Applying artificial intelligence to business and management processes at all levels of an organization offers great potential. The potential to create more agile and adaptable ways of working, make better decisions, increase operational efficiency, and even identify and capture entirely new opportunities.
Realizing this potential is now a priority for companies in most industries - because they want to stay relevant and competitive.
Before developing and implementing an AI strategy, leaders need to determine where AI, data analytics, and machine learning can fit into their organizations and how they can use these tools to create value. The first step is to evaluate the current landscape of artificial intelligence.
Matthew Mitchell, Professor of Economic Analysis and Policy at the Rotman School of Management, says:
“Everything is on the map. There are companies that are world leaders in understanding what these technologies can do and already implement them. This includes the usual suspects of tech companies and institutions in the marketing space, and companies like banks and airlines - a major customer service aspect for their business. We also see that many companies say "Our company needs an AI strategy". Often they don't really know that this AI strategy will be there to accomplish, and they can juggle too much about what these technologies can do for them, because they don't know the importance of it. ''
“Again, the answers are all over the map,” Mitchell said, considering the current state of development of technologies and they are now in a mature stage. I think you can use the word 'mature' in the sense that people know how to use these technologies and the technologies increase efficiency for large numbers of companies in some areas. In other areas, I think it's a very remote prospect. ”
The key to getting started with artificial intelligence and machine learning is that there are some people in the firm who have a general understanding of what artificial intelligence algorithms do. As Mitchell points out, these algorithms "mostly try to solve classification problems - deciding from which category something comes from - customer, product, or from another data point based on the data. If someone contacts your call center, the algorithm is what they hear and calculate." From words, they can understand what their problem is and decide how best to move forward, whether to be sent to a person or some other kind of solution. "
Call centers are a great example of what technology can do. It can categorize people, tasks, and issues, and help determine where resources should be allocated. When it comes to a call center, who are the people who really need to talk to a person?
"In targeted marketing, this technology is useful in classifying what kind of buyer it is. In some companies, AI also helps HR decisions by scanning candidates. Understanding what problems AI is solving well will help you understand where it can add value to your organization."
"There is no one-size-fits-all solution. You cannot leave this technology to your HR department or customer service department. It starts with a high level of knowing what this technology is capable of."
Start with the problem
Mitchell believes that rather than seeing this through the 'new technology' prism, you should start with the business problem you want to solve. "You have to start with someone who has a problem and understand how these algorithms work, they should be able to communicate with someone with practical knowledge so they can implement these processes together."
How this information can be obtained can depend on many factors. While a large bank may have a technical personnel department with a background in data science and machine learning, with a thousand employees attacking all business problems in the firm with their technical training, for many other firms these technologies are what you don't do.
There are things you can outsource and buy from companies that have technical knowledge. “The important thing is to understand when there is a match between a business problem and this type of technology,” says Michell. "If this match is missing, your data will not be successful no matter how technically complex your data is, and the truth is, if you have a great match, you may not need to hire your own technicians."
This concept is central to the thought behind the program at Rotman, led by Mitchell: Put AI to Work: Managing with Machines.
The idea is to address some ideas about how modern artificial intelligence works and when it doesn't work in a non-technical way. Participants will be those who need to find out how artificial intelligence fits their firm. "We are not looking for super technical people who want to learn the absolute essence of how these algorithms work. We are looking for leaders who want to understand the decision-making level I mentioned earlier, the technologies, the environment, and the problems that need to be solved."
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