A lot is at stake for companies as this digital transformation takes hold. These transitions in a company’s life are often where they can get stuck. In this super-charged environment, acquiring your way to innovation no longer works — the only way to stay innovative is to invest in research and development.
Higher R&D productivity yields higher profits, growth, and market value per dollar of R&D. Product life cycles have shortened by about 25 percent over the past 15 years, while product variety has more than doubled, according to Roland Berger, a global strategy consulting firm based in Munich. The shorter life cycle of a product or technology softens a company’s ability to profit from it, which in turns puts pressure on the company to introduce new products faster. Move away from purely digital products and services and two additional trends emerge – the decline in the rate of breakthrough innovations, and the increasing complexity of new technologies.
In short, companies have less time to develop ever-more costly technologies. Couple that inherent tension with management’s short-term focus on the bottom line, and it becomes clear why many companies avoid embarking on long-term R&D efforts and opt instead for incremental improvements.
This is where Open Innovation increasingly comes in, especially for companies that want to move beyond theoretical ideas to develop real-world products and services that can extend their core business into new markets. Open Innovation (OI) is a paradigm that encourages companies to look for technological solutions outside of their in-house R&D operations. OI strategies can bring in fresh perspectives from outside teams, and new ideas from research partners.
Various Types of Real-World OI Partnerships
Truly creative R&D happens when ideas cross-pollinate between industries, in which technologies from one market are applied to an entirely different set of problems and customers.
One common OI model involves collaboration with universities and research laboratories. However, most academic teams are not intimately familiar with the importance of generating positive returns, and they aren’t pressed to manage profit and loss or contribute to financial success. In short, they don’t make very business-savvy partners.
R&D partnerships between companies, often with suppliers or competitors, are another common mode of OI. The difficulties here arise from how to structure the partnership and how to organize the project. Before a collaborative R&D effort can begin, each side should disclose sensitive information about its R&D needs and capabilities. Even when honoring a non-disclosure agreement, two teams might be forced to reveal information that can indirectly suggest a stealth-mode strategic direction or a company weakness.
Another OI model involves intermediaries such as NineSigma, InnoCentive, YourEncore, and Yet2.com, which help by serving as networking platforms. They connect companies which need solutions with organizations and individuals that can provide the answers.
The OI paradigm has been adopted by many of the world’s leading multinationals across a broad range of vertical industries. For instance, General Electric is an industrial behemoth that has embraced the OI paradigm. is a consulting-like service that GE developed to “crowd-power your business and provide you with cost-effective results at lightning fast speeds.”
Another one of GE’s OI projects is First Build, a co-creation community backed by GE Appliances. First Build is focused on solving problems and creating new home appliance products. The most impressive of all GE OI projects, however, is Ecomagination, a growth strategy to enhance resource productivity while reducing the environmental impact of industry at a global scale. Ecomagination has generated over $200 billion in revenue to date.
P&G Case Study: Explainable AI for Skin Care
In another example, major consumer packaged goods maker Procter & Gamble Co. partnered with PARC to develop an “Explainable AI” system that enables machines to “speak” with users on an understandable human level.
The resulting Olay Smart Skin Care Advisor analyzes selfie photos of consumers by using artificial intelligence engines to diagnose and suggest the best combination of cosmetics and skin care products for each user’s complexion and hair/eye colors.
The conceptualized solution was defined as a smartphone app that could perform a personalized skin diagnosis by using selfie photos to make recommendations for product and regimen changes. Repeated use would improve results and provide deeper insights.
PARC framed the Olay project into two capability clusters – the technical solution and the user experience. For the technical cluster, computer vision experts developed software to help control variables like lighting, camera distance and facial expression. Machine-learning specialists trained models to detect the presence of target skin features, using Olay’s human-graded image databases as ground truth.
For the user experience cluster, social scientists worked with Olay Product Research specialists. The team observed and interviewed Olay customers to frame hypotheses about drivers of believability in the recommendations, enjoyment of the app, and sustained use.
Since being released in March 2017, the app has reached several notable milestones for Olay, including over 1 million visits worldwide and 300,000 completed consultations. App users exhibited 3.5 times greater purchasing intent than regular Olay.com visitors and double the level of engagement with Olay product recommendations.
Open Innovation Holds the Key to Unlock R&D
We are seeing closer integration all the time between the digital world and the physical world. This should force us to think deeply about innovating at the intersection of hardware and software, the cyber and the physical.
If predictions hold, we should also expect that the cost of developing new technologies will only increase, complexity will continue to rise exponentially, and it will take groundbreaking innovations to dominate the marketplace. For any one company to get there, it will have to collaborate with an ecosystem, especially on the R&D side.
At its best, Open Innovation really exists at the outer boundaries of R&D. It’s all about translating deep science and technological investigation into commercially viable pathways for productization. That translation is never easy, but it will increasingly require extended R&D partnerships to ensure market success.