Decision Intelligence (DI) is the commercial application of artificial intelligence (AI) to business decision-making processes in all areas. It is outcome-driven and must meet commercial goals. Organizations use Decision Intelligence to optimize every department and improve business performance.
What is the significance of Decision Intelligence?
Decision Intelligence allows businesses to use artificial intelligence (AI) and data to make quick, accurate, consistent decisions and address specific business needs and problems. It enables the collection and modeling of data using machine learning to predict accurate outcomes for optimal commercial decision-making.
What Decision Intelligence is not is the complete removal of humans from the decision-making process. It's about empowering humans with AI and a more holistic, accessible view of all of their business data so they can make the best decisions possible.
It enables businesses to process and predict data in order to make more informed decisions at all levels of the organization and gain better visibility into their operations while driving game-changing commercial outcomes.
What exactly is the distinction between Decision Intelligence (DI) and Artificial Intelligence (AI)?
AI is the theory and development of algorithms that can perform tasks that were previously only performed by humans, such as decision-making, language processing, and visual perception. Decision Intelligence, on the other hand, is a practical application of AI to the commercial decision-making process.
It suggests actions to address a specific business need or to solve a specific business problem. Decision Intelligence is always commercially focused and powers large-scale business decision-making for organizations across multiple industries.
Decision Intelligence is uniquely positioned to assist in making sense of massive amounts of data, especially when a clearly defined outcome or metric is measurable. The following are the primary advantages of decision intelligence:
● Automate and accelerate the discovery of insights in order to deliver actionable recommendations.
● Continuously uncover hidden drivers of business change to keep a pulse on KPIs without hours of manual analysis, allowing an organization to act in real time to capitalize on opportunities and address problems.
● Make number-intensive data and business analytics metrics understandable to non-expert analysts.
● Contextual intelligence is used to make data more understandable and useful by explaining how KPIs and other data are relevant to end users.
● Allow for better-informed, more data-driven decision-making that is also faster than traditional BI. Allow users to drill down to see more granular data to support a user's decision and its impact, while also providing them with actionable insights and recommendations for decision-making.
When it comes to decision-making, it can be difficult not only to make the decision but also to live with the decision if it does not turn out well. The following are some common decision-making challenges.
● Incomplete Information: A lack of available information places decisive leadership in a sea of uncertainty, making it impossible to interpret that information and make the best decision.
● Information Overload: Traditionally, most people believed that having more information and data at your disposal would allow you to make better decisions.
● Time Constraints: Time constraints can put additional pressure on you to make a business decision faster than you anticipated.
● Uncertainty: Another challenge for organizational decision-makers is uncertainty. Even though organizations face uncertainties on a regular basis, it can be difficult for even the most experienced leader to overcome uncertainty about the future.
Using AI-powered business intelligence solutions allows businesses to make better and faster decisions within critical business processes. Companies can thus not only reap the full benefits of being data-driven but also consider the widest range of relevant information when deciding on the next step.