On this week’s episode I get to talk to a well-known industry expert and dive into a subject many of you may be confused and excited about in equal measure.

Melissa Drew, Associate Partner at IBM is my guest to dive into the topic of use cases for AI in procurement applications. We tackle where we are, where we’re headed and what some of the opportunities and limitations could be for this technology in this space.
Use cases, opportunities and limitations of AI in Procurement
Melissa starts off by giving a quick whistle-stop tour of her career so far, which includes 27 years in the procurement space in one form or another.

I then begin by asking the question of whether AI and robots are about to take over our lives.
Are robots about to take over our jobs?
Melissa gives a very important definition that AI and cognitive technologies are actually different things, and that what we commonly refer to as AI in digital procurement terms is in fact what she would define as cognitive technologies.

The difference being, in the example she gives, that AI in driverless cars is constantly evolving and developing its behaviour as it learns from different scenarios that it is being exposed to. Whereas on the other hand, cognitive technology that’s often used in digital procurement technology is highly reliant on the human that is programming the algorithm telling the machine what it should actually do.
AI is only as intelligent as the human programming it
In order for outcomes to be representative, a human has to “teach” the models. Teaching the models can only be done by collecting data. But HOW we collect the data and from which sources obviously impacts the programming that is written around how the machine interprets this.

Melissa gives the example of Amazon’s CV screening algorithm that was unknowingly biased towards male candidates because of the way that it had been programmed based on the data it had been fed. Inadvertently, it had been shown more male applicants during its programming and as such had developed a bias towards male candidates.
What has contributed to improvements to AI over the past 20 years?
AI was originally used to augment human behaviour; to take things that we tactically need to do repetitively as a process and just do them faster and more accurately.

Synthesizing, cleaning and categorising complex procurement spend data used to take Melissa about 9 long weeks. Back in 2004, when she started applying AI to be leveraged for repetitive tasks in spend analysis and categorisation, it reduced this task from 9 weeks down to several hours. That’s the potential that AI has.

As Melissa explains, what has changed over the past few years is the infrastructure that organisations have to house the data, as well as the improved ability to be able to collect and process it all.
Why we still need humans to interpret and assess AI’s work
How do we ensure that we are collecting all of the right data? Are we using the right breadth of data. Why is AI making the decision or recommendation?