Consider what challenges AI might help marketing overcome. And how might these differ from the challenges faced by finance? With these two lines of business earmarked as areas of strong opportunity for AI, considering these questions is critical to understanding how IT can develop AI capabilities that will help make the business more effective as a whole.
The CMO: Building deeper connections with customers
Adoption of AI-based technologies within marketing is fairly mature relative to the enterprise a whole, and most marketing leaders anticipate AI will be the technology area that experiences the most growth over the next two years
According to Salesforce, 51 percent of marketing leaders already use AI, with more than a quarter planning a pilot in the next two years.1
51% of marketing leaders already use AI, and more than a quarter are planning a pilot within the next two years.2
Data is critical to decision making for marketers, and as they pursue the ability to develop personalized offers or communications, IT can offer a solution in the shape of natural language processing.
Consider the value marketing can generate from understanding and responding intelligently, at scale, to sentiment on social media, for example. Or how dynamic price optimization, using machine learning, could help them find the sweet spot between pricing and sales trends (similar algorithms could also generate best next offers or complementary product recommendations).
Marketing generates data on a large scale and BigDL was created by Intel to bring deep learning to big data. It is a distributed deep learning library for Apache Spark* that can run directly on top of existing Spark or Apache Hadoop* clusters, allowing development teams to write deep learning applications as Scala* or Python* programs.
Because it is optimized by design to run on Intel® architecture, BigDL can be applied to the data being captured and analyzed by current marketing-related advanced analytics solutions with minimum disruption, making it an excellent route to building an organization’s AI capabilities and generating business value.
BigDL uses Intel® Math Kernel Library (Intel® MKL) and multithreaded programming in each Spark task. This helps it to achieve high performance, enhancing deep learning performance as compared to out-of-the-box open source Torch* or TensorFlow* on a single-node Intel® Xeon® processor.
Watch: Artificial Intelligence is transforming the way enterprises use natural language processing to improve customer experience.
The CFO: Managing risk, fighting fraud, and automating business as usual
A recent survey by EY found that 69 percent of CFOs believe automation and advancing technology will fundamentally change their roles, while 65 percent say standardizing and automating processes will be a significant priority for the finance function.3
EY cited examples where AI could transform the way finance processes routine transactions, for example, with systems being given “the authority to declare something out of bounds or to respond in a particular way to anything unusual”. And risk management could be improved by AI systems that identify “patterns in large data sets that are indicative of fraud or other concerns”.4
AI systems can access and analyze much larger datasets than humans. For one insurer, an Intel®-based AI platform looked at more than a hundred thousand claims and identified a fraud ring involving dozens of claims and even more participants. Within hours of setup, it discovered another ring responsible for fraudulently claiming millions of dollars a year.5 AI can give finance leaders peace of mind that among all the transactions passing through their line of business, there’s a reliable, automated net in place to catch any potentially fraudulent ones.
69% of CFOs believe automation and advancing technology will fundamentally change their roles.6
The AI you need on the Intel® architecture you already know and trust
Your current Intel® Xeon® processors provide an ideal opportunity to prove the value of AI from a flexible foundation
But deploying AI successfully is about more than getting the technology7 right. Understanding where key lines of business stakeholders are in their own AI journeys is an important foundational step for ensuring that AI can transition from being an impressive novelty to a business-critical analytics function.