Decision Management Solution Blog

Real-world problems require ML/AI algorithms that turn your data into insights to be combined with policies, regulations, best practices and business domain expertise. Only when ML/AI algorithms are combined with business rules can decisions be made and business value created. Treating business rules and ML/AI technology as a pair ensures your algorithms operate inside the […]

Focusing on decisions and making decision services a core element of your IT infrastructure ensures your ML/AI algorithms can be effectively integrated into your business processes and systems. With your IT department managing the decision service architecture, the ML/AI team can focus on building the algorithms that will make a difference not on being their […]

The combination of decision models, built using industry standard notation like the Decision Model and Notation or DMN standard, and business rules creates transparency in your decision making. Decision models and business rules ensure that business owners, legal and compliance, and ML/AI teams can all participate in reviewing your decision-making. Because you know which business […]

Before you “listen to the data” you need to listen to the business. Focusing on decisions and building decision models based on business know-how puts your whole ML/AI program on a business footing. Using industry standard notation like the Decision Model and Notation or DMN standard enables senior executives and others across the business, IT […]

Today’s businesses are heavily digitized and must be structured so that while interacting through digital channels, the value gained from customer experience between humans and digital touchpoints can be handled seamlessly and effectively. As a result, the line between business and technology is gradually blurring. In addition, most organizations still face many challenges with the […]

by James Taylor, CEO Decision Management Solutions “Enterprises waste time and money on unactionable analytics.1” This quote from Forrester succinctly states why machine learning (ML) projects often never progress beyond the pilot stage—a common form of “purgatory” and a topic I presented recently at this year’s Machine Learning Week. Why many ML projects disappoint ML […]

The potential for artificial intelligence (AI) to transform business in positive ways has been enthusiastically embraced by many forward-thinking enterprises. While AI promises to accelerate improvements in how we do business, enthusiasm for this technology needs to be tempered by thoughtful consideration of the ethical implications. My respected colleague Neil Raden addresses this very issue […]