This article describes that groups of people and computers combined will be able to provide collective intelligence, sometimes referred to as “cyber-human.” What relation does this have to improving product development?
There is an excellent article by Thomas W. Malone in the latest MIT Sloan Management Review (Summer 2018), called “How Human-Computer ‘Superminds’ Are Redefining the Future of Work.” What I find much more compelling than this title is the byline, “Virtually all human achievements have been made by groups of people, not lone individuals. As we incorporate smart technologies further into traditionally human processes, an even more powerful form of collaboration is emerging.”
Drotar and Morrissey, Co-founders of Strategy 2 Market, have seen countless product development mishaps over the years. These mishaps boil down to poor decision-making and the limited ability to identify and manage product development uncertainty and risk. These mishaps include, but are not limited to the following examples:
- Companies having a hard time keeping up with continuous change in technology, competition, regulations
- Products languishing in development for months or even years since the technology is new and evolving
- Companies creating a new product that the existing sales channel cannot sell
- Elegant product solutions that customers are not interested in buying
Improving project team decision-making through the assistance of computers/ Artificial Intelligence (AI) is a key asset that product development teams can benefit from to reduce the number of product development mishaps.
Drotar and Morrissey created a “cyber-human” tool called the Product Risk Framework® (PRF). The PRF enables and supports Malone’s key elements of an intelligent system: create possibilities for action, decide which actions to take, sense the external world, remember the past and learn from experience.
The PRF collectively incorporates smart technology/AI into the human process of developing products. Ultimately, leading to better decision-making. Here is how the cyber-human collaboration works:
- The PRF provides a framework and process for cross-functional identification of risks and uncertainties on individual new product ideas
- As the project team works through this framework, the PRF captures all the relevant information on risks identified, as well as the team’s assessment of the risk from multiple dimensions
- The PRF categorizes risks into priority categories, allowing the team to quickly surface the most important risks
- The project team assesses project viability using information collected in the PRF and selects the most important risks to resolve
- The PRF provides reports and graphics that enable objective communication and transparency of product risks during project portfolio prioritization
- As the team resolves the most important risks, the PRF monitors progress and produces reports and graphics
- The PRF builds new learnings into the frameworks and models for future use
Companies are showing an interest in these intelligent systems for product development. Mary and Kathy are recipients of a NSF STEM I-Corp grant for the Product Risk Framework. During their grant research they found senior product development leaders, across industries, embraced the idea of sharing anonymized information (1) on product development risks and uncertainties to improve product development efforts.
So, what does this future world of PRF collective intelligence look like for you and your product development team?
- Reduced product uncertainty and risk, leading to increased product success
- A unified view of the product across disciplines
- Ability to react to unexpected threats and disruption
- Optimized resources
- Community of users collectively improving product development
Collectively, computers and humans can help product developers to make better decisions. Visit our Product Risk Framework website or contact us to learn more about the Product Risk Framework business intelligence tool. You can also read our latest Summer 2018 white paper and case study.
(1)Providing anonymity of information collected from participants means that either the project does not collect identifying information of individual subjects (e.g., project name, proprietary details, etc.), or the project cannot link individual responses with participants’ identities.