Every day, innovative companies are finding new and exciting ways to use artificial intelligence (AI) and machine learning (ML) for smarter decision-making. It’s a topic we often cover on the APEX of innovation, including how companies are adopting AI across finance, technical, and IT teams, essential strategies for upping your AI game, and the components of a successful AI project.
Now, we’re looking at the impact of AI and ML on today’s business intelligence (BI) solutions, which have undergone a rapid evolution over the past few years, providing easier and faster data analysis and access for employees. This includes the development of data visualization applications, which help employees see and interpret information more rapidly with self-service dashboards and easy-to-use reporting and analytics tools. By providing a more efficient way for employees from across departments to access and use data for decision-making, businesses can quickly identify business trends and opportunities.
A recent CIO article on the topic details how AI and ML technologies are ushering in an era of “decision support platforms” that are becoming integral parts of company workflows. According to the article, “Business intelligence systems and strategies are being augmented with AI and machine learning to provide decision-making context and recommendations across the enterprise.” This approach is most commonly used by companies in the form of recommendation engines and analytics applications that predict what consumers will buy, need, or watch next.
A break-through example of embedding predictive analytics into enterprise workflows comes from TIBCO customer BMO Financial Services Group, which includes the Bank of Montreal. BMO uses the technology to provide customers with highly personalized experiences, including the right product and service offers at the right time. By enabling real-time information, identifying key incidents, and automating processes, BMO can make smarter decisions on how best to engage and serve customers.
“We use real-time event processing to trigger actions based on the right events, so our response to customers is relevant,” says BMO’s Vice President of Customer Analytics, Gayle Ramsay. “As digital marketing and social media have become more important, our customers are expecting us to know them in real-time. We wanted the capability to serve them and provide relevant offers whether they interacted with us in a branch, through a call center, or in digital space.”
As a result of this new approach, BMO has increased their customer satisfaction scores, and their customer acceptance offers are three times higher. Moving forward, the bank plans to enable real-time decisioning and predictive analytics across even more channels, building on its current foundation.
If you’d like to learn more, you can read the complete BMO success story.