Decision Intelligence

Description

OVERVIEW OF DECISION INTELLIGENCE

Decision Intelligence (DI) is the commercial applications of AI to the decision making process of every area in a businesses. It is outcome focused and must deliver on commercial objectives. Organizations are using Decision Intelligence to optimize every single department and improve business performances.

Decision intelligence is a trending field that combine business intelligence (BI) and artificial intelligence (AI) to improve organizations’ decision-making processes. DI uses data and predictive model to make business decisions faster, easier, more accurate, and more consistent. With the rapidly increasing urgency to digitize and gain competitive value from new technologies like artificial intelligence (AI) and machine learning (ML), decision intelligence is emerging as a solution that can connect decision support, decision management, and complicated systems applications. Decision intelligence applies data science within the frame of business problems, and is attained by taking stakeholder behaviors into account to affect adoption and decision making. Frequently, it’s a fusion of business intelligence, data science, management, and decision modelings.

Why is Decision Intelligence important?

Decision Intelligence empowers companies to use AI and data to make fast, accurate, consistent decisions and address certain needs and problems in their business. It allows data to be gathered and modeled with machine learning in order to predict accurate outcomes for optimal commercial decision making.What Decision Intelligence is not is removing humans from the decision making process entirely. It’s about empowering humans with AI and a more holistic, accessible view of all of their business’ data to enable them to make the best decision possible.

Applications & Use Cases

Financial services: Processing credit, mortgage, and car loan credit applications based on the client’s income, credit score.

Retail: Inventory and fulfillment optimization and warehouse management according to demand forecast. 

Logistics: Real-time truck and freight optimization to reduce superfluous transportation and costs.

Talent management: Use decision intelligence and intelligent apps throughout the hiring and employee evaluation process. HR departments can use intelligent apps to track potential employees through the application, interview, and hiring process. And they can monitor current employee satisfaction to better understand retention and predict future hiring needs.

Pricing: Automated systems can adjust prices based on data thresholds. With the large volume of transactions, companies can apply multiple decision-making frameworks to test, iterate on, and refine decision processes and AI models. Use intelligent apps to break down data silos and get data across the organization to ensure you have the most up-to-date information. This is especially beneficial for transaction-heavy businesses, such as airlines and pharmaceutical companies.

Infervision – Healthcare
Image recognition and analysis, an essential part of AI and machine learning, can be used for diagnostic purposes in healthcare, thereby saving lives.Cancer is becoming one of the leading causes of death in recent years. Radiologists use CT scans for the diagnosis of cancer.The radiologists have to go through numerous CT scans every day for the diagnosis. It sure is a time-consuming and tedious task.China, which is lacking radiologists to review over 1.4 billion CT scans every year, is looking towards AI to fill this gap to some extent.With the ratio of doctors to scans being incorrectly balanced, overworking healthcare professionals might suffer from fatigue, which can inturn cause errors.Infervision has developed an AI and trained with suitable algorithms to review the CT scans and detect any early signs of lung cancer.It makes the job easier forradiologists because they can simply use the data from the AI and can diagnose for cancer more accurately and efficiently, treating it that much more effectively.