Role of artificial intelligence or AI in procurement

How Artificial Intelligence Can Clean and Enrich Your Spend Data

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The procurement industry is undergoing a rapid transformation. Once considered a laggard in the race to digital, the industry is evolving into a strategic function that's driven entirely by data and insights. While data is crucial to driving these transformations, bad, unorganized, or dirty data is considered no lesser than a bane for the procurement industry.

In this article, we'll be focussing on breaking the myth of “bad data in, bad data out,” based on a conversation with Mosaira Rhodes, Lead Data Scientist at Ignite Procurement. We seized this opportunity to delve into the complex topic of AI, data science, and machine learning using her subject expertise and insights as the guiding light.

Watch webinar: AI in Procurement: From Bad to Good Data

The Rise of AI in Procurement

AI or Artificial Intelligence in simple terms is a substitute for real intelligence as it mimics human abilities to sense, think and act. This human-like intelligence or AI can be exhibited either by a computer, robot, or software. Just like humans, AI also learns from examples and experiences and is thus capable of recognizing objects and patterns, understanding and responding to language, solving problems, and making decisions.

Machine Learning on the other hand is a subset of Artificial Intelligence application that learns by itself and Natural Language Processing or NLP, a subfield of AI that aids computers to understand human language. There's been enormous growth and research in these domains that have enabled data-driven organizations to turn massive amounts of information into actionable insights.

Procurement is an information-intensive process where professionals are surrounded by data but starve for insights because most of the time, the quality of this data is poor. This is where AI can come to the rescue. AI can organize, clean, enrich, and then finally analyze even bad, incomplete, inaccurate, duplicate, and invalid data to extract the pieces of information you precisely need.

What Does Dirty or Bad Data Really Mean?

Data comes in many forms and its quality is directly proportional to how clearly it can represent a given situation. If the picture it represents is bad, it leads to decisions that are made under the wrong premises that can sometimes prove disastrous.

What's bad data?

How AI Can Help in Procurement 

As Rhodes mentioned, a lot of companies see bad data as a huge barrier to pursue data-driven procurement. While there's no questioning of the vast potential and capabilities of Artificial Intelligence, and the benefits that it can bring to the industry, many companies still find it hard to understand and that's why refrain from using it.

Furthermore, procurement lags in using data. Many organizations still believe in running old-school procurement practices and prefer the human touch over using AI. They need to take a closer look at what AI can do for them. They need to envision a future in which they can create a robust spend data foundation even from data that lacks structure and nomenclature, is full of manual errors, and often has missing information.

But the procurement industry is in the midst of a sea of change. In the Deloitte 2018 Global Chief Procurement Officer Survey, 45% of the CPOs said they were using, piloting, or planning to use AI. Yet, many consider this advancement just another technology hype but the truth is, the impact that AI can have on procurement is wider and deeper.

Of all the technologies of AI, the one that is most relevant to this context is text analytics and NLP. A decade ago, a majority of the companies were dealing with the problem of not having enough data but now the tables have turned. And with time, this database will only grow. Luckily, to handle this demand, we have the NLP solution which is getting better each day.

NLP can extract information from complex or raw data sources. Using advanced computer linguistics and analytics, it can sift out real-time, granular insights contained within data and make the information accessible and understandable. The AI-driven spend analysis then goes many steps further to enrich this data with things like:

  • Text- and Natural Language Processing-based data streamlining.
  • Continuous data audits to check for data accuracy.
  • Accurate spend classification where transactions are correctly tagged to the referred items.

These steps mark the beginning of a strong data foundation that's the lifeblood for enabling an effective procurement initiative. However, the key to ensure its effectiveness lies in doing it consistently, sustainably, and in a scalable manner.

We at Ignite Procurement believe that every company can unlock the monumental benefits of strategic and data-driven procurement, both in terms of impacting the bottom line as well as how procurement can contribute toward the larger organizational goals.

Want to take a closer look at how AI can be a game-changer for assessing and transforming dirty data, sign up for our upcoming webinar about “AI in Procurement: From Bad to Good Data” here.


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