We live in a world of instant changes where decision-making acquires more significance and commercial importance. Data and information are valuable resources that can either bring to the overall business success or lead to substantial commercial losses. But what if digital technologies can assist decision-making, enabling people to become more informed and confident in attaining strategic or operational goals? Decision Intelligence comes on the stage for precise, insights-driven, timely decisions.
Artificial intelligence is a theory and practice that allows machines to perform tasks and operations that only humans can usually handle (e.g. language processing or visual perception). Decision intelligence, in its turn, is a commercial application of AI decision-making, allowing companies and businesses to take full advantage of data-driven decisions in the commercial sphere.
DI always carries a commercial connotation, focusing on figures and statistics. For instance, when planning to expand the market share, launch new products, or even hire new employees, we imply DI to assist us. Considering the abstract situation, let’s suppose that AI has developed an algorithm that predicts the demand for a specific product in marketing. Finally, it becomes DI once the marketing team implements that same specific product decision created on the base of an AI-powered prediction. Hence, artificial intelligence decision-making is a phenomenon that serves as a peculiar template for DI commercially-inclined actions.
Now, we have understood how AI and DI are interconnected. But still, there is another definition concerning DI that needs to be explained. Let’s clarify what Business Intelligence (BI) is and learn about its main features that are different from DI.
Business Intelligence is a broad definition that unites business analytics, data visualization, data mining, data tools and infrastructure, and other practices to assist decision-making.
One of BI’s important features is filtering data to extract insights. Decision intelligence goes further since it creates an impactful interconnection of technical performance and business objectives to create driving-forward decisions. Business Intelligence helps to prepare the foundation for DI-based decision-making, analyzing, visualizing, and extracting insights using such tools as Tableau, Power BI, and others.
Summing up, decision intelligence can be illustrated as a combination of data science, overall management, and business intelligence. It extracts the information gathered by stakeholders, managers, and CEOs and channels this structured flow in the direction of making data-driven decisions.
Experts define three types of how DI can assist in the decision-making process. They depend on the level of autonomy and the degree of human interference in the process.
The first level is decision support which involves machinery logic and auxiliary tools such as analytics, alerts, and data exploration. At the same, humans make decisions solely by themselves at this level.
Decision augmentation is the second level where machines play a more proactive and significant role. They analyze and evaluate data and generate recommendations and predictions while responsible persons review and validate them. Humans can instantly accept the proposal or cooperate with a machine to amend and improve it.
The third level implies decision automation, which significantly reduces humans’ involvement with the total delegation of tasks execution to machines. Devices automatically utilize rules, instructions, and AI-based predictions, while humans act as high-level supervisors who monitor the risks and are ready to review outcomes.
It is worth mentioning that this three-level model is not rigid and has the opportunity to change. Moreover, some decisions do not require augmentation or automatization since they are infrequent and sensitive, and humans cope with them in a better way. Nonetheless, an effective DI model includes three levels with the ability to downgrade or lift the level of automation to fit some particular needs.
Decision intelligence combines technologies (e.g. AI and process automation, machine learning, etc.) to attain economically reasonable decisions. AI and machine learning are intended to collect data and provide structured information to make insights, but they do not deal with decisions’ execution and outcomes. At the same time, business process apps (e.g. robotic process automation, process mining, and process discovery) are task-focused, which means they execute jobs flawlessly but do not contribute to decision-making effectiveness.
Decision intelligence unites AI, process automation, and machine learning, connecting data with decisions and outcomes. It delivers insights based on processed information, uses them to make the right decisions, and provides feedback by checking the decisions’ effectiveness.
A common DI technology usually contains:
More and more international brands in various domains adopt DI technologies. Google opened a department with 17 000 employees coping with DI in 2018. Similarly, Alibaba created a Decision Intelligence Lab to reduce costs and improve efficiency in data analytics, capital arrangement, content process, inventory pricing, and asset allocation in 2018.
Today many companies face issues with the absence of Artificial Intelligence solutions, hence they do not have relevant business scaling and digital transformation instruments in e-commerce. Decision intelligence, on the contrary, eliminates those difficulties and gives companies the capacity to achieve the expected business impact.
Gartner forecasts that 33% of organizations will assign DI analysts by 2023. As a result, more and more entrepreneurs are looking for a broad overview of what decision intelligence is to benefit from innovative technology in search of a profitable and payback solution. Considering our company, Innowise Group keeps pace with delivering DI and BI solutions of any complexity and scope and is ready to offer a business solution that streamlines workflows and boosts sales.
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