In most every example of Artificial Intelligence (AI) in business, there is a chat bot or robot or algorithm that attempts to replace a human. We surely will see replacement via AI, but augmentation is also a value approach. In augmentation, a AI process considers various options and provides a human improved decision options, based on some deeper consideration or at least with more complexity and optionality involved. There are many things we currently do that are doing regularly that are often conducted with limited consideration of options or a poor consideration of complexity. The penalty we experience is in cost, quality of life, and loss of time. Here are some things, that I think, merit an AI process for continued improvement in everyday life.
- Daily Dynamic Scheduling: If you live in any major urban area, your daily schedule is highly complicated by weather, traffic, unexpected client meetings, a sick kid at school, and unanticipated delays. At least in Chicago, we see large changes in commute times based on this. Even a freight train can make a trip 30 minutes longer! However, I’d like to know that first thing in the morning and have meetings moved or rescheduled to accommodate travel time. In places like NYC, DC, and major Asian and European cities, travel times are a function of transit systems. Schedules should be dynamically linked. No more missed meetings, no more stress no more racing to be late. You know (with great confidence) that your AI made the best scheduling possible, and updated your calendar with changes to allow for that to occur.
Improvement: Less time is wasted. What would you do with more time in your life and less stress in being late?
- Achieving Best Pricing on Regular Purchases: There are things we use each day that are, however, purchased less frequently. It means we inventory these items. It means we hold them until needed and deploy capital to hold them. Gas is an example. We buy it, say, once a week. Various home sundries, detergents, and even preserved food and beverage items also have this feature. Why should we ever over pay? Imagine an AI that more or less tracks our purchases, say by examining our online purchases, and tracks or estimates our consumption (some consumption is harder to measure than gas) and pre-negotiates a purchase price and delivery time with a seller. Amazon or Jet would know to send Tide detergent because the AI negotiated a price that is beneficial and you will need Tide in the coming days or weeks. This helps everyone. The seller does not really want to hold inventory and would rationally offer a discount for having the item sold quickly. By learning the seller cost function and offering to purchase at ideal times for the seller, our little AI can get us deals on everything from paper towels to fuel. Imagine if the AI can also be integrated with the grid and allow us to throttle electricity use, so power companies can offer us discounts to consume less (or not at all) during peak periods. Without such an AI learning about costs, we all end up buying when we need or want things, resulting in us generally overpaying.
Improvement: Reduction in spending by consumers. Also, reduction in cost to sellers. The AI removes friction from the market.
- Deep (and On-going) Searching: Most people have a few special hobbies or pastimes on which they spend money. In such pastimes, the goal is generally to have as much fun or quality of experience per dollar spent. It is a constrained optimization, as we would say in economics. Even if we are not price sensitive, we prefer a larger hotel room to a smaller one, if the prices are the same or we prefer better seats at the ball game, when equally priced. However, such deals, especially deals in travel and entertainment are often limited, are quickly taken, and often have optionality that impose opportunity cost. When you find a great deal to Hawaii, you might ask, how this compares to traveling to Fiji or Florence instead? How about a great set of tickets to see the Cubs? What is a great price/seat combination, anyhow? These challenges always cause me some grief. I like to travel with my family and in selecting trips, I wish I had a AI that could think through the web of options. For instance, I might tell the AI, I want to go to Europe on certain weeks, and it returns many comparisons and the best options, not just on price but on experience, as presumably, the AI has gotten to learn my taste. No more reading hundreds of reviews and looking at thousands of pictures on TripAdvisor, your AI has fully identified the “best options” for you. The truth is that our mind cannot process the many options that are available and unless we stumble across a great deal or experience, we are likely missing something that might have been more enjoyable. Such deep and on-going searching can also be deployed to help one find and purchase a special item (yet known) or perhaps locate a special doctor for an elective surgical procedure.
Improvement: Better customer experiences. This increases the utility of the experience, but also gives providers access to consumers that are more likely to marvel at the experience. Everyone wins.
- Cash Management: Many Americans have mortgages, home equity lines of credit, student loans, car loans, kids’ tuition, and savings, simultaneously. Of course, one has savings for various reasons, even in the presence of debt. Every time, I think about the challenges of cash management in personal finance, I think of a former colleague and finance professor that confided (or boasted) to me that he kept a year’s worth of salary in his checking account. He admitted to not knowing what he wanted to do and felt it was his best option, as it preserved all optionality. Like all quasi confessions, he seemed to be seeking my understanding, approval, or at least empathy. I nodded and agreed that his choice did preserve all options. But it came at a great cost. He could have placed the money in short term money markets or used it to pay down recallable debt (like a home equity line) or even lent it to high credit quality borrowers for a short period of time. If finance professors can have this problem in big ways, then we all have it in some way. Firms have this problem, too. Essentially, we give banks float by keeping money in low interest bearing accounts. And we do it even if we don’t want to. Your paycheck gets deposited into a checking or savings account and probably is there for some days, weeks, or months before it is extracted. We can do better, but don’t because the searching and switching costs are too high. Sometimes firms need capital for a few days or weeks to initiate a project or until an invoice is paid. There is an immediate need for capital that inevitably gets filled by high interest rate corporate lines of credit. Imagine pairing those millions of such cases with everyday depositors that would be very happy to get 2%-2.5% for lending money over a couple of days or weeks. Even if you have no debt, it be great to have an AI search for short term investment options and then move your money around (even daily) to deposit or credit seeking organizations that would pay more than is paid by banks for deposits. Also, ideally, we can pay the power utility on the last day possible, so we pick up more days of float and overall have the AI manage the cash flow with expected inflows, outflows, and short-term investments in mind to maximize value. The AI can build in a cash cushion and even a slush fund. It is really about estimation, searching, and execution. It is fair and rational; everyone gets a good deal. The capital holder gets better rates and presumably the organization offering those rates does not have to pay higher rates in traditional channels. Of course, if one does have debt, the AI could help accelerate the debt servicing by looking for surplus capital and wasteful spending in the personal cash flow to pay down the debt.
Improvement: increased returns on surplus capital plus reduction in the cost of capital for multiple borrowing organizations.
- Best Recipe Tonight: This is a fun one as much as a real one. If you regularly cook and enjoy exploring new cuisines (as we do at home), then you have been faced with the challenge that you found a great sounding dish in a recipe, but don’t have or don’t think you have certain key ingredients. This is a mixing problem, which can be solved though optimization and missing ingredients suggest a trip to the store. However, consider inverting the problem, which is mathematically far harder to solve. Start with all the things in your refrigerator and cupboards and consider all the dishes you can make without a trip to the store. Wow! Some of the identified dishes are even better than the one you have been desiring. The options were too many and complex to think through. More constraints can be imposed like maximum cooking times, cuisine types, etc. The possibilities are endless (well not exactly, the possibilities are finite and in the millions, making the problem solvable) but the same algorithms can be used to identify the solutions to your problem. You identify all possible combinations subject to your supply and taste preference (which are just other constraints). Of course, this is most helpful to cooks and others with mixing problems, but it does have real value in reducing unnecessary trips to the store, reducing food waste, and increasing quality of life.
Improvement: this really is about resource allocation and making more efficient use of currently available resources.
In the fun context of preparing dinner, it would bring greater savings, joy, and exploration to life. We can all use that, plus it would be really fun! Let’s hope AI can indeed be fun!
Professor Walker provides keynote talks, seminars presentations, executive training programs, and executive briefings.
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About Russell Walker, Ph.D.
Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University. His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.
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