Wouldn’t it be nice to reach artificial intelligence (AI) nirvana? To have a system that provides real-time, context-aware decisions. One that monitors and auto-corrects manufacturing processes, helps CXOs make strategic decisions, or help retailers determine which deals they should offer.
Sounds great, but not so fast. Though AI-driven decision-making may be achievable (and certainly desirable), your goal shouldn’t be to get there right away. An all-in approach is not only costly, it’s a risky endeavor. Instead, consider an iterative approach. One that generates value throughout the process, sometimes even in a matter of months.
Where Do You Start?
AI starts with data. But not any data. You hear a lot about big data, but data doesn’t even have to be big to yield results. Of course, you’ll need a critical mass, but data diversity is just as important to AI, if not more important than volume. Billions of transactional data sets may not lead to real insights. Though you’ll certainly learn something new, your AI engine won’t really understand the context. Context can generally only be provided through data diversity.
What is Data Diversity?
Data diversity means a mix of transactional, emotional, static, real-time, structured, as well as unstructured data. A broad spectrum of data will better help any algorithm understand the context in which you are making decisions.
How to Apply an Iterative Approach to AI Nirvana
Iteration is about taking baby steps. Incrementally feeding the right kind of data into your cognitive services or intelligence applications.
Here’s how you do it:
Here’s an example of how it all comes together. An online store has an inventory bloat of red sweaters. To shift the garments, the store runs a campaign and offers those sweaters at a steep discount—a rather traditional approach.
But with the help analytics and historical data, the store could run a more diversified campaign, perhaps offering a small discount to those who are more likely to buy that sweater (targeting frequent buyers or those who have purchased similar brands). In this model, the higher discount is reserved as an incentive for those who are less likely to purchase it (infrequent buyers or those who haven’t purchased similar goods in the past).
But wait! As the store feeds the system more rich and diverse data, it slowly turns into an AI-based recommendation engine. Let’s say, it detects an unexpected demand for green sweaters. It’s time to shift gears and follow the system’s recommendation. Instead of the traditional knee-jerk reaction of focusing on selling the excess inventory of red sweaters, even if they’re filling warehouse space, the ROI of investing into a green sweater campaign will outweigh the loss of not promoting the hard to sell red sweaters.
Having the courage to go against the flow and adopt an iterative approach, rather than diving into your big data all at once, can pay dividends and put AI-driven decision-making quickly within your reach. We call it the Minimal Viable Prediction (MVP). And it works.