Innovation, like other business operations, requires a multitude of decisions on a daily basis. Over time, the intricacies of R&D and the responsibilities of its leaders have increased; the number of decisions required from leadership has followed. A growing number of decisions requires an increased amount of time and a greater likelihood of human error. Automated decision making has the potential to make leaders and their teams more effective by taking on some required decisions.
When Can We Automate Decisions?
Automated decision making is extremely effective when decisions are based solely on data. When an artificial intelligence (AI) algorithm has all of the information and rules necessary to choose the best option, it will make decisions with little to no human input. The algorithm will continue to evaluate and improve its choices based on the continuous feedback it receives from previous decisions.
Challenges of Automated Decision Systems
AI-backed, automated decisions are made using available data and set rules. When these inputs are unavailable or of questionable quality, so are the resulting decisions. This limitation makes automated decision making ideal for day-to-day, operational R&D decisions, such as budgeting, compliance, and forecasting. Of course, AI and machine learning (ML) will be reliable decision making tools for more decisions as technology improves.
Automated systems also require oversight and explanations, especially when making sensitive decisions. The potential bias of unsupervised AI has been well-publicized recently, as the European Union’s GDPR “limits the circumstances in which you can make solely automated decisions, including those based on profiling, that have a legal or similarly significant effect on individuals.” Another cautionary tale comes from Boston, where a AI-based decision to change school schedules and bus routes was not well-received, partly due to “the somewhat black-box nature of the algorithm.”
How Do We Automate Complex R&D Decisions?
These challenges become apparent as we attempt to automate more complex decisions using AI and ML. However, it is these complex tasks that are most susceptible to human error and require the most skill and time. Therefore, automating them offers potential time and cost savings that can streamline the innovation process. To take advantage of automated decision making in the R&D department, consider utilizing AI to inform pieces of the decision making process.
Insights backed by AI and ML algorithms, known as augmented analytics, can inform the decisions innovation teams eventually make. By turning to this type of intelligent decision support system, R&D teams can harness the power of AI to make better decisions more quickly. For example, our Technology Vitality Report (TVR) uses Semantic Gist™ to help inventors prioritize their most novel ideas. The AI-enabled search technology within InnovationQ Plus® offers innovation teams insight into their competitors’ recent technological advances. Together, the tools automate multiple key pieces of the innovation decision making process.