Optimistic overview and predictions about innovation.
Original source: The roaring 20’s?
Optimistic overview and predictions about innovation.
Original source: The roaring 20’s?
The key insight was to stop trying to build a mechanical carriage, and instead build something more like a mechanical horse.
we found that the “embarrassment” teams generated 26% more ideas spanning 15% more use categories than their counterparts.
Candor led to greater creativity. Thus, we propose a new rule for brainstorming sessions: Tell a self-deprecating story before you start. As uncomfortable as this may seem, especially among colleagues you would typically want to impress, the result will be a broader range of creative ideas, which will surely impress them even more.
Here’s an an idea for a formula for figuring out how much innovation an organization will do. I never know how good math is for this kind of thing, but it adds structure to programming an organization to be innovative, rather than career advancement seeking:
In organizations, the competing forces can be described as “stake in outcome” versus “perks of rank.” When employees feel they have more to gain from the group’s collective output, that’s where they invest their energy. When they feel their greatest rewards come from moving up the corporate ladder, they stop taking chances on risky new ideas whose failure could harm their careers.
Also, getting people to do new things by manipulating their desire to try new “tricks”:
Companies usually invest in training employees with the goal of better products or higher sales. Send a device designer to a technical workshop, and you’ll probably get better pacemakers. Send a salesperson to a speaking coach, and his pitch delivery might improve. But there’s another benefit: A designer who has learned new techniques will want to practice them. Training encourages employees to spend more time on projects, which reduces time spent on lobbying and networking.
Obtaining FDA approval can be a difficult and long process. “The traditional paradigm of medical device regulation was not designed for adaptive AI or ML technologies, which have the potential to adapt and optimize device performance in real – time to continuously improve healthcare for patients,” the report said.
“The highly iterative, autonomous, and adaptive nature of these tools requires a new, total product lifecycle (TPLC) regulatory approach that facilitates a rapid cycle of product improvement and allows these devices to continually improve while providing effective safeguards.”
If a manufacturer decides that its device that studies retinal scans for diabetic retinopathy can also measure if a patient has high blood pressure or not, it’ll have to contact FDA officials to check if the device can be used for that purpose.
This is probably a good space for that Talebian thinking that goes “start with what’s worked for thousands of years, and probably don’t stop.”
Wardley’s take on riding the diffusion or understand curve:
The uncharted space is where no-one knows what is wanted which forces us to explore and experiment. Change is the norm here and any method that you use must enable and reduce the cost of change. In this part of the map, I tend to use an Agile approach that has been cut right back to the core principles, a very lightweight version of XP or SCRUM.
Of course, as a component evolves and we start to understand it more then our focus changes. Sometime during the stage of custom built we switch and start to think about creating a product. Whilst we may continue to use underlying techniques such as XP or SCRUM, our focus is now on reducing waste, improving measurements, learning and creating that first minimal viable product. We start to add artefacts to our methodology and the activity has more permanence about it as it undergoes this transition. We’ve stopped exploring the uncharted space and started concentrating on what we’ve found. Today, Lean tends to rule the waves here though back in 2005 we were struggling to find something appropriate. The component however will continue to evolve becoming more widespread and defined as it approaches the domain of industrialised volume operations. Our focus again switches but this time to mass production of good enough which means reducing deviation. At this point, Six Sigma along with formalised frameworks such as ITIL then start to rule the waves. Any significant system will have components at different stages of evolution. At any one moment in time, there is no single method that will fit all.
Original source: Exploring the map – Wardley Maps
“And one of the ways you make certain that you don’t have bad processes eat up good peoples’ ideas is you make certain that you remove the bad processes and organize for success.”
Original source: Media Availability with Secretary Mattis at DIUx, transcript
“Once a list of innovation ideas has been refined by curation, it needs to be prioritized. One of the quickest ways to sort innovation ideas is to use the McKinsey Three Horizons Model. Horizon 1 ideas provide continuous innovation to a company’s existing business model and core capabilities. Horizon 2 ideas extend a company’s existing business model and core capabilities to new customers, markets or targets. Horizon 3 is the creation of new capabilities to take advantage of or respond to disruptive opportunities or disruption. We’d add a new category, Horizon 0, which refers to graveyards ideas that are not viable or feasible.”
Original source: What Your Innovation Process Should Look Like
‘“a lack of connection between innovation teams and their parent organization. Teams form/and are taught outside of their parent organization because innovation is disconnected from other activities. This meant that when teams went back to their home organization, they found that execution of existing priorities took precedence. They returned speaking a foreign language (What’s a pivot? Minimum viable what?) to their colleagues and bosses who are rewarded on execution-based metrics. Further, as budgets are planned out years in advance, their organization had no slack for “good ideas.” As a result, there was no way to finish and deploy whatever innovative prototypes the innovators had developed – even ones that have been validated.”’
Original source: How to make innovation programs deliver more than coffee cups
A nice way of explaining Amazon’s success in charts, e.g., as compare to Wal-Mart:
Just thinking aloud without any analysis, it seems liken Amazon is an example of how difficult, long, and confounding doing continual innovation as your business is. Many companies claim to be innovation-driven, but most can just eek out those “incremental innovations” and basic Porterian strategy: they improve costs, enter adjacent marketers, and grow their share of existing TAMs, all the while fending off competitors.
Amazon, on the other hand, has had decades of trying new business models mostly in existing businesses (retail), but also plenty of new business models (most notably public cloud, smart phones and tablets, streaming video and music, and whatever voice + machine learning is).
All that said, to avoid the Halo Effect, it’s important to admit that many companies tried and died here…not to mention many of the retailers who Amazon is troubcibg – Wal-Mart has had several goes at “digital” and is in the midst of another transformation-by-acquisitions. Amazon, no doubt, has had many lucky-breaks.
This isn’t to dismisss any lessons learned from Amazon. There’s one main conclusion, thought: any large organization that hopes to live a long time needs to first continually figure out if they’re in a innovation/disrupting market and, if they are, buckle up and get ready for a few decades of running in an innovation mode instead of a steady-state/profit reaping mode.
Another lesson is that the finances of innovation make little sense and will always be weird: you have to just hustle away those nattering whatnots who want to apply steady-state financial analysis to your efforts.
You can throw out the cashflow-model chaff, but really, you just have to get the financial analysis to put down their pivot tables and have faith that you’ll figure it out. You’re going to be loosing lots of money and likely fail. You’ll be doing those anti-Buffet moves that confound normals.
In this second mode you’re guided by an innovation mindset: you have to be parnoid, you have to learn everyday what your customers and competitors are doing, and do new things that bring in new cash. You have to try.
In contrast to agile, private-sector companies, the public sector does not face any pressure from competition. When it comes time to renew your license, there is only one place for you to do that: and, unfortunately for Americans, that’s the DMV. With no competitive forces, government agencies do not have to innovate or take bold risks when it comes to digital.
And, as ever, being smart about using updated tools and new methods yield huge productivity results:
While running technology for Obama’s WhiteHouse.gov, open-source solutions enabled our team to deliver projects on budget and up to 75% faster than alternative proprietary-software options. More than anything, open-source technology allows governments to utilize a large ecosystem of developers, which enhances innovation and collaboration while driving down the cost to taxpayers.
While open source has different cost dynamic, I’d suggest that simply switching to new software to get the latest features and mindset that the software imbues gives you a boost. Open source, when picked well, will come with that community and an ongoing focus on updates: older software that has long been abandoned by the community and vendors will stall out and become stale, open or not.
With most large organizations, and especially government, simply doing something will give you a huge boost in all your KPIs in the short term. Picking a thriving, vibrant stack is critical for long term success. Otherwise, five or ten years from now, whether using open or closed source, you’ll end up in the same spot, dead in the water and sucking.
From Snap’s S-1, via Ben:
In a world where anyone can distribute products instantly and provide them for free, the best way to compete is by innovating to create the most engaging products. That’s because it’s difficult to use distribution or cost as a competitive advantage—new software is available to users immediately, and for free. We believe this means that our industry favors companies that innovate, because people will use their products.
So, long run growth comes from one thing, and one thing only: Productivity. New and better ways of doing things. New and better products, new and better companies. It doesn’t come from 90% of the things that we talk about. So, the Federal Reserve, stimulus programs, even anti-inequality programs–over 10-20 years, it’s about productivity. Our ancestors may have, you know, you might have had a grandparent who dug coal with a pickaxe; and how did you get so much richer? Not by your union getting him higher wages and he still digs coal with a pickaxe at 20 cents an hour, not 10 cents. It’s because one guy left and he uses a bulldozer. Right? Growth comes from productivity. And productivity–everybody likes growth in someone else’s backyard. Productivity comes from new companies, doing things new ways, and making life very uncomfortable for everybody else. Uber is the great example. Uber is–that’s a great productivity enhancement. It’s putting a lot of people to work who otherwise couldn’t go to work. And the taxi companies hate it. And most of economic regulation is designed to stop growth. It’s designed to protect the old ways of doing things. So, what we need for growth-oriented policies is exactly that kind of innovation, that kind of new companies coming in an upending the status quo, that make everybody uncomfortable and run to their politician to say, ‘You’ve got to stop this.’
I don’t know the politics of economics enough to figure out if that’s a dick thing to say or not, but it sure makes grim-sense. The rest of the interview has some fun mental gymnastics and suave “turns out”’ing.
(And check out the show notes! That’s some intimidating work.)
Capabilities such as creativity and sensing emotions are core to the human experience and also difficult to automate. The amount of time that workers spend on activities requiring these capabilities, though, appears to be surprisingly low. Just 4 percent of the work activities across the US economy require creativity at a median human level of performance. Similarly, only 29 percent of work activities require a median human level of performance in sensing emotion.
Many IT professionals believe innovation in IT pays for itself. But a surprising number of companies I visit aren’t in a position to prove this hypothesis. They typically don’t know what it costs to provide their IT services and can’t quite put a figure on the benefits of IT innovation projects. Without these key data points, they have a hard time quantifying the ROI or payback on any IT project—making it difficult for IT to compete internally with other departments for scarce business funding.
The rest has many considerations for planning our ROI, plus contingencies to muck about with.
For much more detail, see Mindy Cancila’s write-up over at Gartner from earlier this year.
But this can be a toxic formula. The financial optimization algorithm always prioritizes the known over the unknown since the known can be measured and is assigned a quantum of value while the unknown is “discounted” with a steep hurdle rate, and assigned a near zero net present value. Thus the financial algorithm leads to promoting efficiency at the expense of creation. Efficiency may be the right priority when times are difficult and resources are scarce but creativity is the right priority in a time of plenty. And abundance is what being big is all about.
I always like a good three p’s explanation:
We found three key elements that consistently drive innovation: people, processes and philosophies (what we call the 3Ps). We found that highly innovative organisations built their people, processes and philosophies around five fundamental “discovery skills”. Innovators ask provocative questions that challenge the status quo. They observe the world like anthropologists to detect new ways of doing things. They network with people who don’t think like them to gain radically different perspectives. They experiment to test new ideas and experiences. Finally, these behaviours trigger new associations which lead them to connect the unconnected, thereby producing disruptive ideas. An organisation’s investment in and ability to leverage these three facets of innovation sets it apart.
What you get from Technological Revolutions and Financial Capital is an explanation of how new technologies and the resulting changes in society and business (esp. finance) create cyclical 40-60 year boom, bust, and the thriving cycle.
The valuable part isn’t pointing out that there’s a cycle, but rather collecting together the lengthy cause and effect chain of various cycles of time. The account of how financing (that is, how money “seeks” to fund innovation and then “make money from money,” or “rent-seeking” as the kids like to say) is especially interesting. There’s a distinction between “production capital” (money used by companies to create profit by creating/doing some business in goods or services) and “financial capital” (“banks” and investors who seek to “make money from money,” not by producing something) that’s an especially helpful framing to pound into your head. For example, in this later passage, you get a sense of when each type of money “wants” to invest in business:
In the economy, the interrelations between financial and production capital determine the rhythm and the direction of each technological revolution. Financial capital enables the succession of surges. When production capital at the end of a surge becomes conservative, due to having so much investment and experience tired to it, financial capital will break loose and end up either helping the initial big-bang of the next revolution or following it up by backing the new entrepreneurs in spreading it. When financial capital, during the person of installation of a new paradigm, take the economy on a frenzied ride up a paper-wealth bubble, the new modernized production capital will be ready to take over and lead a more orderly growth process, in the “golden age” that sees the full deployment of that revolution.
As with any pattern/model, I have a dangerous urge to fractal down the cycles from 40-60ish year cycles to 10 years or less as a way to model point innovations. It’s probably a bad idea if precision is desired, but as far as describing what otherwise could look like chaos, it might be handy.
All that said, this book is hard as shit to read. I had to start over ¼ through because I’d let too much time elapse between readings.
The last passage is from June 2002, and I’d be eager to see the author’s current WTF analysis on the world economy.
The reality is that it is only the `cash cows’ that are really important—all the other elements are supporting actors. It is a foolish vendor who diverts funds from a `cash cow’ when these are needed to extend the life of that `product’. Although it is necessary to recognize a `dog’ when it appears (at least before it bites you) it would be foolish in the extreme to create one in order to balance up the picture. The vendor, who has most of his (or her) products in the `cash cow’ quadrant, should consider himself (or herself) fortunate indeed, and an excellent marketer, although he or she might also consider creating a few stars as an insurance policy against unexpected future developments and, perhaps, to add some extra growth. There is also a common misconception that ‘dogs’ are a waste of resources. In many markets ‘dogs’ can be considered loss-leaders that while not themselves profitable will lead to increased sales in other profitable areas.
“These old technologies are holding us back. They’re anchors on where we want to go,” he said. “We find the things that have outlived their useful purpose. Our competitors are afraid to remove them. We try to find better solutions – our customers have given us a lot of trust. In general, it’s a good idea to remove these rotating medias from our computers and other devices. They have inherent issues — they’re mechanical and sometimes break, they use power and are large. We can create products that are smaller, lighter and consume less power.”