intents and purposes identical. Now I’ve come to think the model generalizes to pretty much any kind of supposed spectrum of extremes. If I define any two spectrum extremes as ‘A’ and ‘-A’, and draw circle such that they meet, the other side of the circle – their actual opposite – is the mean, μ, or, if the distribution isn’t skewed, zero.

Can that be true?

Is extreme Fragility the same as extreme Anti-Fragility?

Is extreme Freedom the same as extreme Responsibility?

Is extreme Fight the same as extreme Flight?

Is extremely expensive the same as extremely cheap?

In each case, I think the answer is they are: either the outcome is the same, or, as with love/hate, they are separated by a very thin line forming the edge of a cliff.

Once I accept that A is the same as –A, the real opposite of each pair becomes ‘μ’. It’s zero. It’s apathy. It’s doing what everyone else at the middle of the normal curve is doing.

In which case we might think of ‘Anti-Innovation’ as ‘proactively staying the same, irrespective of what’s happening in the external environment’. And that in turn means the meaningful opposite opposite of Innovation is not ‘Anti-Innovation’ it’s ‘Continuous Improvement’.

Speaking as someone who spends the majority of his life staying in hotels, I’d have to say the novelty of the signs encouraging me to re-use my towels was pretty short-lived. Now, probably ten years since the signs first started appearing in hotel bathrooms around the planet, they’ve come to feel like some kind of dripping-tap torture.

The reason for the torture comes through the knowledge that, despite knowing the signs have never worked, somehow the hotel industry still hasn’t worked out how to dig themselves out of the hole it’s dug for itself.

It’s not as if the reasons for the counter-productive outcomes the signs produce are that difficult to diagnose. We can all read the words, but we also, too, make a swift interpretation of what the hotel management actually means. Something along the lines, ‘our laundry bill is really high, help us to lower it, please, even though we won’t be passing any of that saving along to you. Thanks.’

The signs are a clear signal of an industry that, ironically – given that they’re fundamentally in the ‘people business – doesn’t understand what drives peoples’ behaviour.

What’s really annoying about this is that we know the human behaviour story pretty much boils down to the ABC-M quartet of drivers we so frequently talk about in our workshops. Human Intangibles Rule #1: when you change anything, make sure stakeholders perceive perceptions of their Autonomy, Belonging, Competence and Meaning have all become better than they were before the change.

Look at the ‘save our planet’ towel re-hanging signs through this ABC-M lens and it becomes more than obvious why the signs would never work:

Autonomy – by telling people they have ‘the choice’ while at the same time shaming them  into hanging up their towel, the sign speaks from a higher moral ground and thus takes away autonomy.

Belonging – while in theory playing on the idea that we all have a collective responsibility to do something about the environment, the only tribe that wins were I to hang up my towel is the hotel chain and their lowered laundry bill, and that’s a tribe I don’t belong to.

Competence – by telling me something that I’ve already known about for the last decade, the sign implicitly assumes I’m an incompetent idiot.

Meaning – saving the planet is highly meaningful; re-hanging towels is such a trivial part of that story that by attempting to make a connection between the two the enormous contrast in effect exaggerates the meaninglessness of the act of re-hanging towels.

So, let’s try another approach. This time based at least in part on the ‘ABC-M gets better’ heuristic:

These approaches do a better job of re-framing the Belonging part of the story. We’re now in effect asked to become part of the majority of people that re-hang their towels. I can imagine that it had the impact that it had. 26% more people hang their towels.

Now how about this one:

Autonomy – the guest is in control. They even get to write on the card and tell the hotel management what to do with the money they save.

Belonging – the guest gets to be part of the (majority) tribe of towel re-hangers. Plus they get to pass the money the hotel saves onto a tribe of their choice.

Competence – by telling the guest what the towel re-hanging exchange rate is in terms of environmental benefit and cost saving, the hotel helped make them better informed and therefore their competence went up.

Meaning – by connecting the trivial act of re-hanging my towel to a financial gain to something the guest cares about, the hotel just made the act a meaningful one.

Big Data is great for finding correlations. The Bigger the Data, the better the correlation.

But correlation, no matter how good, has nothing at all to do with causation.

Which is a pity, because causation is the only stuff that any of us ought to act upon. When we confuse correlation and causation, the only certain result is we make matters worse.

When we talk about looking for needles in haystacks, correlation is hay. Needle is causation. Causal relationships between things. Contradictions. Insight. Insight is contradiction is causal relationship.

When we talk about enterprises being able to act with confidence, what we mean is we have found an actual needle, that we have uncovered a meaningful causal relationship that no-one spotted before.

As the world gets ever more complex, busy and interdependent, hay becomes easier and easier to find. Needles, conversely, do not. Moreover, if we believe all of the findings of TRIZ since 1946, that insight is causation is contradiction is needles, the number of needles is finite. And if we already found most of them, there’s not a lot of point in gathering more hay.

Big Data is hay. The best way to find needles is to find them before they get dropped in the hay. Better yet, is go visit the needle factory. You’ll recognize it when you see it, it has a big ‘TRIZ’ sign outside.

I broke one of my main rules today. I went on TripAdvisor to look up a hotel I’m scheduled to stay at. Sometimes it’s good to break rules. Sometimes breaking the rules confirms why you had the rule in the first place.

The no-TripAdvisor rule exists because I spend most of my life in hotels, and have never yet met anyone with anything sensible to say about their stay. Not that I’m blaming or criticizing, just that unless you visit too many hotels than is good for your sanity, you don’t really have a means of calibrating how good one is relative to the other fifty in town.

The reason I broke the rule is because I wasn’t going to be alone on this trip, and my better half told me I had better not let her down.

The hotel I’d chosen had 306 reviews posted. Surprise, surprise, the average rating was a shade over 4 stars. I thought I’d better look at one of the non-4-star efforts. Like this one, posted a few days ago by ‘Penelope27’ (name changed)

This hotel was well sited for my work purpose. I arrived and checked in with the usual formalities, my room was spacious and clean. No view. Usual facilities ….. tea/coffee, UHT milk, nice cookies but only on first night. I ate on my first evening, a simple soup, well presented, cheap and cheerful. Was hot and had flavour. Came with fresh bread. The menu was basic pub grub which I guess people want. I prefer whole healthy foods. There was a salad bar but included lots of mayo ….. I don’t do dairy.  Two bar staff, male, were really friendly and helpful. I asked if they sold chocolate, I fancied after my soup, the young man behind the bar suggested a few shops I could visit, then after a few minutes came to my table with a chocolate flake (obviously for deserts) and a big smile. I was impressed with his thoughtfulness and acted upon his instincts to please. In the morning I was treated to a huge breakfast bacon and mushroom bap. the young lady serving was helpful and friendly. I asked for herbal tea and was told there wasn’t any, then I was offered some loose tea on the shelf, peppermint which was lovely. My second morning was different, my breakfast bap was 4/5 mushrooms less, and I was told abruptly by a different server that peppermint isn’t in the breakfast deal and therefore was denied it, despite my saying I had it the previous morning. What a shame ……. I felt quite upset that the “special loose tea” was not included in the breakfast. A small spoonful of tea leaves which would have completed my morning breakfast was denied by an indignant young man, who did not offer an alternative tea, just smirked and walked off. Other diners refused a hot drink in the morning, surely one small of tea is not too much to ask???? Other than that …… it was ok, some staff were really kind and friendly especially the lad who offered chocolate. made my stay really warm and welcoming ….. isn’t that what counts????????

So the question now is should Penelope27 influence my decision to stay at the hotel?

The three-star rating Panelope27 offered suggested I should probably think about changing my booking to another hotel.

But, just who is Penelope27?

I thought I should run her review through the PanSensic tools.

This is what I learned:

She is a Myers-Briggs ENTP, and so therefore quite unusual.

She is great at finding problems.

She has some rather fixed ideas about what ‘good’ and ‘bad’ are.

She is extremely naïve and innocent.

She is rather self-absorbed and ‘entitled’, and therefore probably GenY, although, if I had to speculate, at the older end of the scale, higher than ‘27’, but lower than 34.

Should I listen to her review?

Answer: no.

Ditto the other 305 reviews.

In fact the only vaguely useful content out of all of the reviews came from the hotel’s proprietors. Where they had occasion to respond to a comment, they revealed themselves to be:

Diligent, paying a lot of attention to detail, probably ESTP.

Flexible.

Calm and unphased by idiots.

Empathic enough to know when to stay out of the way.

I think I will like our stay at the hotel.

I think, too, that my no TripAdvisor rule stands firm. Although I might see if we can set up a TripAdivsor API that strips out all the reviews and just leaves behind the responses from the staff. That seems to be the only meaningful stuff.

Hmm. I wonder if TripAdvisor know about this? Maybe they already know the only point of encouraging millions of Penelope27’s to write meaningless reviews is to provoke meaningful responses from the hotel staff? That’s what I’d call borderline genius.

Every organisation on the planet these days seems to be wanting an ‘innovation culture’. Or at least they do until they understand what they’re asking for. The first problem is being able to make any kind of meaningful measurement of the prevailing ‘culture’. The second is knowing what the measurement needs to change to.

The biggest programme of PanSensic R&D activity at the moment is creating a suite of psychometric tools. Although initially conceived as a means of allowing individuals to assess themselves through the various different lenses (Belbin, Kirton, DISCUS, etc), because the system needs only a pile of narrative to do its job, it means we’re able to do things that the traditionally questionnaire-based assessment tools have never been able to. Like measuring the Myers-Briggs profile of a whole enterprise.

At first, we thought that when we analysed masses and masses of narrative authored by many different individuals within an enterprise, we’d end up with a meaningless cancelling out of the various different measurement axes. Such that, for example, all of the ‘extroverts’ in the enterprise would be matched by an equivalent number of ‘introverts. So far that hasn’t been the case. Here’s the sort of picture we seem to end up with:

The picture is typically constructed by feeding company reports, website content, and ideally (anonymized) email traffic into the PanSensic engine. What it then spits out is a set of psychometric profiles of the overall organisation. In the case of the image presented above, we can say previously inconceivable things like ‘this organisation is an ISTJ’.

And what that in turn allows us to do is to correlate to the types of change that kind of ‘person’ tends to be comfortable with. The following image plots the sixteen different Myers-Briggs profiles relative to the seven main types of change needed at different stages of an evolutionary s-curve:

ISTJ’s are your great ‘do things the right way’ optimisers. They’re not innovators, and the expression ‘innovation culture’ tends not to be part of their vocabulary. Unless it’s something like when they say, ‘innovation culture? Isn’t that when they make everyone wear facepaint and dance like a tree?’

This ISTJ organisation, in other words, is going to find the journey to an ‘innovation culture’ a long and probably tortuous one. But at least we now know where we’re starting from and, once we’ve also psychometrically profiled their competitors and aspirations, we know where their destination should be. And that the journey, initially at least, should keep the tree choreography instructions in a triple-padlocked cupboard.

Ah, the World Economic Forum. If ever an organisation epitomized the fallacy of measuring what’s easy rather than what’s important, it is this esteemed body of blinkered economists.

Joy of joys, they’ve just published the results of their annual Global Competitiveness Report. Which these days includes a specific ‘most innovative country’ section. Not that it has anything useful to tell anyone, but here is the ‘top 10’ according to the survey:

The reason the result is essentially meaningless is largely because the WEF apparently has no idea what ‘innovation’ means. According to the way the study has been conducted, it apparently has something to do with:

1. The amount of money being spent on R&D by companies
2. Quality of academic research institutions based on the number of citations of academic journal articles
3. ‘capacity for innovation’ on a 1-7 Likert scale.

The first criterion conveniently forgets the fact that 98% of innovation attempts fail. Innovation, in other words, has almost no correlation at all with the amount of money companies throw at it. In many ways, what the WEF have unwittingly collated is a measure of innovation inefficiency.

If you thought a 98% innovation attempt failure rate was bad, things get twice as bad when we shift the focus to the academic sector. In the EU right now, on average every million Euros invested in academic research is going to deliver slightly less than 10,000 Euros back in useful return. So measuring innovativeness based on university-anything is akin to counting sheep two years after you opened all the gates.

Or maybe, to extend the metaphor a step further and look at the third WEF calculation criterion, it’s a bit like asking a random passer-by to rank your field on its sheep-iness. On a 1-7 scale. Ah yes, that pasture looks like a five. Whereas that Albanian one over there looks like a four. Not only is the analysis meaningless, there is no-one on the planet that can make any of the cross-nation comparisons in any kind of sensible fashion. So much for that idea.

Above and beyond these three ill-fitting criteria, the WEF study also builds an analysis of patents into their findings. With a mere 97% of patents never making any money for their assignees, I guess you could say that this is one of the better measures. Having a patent granted is at least some kind of indication that you have an intention to monetize a new idea. The big problem, of course, given that inconvenient 97% number, is that measuring quantity of patents can only ever be a tiny part of the story. Admittedly it’s an easy thing to measure, but, per the introduction to this diatribe, just because something is easy to do shouldn’t justify doing it. Far better in this kind of situation would have been to try and measure innovativeness using something like patent quality.

That’s what we set out to do when we built the ApolloSigma measurement method: number of patents is almost pointless, how well they’re able to be monetized and how ‘future-proof’ they are is the thing you need to be able to quantify.

When we look at the global innovativeness story through the ApolloSigma lens, looking at the ‘Star’ patents granted during the year 2014, plotted against the number of patents generated in a country per head of population, here’s what we end up with:

First thing to say looking at this picture, I guess, is ‘sorry Finland, you’re not the most innovative nation on the planet’. Depending on how we interpret the graph, you’re barely in the top 10. Whereas, neighbour Denmark is. And moreover, if we just look at the quality of patents, Denmark was, by some margin the most effective patenter on the planet last year.

Okay. So much for patents. We’re still – in most parts of the world at least – falling in to the trap of assuming that ‘innovation’ is all about science and technology. These days the definition needs to include all of the business-model step-change stories. Maybe that’s where we should look next. See what a Top Ten list looks like through that lens….

I started to become conscious of the phenomenon a few months ago. Now I see it everywhere. I look at a movie poster and it’s full of 4 star reviewer ratings. I pick up my music magazine and every new album seems to get 4 stars. Everything, it seems, gets 4 stars.

So I got the SI research team to dig a bit deeper. Regarding my music magazines, it turns out I was wrong. Only two-thirds of albums get 4 stars. Nearly all of the rest get 3. The net effect, however, is precisely the same: the reviews tell me absolutely nothing about what I should consider spending my disposable income on this month. Same with the film I might go and see. Or the hotel I choose to stay in. When everything is about the same ‘pretty good’ standard as everything else, reviews have become meaningless.

Maybe that’s the point?

But then again, what if it isn’t? Humans are natural ‘satisficers’. When faced with a problem – like what music to buy – we more often than not allow ourselves to fall on a solution that does the job we want satisfactorily. But we’re also incorrigible changers. We love change. We especially love change when we get bored with the status quo.

And when the status quo is 4-star everything, we all become bored.

Now one possible response to this boredom is that I go on ebay and click any old ‘buy it now’ button and see what happens. Like a game of Russian Roulette. Only one in which every chamber in the gun contains the same 4 star bullet.

Which then lead me to think perhaps we need to dig a bit deeper and see why society is on this 4 star trajectory. Strictly speaking, I should probably say ‘Western society’, although in my experience, most other parts of the globe seem to be on the same basic path.

A Perception Map seemed to be a good next step. So we set about compiling a list of reasons why the migration to 4 stars is happening. Here’s what we found:

Everything gets 4 stars because:

1. Everything is (perceived to be) getting better
2. Producers increasingly all learn a success formula
3. More mediocre (1, 2 and 3 star) solutions quickly get eliminated from the market, or never make it to market in the first place
4. Critics are increasingly expected to ‘play nice’ (and in today’s social media world, we’re all critics)
5. There are too many critics, and so its always possible for producers to find a critical mass of reviewers prepared to say 4 star things
6. Rate of content production decreases and so an incredible amount of content gets created, so there’s lots of noise and consequently the 5 star cream never gets a chance to rise
7. Audiences that have never experienced (or can’t remember) great content, don’t know what it looks, sounds or feels like
8. Negative criticism is viewed as socially unacceptable
9. More and more new content suffers from ‘Emperor’s New Clothes’ Syndrome

And here’s what happens when we link those comments using the ‘leads to’ question:

At first sight this picture doesn’t seem to look so bad. Everything seems to lead to things getting better. But scrape beneath the surface by walking yourself around the loop and what becomes clear is that this apparent virtuous cycle is actually a slow but invidious death spiral. Everything is perceived to be getting better, but actually isn’t because nobody dares say anything negative. Least of all that we might all increasingly be finding ourselves surrounded by 4 star naked Emperors. If we’re going to escape, someone needs to solve this contradiction:

 The trick to finding 5 star talent, to creating 5 star music seems to come down to our ability to create a community of critics that can say things that spark artists to do better while at the same time being perceived as playing nice.

Or maybe forget the nice part? Maybe the best way to be nice is to tell people the uncomfortable truth?

Sounds like a job for Tough Generation X Nomads. Ready or not, come 2022, that’s what society will get. (Evil grin.)

In the meantime, we need to learn more subtle ways of playing nice and not playing nice. But, hey. Wait a minute. Maybe that’s exactly what a 4 star review has become? Maybe the critics of the world are indeed sending artists everywhere a covert message: a 4 star review is the new mediocre. Maybe its real message is ‘if you’re really an artist, you’re not supposed to settle for using the 4 star formula.’

Ever since the early 1970s when Genrich Altshuller declared there should be no more work on the tool, the Contradiction Matrix has polarized the TRIZ community. In the East, and particularly with TRIZniks from the original former Soviet countries, the heart of the polarization can be seen with the frequent attempts to rubbish any attempt to update the tool.

‘Don’t touch TRIZ’ has long been a battle cry of the TRIZ traditionalists. But then at the same time, if we look at the curriculum of any TRIZ education programme it inevitably contains a significant portion devoted to the original Altshuller Matrix. So what we end up with are great numbers of people being taught how to use a tool that everyone in the TRIZ world knows is outdated and more often than not points users towards solutions that are either irrelevant or, worse, entirely inappropriate. Or, put another way, if you use a 1970s tool, you shouldn’t be too surprised to get a 1970s answer.

In the West, the polarization usually takes a different form. If there are only 40 Inventive Principles, the argument goes, why bother with the tedious task of looking up numbers in the Matrix? On one hand there is a lot to be said for this approach: for a team working on a real problem, one might rightfully say, it would be foolish for them not to examine all of the Principles to see how they might contribute to the eventual solution. On the other hand, there is a growing demand for efficiency in the innovation process. And, whichever way we look at it, randomly brainstorming through 40 Inventive Principles is not efficient.

It’s inefficient on two counts; firstly it means we spend less time thinking about what the contradiction we want to solve really is. This means we’re less likely to find ourselves working on the ‘right’ problem. And spending time working on the wrong problem is about as inefficient as it is possible to get.

Secondly, when we do use the Matrix its main job is to provide us with a ranked list of Principles that we can systematically apply to our problem. The key word here being ‘ranked’. The current version of the Matrix we’ve built to tackle technical problems now benefits from over five million case-study data points. This means that when the Matrix tells us that an Inventive Principle is the most frequently used Principle to solve our chosen problem, it means that literally tens of thousands of people have used precisely that Principle to solve the problem. Not to mention the four billion years of biological evolution that our research has also reverse-engineered and added to the tool.

The Systematic Innovation research team continues to devote significant time and energy to updating the Matrix. In a typical month, thanks to the advent of smart contradiction-finding software tools, we’re able to analyse several thousand new patents and academic journal papers, find the contradictions, reverse engineer how they have been solved and add the findings to the Matrix. We do that because we spend most of our time working for clients that are serious about innovation, clients that know it’s more important to find the right problem and deliver the best solution than it is to tell the world about how they did it. The current and all of our future Matrix tools are developed with these serious users in mind.

Efficiency looks set to be the dominant innovation driver in the next ten years. And yet, somehow, TRIZ usage is currently on the decline in many parts of the world. Herein lies another intriguing paradox: how can it be that the methodology that has the most to contribute to efficient problem definition and solving is being used less and less?

Whether TRIZ will survive into the future probably has much to do with the aforementioned polarisations and paradoxes and how well they get solved. If, indeed, they do ever get solved. Another odd aspect of the TRIZ community is that it often seems reluctant to apply its own tools to the problems it encounters. One thing is clear, however, and that is if the TRIZ community is to turn around the current decline, it needs to grow the community of invisible serious users and help them increase the rate of tangible, visible, success stories. Hopefully using tools – like the latest Contradiction Matrix – designed to deliver useful outcomes.

“Often, if there’s something that I want to do, but somehow can’t get myself to do, it’s because I don’t have clarity. This lack of clarity often arises from a feeling of ambivalence – I want to do something, but I don’t want to do it; or I want one thing, but I also want something else that conflicts with it.”

Gretchen Rubin

‘I don’t know’, ‘I don’t mind’, ‘I’m easy either way’, ‘I’m happy to go with the flow’.

All statements that come out of our mouths accompanied by a faint sense of guilt. Somehow society has conditioned us to have clear and definitive opinions about stuff. Not holding such clarity often means we’re accused of being a ‘fence-sitter’, ‘indecisive’ or ‘dithering’. As if they’re necessarily bad things.

Which, of course, they sometimes are. That’s because there are two very different ways by which we can be ambivalent about a situation. Both can be seen vividly in our ‘new’ kitchen. Where, somehow, we managed to spend just over a year not making a decision about what tiles to put up.

I’m the lead ‘negative ambivalence’ part of the kitchen tile story. I can’t make my mind up because I’m too apathetic to stir my brain into action to contemplate any of the alternatives on offer. The landslide of samples that have been and gone over the last twelve months have been a mere rainbow-coloured blur in my mind, because frankly I don’t care what colour(s) the tiles end up being. Pale-burgundy is much the same as duck-egg-blue in my head, tile-wise.

The positive side of the ambivalence story is that the pale-burgundy-duck-egg-blue debate has, until very recently, still been ongoing because there’s a dilemma that needs solving. In this positive side of the story, the ambivalence has been useful incubation time for playing out the consequences of the two-sides of the dilemma, and filling in the  knowledge gaps. And, moreover, done in such a manner that I’m now confident that we have ended up with a solution in which the dilemma has well and truly been transcended. It is the ‘right’ solution.

Proactive ambivalence is about recognizing the presence of a conflict – there are advantages on both sides of the fence – and proactively working out how the conflict can be resolved without making a trade-off.

Take me outside the kitchen and thinking about music, and I’m much more inclined towards this kind of proactive ambivalence. Example. Paul Simon and Sting are touring the UK together at the moment. Should I go and see them? I’m not sure:

I think Paul Simon is one of the greatest songwriters that’s ever lived, but, I don’t think he’s a great live act. Conversely, I think of Sting as a pompous, moralistic egotist, but (damn him) that he also possesses a genius-level kind of leadership and feel when it comes to getting the most out of musicians on stage. So does the combination of the two of them equate to the best of either worlds, or the worst? Either way, though, in all likelihood, they’ll never tour together again, so whatever happens it will be ‘unique’. Couple all that with the more practical issue of the (near-stratospheric) price of the tickets and the fact that I’ll spend seven hours in the car getting to and from the gig, and there’s an awful lot to be proactively ambivalent about:

I’m fortunate here to have a whole toolset designed to help transcend these kinds of conflict and contradiction, but even with all of them at my disposal, the solution process can still take time. In the large majority of cases, the non-emergence of a solution is indicative of the fact that there is still some important missing information. Part of the proactive ambivalence, in this situation becomes working out what’s missing and then finding it. How could I find out whether the Simon/Sting combination is good or bad? Check out the reviews.How could I avoid having to spend seven hours in the car? Find a client or clients in the same city the morning after the gig? How could I avoid having to take out a second mortgage to fund the ticket? Look for cut-price auctions on the ticket resale websites.

Proactive ambivalence. The Boy In The Bubble, seeks Message In A Bottle, finds bottle, transcends bubble, agrees the duck-egg-blue kitchen tiles look great. All is well with the world.