Adding artificial intelligence, machine learning, and other cognitive interactions to traditional business processes and applications enables greatly improved user experience and productivity. These technologies are already impacting all levels of business including finance, marketing, human resources, and sales.
Proponents point to incremental changes in the way we work with AI and ML. From the auto-complete or autocorrect on your phone, to chatbots, and virtual assistants like Siri and Alexa. But larger implementations are also taking hold. The New York Metropolitan Transit Authority (MTA) is using machine learning to understand rider patterns and will be changing color coding of their maps to help commuters and tourists get to their destinations faster.
However, these visible AI and ML projects are more the exception than the norm. Industry experts point to massive interest and investments over the next few years:
To help put these trends in perspective, we invited Kirk Borne to the Oracle Analytics Advantage podcast to discuss the value of AI and Machine Learning for business decision-making. Borne is often touted as a worldwide influencer on data science and one of the leading thinkers on the topic of large scientific databases and information systems.
Borne currently consults clients for Booz Allen Hamilton. He's a researcher, blogger, data literacy advocate, TEDx speaker, and author of several books. He's even a project scientist for NASA as part of its Space Telescope Science Institute.
During his interview, Borne explained that the successful evolution of AI and ML in business should be linked to solving the questions at hand
"I like to remind people that machine learning is an algorithm that learns from experience—it detects and recognizes patterns in data," Borne says. "So, if you are classifying a disease or if you are classifying a customer, or information in your weblog, once you see that pattern, you can take the appropriate action."
While the promise of AI and ML are encouraging, one strong barrier to adoption is a cultural one, according to Borne.
"If you are going to have AI help you in decision making, that means letting go of some authority of your own," Borne noted with a nod to top executives in contrast to mid-level managers who may see AI and ML as a threat to their jobs.
To help mitigate cultural changes, Borne advocates for a group of data experts instead of a single decision-maker when it comes to implementing AI and ML.
"Whether the person is the chief data officer, chief data scientist or chief algorithm officer—which is a term I heard the other day—this person should have the view that people should be empowered to speak up when they see something in the data that can help improve the business they are empowered to bring that forward – it's not just the executive suite or the data scientists."
Thankfully, many companies are already setting themselves up for success by adopting a cloud-based analytics infrastructure, which he says is needed to keep costs down.
"If all you need are a few minutes to process data and some of these major cloud providers are providing the tools that also use the cloud—then you don't have to incur any more costs," he says.
Borne is also eager to change the meaning behind the acronym of AI. Whereas most people identify AI as artificial intelligence, Borne suggests businesses think of decision-making with the "new AI" where the letter A could mean Accelerated, Actionable, Adaptable, Amplified, Assisted, or even Augmented.
"I think taking full advantage of artificial intelligence is a stretch even for the major companies that announce they are 'AI First,'" Borne says. "Even the ones that are fully using AI tell me they are still on the growth curve. Your average company has typically not even started with AI, which means there are a lot of opportunities to get started."
To hear the full interview, check out the Oracle Analytics Advantage podcast by clicking on the podcast photo below and visit the Oracle Analytics Cloud website to see how to apply AI and machine learning to your data strategy.
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