Is having your procurement platform based on generative AI a ‘silver bullet’ breakthrough? I was recently involved in a discussion which got me thinking about the implications for business operations. As procurement is often tasked to leverage new ideas into business benefits, what does all this all mean………..

Quick Definition Fact Check

• Generative AI generates new ‘creative’ content – written articles, art, music; think ChatGPT, Dall-E2, AIVA . Content represents the ability to communicate ideas and depends on the context and purpose. In the procurement world, examples include How to Guided Buying, Helpdesk FAQ, and data visualization. Generative AI is trained using ‘unstructured’ data and learns from data patterns.

• Predictive AI utilizes data to generate predictions to support decision making. These insights are used by supply chain, finance and procurement functions to improve forecasting, optimization, fraud detection etc. and helps us make sense of the ocean of historical data that exists within an organization. Predictive AI supports the relevant classification of datasets, data correlation and trending to turn data into strategy formulation. Predictive AI is normally associated with ‘structured’ data.

Procurement frustrations

According to a survey back in 2020, 82% of supply chain leaders experience frustrations with AI (Secondmind, AI System Survey).

The biggest frustrations around AI were caused by a lack of reliable data (37%) and “rigid processes and internal structures” which prevented quick responses to changing market conditions (41%). 

AI relies on a large quantity of good reliable data.

Now for the broken record

i) Poor Quality Data – we all know this challenge!

The report said 96% felt this affected their ability to make effective decisions, with 50% saying they had to spend significant time manually analysing and interpreting the data to help inform decisions, and 31% highlighting expensive forecasting and planning mistakes.

Bad data or out of date data will corrupt the insight – Garbage in, Garbage out.

ii) Organizational barriers – we all know this challenge!

It takes a village to respond to an event, and any organizational disconnect, lack of alignment and barrier to react promptly to data-driven insights will hinder an organizations ability to leverage AI output.

Conclusion

Generative AI is considered core to this growth of AI; the AI market was valued at $ 136 billion in 2022, and predicted to grow at a compound annual rate of 37.3% from 2023 to 2030 (CIPS, Supply Management July 2023). However it is clear that presently it is still humans with the relevant expertise and experience supervising the inputs and maintaining responsibility for interpreting the outputs. The understandable concern is the need to establish oversight to ensure standards for responsible AI practices.

Generative AI will not solve Poor Data or Organizational challenges; and our ‘call to action ‘is the need to address these pivotal factors to best leverage the benefit of AI advancements. Any breakthrough is dependent on getting our house in order!

Final Note: Generative AI and Predictive AI are complimentary and becoming more symbiotic. Generative AI generates content for processes and can be used to create synthetic data for Predictive AI. Generative AI is able to use predictive processes to generate the next unit of content.

Help set the path. Champion data quality and organizational agility