Credit by Julie de la Kethulle de Ryhove
We're using the most cutting-edge technology to innovate innovation. Here's how we're embracing machine learning, one digital tool at a time.
Why we’re creating Tech Lab
We don’t believe that artificial intelligence or machine learning will replace innovation consultants. On the contrary. It’s innovation consultants who use AI that will replace innovation consultants who don’t. That’s why we’re creating Tech Lab, so our team and our clients’ teams will be able to innovate like superheroes, from augmented problem exploration to automatic business model validation.
Over the years, innovation has evolved from being a ‘checkbox politic’ (“We innovate!”) to a structural part of organizations. As innovation becomes more integrated into company strategy and culture, the need to prove its impact is becoming larger. Using technology, we’re going to start measuring small parts of the innovation method to quantify our impact as innovation consultants, as well as the impact of our clients.
Our current innovation methods will be picked apart to investigate which ones can (and should) be automated or augmented, and which ones are better kept “human”. The test will be whether the technology is better than what we can still do ourselves.
Here’s how Tech Lab will work. First, we’ll use the existing technological tools of third parties to test existing innovation methods and come up with new ones. If those don’t suffice, we’ll build integrations between different tools. If that fails, we’ll build our own solutions with a team of developers.
More efficient and effective innovation methods
Until now, the front end of innovation has been a fluffy, mysterious art form. And it currently involves a lot of manpower. We depend on human creativity to dream up creative ideas, and empathy to identify problems worth solving. We rely on a CEO’s intuition about investment in ambitious but ambiguous innovation projects, and on human-led market validation. To make this process more efficient, we want to build new business using technology.
Our mission with Tech Lab is to engineer a future where innovation can be turned into an algorithm, and even codified.
We don’t pretend to have all the wisdom or know-how to build the innovation methods we envision. That’s why we want to co-create it with clients, academics, developers, and even artists, mathematicians and zombies from startup graveyards. (They exist, we’ve seen them).
Tech Lab: the deliverables
The deliverables of Tech Lab won’t look familiar to the tools and guides we have available in physical form. It’s not about replicating the current innovation methods with a digital, automated version. Giving out iPads with business model kits might impress, but it’s not our mission to have fancy digital tools unless they prove to be more efficient or effective than the methods we use today. We want what we make to be useful, not just a gimmick. Whatever we build will be a means to an end. Since the costs of technology decrease exponentially with time, we can afford to have an effective but inefficient version at first. As long as it’s as efficient as the alternative, we can make it more efficient and scalable later on.
We’re dreaming big. For example: What if we could validate ideas so quickly and cheaply that we could skip the problem validation phase altogether and do instant real-time solution validation instead? What if a machine could automatically generate 100 business models, validate the 10 most promising ones, and suggest an investment decision?
How others are innovating innovation methods
Innovation methods have already been innovated in different ways. We’re spotting new tools every week that deliver on the promise to innovate parts of the innovation methods, for example buffl, a mobile survey tool for designers.
There are the obvious innovation management tools such as idea management platforms. But prototyping, for example, has significantly accelerated. What 3D printing did for physical prototyping, Figma, Invision, Sketch and many more are doing for digital prototyping tools. They are becoming increasingly easy to use, even when going from low fidelity to higher fidelity prototypes. Invision, for example, makes it easier to do engineering hand-offs after a project.
More and more corporates are releasing their own Internet of Things (IoT) devices and IoT applications in the form of development kits. GE’s Predix development platform and Orange’s nb-IOT rapid development kit) were built at maker spaces for their employees and are active in the world of open software development platforms and different types of APIs (e.g. Github and Tensorflow).
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