Open innovation will take on a new meaning as AI will scan internal and open data to find the best ideas, write Alessandro di Fiore and Simon Schneide

di Fiore, Alessandro

Schneider, Simon

LSE Research Online

2/8/2017Crowdscanning: The future of open innovation and artificialintelligenceblogs.lse.ac.uk /businessreview/2017/02/08/crowdscanning-the-future-of-open-innovation-and-artificial-intelligence/Imagine a future where the CEO of a manufacturing firm turns to his Chief Innovation Officer and asks: “Bob, howcan we innovate on our product and save 10 per cent of production cost to stay competitive against low-cost firms?” So far this seems to be a normal conversation that occurs daily across organisations around the world.Except that ‘Bob’ is really an AI computer program on the CEO’s iPhone.A study from Oxford University says that A.I. will replace up to 45 per cent of jobs within 20 years. There is a lot oftalk about how such intelligent systems and chatbots will eliminate low level jobs, customer service and repetitivetasks. Let’s review, as examples, some actual practices where Artificial Intelligence (AI) is already changing the wayjobs are performed.Customer serviceSmart chatbots are already being used to interact with customers online. They not only repeat pre-defined answersbut can actually ‘think’ and help customers order menus at Taco Bell or get tickets from Ticketmaster.Recruiting and HRMany of the current functions of a recruiter consist of standard tasks that a smart machine can do as well as ahuman. Traditionally, recruiters receive the needed requirements for a job, search candidates, screen best fits, checkavailabilities and then present best candidates back to hiring manager. These five fundamental tasks will be takenover by the ‘AI Recruiter’ in the next few years. Some initial new technologies and startups likeZyncd, Wade&Wendy and Mya are first generation A.I. recruiting tools that will shake up this market.But will A.I. reach the top echelon of innovation jobs too?1/4While the world seems to agree that repetitive tasks will be outsourced to machines, there is a wrong belief thatadvisory roles and creative jobs will be safe from A.I. While there is surely a lot of truth to that, there is also a lot ofarrogance in such thinking and underestimating the creative power of A.I.We have seen IBM Watson going into helping and potentially replacing white-collar jobs in financial advisory andhealth care diagnosis. Ok, but what about innovation? We have been trained about the power of open innovation forenterprises and how accessing creative minds inside and outside the firm is bringing in the ‘unusual suspects’ ofdisruptive ideas. For a decade the world has been obsessed with how to open up to these creative minds inside afirm and outside to bring in new ideas.A.I. will inverse open innovation in many ways.Many famous OI platforms have struggled in recent years: Quirky filed for Chapter 11 protection over a year ago,while Topcoder and a few others have been sold or are in process of being acquired because their business modelhas been failing. In Quirky’s case the main problem was that the ideas from the crowd just weren’t innovativeenough and the assessment of the ideas was done by users who had no idea about the market or how to developand commercialise a new product.The main problem of the Quirky community wasn’t the shortage or the quality of their new ideas. The problem wastwofold: first, the ideas just weren’t radically novel breakthroughs but rather small add-on observed by members ofthe public. A good example is the Quirky flexible ‘Powerstrip’ which is a traditional power strip that is put togetherwith flexible pieces so that it can bend around furniture. Surely a good idea, but not something that will turn into a$100 million-plus business by itself. Its second problem is that the community wasn’t led by a crowd with productdevelopment experience or at least an industry experience. As a result Quirky developed a far-flung portfolio ofproducts in 26 different categories, managed and developed by a crowd of innovators with too general skills andbroad knowledge.From crowd-suggested to crowd-scannedOpen innovation takes on a new meaning as AI will analyse what’s out in the open digital world (Internet). Combininginternal data and open data will lead to finding the best ideas suggested by the algorithm. From crowd-suggestedto crowd-scanned, as companies will still be picking up the brains and the intelligence of the outside world andcrowd but in a more informed and effective way, avoiding so the issue of Quirky and other similar platforms.AI will be meshed into the open innovation strategy of big firms to innovate better, which brings us back to the aforementioned scenario where the CEO is asking his virtual CIO Bob about how to innovate better at his firm. Let’sexplore that case and how that will work when AI meets OI.Bob, the AI program, will first access all the internal data of the firm to understand where the problems lie. Now it canscan for innovations on the web and look at startups and inventors  —  who all have a large digital footprint  — patent databases and scientific publications for information on solutions for its problems.For instance, a typical start-up leaves a digital footprint on many public sources on the Internet:Startups are very vocal about their ideas on blogs and social media revealing case studies, customers andmilestonesTheir founding history, address, directors and revenues can be accessed from national registries (CompaniesHouse for the UK)Their investment and funding history can be seen from press releases and the Crunchbase webpageInnovations can be searched on the patent databases of USPTO and the European Patent OfficesTheir technology ideas can also be tracked with sources, from speeches at events, and the team’s personal2/4footprints and skills.Artificial intelligence can compute millions of correlations taken from all these dispersed signals on the web thatwould take humans years to make. While humans might not even see how a non-industry startup filed a patent foran innovation that might be relevant for them, an AI program can correlate all public information about this startupand understand that it sits on a perfect solution for your problem.The OI process of the future, supported byAI follows a four-step process:1. Identify the problem statementKey to this new OI-AI model is that while the CEO understands the results of his problems, i.e. declining revenues,the AI will assist in finding out the root causes for it. In our ten years of open innovation experiences, we often sawthat wrong understanding was the key problem in a company’s innovation strategy.For instance, accessing all the chatbot data that the customer service AI has gathered in chats with customers,finding out that 15 per cent of customers report problems right after receiving the product in the mail, pointing to aproblem with how product is shipped.2. Translate into a search algorithmHaving understood the problem where innovation needs to focus, the AI bot will now search for packaging solutionsacross industries, understanding which novel ideas receive great online feedback and are used by competitors.3. Look at matches and refineThe results of this search will surely not be perfect the first time as the AI will need to learn from the firm’spreferences and implied knowledge that it cannot access with data. Therefore, OI practitioner’s job will be to teachthe system so that it becomes smarter over time and creates the right matches.There are already several companies offering such search tools, like Ideapoke.com, which is a B2B platform thathelps businesses find technology solutions by looking at public sources, the web and strategic partners.4. Contact innovators and integrateThe final step after having identified a few good matches is to make contact with these firms and evaluate how tocooperate or integrate the innovation in the client’s process. This step is very human-centric and we predict thisarea is where the main value of OI practitioners will lie in the future.Impact on enterprises to run open innovationIn many industries and sectors, AI will take over repetitive and data-heavy tasks in the future. For open innovationthis means that the scanning, searching and matching part of the work can be replaced by robots. However, thesecond part of OI is to work with the matched outside innovators on how to integrate ideas into large enterprises,which will require human-to-human interactions, discussions and negotiations. Just finding the right idea is half thebattle and OI practitioners will focus more on the integration part as a result of it, leaving the hunting and matchingto the machines.♣♣♣Notes:The post gives the views of its authors, not the position of LSE Business Review or the London School ofEconomics.3/4Featured image credit: Coding, by Pexels, under a CC0 licenceBefore commenting, please read our Comment PolicyAlessandro Di Fiore is the Founder and CEO of the European Centre for Strategic Innovation(ECSI) and ECSI Consulting, based in London. He is a consultant, author and media commentatoron strategy and innovation. Alessandro has worked in management consulting for more than twodecades. Along the way he has advised more than 25 Fortune 500 companies throughout theworld on strategy and organisational change. He started his career as a product manager atColgate-Palmolive. He went on to work at Gemini Consulting, a large international consultinggroup, where he was responsible for Life Sciences in continental Europe, Managing Director forItaly and global head of its Market Focused Strategy Center of Excellence. In 2000 Alessandrofounded his first European boutique strategy firm, Venture Consulting. The firm was sold in 2008 to Tefen, aninternational management consulting company floated on the Tel Aviv stock exchange. Alessandro went on to foundECSI in 2010 with the aim of creating a truly unique global consulting firm. Email: adifiore@ecsi-consulting.comTwitter: @alexdifioreSimon Schneider is Director at ECSI Consulting, London. Simon started his career as consultantat IBM in New York working on cyber-security and biometrics projects that opened uphis perspective on how emerging technologies can disrupt old industries and create new markets.He invented a security patent that is licensed to IBM which helped him understand how thetraditional ‘closed’ innovation process works in large enterprises. During his MBA at LondonBusiness School he co-founded a crowdsourcing platform business that used open innovationcompetitions to solve R&D challenges for enterprises, and was later acquired by InnoCentive inthe US. Simon became the CEO of Zyncd a German based A.I. startup. In January 2017 I joinedECSI Consulting in London with the ambition and responsibility to accelerate the open innovation practice of thecompany. He serves on the boards of directors for the crowd-funding company CrowdHut and the foreign-languageteaching startup Language on Stage in the UK.  Follow him on Twitter: @simon_crowd Copyright © 2015 London School of Economics4/4

Crowdscanning: The future of open innovation and artificial intelligence

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