How to Make AI Work for Procurement

Building Artificial Intelligence (AI) applications in your business without high quality data is like driving a Ferrari without gas. It’s expensive and it gets you nowhere…. Here’s how to “get the gas” for your Artificial Intelligence projects.

Defining Artificial Intelligence

Before dissecting my claim, it’s important to first state that the field of AI is overwhelmingly vast. Currently, Limited Memory AI is the most promising and useful type of AI for business. Therefore, it is the focus of this article. Limited Memory AI uses machine learning (ML) algorithms that combine various data sets to establish probabilistic knowledge. These data sets can come from facts (e.g., measurement conversion rates), past events (e.g., previous system transactions), simulations (e.g., weather projections) or even previous algorithm executions (e.g., a toy car better recognizing a wall after hitting into it multiple times).

In essence, the goal of these algorithms is to iteratively crunch data, recognize patterns and establish principles to make predictions within a confidence interval. Rules can then be build around those predictions and confidence intervals to automate actions within your systems.

As you can see, to effectively harness Limited Memory AI, you need quality data. It must be complete, available & accurate/trustworthy. Without it, system predictions are worse than human predictions (which are not great to begin with…). So, how can you ensure you have a solid data foundation on which to build your organizational AI capabilities? The answer is simple enough but attainment is difficult...

Building a good data foundation requires mastery of data quality management process for three types of system data: organizational data, master data and transactional data. In short, this involves establishing a set of standard processes and tools to govern your data’s lifecycle (creation to decommission). How do we do this?

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