AI-driven Master Data Cleansing – Adriano Garibotto from Creactives

AI is a term that often scares anyone who is not familiar with the technology and the application. In this episode, we're going to cover some of the different use cases for AI in the procurement space, and then dive into master data as a specific case study. Adriano Garibotto, Co-Founder and Chief Sales & Marketing Officer of Italian procurement data management company Creactives is my guest on this week's show to break down this not-as-scary-as-it-sounds technology!

Using AI to Clean Procurement Data

Creactives originally set out as a cost reduction consultancy for indirect procurement back in the early 2000s, focusing specifically around optimisation of MRO spend in manufacturing businesses. The constant challenge of poor data, inaccurate or missing taxonomies and battling with free text PO descriptions is what ultimately led them down the path of creating a software business to solve this problem at scale. Origins of what Creactives is today comes from some of the early stage AI utilised by their consultancy around 15 years ago. Together with a collaboration with the University of Verona, they then doubled down on developing a software solution which can help to classify, clean and structure complex master data from multiple ERP systems and sources.

Some examples of what AI can do in the Procurement space

AI must be applied to specific fields to provide tangible solutions. The fundamentals of procurement can be broadly classified into the following 4 questions:

1. What am I buying?
2. From whom do I buy?
3. What price am I paying?
4. Who is doing the buying?

Answering these questions is not easy, especially in large, enterprise level organisations with legacy systems i.e. different ERP systems, multiple languages and complex supply chains. AI can play a strategic role in the harmonisation of the data and helping to create a unique visibility. This is the fundamental building block which leads to other opportunities to use AI in the procurement space.

How can AI be leveraged as a catalyst for positive change?

Addressing resistance to technology is a change management issue, as Adriano explains. At its core, getting the right organisational structure in place is the key to success. Data preparation historically required a large amount of work from procurement professionals. There is the classic Pareto of 80% of the time being taken doing the preparation, and only 20% conducting the actual added value activity for which the clean data is necessary. If AI is able to do the lion's share of the 80%, this then allows strategic resources to be freed up to focus on more value-added activities which can actually implement the changes and the projects to drive the costs down, or reduce the supply chain vulnerability, or whatever the higher goal of the activity may be. Data itself on its own doesn't intrinsically have value - it is the enabling factor that facilitates the journey to be able to deliver the value.

Is category management dead as an organisational structure?

Adriano makes a controversial - but very valid - point that the category management architecture of procurement organisations which has been dominant for the past 20 or so years will be rendered irrelevant by AI. The way procurement teams operate in future will be beyond the category model, as a result of data being the driver of how organisations drive value in their business. Product launches and lean activities cut across numerous different categories, and the design of procurement departments must consequently adapt to this.

To what extent can AI perform data classification?

Adriano uses a nice example of comparing master data and AI solutions to motor vehicles. The more sophisticated the engine is, the more sophisticated that the fluid going into the engine needs to be. The same applies to procurement data and the AI...