The power of co-creation
Companies are struggling to identify innovative solutions for breakthrough products and services, compounded by a lack of method to bring them to fruition.
Co-creation—the concept of gathering teams from across an enterprise to design, prove, and launch innovative programs—is an increasingly vital tool today, says Lori Victor Feller, IBM Garage Global Leader. AI and analytics capabilities can accelerate projects in many business units, but when they’re launched without know-how, adequate testing, and value verification, they’re at risk of failing.
“Given limited AI and analytic skills in the marketplace,” says Feller. “You can do three things: acquire the skills, acquire a partner, or purchase the skills.” The co-creation model is the partner strategy, which she champions through her work with IBM Garage.
Here are a few more of Feller’s rules for successful co-creation.
You need talent as much as you need technology
“You have to understand the outcomes you want to achieve – and people decide on these outcomes, not the technology. You can’t ask a computer to understand the human experience. Your smartest people need to train these computers to get the desired results.”
Build dedicated space for co-creation
“This is not remote work. Because your teams are rapidly ideating and developing new AI and analytics models and solutions, they need to be located together. That’s what speeds up the cycle of innovation. For high-level tasks like generating ideas, you need face-to-face meetings – it’s just faster that way. Once you move on to repeatable, rote tasks, it’s OK if people work together remotely. This is what we do with IBM Garage – we are onsite with our clients so we can work with them to prove out innovative, cutting-edge ideas and hypotheses.”
Be prepared to test, and test some more
“In our IBM Garage projects, we create a learning-by-doing environment, and part of that is recognizing that you’re going to iterate. We’re not going to wait until the outcomes are perfect to start generating insights. Over time, you should be able to make better and faster decisions because you have information that you didn’t have before.”
It’s OK to start with imperfect data
“Many organizations don’t start AI and analytics projects because they say their data is not in great shape. People get stuck on the idea that their data won’t yield any useful results. Kickstart the project with the data that is in good shape. You can add other data sets later – or enhance your data to external data sets that can flesh out results.”
Never lose sight of the end goal
“For projects to work, you need a maniacal focus on business value. Whatever you’re creating needs to be aligned with business strategy and objectives.”