by Anna Frazzetto

What can machine learning do for your business right now?

May 30, 20184 mins
AnalyticsArtificial IntelligenceBusiness Intelligence

As a few companies race to monetize the kind of machine learning that focuses on teaching robots to behave more like humans, many companies are grappling with how to leverage artificial intelligence and smart machines.

artificial intelligence / machine learning / binary code / virtual brain
Credit: Thinkstock

Is machine learning for the future or is it for your business right now? It’s a question lots of business leaders are asking as each day new instances of AI and machine learning push the boundaries of what we thought we knew about technology. One recent and powerful example was Google’s May 8 demonstration of its Google Assistant booking hair salon and restaurant reservations with conversational clarity that was both inspiring and unnerving. Many business leaders would be forgiven for a twinge of panic. If machines are learning that quickly, how behind is my business right now when it comes to leveraging machine learning?

Good news: this is just the beginning

While its part of Google’s job to be the maverick, breaking ground with embryonic and advanced technologies, most CIOs are tasked with delivering solutions that yield business results. Outside of the companies racing to build self-driving cars or lead the way in global face recognition systems, few companies have figured out how to monetize the kind of machine learning that focuses on teaching robots to behave more like humans. Despite that fact, there is tremendous investment pouring into AI and machine learning. The International Data Corporation (IDC) predicts that investment in cognitive and artificial intelligence systems will grow to over $52 million by 2021. 

Right now: let the machines be machines

Where should CIOs focus their machine learning budgets and vision? Rather than looking for ways that technology can behave more like humans, CIOs can and should take more advantage of the some of the distinctly non-human capabilities computers have. For example, computers today can process and analyze large, complex data sets in milliseconds. Add machine learning capabilities to that colossal, rapid analytical ability and suddenly you have a business intelligence (BI) tool that can crawl ever-growing, complex business data, watch for trends, analyze what it finds and provide insights and potential solutions.

Business intelligence: where the action is

Business intelligence right now, with its ability to combine big data analytical work with machine learning capabilities, is where we are seeing several different industries make strategic forays into artificial intelligence. Here are a few examples of how machine learning is already hard at work across several industries:

  • Financial services: The financial services sector is a data-rich sector. Every customer transaction and engagement produces data that can be analyzed to improve services and satisfaction. Using machine learning, financial institutions can watch for patterns in customer behavior, from spending to saving to investing, analyze the trends and offer up custom financial advice and products.
  • Retail: Online retailers are known for their ability to gather and leverage customer data in order to maximize sales and satisfaction. This kind of information gathering and learning is expanding across retail everywhere as retailers look to business intelligence and smart machines to rapidly determine when and where the best time is to offer discounts, change products or revise pricing. You see this with surge pricing in companies like Uber and Lyft who leverage weather, traffic and event data to determine pricing in real time. Business intelligence tools will continue to be able to go deeper and broader, looking for buying trends across geographies, over various timespans and across demographics and helping businesses better know and anticipate their customers.
  • Healthcare: In healthcare, deep learning algorithms are working to help with medical diagnoses. For example, Stamford developed an algorithm capable of diagnosing skin cancer by learning from the data and imaging fed into it. Across much of the medical field, computers are being trained to study imaging and identify potential issues and abnormalities. Algorithms are also beginning to improve clinical trials and pharmaceutical development by combing data sets across various sources (labs, institutions, studies) in order to help identify success factors and/or potential issues.

The smart machines are here

While they might not yet be driving our cars and building our houses, smart machines are hard at work turning the data we have into answers we need. For business leaders wondering where to begin with machine learning, the answer is in the question. What are the hardest questions your business is looking to answer? If there’s data to explore, there are smart machines and business intelligence tools equipped to help your business with the answers.