A seasoned procurement veteran predicts that procurement and supply chain jobs need to evolve, and perhaps already are evolving, to include data expertise in their requisite skill sets, driven by the advance of AI-powered automation and its continuously reformable algorithmic learning.
Writing in Future of Sourcing, Steven Sargent, a procurement pro with over 30 years’ experience, emphasises that AI initiatives are not autonomous of human intervention: to succeed, they need human data experts who can help refine the primary purpose of the technology, which is currently to augment the human workforce.
In the future, AI may replace tedious tasks, freeing people to focus on more human and creative processes. For now, the technology needs human talent with new skills to work beside it — skills that take time to gain through retraining and on-the-job experience.
Currently, the most potent form of learning that AI-machines can perform is via ‘human augmentation’ — through interaction between their reformable algorithms and human agents.
This, Sargent says, has implications for procurement jobs, both for permanent employees and procurement and supply chain interims. Currently, most procurement professionals possess skill sets that do not overlap with algorithm development.
But that’s an obstacle waiting to be solved: Sargent believes that new-gen procurement jobs will increasingly need data expertise and familiarity with machine learning and that tomorrow’s professionals will develop on the job through day-to-day experience and ongoing professional development, rather than being hired straight out of university.
He writes: “Critical to the role are knowing what data sources are useful to resolve AI issues, being aware of how data is used in algorithms, assessing data quality, and cleaning it.”
Specialist procurement and supply chain recruitment agencies are about to start looking for talent who not only show aptitudes in strategic sourcing, category management, and opportunity assessment but also data analytics and AI algorithmic learning.