Building an AI-Ready Procurement Department
It’s fun to see what wild hallucinations ChatGPT returns with absurd prompts.
By now, we’ve all experimented with artificial intelligence to some extent. Perhaps you’re already using some tools with AI integration for supplier management or contract reviews.
However, successfully leveraging AI requires more than just adopting new technology; it demands a shift in corporate culture and a workforce equipped with the right skills.
The Need for Skilled Talent
As AI moves from a what-if to a must-have, organisations realise the growing need for employees with specialised skill sets. Roles such as data scientists and data engineers will work alongside procurement teams. These professionals play a crucial role in harnessing the power of AI to streamline procurement processes, improve efficiency, and enhance decision-making.
However, recruiting such talent is only half the battle. Companies must also focus on upskilling their existing workforce. The rapid pace of technological advancement means that employees need to be continuously trained in new skills, such as machine learning operations and data analysis. In dynamic industries like procurement and supply chain management, decisions must often be made in real time.
Cultural Impact of AI Implementation
Change is never easy. Have you gone through implementation of a new ERP or procure-to-pay solution? The technical hurdles are only part of the process. In the push to adopt new tools, it’s easy to underestimate the impact on people and processes.
While the technology is exciting, it’s critical to consider how it will affect the workforce and business operations. Companies must be intentional about integrating AI, ensuring that it supplements the existing labour force rather than replacing it.
Like any significant initiative, engaging the user base early and often throughout the project will lay the foundation for success. Expose users to the project early to provide comfort and awareness so they will stay engaged along the way. Build leading and lagging adoption and success metrics as the project moves from development to deployment.
Users must understand what AI can do and what it can’t do. AI should be viewed as a tool that makes jobs more efficient and enhances employees’ roles rather than as a threat. For instance, while there is a lot of buzz around generative AI and prompt engineering, focus on the broader aspects of AI, such as machine learning operations and the ability to industrialise AI at scale.
For the most part, AI is embedded behind the scenes in tools you use daily rather than an extra step, like when you ask ChatGTP to write an email for you.
Lay the Foundation for AI Success
The phrase “Garbage in, Garbage out” was common in the early days of computers decades ago, and it’s never been more true.
For AI to be truly effective, organisations must first establish a strong data foundation. This includes having the necessary data collection systems in place, ensuring data accuracy, and maintaining a robust data abstraction layer to contextualise information. Without these foundational elements, even the most advanced AI systems will struggle to deliver meaningful insights.
Building that data foundation means moving away from trusty spreadsheets to embracing data integration and the analytics that result from it. Spreadsheets are the backbone of many operations but limit the organisation’s ability to build a holistic set of data and applications.
As the company progresses away from spreadsheets, it can integrate internal data with external sources, suppliers, and logistics groups to improve the model’s performance. You can replace garbage with “Value in, Value out.”
The organisation must be ready for the transition. Without the data foundation and the resources and structure in place, the effort will likely fail.
Most importantly, implementing AI should not be siloed within specific functions. Instead, it should be a collaborative effort involving cross-functional teams, including business product owners, data scientists, software engineers, and value realisation teams. These teams should work together to drive a consistent AI strategy across the organisation, ensuring that AI initiatives are aligned with broader business goals.
Visibility and Decision-Making
One of the most significant benefits of AI in procurement is its ability to provide enhanced visibility across the entire supply chain. AI-driven insights allow companies to identify potential issues before they escalate, whether it’s a supply chain disruption or a regulatory change that could impact operations. By automating lower-level alerts and focusing attention on more critical issues, AI helps procurement teams make more informed decisions quickly.
Furthermore, AI enables a deeper understanding of complex issues by providing a conversational layer that allows users to interact with data more intuitively. This capability is particularly valuable in fast-paced environments where traditional data analysis methods may be too slow to provide actionable insights.
Conclusion
To help cut through the noise around AI, focus on finding solutions to specific business problems rather than some magic-wand AI overlord. Use that business challenge to narrow the field for the solutions that you consider.
The successful adoption of AI in procurement is not just about implementing new technology—it’s about creating a culture that supports innovation and continuous learning. Organisations must invest in building a skilled workforce, establishing strong data foundations, and fostering cross-functional collaboration.
By doing so, they can unlock the full potential of AI, driving greater efficiency, visibility, and strategic decision-making in their procurement processes.
