What is Decision Analytics?
Derived from Google Research and Development, DI combines behavioral research with AI/ML-based decision making for data-driven technology application. It enables the structured discovery of decision points and their decision context, which incorporates who the decision maker is, the default decision in the absence of data, what alternatives a user might choose, and what supporting data could be used to validate an alternative.
By analyzing business processes and workflows to identify how and where employees make decisions and the context of those choices, leaders can better understand where AI/ML can improve decision-making processes. As a result, DI can help identify where AI/ML or other statistical and rule-based techniques enable users to make better decisions. Over time, DI learns users’ preferences as they relate to trusting and consuming AI/ML and adapts accordingly for maximum adoption.
It has been shown that humans are not ideal decision makers, both because we are influenced by internal biases and because we tend to use mental shortcuts, known as heuristics, that enable us to circumvent rational processing of complex information. By contrast, we are highly skilled at making personal connections. The application of DI enables humans and AI/ML to adapt to the strengths of the other and focus respectively on their areas of expertise. This, in turn, drives better business value and ROI from AI/ML investments.
Use Case Discovery: Driving AI/ML with DI
By mapping user workflows and business processes and examining the decision analytics, as well as the heuristics and biases that influence business-critical decisions, leaders can employ DI to assess how to support better decision making and evaluate tools and resources to help. In turn, they can leverage DI to uncover key functions within the organization where AI/ML can be implemented for optimal adoption and ROI.
Employee insight is essential to the success of the developmental cycle for decision support and intelligence. Leaders should identify key personas as they are discovering Decision Analytics. These are key inputs for determining the requirements for improved decision support. It’s also critical to understand the importance of dimensions such as diversity, fairness, explainability, and actionability. These inputs help define the requirements and the deliverables for the AI/ML projects, as well as aid in determining how to deliver insights to support the user’s trust in the AI/ML recommendations.
Still, building AI/ML applications that users trust and want will not automatically guarantee adoption. The user experience (UX) of AI/ML-generated insights lags behind the UX for traditional tech. Thus, the adoption of AI/ML can be greatly enhanced by integrating AI/ML output in ways that users can easily understand, such as in a dashboard. However, the preference for how to consume information may vary by user persona. For this reason, it’s crucial to analyze the personas and weigh the relative cost of building the tools that best integrate into each user type’s workflow.
To be truly successful, AI/ML must be complemented by precisely defined key performance indicators (KPIs), both from the machine learning perspective and that of the users. These criteria help define and focus the goals of the AI/ML and provide a business justification for continued investment.
A Path to Better AI/ML Implementation
Human nature is such that we will never be able to fully predict what will encourage staff to adopt new tools and processes. However, DI offers a critical bridge to better make these judgment calls based on real-world patterns in behavior. This will contribute to targeting AI/ML on providing Decision Analytics where it’s most impactful. By taking these data-driven steps to ensure the best possible implementation, AI/ML can do what it was created to do: support better, faster, and more personalized decision-making across industries, including life sciences.
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