Shipulski On Design

Archive for the ‘Innovation’ Category

Here’s how innovation goes:
(Words uttered. // Internal thoughts.)

That won’t work. Yes, this is a novel idea, but it won’t work. You’re a heretic. Don’t bring that up again. // Wow, that scares me, and I can’t go there.

Yes, the first experiment seemed to work, but the test protocol was wrong, and the results don’t mean much. And, by the way, you’re nuts. // Wow. I didn’t believe that thing would ever get off the ground.

Yes, you modified the test protocol as I suggested, but that was only one test and there are lots of far more stressful protocols that surely cannot be overcome. // Wow. They listened to me and changed the protocol as I suggested, and it actually worked!

Yes, the prototype seemed to do okay on the new battery of tests, but there’s no market for that thing. // I thought they were kidding when they said they’d run all the tests I suggested, but they really took my input seriously. And, I can’t believe it, but it worked. This thing may have legs.

Yes, the end users liked the prototypes, but the sample size was small and some of them don’t buy any of our exiting products. I think we should make these two changes and take it to more end users. // This could be exciting, and I want to be part of this.

Yes, they liked the prototypes better once my changes were incorporated, but the cost is too high. // Sweet! They liked my design! I hope we can reduce the cost.

I made some design changes that reduce the cost and my design is viable from a cost standpoint, but manufacturing has other priorities and can’t work on it. // I’m glad I was able to reduce the cost, and I sure hope we can free up manufacting resources to launch my product.

Wow, it was difficult to get manufacturing to knuckle down, but I did it, and my product will make a big difference for the company. // Thanks for securing resources for me, and I’m glad you did the early concept work when I was too afraid.

Yes, my product has been a huge commercial success, and it all strarted with this crazy idea I had. You remember, right? // Thank you for not giving up on me. I know it was your idea. I know I was a stick-in-the-mud. I was scared. And thanks for kindly and effectively teaching me how to change my thinking. Maybe we can do it again sometime.
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There’s nothing wrong with this process; in fact, everything is right about it because that’s what people do. We’ve taught them to avoid risk at all costs, and even still, they manage to walk gingerly toward new thinking.

I think it’s important to learn to see the small shifts in attitude as progress, to see the downgrade from an impossible problem to a really big problem as progress.

Instead of grabbing the throat of radical innovation and disrupting yourself, I suggest a waterfall approach of a stepwise ratchet toward problems of a lesser degree. This way you can claim small victories right from the start, and help make it safe to try new things. And from there, you can stack them one on top of another to build your great pyramid of disruption.

And don’t forget to praise the sorceres and heretics who bravely advance their business model-busting ideas without the safety net of approval.

There are always too many things to do, too much to work on. And because of this, we must choose. Some have more choice than others, but we all have choice. And to choose, there are several lenses we look through.

What’s good enough? If it’s good enough, there’s no need to work on it. “Good enough” means it’s not a constraint; it’s not in the way of where you want to go.

What’s not good enough? If it’s not good enough, it’s important to work on it. “Not good enough” means it IS a constraint; it IS in the way; it’s blocking your destination.

What’s not happening? If it’s not happening and the vacancy is blocking you from your destination, work on it. Implicit in the three lenses is the assumption of an idealized future state, a well-defined endpoint.

It’s the known endpoint that’s used to judge if there’s a blocking constraint or something missing. And there are two schools of thought on idealized future states – the systems, environment, competition, and interactions are well understood and idealized future states are the way to go, or things are too complex to predict how things will go. If you’re a member of the idealized-future-state-is-the-way-to-go camp, you’re home free – just use your best judgment to choose the most important constraints and hit them hard. If you’re a believer in complexity and its power to scuttle your predictions, things are a bit more nuanced.

Where the future state folks look through the eyepiece of the telescope toward the chosen nebula, the complexity folks look through the other end of the telescope toward the atomic structure of where things are right now. Complexity thinkers think it’s best to understand where you are, how you got there, and the mindset that guided your journey. With that knowledge you can rough out the evolutionary potential of the future and use that to decide what to work on.

If you got here by holding on to what you had, it’s pretty clear you should try to do more of that, unless, of course, the rules have changed. And to figure out if the rules have changed? Well, you should run small experiments to test if the same rules apply in the same way. Then, do more of what worked and less of what didn’t. And if nothing works even on a small scale, you don’t have anything to hold onto and it’s time to try something altogether new.

If you got here with the hybrid approach – by holding on to what you had complimented with a healthy dose of doing new stuff (innovation), it’s clear you should try to do more of that, unless, of course, you’re trying to expand into new markets which have different needs, different customers, and different pocketbooks. To figure out what will work, runs small experiments, and do more of what worked and less of what didn’t. If nothing works, your next round of small experiments should be radically different. And again, more of what worked, less of what didn’t.

And if you’re a young company and have yet to arrive, you’re already running small experiments to see what will work, so keep going.

There’s a half-life to the things that got us here, and it’s difficult to predict their decay. That’s why it’s best to take small bets on a number of new fronts – small investment, broad investigation of markets, and fast learning. And there’s value in setting a rough course heading into the future, as long as we realize this type of celestial navigation must be informed by regular sextant sightings and course corrections they inform.

Image credit – Hubble Heritage.

Inspiration is the fuel for imagination and imagination is the power behind innovation.

All companies want innovation and try lots of stuff to increase its supply. But innovation isn’t a thing in itself and not something to be conjured from air – it’ a result of something. The backplane of innovation, its forcing function, is imagination.

But imagination is no longer a sanctioned activity. Since it’s not a value-added activity; and our financial accounting system has no column for it; and it’s unpredictable, it has been leaned out of our work. (Actually, she’s dead – Imagination’s Obituary.) We squelch imagination yet demand more innovation. That’s like trying to make ice cream without the milk.

No inspiration, no imagination – that’s a rule. Again, like innovation, imagination isn’t a thing in itself, it’s a result of something. If you’re not inspired you don’t have enough mojo to imagine what could be. I’ve seen many campaigns to increase innovation, but none to bolster imagination, and fewer to foster inspiration. (To be clear, motivation is not a substitute for inspiration – there are plenty of highly motivated, uninspired folks out there.)

If you want more innovation, it’s time to figure out how to make it cool to openly demonstrate imagination. (Here’s a hint – dust off your own imagination and use it. Others will see your public display and start to see it as sanctioned behavior.) And if you want more imagination, it’s time demonstrate random acts of inspiration.

Inspiration feeds imagination and imagination breeds innovation. And the sequence matters.

Image credit – AndYaDontStop

Big Data is powerful – measure what people do on the web, summarize the patterns, and use the information for good. These data sets are so powerful because they’re bigger than big; there’s little bias since the data collection is automatic; and the analysis is automated. There’s huge potential in the knowledge of what people click, what pages they land on, and what place the jump from.

It’s magical to think about what can be accomplished with the landing pages and click-through rates for any demographic you choose. Here are some examples:

• This is the type of content our demographic of value (DOV) lands on, and if we create more content like this we’ll get more from them to land where we want.
• These are the pages our DOV jump from, and if we advertise there more of our DOV will see our products.
• This is the geographic location of our DOV when they land on our website, and if we build out our sales capacity in these locations we’ll sell more.
• This is the time slot when our DOV is most active on their smart phones, and if we tweet more during that time we’ll reach more of them.

But just as there’s immense power knowing the actions of your DOV (what they click on), there are huge assumptions on what it all means. Here are two big ones:

1. All clicks are created equal.
2. When more see our content, more will do what we want.

Here is an example of three members (A, B, C) of your demographic of interest who take the same measurable action but with different meaning behind it:

Member A, after four drinks, speeds home recklessly; loses control of the car; crashes into your house; and parks the car in your living room.

Member B, after grocery shopping, drives home at the speed limit; the front wheel falls off due to a mechanical problem; loses control of the car; crashes into your house; and parks the car in your living room.

Member C, after volunteering at a well-respected non-profit agency, drives home in a torrential rain 15 miles per hour below the speed limit; a child on a bicycle bolts into the lane without warning and C swerves to miss the child; loses control of the car; crashes into your house; and parks the car in your living room.

All three did the same thing – crashed into your house – but the intent, the why, is different. Same click, but not equal. And when you put your content in front of them, regardless of what you want them to do, A, B, and C will respond differently. Same DOV, but different intentions behind their actions.

Big Data, with its focus on the whats, is powerful, but can be made stereoscopic with the addition of a second lens that can see the whys. Problem is, the whys aren’t captured in a clean, binary way – not transactional but conversational – and are subject to interpretation biases where the integers of the whats are not.

With people, action is preceded by intent, and intent is set by thoughts, feelings, history, and context. And the best way to understand all that is through their stories. If you collect and analyze customer stories you’ll better understand their predispositions and can better hypothesize and test how they’ll respond.

In the Big Data sense, Nirvana for stories is a huge sample size collected quickly with little effort, analysis without biases, and direct access to the stories themselves.

New data streams are needed to collect the whys in a low overhead way, and new methods are needed to analyze them quickly and without biases. And a new perspective is needed to see not only the amazing power of Big Data (the whats), but the immense potential of seeing the what’s with one eye and the whys with the other.

Keep counting the whats with traditional Big Data work – there’s real value there. But also keep one eye on the horizon for new ways to collect and analyze the whys (customer stories) in a Big Data way.

Collection and analysis of customer stories, if the sample size is big enough and biases small enough, is the best way I know to look through the fog and understand emerging customer needs and emerging markets.

If you can figure out how to do it, it will definitely be worth the effort.

There are three types of innovation: innovation that creates jobs, innovation that’s job neutral, and innovation that reduces jobs.

Innovation that reduces jobs is by far the most common. This innovation improves the efficiency of things that already exist – the mantra: do the same, but with less. No increase in sales, just fewer people employed.

Innovation that’s job neutral is less common. This innovation improves what you sell today so the customer will buy the new one instead of the old one. It’s a trade – instead of buying the old one they buy the new one. No increase in sales, same number of people employed.

Innovation that creates jobs is uncommon. This innovation radically changes what you sell today and moves it from expensive and complicated to affordable and accessible. Sell more, employ more.

Clay Christensen calls it Disruptive Innovation; Vijay Govindarajan calls it Reverse Innovation; and I call it Less-With-Far-Less.

The idea is the product that is sold to a relatively small customer base (due to its cost) is transformed into something new with far broader applicability (due to its hyper-low cost). Clay says to “look down” to see the new technologies that do less but have a super low cost structure which reduces the barrier to entry. And because more people can afford it, more people buy it. And these aren’t the folks that buy your existing products. They’re new customers.

Vijay says growth over the next decades will come from the developing world who today cannot afford the developed world’s product. But, when the price comes down (down by a factor of 10 then down by a factor of 100), you sell many more. And these folks, too, are new customers.

I say the design and marketing communities must get over their unnatural fascination with “more” thinking. To sell to new customers the best strategy is increase the number of people who can afford your product. And the best way to do that is to radically reduce the cost signature at the expense of features and function. If you can give ground a bit on the thing that makes your product successful, there is huge opportunity to reduce cost – think 80% less cost and 20% less function. Again, you sell new product to new customers.

Here’s a thought experiment to help put you in the right mental context: Create a plan to form a new business unit that cannot sell to your existing customers, must sell a product that does less (20%) and costs far less (80%), and must sell it in the developing world. Now, create a list of small projects to test new technologies with radically lower cost structures, likely from other industries. The constraint on the projects – you must be able to squeeze them into your existing workload and get them done with your existing budget and people. It doesn’t matter how long the projects take, but the investment must be below the radar.

The funny thing is, if you actually run a couple small projects (or even just one) to identify those new technologies, for short money you’ve started your journey to selling new products to new customers.

The Holy Grail of marketing is to identify emerging customer needs before anyone else and satisfy them to create new markets. It has been a long and fruitless slog as emerging needs have proven themselves elusive. And once candidates are identified, it’s a challenge to agree which are the game-changers and which are the ghosts. There are too many opinions and too few facts. But there’s treasure at the end of the rainbow and the quest continues.

Emerging things are just coming to be, just starting, so they appy to just a small subset of customers; and emerging things are new and different, so they’re unfamiliar. Unfamiliar plus small same size equals elusive.

I don’t believe in emerging customer needs, I believe in emergent customer behavior.

Emergent behavior is based on actions taken (past tense) and is objectively verifiable. Yes or no, did the customer use the product in a new way? Yes or no, did the customer make the product do something it wasn’t supposed to? Did they use it in a new industry? Did they modify the product on their own? Did they combine it with something altogether unrelated? No argument.

When you ask a customer how to improve your product, their answers aren’t all that important to them. But when a customer takes initiative and action, when they do something new and different with your product, it’s important to them. And even when just a few rouge customers take similar action, it’s worth understanding why they did it – there’s a good chance there’s treasure at the end of that rainbow.

With traditional VOC methods, it has been cost prohibitive to visit enough customers to learn about a handful at the fringes doing the same crazy new thing with your product. Also, with traditional VOCs, these “outliers” are thrown out because they’re, well, they’re outliers. But emergent behavior comes from these very outliers. New information streams and new ways to visualize them are needed to meet these challenges.

For these new information streams, think VOC without the travel; VOC without leading the witness; VOC where the cost of capturing their stories is so low there are so many stories captured that it’s possible to collect a handful of outliers doing what could be the seed for the next new market.

To reduce the cost of acquisition, stories are entered using an app on a smart phone; to let emergent themes emerge, customers code their own stories with a common, non-biasing set of attributes; and to see patterns and outliers, the coded stories are displayed visually.

In the past, the mechanisms to collect and process these information streams did not exist. But they do now.

I hope you haven’t given up on the possibility of understanding what your customers will want in the near future, because it’s now possible.

I urge you to check out SenseMaker.

There are many flavors of innovation – incremental, disruptive, and seven flavors in between. And there is lots of argument about the level of innovation – mine’s radical and yours isn’t; that’s just improving what we already have; that’s too new – no one will ever buy it. We want to label the work in order to put it in the right bucket, to judge if we’re doing the right work. But the labels get in the way – they’re loaded with judgments, both purrs and snarls.

Truth is, innovation work falls on a continuum of newness and grouping them makes little sense. And, it’s not just newness that matters – it’s how the newness fits (or doesn’t) within the context of how things happen today and how customers think they should happen tomorrow. So what to do?

Customers notice the most meaningful innovations, and they notice the most meaningful ones before the less meaningful. Evaluate the time it takes a customer to notice the innovation and there may be hope to evaluate the importance of the innovation.

The technology reduces cost, and at the end of the month when the numbers are rolled up the accountants can see the improvement. This is real improvement, but there’s a significant lag and the people doing the work don’t see it as meaningful. This one’s a tough sell – buy this new thing, train on it, use it for three months, and if you keep good records and do some nifty statistics you’ll see an improvement.

The technology reduces scrap, and at the end of the week the scrap bin will be half full instead of fully full. Scrap is waste and waste reduction is real improvement. This is an easier sell – buy it and train on it and at the end of the week you’ll notice a reduction in scrap. This is important but only to those who are measured on scrap. And today the scrap is emptied every week, now we can empty it every other week. The time to notice is reduced, but the impact may not be there.

The technology increases throughput, and at the end of the shift the bins will be fuller than full. Here – try it for a shift and see what you think. If you like it, you can buy it. I’ll be back tomorrow with a quote. This is noticeable within eight hours. And at the end of eight hours there are more things that can be sold. That’s real money, and real money gets noticed.

The technology makes the product last two hours instead of one. Here – try it for a couple hours. I’ll go get a coffee and come back and see what you think. You won’t have to stop the machine nearly as often and you’ll put more parts into finished goods inventory. The technology gets noticed within two hours and the purchase order is signed in three.

Where the old technology was load, this is quiet. Don’t bother with ear protection, just give it a go. Pretty cool, isn’t it. Go get your boss and I’ll sell you a couple units right now. This one shows its benefits the end user right away – first try.

The most meaningful innovations get noticed instantly. Stop trying to label the innovation and simply measure how long it takes your customer to notice.

It’s pretty clear that innovation is the way to go. There’s endless creation of new technologies, new materials, and new processes so innovation can create new things to sell. And there are multiple toolsets and philosophies to get it done, but it’s difficult.

When doing new there’s no experience, no predictions, no certainty. But innovation is no dummy and has come up with a way to overcome the uncertainty. It builds knowledge of systems through testing – build it, test it, measure it, fix it. Not easy, but doable. And what makes it all possible is the repeatable response of things like steel, motors, pumps, software, hard drives. Push on them repeatably and their response is repeatable; stress them in a predictable way and their response is predictable; break them in a controlled way and the failure mode can be exercised.

Once there’s a coherent hypothesis that has the potential to make magic, innovation builds it in the lab, creates a measurement system to evaluate goodness, and tests it. After the good idea, innovation is about converting the idea into a hypothesis – a prediction of what will happen and why – and testing them early and often. And once they work every-day-all-day and make into production, the factory measures them relentlessly to make sure the goodness is shipped with every unit, and the data is religiously plotted with control charts.

The next evolution of innovation will come from systematically improving people systems. There are some roadblocks but they can be overcome. In reality, they already have been overcome it’s just that no one realizes it.

People systems are more difficult because their responses are not repeatable – where steel bends repeatably for a given stress, people do not. Give a last minute deliverable to someone in a good mood, and the work gets done; give that same deliverable to the same person on a bad day, and you get a lot of yelling. And because bad moods beget bad moods, people modify each other’s behavior. And when that non-repeatable, one-person-modifying-another response scales up to the team level, business unit, company, and supply chain, you have a complex adaptive system – a system that cannot be predicted. But just as innovation of airliners and automobiles uses testing to build knowledge out of uncertainty, testing can do the same for people systems.

To start, assumptions about how people systems would respond to new input must be hardened into formal hypotheses. And for the killer hypotheses that hang together, an experiment is defined; a small target population is identified; a measurement system created; a baseline measurement is taken; and the experiment is run. Data is then collected, statistical analyses are made, and it’s clear if the hypothesis is validated or not. If validated, the solution is rolled out and the people system is improved. And in a control chart sense, the measurement system is transferred to the whole system and is left to run continuously to make sure the goodness doesn’t go away. If it’s invalidated, another hypothesis is generated and the process is repeated. (It’s actually better to test multiple hypotheses in parallel.)

In the past, this approach was impossible because the measurement system did not exist. What was needed was a simple, mobile data acquisition system for “people data”, a method to automatically index the data, and a method to quickly process and display the results. The experimental methods were clear, but there was no response for the experiments. Now there is.

People systems are governed by what people think and feel, and the stories they tell are the surrogates for their thoughts and feelings. When an experiment is conducted on a people system, the stories are the “people data” that is collected, quantified, and analyzed. The stories are the response to the experiment.

It is now possible to run an experiment where a sample population uses a smart phone and an app to collect stories (text, voice, pictures), index them, and automatically send them to a server where some software groups the stories and displays them in a way to see patterns (groups of commonly indexed stories). All this is done in real time. And, by clicking on a data point, the program brings up the story associated with that data point.

Here’s how it works. The app is loaded, people tell their stories on their phone, and a baseline is established (a baseline story pattern). Inputs or constraints are changed for the target population and new stories are collected. If the patterns change in a desirable way (statistical analysis is possible), the new inputs and constraints are rolled out. If the stories change in an undesirable way, the target population reverts back to standard conditions and the next hypothesis is tested.

Unbiased, real time, continuous information streams to make sense of your people systems is now possible. Real time, direct connection to your employees and your customers is a reality, and the implications are staggering.

Thank you Dave Snowden.

From the outside it’s unclear how things happen; but from the inside it’s clear as day. No, it’s not your bulletproof processes; it’s not your top down strategy; and it’s not your operating plans. It’s your people.

At some level everything happens like this:

An idea comes to you that makes little sense, so you drop it. But it comes again, and then again. It visits regularly over the months and each time reveals a bit of its true self. But still, it’s incomplete. So you walk around with it and it eats at you; like a parasite, it gets stronger at your expense. Then, it matures and grows its voice – and it talks to you. It talks all the time; it won’t let you sleep; it pollutes you; it gets in the way; it colors you; and finally you become the human embodiment of the idea.

And then it tips you. With one last push, it creates enough discomfort to roll over the fear of acknowledging its existence, and you set up the meeting.

You call the band and let them know it’s time again to tour. You’ve been through it before and you all know deal. You know your instruments and you know how to harmonize. You know what they can do (because they’ve done it before) and you trust them. You sing them the song of your idea and they listen. Then you ask them to improvise and sing it back, and you listen. The mutual listening moves the idea forward, and you agree to take a run at it.

You ask how it should go. The lead vocalist tells you how it should be sung; the lead guitar works out the fingering; the drummer beats out the rhythm; and the keyboardist grins and says this will be fun. You all know the sheet music and you head back to your silos to make it happen.
In record time, the work gets done and you get back together to review the results. As a group you decide if the track is good enough play in public. If it is, you set up the meeting with a broader audience to let them hear your new music. If it’s not, you head back to the recording studio to amplify what worked and dampen what didn’t. You keep re-recording until your symphony is ready for the critics.

Things happen because artists who want to make a difference band together and make a difference. With no complicated Gantt chart, no master plan, no request for approval, and no additional resources, they make beautiful music where there had been none. As if from thin air, they create something from nothing. But it’s not from thin air; it’s from passion, dedication, trust, and mutual respect.

The business books over-complicate it. Things happen because people make them happen – it’s that simple.

If you’re working in a company you like, and you want it to be around in the future, you want to know if it will grow.  If you’re looking to move to a new company, you want to know if it has legs – you want to know if it will grow. If you own stock, you want to know if the company will grow, and it’s the same if you want to buy stock.  And it’s certainly the case if you want to buy the whole company – if it can grow, it’s worth more.

To grow, a company has to differentiate itself from its competitors.  In the past, continuous improvement (CI) was a differentiator, but today CI is the minimum expectation, the cost of doing business.  The differentiator for growth is discontinuous improvement (DI).

With DI, there’s an unhealthy fascination with idea generation.  While idea generation is important, companies aren’t short on ideas, they’re short on execution.  But the one DI differentiator is the flavor of the ideas.  To do DI a company needs ideas that are radically different than the ones they’re selling now.  If the ideas are slightly twisted variants of today’s products and business models, that’s a sure sign continuous improvement has infiltrated and polluted the growth engine. The gears of the DI engine are gummed up and there’s no way the company can sustain growth.  For objective evidence the company has the chops to generate the right ideas, look for a process that forces their thinking from the familiar, something like Jeffrey Baumgartner’s Anticonventional Thinking (ACT).

For DI-driven growth, the ability to execute is most important.  With execution, the first differentiator is how the company investigates radically new ideas.  There are three differentiators – a focus on speed, a “market first” approach, and the use of minimum viable tests (MVTs).  With new ideas, it’s all about how fast you can learn, so speed should come through loud and clear.  Without a market, the best idea is worthless, so look for “market first” thinking.  Idea evaluation starts with a hypothesis that a specific market exists (the market is clearly defined in the hypothesis) which is evaluated with a minimum viable test (MVT) to prove or disprove the market’s existence.  MVTs should error on the side of speed – small, localized testing.  The more familiar minimum viable product (MVP) is often an important part of the market evaluation work.  It’s all about learning about the market as fast as possible.

Now, with a validated market, the differentiator is how fast company can rally around the radically new idea and start the technology and product work.  The companies that can’t execute slot the new project at the end of their queue and get to it when they get to it.  The ones that can execute stop an existing (lower value) project and start the new project yesterday.  This stop-to-start behavior is a huge differentiator.

The company’s that can’t execute take a ready-fire-aim approach – they just start.  The companies that differentiate themselves use systems thinking to identify gaps in resources and capabilities and close them. They do the tough work of prioritizing one project over another and fully staff the important ones at the expense of the lesser projects.  Rather than starting three projects and finishing none, the companies that know how to do DI start one, finish one, and repeat.  They know with DI, there’s no partial credit for a project that’s half done.

All companies have growth plans, and at the highest level they all hang together, but some growth plans are better than others.  To judge the goodness of the growth plan takes a deeper look, a look into the work itself.  And once you know about the work, the real differentiator is whether the company has the chops to execute it.

Image credit – John Leach.

It’s harder than ever to innovate, and getting harder.

The focus on growth can be empowering, but when coupled with signed-in-blood accountability, empowering turns to puckering.  It’s an unfair double-bind. Damned if you try something new and it doesn’t work, and damned if you stay the course and don’t hit the numbers.  The most popular approach seems to be to do more of what worked.  A good approach, but not as good as it’s made out to be.

Doing more of what worked is good, and it works.  But it can’t stand on its own.  With today’s unreasonable workloads, every resource is fully booked and before doing more of anything, you’ve got to do less of something else.  ‘More of what worked’ must walk hand-in-hand with ‘Stop what didn’t work.’  Without stopping, without freeing up resources, ‘more of what worked’ is insufficient and unsustainable.

But even the two together are insufficient, and there’s a much needed third leg to stabilize the stool – ‘starting new work.’  Resources freed by stopping are allocated to starting new work, and this work, also known as innovation, is the major source of growth.

‘More of what worked’ is all about productivity – doing more with the same resources; and so is ‘stopping what didn’t work’ – reclaiming and reallocating ineffective resources. Both are important, but more importantly – they’re not innovation.

As you’re well aware, the rules are changing faster than ever, and at some point what worked last year won’t work this year. The only way to stay ahead of a catastrophe is to make small bets in unproven areas.  If the bets are successful, they turn into profitable innovation and growth. But the real value is the resiliency that comes from the ritualistic testing/learning cycles.

Going all-in on what worked last year is one of the riskiest bets you can make.