Implementing AI in Business: A 4-Step Guide for Managers

Andrew Ng, one of the world’s most prominent thinkers on AI, says the best way to implement AI into your business is to start small. In a recent talk, he gives the example of his own experience at Google and how he used Machine Learning (ML) to add value to speech recognition and Google Maps before tackling bigger business problems in the advertising department. Before managers even start thinking about AI, though, they must first identify customer problems and consider all possible solutions to solve them. Below are the 4 steps a manager should follow when using ML to solve business problems.

Step 1: Challenge your Assumptions

One of the biggest mistakes companies can make in the new world of AI is to look for or create problems because they think AI would do a good job of solving them. They create chatbots that nobody asked for or automate tasks that benefit from human touch. By speaking with customers and identifying their critical pain points, the company can avoid creating problems and start solving them. Only once proper customer discovery has been completed can the manager move on to identifying the best technologies to solve the problem.

Step 2: Consider All Solutions

Managers can add immense value with an understanding of the strengths and limitations of AI. Good leaders can craft strategy with AI in mind, but it takes great leaders to understand situations in which using AI would not be beneficial. By understanding problems that cannot be solved by AI, managers help save the company wasted time and money. Once the manager has decided that AI is (or isn’t) a good fit. They need to identify available data sources.

Step 3: Find the Data

When considering all solutions, there should be a preliminary assessment of the data available and whether or not there is enough data to successfully train ML algorithms. Moving on to step 3, the manager must consider insights ML algorithms provide.

For example, a bank is hoping to alert customers when they detect fraud in their accounts. In order to create an algorithm to detect fraud, it will need to train on examples of fraudulent transactions that occurred in the past. In this case, the data needed is transactions with a label of ‘fraud’ or ‘not fraud’ for each.

Step 3 can go terribly wrong if steps 1 and 2 were not properly completed. Without a definitive customer problem to solve and an analysis of the best solution, it will be terribly difficult to identify the proper data sources needed. By completing the prior steps correctly, the manager will have a strong basis for discussion with the data scientist and can ensure that the whole team is working towards a solution that creates the greatest value for customers.

Step 4: Identify the Best Algorithm

Once the data scientist has collected all of the data, and completed some preprocessing, managers can help determine which algorithm is the best fit.  Managers do not need to know the specifics of algorithms or what they are called, how they work, etc. Instead, they only need to establish an acceptable level of error in output and the level of explainability of the algorithm.

Would it be acceptable to the business if the algorithm for detecting fraud was correct only 50% of the time? By establishing metrics and measurements for success, managers can ensure the data scientist is working towards a common business goal.

Leaders must also determine the importance of model explainability. Customers may not wish to know how the bank decided a transaction was fraudulent, but it may be very important to explain why an algorithm rejected their loan application.

By understanding customers expectations, and how they interact with an algorithm’s outputs, managers can greatly improve the customer experience and avoid creating problems with algorithms that are not explainable and inaccurate. Carefully following the steps above will lead to high value ML solutions that add direct value to the business.