Who's The Bot? Driving Business Change with Artificial Intelligence
AI is like Big Data analytics on steroids, and the technology is poised to deliver great things for retail, manufacturing, and more. When the unbelievable happened—machines defeated top chess masters—artificial intelligence (AI) got more buzz than a celebrity scandal. Today, AI is on the verge of making a big mark in the enterprise.
Machine learning, a subset of AI in which machines can learn without being programmed, has particularly great promise as a business tool. It’s like Big Data analytics on steroids.
“… Machine intelligence exhibits the ability to not only learn from data, but to actually clearly articulate answers and discover why,” explains Michael Schmidt, founder and CTO of machine intelligence platform Nutonian, in a TechCrunch article. “That is, machine intelligence is the first instance of machines teaching the human and relaying brand new discoveries automatically.”
As large enterprises prepare for the new AI landscape, some smaller firms have already deployed applications. Wellframe, a healthcare software developer, unveiled a new AI-based product that customizes treatments for individuals. Lendified, a Canadian online lender, acquired Mentio Technologies for its automated cash flow forecasting product. Mentio’s software gives Lendified the ability to review creditworthiness and purportedly provide reliable projections of a company’s balance sheet. And it can perform these tasks almost instantly.
Enterprise AI analysts envision applications that can do many other things, including:
- Monitoring call center agents’ conversations with customers and compare them to the agent-generated reports of those interactions. The program could rate agent performance and enhance training programs.
- Analyzing factors that cause metal produced in mass quantities to lose physical strength. Humans can do this forensic work, but AI will be able to do it much faster.
- Evaluating the variables and relationships that impact sales in a retail shop and learn how to improve outcomes. Relationships between, say, weather patterns and marketing spending could reveal whether the retailer is stocking optimal amounts of specific products and predict when it should change its product mix to accommodate new tastes.
- Figuring out how to reduce incidences of faulty component production by pinpointing where problems occur on the manufacturing assembly line.
As more AI applications come online in the next few years, they will begin to learn from each other. “These [programs] will work together and exchange information instantaneously to solve problems in the same way team members collaborate to find solutions in today’s world,” writes Jon Lee, co-founder and CEO of ProsperWorks. “Over the next decade, it’s…possible that entire business divisions will be run by AI programmers with minimal human oversight.”
To exploit this powerful, new Big Data analytics technology, enterprises will need data scientists and statistical analysts. These specialists will be in high demand, so enterprises should step up efforts to hire them, if they haven’t done so already. Now is the time to lay the foundation for the next advance in AI—enterprise AI.