AI use cases in the enterprise
Nothing creates a sense of urgency like reports that your competitor is realizing double-digit improvements in key metrics through their use of AI, as CIOs stated in HBR Analytic Services report, An Executive's Guide to Real-World AI: Lessons from the Front Lines of Business.
“If you believe every company is a data company and every company gets leverage out of being a [data] model company, then the first-mover advantage that we’ve seen in the tech space starts to apply much more broadly,” says Bill Mayo, CIO of the Broad Institute, a biomedical and genomic research center in Cambridge, Massachusetts. “If a competitor can leverage data to make some hundredfold improvement for the customer, they can easily pull away from, and ultimately dominate, the market in no time flat.”
PwC recently analyzed some 300 AI use cases and found the most immediate benefits would accrue in industries such as healthcare, retail, and automotive. In reality, most businesses across sectors will benefit from AI-enablement.
“As organizations digitize, they are flush with data. As a result, there is a real need to organize it properly and get to its hidden business value,” says Jarvinen. “AI is most effective as a tool to augment human employees and teams faced with the need to analyze massive amounts of information. With AI, teams can leave boring tasks to the technology to focus on the higher-order need.”
[ Can AI solve that problem? Read also: How to identify an AI opportunity: 5 questions to ask. ]
AI is best matched with problems for which large amounts of historical data already exist, or for which data can be simulated very quickly. “Since AI models are only as good as the data they learn from, the data can make or break the quality of the solution,” Havens says. “Luckily, there are many types of problems for which lots of data exist.” Think customer service, cybersecurity, fraud detection, marketing, network management, predictive maintenance, and supply chain automation, for a start.
Business leaders should look for critical business junctures, particularly those problems that have a large amount of well-defined and authoritative data in which the outcomes that you want to affect are embedded in the data, says Andrews. In situations where you can ultimately measure the impact of the AI – whether a customer had a positive or negative experience, whether a client purchased the tool or abandoned the buying process, whether the aircraft engine was actually in need of maintenance or the prediction was off – the system can actually improve over time.
As we’ve recently explored, machine learning is probably the most common form of AI in action today at enterprises. Consider this example of how machine learning helped solve a problem at Beth Israel Deaconess Medical Center: Its operating room capacity was stretched thin.
“Machine learning using data from a million patients – including OR times of the past, procedures done, and patients’ disease, gender, age, comorbidities, medications, etc. – determines how much OR time is needed for any given patient,” the HBR report notes. The medical center freed up 30 percent of OR capacity as a result.
What AI isn’t
AI should not be viewed as a hammer for every nail in the business. It’s best suited to certain types of business challenges. What’s more, applied AI is in its earliest stages of development. “AI is not a panacea to solve the entirety of the world’s problems,” Havens says. “Rather, it’s a technological toolkit for specific challenges that can be addressed through an AI-augmented approach [when it is] worth the investment to do so.”
There’s no magic to AI, says Gartner’s Andrews. “Think of AI as the ability to achieve betterment of a key, well-defined enterprise task at scale,” he says. “AI can’t eat your dinner for you, but it can definitely do a great job of matching the silverware to the food.”
[ Want lessons learned from CIOs applying AI? Get the full HBR Analytic Services report, An Executive’s Guide to Real-World AI. ]
