“Incontinent the void. The zenith. Evening again. When not night it will be evening. Death again of deathless day. On one hand embers. On the other ashes. Day without end won and lost. Unseen.” Samuel Beckett

We use the expression ‘successful step-change’ to define innovation because it takes us right to the heart of the world of s-curves. The ‘step-change’ in question being the jump from one curve to the next. We usually draw the s-curve-jump story to look something like this:

There are no hard and fast rules about the relative positioning of two adjacent s-curves, but we know for sure that a big part of the innovation management job is successfully managing the gap between the two. And specifically the void defined by this area:

Whenever an enterprise embarks on an innovation project, they essentially make a decision to jump off the top of their current curve into the unknown of the coming ‘next world’. However we choose to measure the vertical axis defining the s-curve, this jump almost inevitably results in things looking and feeling worse than we felt when we were safely ensconced on the top of our current cliff. The trick to our descent and then – hopefully – rise up the next s-curve is to reach the point where the height we’ve ascended to on the new curve reaches the level we’re at on the top of our current curve. This is what the shaded area is all about.

Managing the Void, then, is all about minimising the area of this shaded region: the smaller the area, the faster everyone accepts that the innovation attempt being made has been successful. Or, put another way, the smaller the void area, the less time and money we have to invest in getting the project out of the red and into the black.

Given the fact that 98% of innovation attempts still end in failure, it is probably fair to say that the majority of enterprises on the planet are uncomfortable when it comes to managing the void. Most enterprises still see the world through Operational Excellence eyes. Which means they focus on improving what they have, rather than jumping off a cliff into some mysterious, unknown ‘void’.

As far as we can see, when we look at the 2% of innovation attempts that end in success, there are five basic strategies for managing the Void. None is mutually exclusive, and if we really were ‘managing’ our s-curve jump we would no doubt look to adopt as many of the five strategies as makes sense given our available resources. Here are the five in graphical terms:

And here’s what each of the five means in practical terms:

1)    ‘Start Earlier’ – in many ways the easiest of the five strategies to engineer and manage, but, alas, in most enterprises, the strategy that gets adopted the least often. One of the best ways to manage the step-change from one s-curve to the next is to start work before we reach the top of the current s-curve. Starting to look for the new curve when we’re on the steepest climbing part of the current curve makes the most sense since that’s when our margins and cash-flows are at their strongest. The problem, however, is that most organisation leadership teams think that the best way to manage their affairs during the exhilarating climb is to put all of their attention on the job of maximising margins and revenues. Nobody, it seems, wants to be seen to be the killjoy that tells everyone that the future won’t always be so rosy, and we should start planning now for future rainy days.

2)    ‘De-Traumatise’ – when people are stood on the top of their current s-curve, it means they’ve done an awful lot of hard work to get there. Their system has been massively optimised, everything has been worked out, and everyone has settled in to their comfort zone. No matter how good the new solution that kicks-off the start of the next s-curve is in reality, it looks and feels worse to everyone who looks at it from the perspective of the beautiful position of the existing system. Managing the Void strategy 2) is thus all about managing the psychology of change and the innovation version of the Kubler-Ross Grief Cycle – first we experience the shock of the new, then we deny it, then we get angry, then we try and bargain our way out of the difficult situation, then we get depressed, then we accept a little realism by testing the new, until, finally, we accept the change, and realise, it’s not as far down as it looks. ‘De-traumatising’ is about letting people still safely positioned on the old s-curve see that getting worse to get better is sometimes just how the world works.

3)    ‘Climb Faster’ – the strategy that sits at the heart of ‘Lean-Startup’ and Design-Thinking – methods and processes that are all about making lots of rapid iterations of the new solution, exposing them to customers, learning from their reactions, and building new iterations as swiftly (and as cheaply) as possible. ‘Fail-Fast, Fail-Forward’ is the frequently heard mantra of teams comfortable operating in the Void: we can’t know everything right now, so our job is to try new things, learn from it and use the learnings to design the next iteration of the solution.

4)    ‘New Measures’- perhaps the least immediately visible of the five possible strategies, the ‘new measures’ strategy is about recognising that in a large majority of cases, when innovation happens, it comes alongside new ways of measuring ‘success’. When JCB first invented the hydraulic earth-mover, for example, it was significantly inferior to the industry-standard cable-driven earth-movers in terms of earth-moving capacity, but what JCB understood before anyone else was that ‘value’ to a customer wasn’t just about how much earth could be lifted in a single bucket-load, it was also about how easy it was to get the earth-mover on-site, how manoeuvrable it was once it arrived, and how flexible it was. It was ultimately about earth-moving productivity, and if you spent a week less time getting the earth-mover on site, that more than compensated for the inferior bucket load. None of which were measured by the cable-driven earth-mover companies. When JCB showed earth-moving contractors how to re-frame and re-define their success criteria, that was when their business really took off.

5)    ‘Unlearn’ – In some ways analogous to strategy, 2), but the focus in the ‘unlearning’ strategy is to get people to recognise that being on top of our current cliff is not as good as we think it is. It means key parts of the learning to get to the top of the current curve has become ‘waste’ and therefore needs to be thrown away. No-one likes to think of devoting lots of hard work to create what looks like irrelevant outputs. Until such times as everyone understands that this kind of ‘un-learning’ is an inherent part of the step-change process, s-curve jumps are always going to appear difficult. Managing the Void strategy 5) is therefore largely about educating people to understand this is how the world works – fundamental means fundamental – and that we periodically unlearn stuff in order to protect the future stability of the enterprise.

Operational Excellence versus Design Thinking. Compare And Contrast.

The Regional Education Board Operational Excellence Sense Radar hears a rumour that the budget for next year is going to be cut again.

The Management, as ever, moves swiftly and launches an investigation. Being a ‘budget’ problem, the investigation is handed directly to the Accounts team, with an ‘urgent’ priority code. The team jump to action and ‘run the numbers’. True to form, they do this very quickly. The message back to the top is, ‘we’re in trouble’.

Management asks for options.

The Accounts team run some more numbers, draws up an elegant cost-per-pupil distribution curve for all the schools in the region and come back with three options. The way they’ve been taught. Two of the options are visibly ridiculous – also the way they’ve been taught – and the other one involves closing the two worst schools on the distribution curve. They present their findings to Management. Management asks about the two worst schools. It turns out – no surprise – that they are the two smallest schools in the region. This is good news. Closures always mean protest, but closing the two smallest schools means the smallest amount of protest. The Management team declare themselves happy with the analysis and, being dynamic thrusting types, they announce the decision to their masters. A week later, the story goes public.

Two weeks after that the first lawsuit arrives. From one of the parents at one of the two soon-to be-closed schools. ‘How can it possibly be’, the suit charges, ‘that the school with the highest academic record in the region is going to be closed?’ Management look at the letter and do the only sensible thing. They pick up the phone, dial the Lawyers and tell them, ‘we have a problem for you to come and fix’.

So much for Operational Excellence thinking.

It’s a story based on a real situation. At this point in time it is ongoing. The lawyers are hard at work sending each other letters. On one level, we don’t as yet know what the outcome will be. On another, several things are already crystal clear:

–   The outcome will be win-lose. Either the Education Board will win, or the parents will win.

–   The teachers and pupils at the school will be caught in the middle, unwitting victims of a battle over their futures.

–   The lawyers on both sides of the argument have no incentive to make a swift resolution. The longer the fight goes on, and the more acrimonious it becomes, the more money they will make.

On too many levels, it is a depressing story. Perhaps the most depressing part is how quickly the Operational Excellence-driven legal downward-spiral took hold.

Here’s how things might’ve played out if the Operational Excellence blinkers had been removed from Management’s eyes:

As part of their ongoing search for contradiction-solving opportunities, a member of the management team looks at the latest cost-per-pupil distribution curve prepared by the Accounts Department, and realises this would make a pretty good contradiction to try and solve at the next Management design-day. She draws the contradiction up in a way that will bring some structure to the discussion:

At the next design-day, the team spend a few minutes looking at the picture and someone shouts out, ‘this would be a great one for us to get all the parents, teachers and officials together to see what win-win solutions we can come up with.

Two weeks later, forty people turn up to a Saturday morning ideation session. They quickly agree on where they all ideally want to get to. Then spend an hour writing down all the reasons that might prevent the ideal from being achieved. That list then got turned into a perception map, which revealed what the key barriers were. Now capable of seeing where they were trying to get to and what was stopping them, they spent the last hour of the session working in small teams to generate solution ideas. They filled a wall with Post-It notes, clustered them, and then gave everyone three stickers so they could vote on their favourite ideas. Some people voted on things that could be done quickly, some on things they volunteered to take away and work on for the next semester.

Back in the office the following week, the Management team was still buzzed at the excitement and passion of the parents and teachers from the Saturday session. They heard a rumour that the education budget cuts next year were going to be bigger than ever, and smiled. Taking 75% out of the Legal budget suddenly seemed like a no-brainer.  

There’s been a lot of discussion in the Big Data Analytics community recently relating to the accuracy of the analyses providers are delivering to their customers. For the providers the discussion has rapidly devolved into a ‘mine’s bigger than yours’ race to be able to claim 100%. It’s not uncommon to already see numbers in the mid 90s percent. Which sounds good. At least until we start to examine the dysfunctional nature of the industry: lots of money being spent on analyses, but almost no apparent tangible benefit being delivered.

How can it be that 95+% ‘accurate’ analysis capability produces no real impact? Does it mean that all the benefit is in the final 5%? Or that the industry has defined ‘100%’ incorrectly?

‘100% of what?’ feels like a good place to start an exploration of the subject.

The answer, as far as I can tell, is something like ‘not a lot’.

Just because a BDA algorithm can pick out keywords, and synonyms and make some kind of semantic context check, and – as some of the most advanced algorithms are now claiming – to be able to identify ‘fakes’ (e.g. false reviews planted by robots), does not mean that what we end up with is ‘100% accurate’. At least not in any meaningful way. Anyone acting on this kind of ‘100% accurate’ analysis is as likely to make the wrong decision as they would have done having acquired no data.

‘100% accurate’ in the current BDA context turns out to actually mean ‘100% accurate assuming the world works in purely tangible ways’. Computers and data analysts love tangible things. Mainly because they’re easy to measure.

But 100% tangibly accurate has nothing at all to do with 100% meaningfully accurate. People are emotional creatures. People make decisions for two reasons: ‘the good reason and the real reason’. Tangible analysis is all about capturing the good reasons and nothing at all to do with capturing the real reasons.

If we’re to capture what drives peoples’ behaviour the analytics need to delve deeply into the world of intangibles because this is where we find all the ‘real reason’ stuff. Things like:

–   Does the data come from a person who is psychometrically relevant to my target audience (e.g. if you’re trying to test a mass-market toothbrush design and all the product reviews you’re analysing are coming from Feudal-thinking, ENTPs, they’re not going to tell you anything useful at all about the future mass-market appeal of your design)

–   Does the data come from a person with a relevant opinion about the subject? See my earlier TripAdvisor case study – is it sensible to listen to the comments of a person that stays in a hotel once a year? Is it sensible to listen to the comments of a person that tends not to be listened to by other people? Sometimes, maybe it is (if we’re designing products for dimwits), but the important point is that I would be well advised to understand the difference between the two and listen to only the relevant people.

–   Does the data come from a person who is speaking reliably about the subject – are they telling the truth in other words or are they playing one of the 4Gs game:

–   Can the analysis identify that the person’s behaviour is going to be consistent and congruent with what they’ve said. People often say one thing and then do something completely different. Back to the good-reason/real-reason dilemma, ‘congruent’ data means data that has successfully captured the between-the-lines ‘real-reason’ content.

Only when an analysis capability is able to achieve these four intangible things – Representative-Relevant-Reliable-Congruent – should we be talking about ‘100% accurate’. 100% accurate, to my mind, means we’ve accurately captured what people mean rather than what they’ve merely said.

‘Sheep in Fog’ is the metaphor I’ve been carrying around for a while now when I look at the innovation activities taking place – or not taking place – within a majority of organisations around the world right now: very little bravery and even less direction clarity.

It’s also, as it happens, one of my favourite poems by Sylvia Plath. Well, ‘favourite’ is probably too strong a word. ‘Admire’ is probably better. The poem is overwhelmingly bleak and I’m only glass-half-empty-level bleak. I’ve always read it as a list of metaphors describing how she felt about the world at a particularly difficult time in her life.

Only lately have I come to connect my innovation metaphor to the poem. The more I think about the connection, however, the more I think Plath’s words tell us about the ‘Hero’s Journey’ from an innovator’s perspective. Four things stand out for me.

First, she uses personification – the stars ‘regard me sadly, the train has ‘breath’, and the fields ‘threaten’ her. All of this creates a sense that nature pities her, or finds her presence problematic. She does not belong in it. Plath as the prospective innovation Hero, and nature as the ‘efficiency engine’, everyday world she unwittingly finds herself in.

Secondly, she uses enjambment, a poetic device in which a single sentence is broken across two verses. Her discussion of the horse in the second stanza extends into the third, while the discussion of the morning is split between the third and the fourth. The technique is used to great effect in the poem to link all of the disparate metaphors together to create a profound sense of estrangement and uneasiness. While I’m sure Plath had no overt conception of s-curves and the idea of the innovator as the navigator between one s-curve and the next, it feels that, somehow, instinctively, she did. The enjambment makes an awful lot of sense, in other words, as a representation of the discontinuous jump between the current world and the next:

Thirdly, the title of the poem references how Plath (the ‘innovator’) feels – a lost sheep wandering in a murky and meaningless world. She feels like she continually disappoints those around her, all while she sees the world blackening. There is a clear paradox here in that she calls this terrifying place a ‘heaven’.  This dark, fatherless heaven was used by Plath as a telling metaphor for her personal life, but perhaps it makes for an even more powerful metaphor to represent the ‘Ordeal’ of the Hero’s Journey? This ‘dark heaven’ is the Contradiction.

The Hero’s Journey stage connection follows, too, fourthly, when we step back and connect the poem to Plath’s life. Plath didn’t prevail over her Ordeal. In the Hero’s Journey, after the Ordeal, something has to die. Plath killed herself a month after the final changes she made to the poem. She never made it out of the fog. In this regard, the poem is certainly bleak and hopeless. But then again, perhaps, all the more sophisticated because it captures, in a very few lines, a profound ambivalence towards death. It is in this regard too, I think, a telling metaphor for the life of the innovator, and the (98%) likelihood that the innovation-sheep don’t make it out of the fog either.

Call it a warning. Or a roadmap. Or, maybe, just a call to arms.

In that regard, finally, I find much to think about in the final changes Plath made to the original draft of the poem in those final weeks of her life:

Whenever anything happens it happens because there is a viable system. Sometimes stuff happens unexpectedly: we didn’t know there was a system, but it turned out there was. Sometime we decide to be proactive and make stuff that we want to happen happen. This requires us to create a viable system.

Strip the world back to first principles, and we see that a ‘viable system’ contains a minimum number of pieces. Depending on how you cut up the jigsaw, that minimum number is six. TRIZ calls it the ‘Law Of System Completeness’. If we have an intention to deliberately and successfully change something, it requires a viable system and that viable system needs these six pieces:

Sometimes we think we’ve designed our change system to include them all. Sometimes we’re right and sometimes we’re wrong. Sometimes people tell us that we have all the pieces we need and still we don’t get the successful change we were expecting.

When our change attempt doesn’t go as well as we expected, it is because one or more of the pieces of our jigsaw are missing. Or not working properly.

The question, then, becomes which one. Or ones.

The best way to answer that question is to look at the symptoms we’re experiencing. Different symptoms come from different missing jigsaw pieces:

If the symptom is anarchy, the cause is a lack of shared vision about the change objectives.

If the symptom is constipation (lots of input, but no output), the cause is a lack of pressure for change from our intended customers.

If the symptom is getting stuck in cul-de-sacs, feeling paralysed and not knowing what to do next, the cause is a lack of relevant knowledge and/or brain-power within the team.

If the symptom is spinning wheels, the cause is lack of a realistic work plan.

If the symptom is everyone heading towards a nervous breakdown, the cause is a lack of capacity to execute.

If the symptom is random oscillation in directions or outcomes, the cause is a lack of relevant metrics.

If you have more than one symptom, the cause is a lack of understanding of systems in general and the Law of System Completeness specifically. Go directly to First-Principle-Jail, do not pass Go, do not collect $200.

“I am becoming convinced that confronting people with ‘facts’, although necessary to better understand our predicament, will be almost completely ineffectual when it comes to altering our course… facts are secondary to accessing raw emotions when it comes to change…”

Nate Hagens

The large majority of all of the work we do with the ABC-M tetrad model is about providing clients with a better understanding of the intangible needs of their customers. A few of the braver ones are now also beginning to ask whether the model is also applicable to their employees. The nice thing about universal models, like ABC-M, is that the answer is an easy ‘yes, this also applies to the people within your organisation.’

That said, I suspect the reason few organisations are as yet asking the question is their instincts are telling them they won’t like the answer.

The simple rule, when we’re thinking about customers, is that innovation occurs when Autonomy, Belonging, Competence and Meaning all get better.

The corollary when switching the model to look inside organisations is perhaps something like, ‘success happens when employee Autonomy, Belonging, Competence and Meaning all get better’. ‘Success’ in the context of the workplace can be any number of things. Successful change. Successfully engaging people in their work. Successful project outcomes. Etc.

And therein lies the problem – or ‘problems’ – for most jobs and most organisations. Managers and leaders know that Autonomy, Belonging, Competence and Meaning for the most part don’t get better when people step across the company threshold and turn themselves into employees:

Autonomy – I often hear people saying, ‘I love change, I hate being changed’. What they hate, I think, is the loss of autonomy that occurs when managers ‘inflict’ change on their charges. For most of us, the moment we step into the office, we know that our level of Autonomy just took a turn for the worse: we used to be in control, now our boss is.

Belonging – if management have done their job in any way well, this is the easiest of the tetrad to get right. When people feel loyal to the organisations they work for, their sense of Belonging increases. They feel a sense of pride to be part of the team. The polo shirt with the company logo on it, or the team lanyard are both symbols of increased Belonging… or rather, they are provided people are actually proud to be ‘wearing the shirt’. One often gets the sense in some organisations that wearing the company logo detracts from a person’s true sense of Belonging – the logo being a sign that, by forcing them to wear it, you’ve just removed them from the (cool) tribe they were a member of before they stepped into the office and forced them to join your very uncool work tribe.

Competence – it is sometimes said that there are only three universal taboos – never criticise a person’s religion, life-partner or work. The last of these three is all about the Competence we feel when we know we’re good at our job, and how we really don’t like it when that competence comes in to question in any way. When people say, ‘I love change, I hate being changed’, what they’re typically also implying is, ‘provided it doesn’t make me feel like an incompetent idiot’. Change is uncomfortable for everyone because almost inevitably it causes our perceived level of Competence to dip, albeit hopefully temporarily. One of Apple’s biggest insights over the years has been to create products where, from the moment the customer opens the box, they feel more Competent than they were when the lid was still taped down. When we have to open a user-guide, our sense of Competence goes down. What Apple learned with their intuitive user interface design is what most organisations still need to learn when people enter the workplace: we all need to feel like we’re good at stuff. And we need to be able to demonstrate that competence to the people around us.

Meaning – the really tough one. To the extent that several clients have in effect asked us to remove it from the tetrad when we’re trying to help them measure what’s going on in their workplace. ‘Making ABC better’ is something they can live with. Making things more ‘meaningful’ is much more difficult. And the honest truth in far too many organisations right now is that a very large proportion of the work we ask people to do is worse than meaningless. Over time, one hopes, all the meaningless work will be eliminated (or given to the robots to do), but right now, for the most part, measuring Meaning – or the lack thereof – is a surefire way of depressing a majority of the people in your organisation. Nothing ever improves, of course, until we are able to measure it. Which is why the more enlightened organisations are now beginning allow for the ‘meaningful-ness’ of work to become something they should be measuring and sharing around the organisation. We’re still a long way away from the ‘war on meaningless work’ that is probably needed in most parts of society, but at least putting it on the radar – and letting managers know it is measurable – is a small step in the right direction.

And if that sounds like uncharacteristic optimism on my part, it probably is.

(PanSensic already has an ABC-M narrative analysis lens. If you’re feeling brave and want to explore how close your organisation or your employees are to achieving the ‘ABC-M all get better’ business success criterion, give me a shout.)

Back in 1994 when I first started describing myself as an ‘innovation consultant’, no-one seemed to recognise the term, never mind know what I did. Today, it feels like there are a million and one innovation consultants. I think there are many reasons for this, not least of which is that the world is in the midst of an innovation wave and a lot of frustrated corporate ‘innovators’ have found that it is easier to set up by themselves than it is to try and innovate in a big-company environment. The big-companies, it seems, still don’t really get it when it comes to innovation. As evidenced by the fact that 75% of innovation comes from small companies.

All that said, whenever an organisation – big or small – is thinking about innovating, and deciding they might benefit from some external assistance, the new problem they face is an apparently overwhelming amount of choice. A million and one innovation consultants with ten million and ten different messages. I just conducted one of our periodic reviews of the state of the art and I’d have to say the main feeling I was left with was one of deep sadness. So much choice and so little understanding of what innovation is about. Talk about the blind leading the blind.

I thought it might be time to start putting together a sort of user guide to help the bewildered become a little less bewildered.

Before we get to the guts of a prototype ‘how to choose the right innovation consultant’ process, there are a couple of questions prospective innovators might want to ask before they start actually talking to prospective consultants.

Question Zero: Do We REALLY Want To Innovate?

In my experience a fairly large proportion of ‘prospective innovators’ find themselves in such a position with a high degree of reluctance. They’ve been handed the challenge by a boss who, I think most believe in their heart of hearts, isn’t really interested in actually changing anything: there is a need to look busy, but, heaven help us if it ever comes to anything requiring a serious decision. Innovation tokenism.

If the real – heart of hearts – answer to this question is ‘no, we really don’t want to innovate’, your best bet is to choose your ‘innovation consultant’ on the basis of either a) they are the coolest and most fun, or, b) being able to say I worked with this one will be good for my CV.

Right now, if answer a) is the one you favour, you’re probably going to go for one of the swarm of under-employed, under-talented Hollywood sci-fi scriptwriters that seem to be doing the rounds at the moment. You’ll have fun (I can speak from experience), but you’ll learn absolutely nothing of any value at all, innovation-wise.

If b) is your answer, you need to go to the consultant with the highest daily rate and/or public cachet. Presence of words like ‘Stanford’ or ‘Silicon Valley’ are helpful indicators. Again, as with option a), don’t’ expect to actually learn anything of relevance to either innovation in general or your organisation in particular.

Question Zero-Point-Five: Is An Excuse For Failure More Important Than Success?

This is the plausible deniability question. 98% of innovation attempts end in failure, and in a lot of organisations, finding yourself in charge of one of the 98% failures can be very career limiting. If that’s your situation, the answer to the ‘which innovation consultant?’ question is very simple: you’re going to choose one of the Big Five consulting companies. Your project will still have a 98% likelihood of failure, but at least when things do go wrong, you won’t be blamed for the failure. Or the enormous consulting bill.

Okay, so now to the proper model. The one for people that have a genuine desire for their project to end up in the 2% success category. Here’s a hierarchy of questions you need to ask of your prospective innovation consultant candidates. The basic idea of the hierarchy is, if they fail one question, there’s no point advancing to the next question because their failure already dooms you to the 98% failure bucket.

Question One: Does The Consultant Understand ‘Good’ & ‘Real’ Customer Outcomes?

Given the fact that there are only two ways to innovate and that one of them is offering customers a new outcome (or ‘function’ or ‘job’ – different words, same meaning), a really good early question to a prospective consultant is how they set about identifying such ‘new outcome’ needs. Given the widespread use of words like function, job and outcome, and the presence of multiple types of ‘function database’, it’s fairly likely unless you’re particularly unlucky with your list of candidates, that they will be able to talk to you about tangible outcomes. The real decider, therefore, here is how they respond to probing questions about how they propose to bring the intangible customer outcome needs into their support of your project. This is the point where you might start to get the blank looks. As a test if/when this look appears, you can test whether they are properly out of their depth by asking to explain the relevance of the JP Morgan aphorism, ‘people make decisions for two reasons, the good reason and the real reason’. If they manage to bluff their way towards an answer that hints they’ll use any kind of customer interview to answer the question, they fail. Got to jail. Do not pass Go.

Question Two: Does The Consultant Understand Contradictions?

If new-outcomes is innovation strategy number one, the other is ‘solve a contradiction’. About 85% of innovations succeed by using this strategy. This also happens to be the test that will allow you to quickly eliminate the large majority of so-called ‘innovation consultants’ from your selection process. Whether you use the words ‘contradiction’, ‘conflict’, ‘trade-off’, ‘condundrum’, ‘paradox’, or any number of other synonyms, unless they can point you towards how they will help you to identify and eliminate contradictions, they’re not going to help you to innovate. Most respondents will answer with a blank stare, others will try and deflect the discussion onto a subject they are more comfortable with. Either way, they fail.

Question Three: Does The Consultant Understand Complex Adaptive Systems?

By the time you reach Question Three, something like 90% of your candidate consultants will have fallen by the wayside. Here’s where you get to eliminate over half of those that remain. Innovation fundamentally means embracing and working under the governing ‘rules’ of complex adaptive systems. You need to know that they understand what a complex adaptive system is. And, more importantly, how that knowledge impacts on the innovation project they’re going to support you through. The early stages of any innovation project are about exploration. Which in turn means identifying and answering ‘the unknowns’. Asking them about the process they propose to navigate you through the ‘fuzzy-front-end’ stages of your precious project, the moment they try and draw a Gantt chart or start talking about pipelines or Stage-Gate, you know they’re not going to be able to help you. Key words and phrases to listen out for in terms of the consultants that do actually understand the connections between complexity and innovation include: ‘emergent’, ‘first principles’, ‘minimum viable demonstration’. Plus, of course, they need to be able to convince you they know how to connect these key words to how they’ll affect how they’re going to spend the minimum amount of (your!) money to make the maximum amount of progress in answering the unknowns.

Question Four: Does The Consultant Understand Analytics?

By Question Four, you’re already somewhere near sifting the genuine cream from the curdled milk. The fourth Question is all about what sorts of analytical measurements and measurement tools are they going to bring to bear to help you get through the exploration and execution stages of your project. The key here is listening out for the sorts of thing they propose measuring. If their list includes all the ‘usual suspect’ measurements (any of the ’75 essential KPIs – see SI ezine worst of 2015 Awards), they’re wasting your time. You need to be listening out for unusual suspect measurements. Things that you know are going to be important (meaningful), but that are traditionally thought to be impossible to measure (‘team morale’, ‘answered unknowns’, ‘frustration’, ‘engagement’, ‘Hero’s Journey stage’, ‘sense of progress’, etc) are the things you need to know. Your consultant needs to be able to demonstrate that a) they know why such measures are important, and, b) how they’re going to make those measurements.

Question Five: Does The Consultant Understand Methods?

At a superficial level, this last Question is about whether your candidate consultant is trying to sell you their method, or the right method for you particular context. If they don’t ask you about the Innovation Capability Level of your organisation, the tools and methods your organisation/team currently uses, or the psychometric profiles of the project team members, the chances are they’re there to sell you ‘their’ method. Easy to catch them out on this question. If they’re trying to push ‘method X’ onto you, ask them for evidence that this is the right thing to bring to bear in your context. Ask them to describe an equivalent situation on a previous project that Method X has worked (if they can, ask them to explain the expression, ‘you can never step in the same river twice’). If they get through that question, the next one is to show you how they’ve done back to back analyses of Method X,Y and Z in order to establish that Method X was indeed the most appropriate one. Don’t worry too much about whether you’ve ever been through this kind of comparison exercise in your own organisation before, at the end of the day, 90%+ of consultants are well versed in just one or two methods, so you shouldn’t have too much difficulty getting them to the point where they are using the word ‘err’ twice per sentence and looking like the they’d rather be somewhere else. Which, as far as your selection process is concerned is precisely where they need to be.

Taken together, those five questions should enable you to swiftly get down to a Top Two or Three. To help make the questions easy to remember, just think about OCCAM:

Beyond that point, your choice is basically going to boil down to your own intangible outcome needs: who’s best going to make you into a hero? Who’s going to stick by you when the going gets – inevitably – rough? Who, to cut through to the simplest answer, is the one that is going to be your innovation Razor?

I’m a big fan of Albert Einstein, but one thing he definitely got wrong was the belief that “Insanity is doing the same thing over and over again, and expecting different results.” It’s an aphorism I still hear churned out unthinkingly by just about everyone in the ‘creativity consultant’ world. As if the statement is some kind of call to arms for clients stuck in the rut they’re perceived to be in. If Einstein said it, the unspoken logic goes, it must be true.

It becomes even truer, the creative consultant believes, when it gets written onto a napkin and a photo of it gets inserted into all of their Powerpoint slide decks.

Fortunately, after not very much searching, it turns out Einstein never said anything about insanity at all. Rather it seems to have been attributed to him by those parts of the ‘creative’ world seeking to inflate their already bloated sense of self-worth.

I’m pretty certain Einstein never really understood complexity theory, so he might have had every excuse for coming to a conclusion that anyone doing the same thing shouldn’t ever expect to get a different result. On the other hand, I’m pretty certain he would have understood the aphorism ‘you can never step in the same river twice’. Heraclitus gave us that little gem around 2500 years ago, when complexity theory definitely didn’t exist.

Perhaps it didn’t need to. Perhaps people had the common sense back then to recognize that it was very frequently the case that people did exactly what they’d always done and ended up getting very different results. Like 88% of 1955 Fortune 500 companies that are no longer with us. They all believed they’d keep being successful by thinking and doing the same old thing too.

The really simple way to make that napkin picture look like the dumb thing that it really is, is to modify it so it looks like this:

Now we’re forced to think about everything around us – Heraclitus’ ‘river’ – and about whether it is sensible to think that it is all staying the same. Think about that for a few seconds and you  have to believe it’s never true. I’m sitting here in a noisy café and my not so good coffee is going cold. In a minute it will probably too cold. Which means I won’t get my full caffeine fix. Which means I’ll probably forget to write something on my job list. Which… you get the idea.

The reason there are so many fragile organisations on the planet right now is that they’ve somehow been brainwashed into believing the same-thinking-same-result mantra is true and, even worse, then connected it to the idea that, because they were doing well a couple of years ago, they just need to keep doing what they’ve been doing. It’s like they’ve become collectively drunk on a cocktail of cognitive flaws – Status Quo Bias, Normalcy Bias, Confirmation Bias and Illusion of Control. To all intents and purposes, you had me at ‘Fortune500’. The ‘environment around us’ doesn’t stay the same even in this stupid café with it’s dated soundtrack, never mind in a commercial organisation of several thousand people.

In a complex world the best we can say is that if we keep doing the same as we’ve always done we will probably get the same result. The closer we are to the edge of chaos, the less probable that same result becomes.There are no guarantees in a complex system because we’re surrounded by a swirling cauldron of interdependent causes and effects. The fact that thinking the same doesn’t necessarily mean we get the same result also tells us – anyone that wishes to be more resilient than the fragile organisation they’re probably working for – that what’s needed is a finely tuned what-around-me-has-changed radar so we can sense what’s changed and shift our response accordingly.

Oh, wait, evolution already gave us one of those. Strike that. What we need is to stay as far away as we possibly can from creativity consultants that teach us how to not use it any more.

“The man who grasps principles can successfully select his own methods. The man who tries methods, ignoring principles, is sure to have trouble.”

Ralph Waldo Emerson

Emerson’s aphorism is one I find myself using a lot these days. The fact that I find myself having to do it is fairly depressing. But then again not nearly so depressing as realising that, after I’ve said it, most people don’t seem to understand what it means.

Sure, I think they nod sagely and are able to get the intended meaning from an intellectual perspective. The problem seems to come the moment we’re asked to contextualise that intended meaning. Then things seem to go awry fairly quickly.

Here’s an example of the sort of thing I see going wrong: The Hype Cycle:

It’s a lovely model of how an innovation attempt made today is likely to progress. Because it’s a lovely model, it gets turned in to a not-so-lovely book, ‘Mastering The Hype Cycle’. And an even less lovely ‘method’ that supposedly allows innovators to navigate the various stages and phases of the Cycle.

I’ve seen lots of them, in fact, commiting ever larger amounts of time and resource to precisely that task. I’ve even seen some bring in Gartner – the discoverers of the model – to run the method for them. No doubt compounding the money part of the resource dimension by an order of magnitude.

Sometimes ‘running the method’ may be the expedient thing to do, but what Ralph Waldo was trying to get us to think about was that we also need to dig deeper. Principle beats method. And in this regard let there be no mistaking the fact that the Hype Cycle is all about method and not about Principles. There are no principles involved in the Hype Cycle, it’s merely an empirically observable characteristic of most modern-day innovation attempts.

Show the Hype Cycle to a control system engineer, however, and they’ll very quickly tell you what the underlying principle of the Hype Cycle actually is. What you have there, they’ll say, is an archetypal under-damped system.

Utilising the Hype Cycle ‘method’ allows a team to manage the Hype Cycle. But only if they understand this underpinning under-damped system principle will they ever come to realise that they’re actually managing something that really doesn’t need to be there. The system only oscillates because there’s not enough damping in the system. If you want to avoid having a Hype Cycle at all, the underlying principle tells us the simple answer involves adding the critical level of damping to the system.

Without wishing to spend too much time attacking the Hype Cycle, when I look at the management literature these days I see an awful lot of method and barely any first principles any more. No, in reality the problem is worse than that. Surely, I thought, someone, somewhere must’ve compiled ‘all’ of the first principle knowledge of the world into a single tome. Turns out they haven’t. Perhaps that’s the next thing I need to add to my job list?

Asked to name the biggest innovations to arrive into the world of cricket in recent times (i.e. the last 30 years – this is cricket, remember) and in most people’s Top Five will be the doosra. A doosra is a particular type of delivery by an off-spin bowler. The doosra spins in the opposite direction to an off break (the off-spinner’s default delivery), and aims to confuse the batsman into playing a poor shot. Doosra means “(the) second (one)”, or “(the) other (one)” in Hindi and Urdu. The delivery was invented by Pakistani cricketer Saqlain Mushtaq. A variety of bowlers have made considerable use of the doosra in international cricket once Mushtaq started having success with the technique in the early 1990s. Users today include Sri Lankan Muttiah Muralitharan, Indian Harbhajan Singh, and South African Johan Botha.

For me it represents a lovely example of what real innovation looks like. Firstly, just looking at the dismayed expression on a batsman that’s just been dismissed by a doosra, it is successful. Second it made a step change (Inventive Principle 13 – the ball unexpectedly moves the other way). Third, it happened while remaining within the tightly controlled rules of the game. And fourth, regarding this compliance issue, like a lot of true innovations, the incumbent’s (i.e. the bowlers that don’t know how to bowl a doosra, or the batsmen that have to face them) frequent response is to try and get the bowling style banned.

Then there’s a second reason I like the doosra. It makes for a lovely simple acronym for the innovation process:

Direction

Outcomes

Obstacle

Switch/Shift/Surprise/Step-change

Resources

Action

Direction – any innovator needs a clear big-picture idea of where they’re heading. A compass that points in the direction of success. In conventional innovation terms that means ‘increasing value’. Which in turn means giving a customer more of the things they like and less of the things they don’t. In cricket terms, the big-picture direction for the team is to win the game, and, bigger still, to reach the top of the world rankings.

Outcomes – once we know the overall compass heading (‘win the game’), we need to zoom-in to look at the specifics of a situation where we think there is an innovation opportunity. If I’m a bowler running in to bowl the very clear outcome I am trying to achieve is ‘get the batsman out’. If I was being really smart, however, I’d recognise that alongside every tangible outcome need is a parallel intangible one. Tangibly my job is to get the batsman out; intangibly my job is to do it in such a way that I make my opponent fearful of facing me next time. If we think about these ‘intangible’ outcome requirements in terms of a competitive game like cricket, it’s all about making the ‘ABC’ (Autonomy – Belonging – Competence) get worse for the opponent. In an innovation environment where I’m trying to make my customer happy rather than take their wicket, my intangible outcome objective is to make ABC get better. Plus, of course, also deliver the very clear tangible outcome benefit. People need their ‘good’ (tangible) reason and their real (intangible) reason for making any kind of change.

Obstacles – once we have a clear idea where we’re trying to get to both at the high-level and in detail, the next job is to focus on ‘what’s stopping us?’ from getting there. This is perhaps the most counter-intuitive part of the innovation process for most people. We tend to veer towards trade-off and compromise. Mainly because that’s what life (and our school systems) have taught us is the optimum strategy. In innovation-world, however, we know that ‘optimum’ is merely a least-poor compromise and that our job is to avoid compromise altogether. Hence we deliberately and purposefully run towards the obstacle in order to ensure we devote our precious time and energy to the most important improvement opportunities. Typical obstacles preventing us from achieving our desired outcomes might be that ‘it costs too much’ or ‘it will take too long’ or ‘it will reduce quality’. The easiest way to find the obstacle is often to listen to the words that come immediately after the word, ‘but’ when we listen to someone commenting on our idea. As in, ‘I want to get the batsman out, but because I’m a spin bowler and therefore bowl quite slowly, the batsman has a longer amount of time to think about how to respond to whatever I do’. (In TRIZ terms, we might think of this situation as a Controllability-versus-Speed contradiction – you might like to look up what the Contradiction Matrix says about that to see how the doosra fits what TRIZ would have recommended!)

Switch/Shift/Surprise/Step-change (I’m not sure which word I like best yet) – having identified the obstacle, the next job is to solve it. Coming up with solutions means making some kind of a step-change to relative to the usual way of doing things. In TRIZ terms this step-change is going to come from one or a combination of the 40 Inventive Principles. Each one represents, in effect, a provocation that says to the problem solver, ‘how would segmenting the problem help you to solve it?’ or, ‘how would ‘doing it the other way around’ help you to solve it?’ With the doosra, the clear switch/step-change involves getting the ball to bounce in the opposite direction to the one the batsman expects. Whether we use the 40 Principles or any other ideation strategy, the job in this ‘S’ stage of the DOOSRA innovation process is to generate our solution ‘clues’ and ideas…

Resources – once we have our provocation-originated solution clues, the next job is to look at what resources we are able to bring to bear to help turn those clues in to a practical reality. Resources are things or sources of energy or sources of information in or around the current system that are not being used to their maximum potential. For Saqlain Mushtaq it was working out how to hold the ball in a different way, and how to apply the necessary switch of the wrist at the right moment during the rotation of his arm that would still allow him to keep his arm – per the rules of cricket – straight.

Action – it’s all well and good to have a good solution to a problem, but we can probably well imagine that Saqlain Mushtaq didn’t just magically dream up the perfect doosra during a live match. Rather, he spent long, hard hours in the nets experimenting and no doubt failing before he got his technique to a point where he thought it was good enough to try in a competitive game. Those ‘hard yards’ of failing, re-thinking, failing again, trying again, failing again, wiping away the tears, and relentlessly persevering through the blood and guts, and stress and frustration is what we might think of as the Action stage of the innovation process. Thomas Edison famously said that innovation was 1% inspiration and 99% perspiration. The Action stage is where all the perspiration is going to happen. Most innovation attempts fail here. They fail because many people fall into the mis-apprehension that once they have a good idea, that’s the hard bit over with. ‘Action’ means recognising that the hard work has barely started yet.

So there’s DOOSRA innovation. The only thing left to think about, just like Saqlain Mushtaq, is that real success comes not just from innovating once, but from going back and doing it again. Doosra becomes teesra (‘the third one’). Which, one day soon, might just turn out to give us a whole new innovation acronym…