Designing Decisions: Define – Part 1

Featured image credits: Michael Kutsche, 2007

Decision Intelligence is the new buzz word and it is one of the top trends on the Gartner Top Strategic Technology Trends for 2022. Gartner’s definition of Decision Intelligence

Decision intelligence is a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes. Those disciplines include decision management (including advanced nondeterministic techniques such as agent-based systems) and decision support as well as techniques such as descriptive, diagnostics and predictive analytics.

Gartner 2022

Though, I don’t see a difference in the approach between designing decisions versus designing for AI. Hypothetically, I have tried to define a Data & Analytics (D&A) Design process.

Decision design (DD) is an iterative design process in which designers focus on how business intelligence decisions are made at various stages of service delivery. In DD, design teams analyze Intelligence (metrics & scope), Business Process (Strategic & Operational), Technology (AI – IP & Data Approach) and involve in tinkering throughout the design process via a variety of research and design techniques, to improve data-driven decision making across business ecosystems.

Deepak Arasu

Businesses today spend less time understanding the problem or it is often overlooked. When products & services are already built, the data-driven decisions made by leaders on interpretations from the orchestration services around existing portfolios may not yield the right focus areas. Realizing the right focus areas need change in the ways organizations work.

If I were given an hour in which to do a problem upon which my life depended, I would spend 40 minutes studying it, 15 minutes reviewing it and 5 minutes solving it.

Albert Einstein

A successful decision is all about the value it creates and the decision-making process, but the latter can be improved. Right from data sources to organizing and visualizing data, AI technologies play a major role in decision making. Computers are better at speed and processing information for human beings to make decisions. So by combining humans in the loop to computers in the group we can make better decisions.

Humans in the loop to computers in the group

Thomas Malone

Below is a modified sample organizational structure of how of how AI team’s in large to small enterprises work.

Figure 1: Enterprise Team Structure modified. Source: Hariharan Subramonyam, Seifert, Colleen, Drucker, Steven, and Oney, Steve. 2021. Designing AI Experiences: Boundary Representations, Collaborative Processes, and Data Tools. Ph.D. Dissertation. University of Michigan, USA. Advisor(s) Adar, Eytan. Order Number: AAI28846644.

Designing Decision Intelligence adds a layer on top of all the technicalities of data science. Data science (core) engineers and AI designers who create solutions (recipes) are frequently working in distinct silos. The AI solution requires design, modeling, alignment, execution, tweaking, monitoring, and accuracy tuning. Collaboration is essential among the ecosystem’s numerous players, particularly between engineers and designers in AI Projects.

Figure 2: United Nations 17 Sustainable goals

I often question, how we can develop a more sustainable life-centered systems, given the UN 17 Sustainable development goals listed above (figure 2)? What part do academics, programmers, designers, corporate executives, and decision-makers play in this journey? What risks and tumors exist?

In the next post, I will write about the processes and tools for Designing Decisions / Artificial Intelligence.