Use cases of mature artificial intelligence applications can help you implement AI more smoothly.
Ameek Singh, vice president of Watson applications and solutions for IBM said that artificial intelligence (AI) does two things really well: "It excels at natural language processing, and it rapidly processes and interprets images." I would add AI's ability to process reams of text rapidly—and to come up with recommended actions are also great features of AI.
SEE: Special report: Managing AI and ML in the enterprise (free PDF) (TechRepublic)
There is so much hype about AI and its ubiquity, fueled by stories about auto-piloted vehicles, creepy facial recognition, and human-free company operations that it's important to sometimes be reminded of AI's core capabilities.
In theory, AI can do all of these things. But the catch for IT managers and AI leaders is to determine where AI is mature enough to be successfully plugged in to a business framework and if there is a match between mature AI and what the company needs to get done.
There already are companies that have found successful alignments between mature AI and corporate business cases.
- In healthcare, AI analyzes images and helps to distinguish benign from malignant skin moles.
- In call centers, AI can be used to analyze the emotional content of customer comments and to inform call center agents in real time about customer sentiment and how to best deal with a particular customer.
- In banking, AI detects unusual credit card usage patterns and uncovers fraudulent behavior before fraud happens.
Business cases like these cement AI's role in the enterprise and help ease CIOs' anxieties because these cases are proven, and they work in production. At the same time, there is room to innovate if the research and development with AI that you're doing holds such immense promise that the risk of early adoption is worth it.
In warehouses and distribution centers, more operations are using robots, with AI providing the intelligence to the machines that automate warehouse pick-and-pack operations. This is occurring at the same time that industry leaders like Amazon tell us that end-to-end warehouse robotic automation is at least a decade away. Still, the ability to innovate and "be first" is so compelling that the risk is worth it.
The bottom line is that you have to know your business and where AI is capable of making a difference now and in the future. Then you have to assess the degree of risk you're willing to assume for the benefits you hope to gain.
If you're in an organization in which upper management reads and attends seminars about AI but really doesn't understand it, it's best to stick with the traditional wisdom of starting small and implementing the AI in production situations where we already know that AI works, provided that they fit in your organization. In other cases, it might be worth taking a risk at being an early AI adopter—if the business case is worth the risk and you have strong support from your AI vendor.
These are the fine lines that CIOs and AI leaders have to navigate as they determine their roadmaps and timelines for AI adoption.
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