The recent conversations around AI adoption in procurement increasingly focus on its potential to completely revolutionise the function. In many cases this may end up being absolutely true, but the greatest challenge facing procurement teams isn’t going to be purely technological - it’s also cultural.
Integrating AI into the organisational technology stack may seem like the priority, but it’s the human element of procurement where the real impact lies. AI adoption requires more than a simple plug-and-play approach. The true success of any AI initiative depends on the readiness of the functional culture to adapt, innovate, and learn from new approaches that AI systems will inevitably give rise to.
The success of any AI implementation is, at its core, determined by the willingness of people to embrace it. Procurement teams need to actively foster a culture of innovation, where new approaches are encouraged, even if they don’t yield immediate results. A real-world example of this challenge was seen in a large UK infrastructure organisation, which attempted to deploy AI-enabled contract lifecycle management software. The system was designed to read, profile, determine patterns, assess risk, flag commercial variances, and store complex subcontract agreements across its supply chain. The expected outcomes included greater visibility, enhanced resilience, reduced risk, and improved margins.
However, despite the clear potential of the technology, the implementation was derailed by resistance from the legal function. Fears of job displacement ultimately overpowered the potential benefits, leading to the initiative’s failure. This case illustrates how crucial it is to address cultural concerns alongside technical ones. Without the buy-in from cross-functional teams and a shared vision of how AI can complement human expertise, such innovations are unlikely to succeed. Therefore, the real challenge in AI adoption lies not just in the technology, but in creating an organisational culture that is ready to innovate and adapt.
A well-defined use case is crucial for AI adoption in procurement. Without a clear understanding of how AI will specifically benefit the function, organisations risk implementing technology that fails to deliver meaningful value. The most successful AI projects are those grounded in real-world challenges, with teams collaborating to ensure AI is solving the right problems.
A FTSE250 Oil and Gas company experienced this first-hand when they deployed optical character recognition (OCR) software—an earlier form of machine learning—across their accounts payable function as part of an efficiency initiative. Unfortunately, the project failed due to a lack of clearly defined requirements. A standard template wasn’t utilised, pre-processing wasn’t properly implemented, and the company took a ‘big-bang’ approach across multiple countries and languages without enhanced training for the remaining staff. Instead of increasing efficiency, the project led to an increase in accounts payable staff to manage exceptions, as well as a supply chain payment backlog that lasted eight weeks.
This example underscores the importance of not only defining the use case, but also ensuring proper planning, training, and execution are in place before deployment. AI should solve specific, well-understood problems to truly add value, and collaboration across teams is key to ensuring it’s implemented correctly.
Nevertheless, it isn’t just about data quality but also, availability. In a spend analytics (for cost optimisation use case), the completeness of the data is fundamental, as without the key metadata, the AI-driven insights will be incomplete – meaning the data will struggle to be structured and then offered up for a decision to be executed.
A significant hurdle in AI adoption is the misconception that AI will instantly solve all procurement challenges. In reality, many procurement functions first grapple with poor-quality data that is unstructured, unclean, and poorly governed. Ironically, AI has the potential to enrich and manage such data, but only if organisations first acknowledge the limitations of their current datasets.
Addressing these data issues requires a strategic approach. Standardising master data fields, limiting the number of staff who can modify supplier data, and harmonising the intake process are essential first steps. For organisations at the early stages of their journey, introducing a manual gatekeeper to oversee data governance is crucial. As organisations mature, they can automate these governance processes by integrating validation through APIs.
We are still in the early stages of AI adoption in procurement, and expectations must be managed accordingly. Rather than expecting AI to provide perfect solutions from day one, procurement teams should focus on improving data quality in tandem with AI implementation. This ensures that AI solutions have a solid foundation to build on and deliver real value.
Starting small with manageable pilot projects allows teams to demonstrate quick wins, building confidence and momentum for larger-scale AI adoption. By learning from past digitalisation efforts, procurement teams can avoid previous pitfalls and chart a more successful course for AI.
Agility also enables organisations to iterate rapidly, refining their AI strategy as they go. For example, a procurement team might initially deploy AI to optimise supplier selection based on cost and delivery speed. However, as market conditions evolve—such as in today’s complex geopolitical landscape—they can quickly adapt the algorithm to prioritise new factors like supplier diversity or sustainability. This ensures that AI remains aligned with broader business goals while being flexible and adaptable to the procurement function’s changing needs.
By staying agile, organisations can ensure that AI not only solves immediate problems but continues to evolve in a sustainable way that meets long-term objectives.
While AI can and will significantly enhance procurement processes in the future, human intelligence and collaboration will always be the determining factor in its success. AI should be seen as a tool that complements human expertise, rather than replacing it. The procurement stakeholder must remain at the heart of every AI initiative, using the technology to enhance decision-making, not to dictate it.
Striking the balance between AI and human intelligence ensures that procurement teams can leverage the full potential of AI while still applying the critical thinking and judgement vital to the function that only human beings can provide.
Fundamentally, the future of procurement lies in how effectively AI is integrated into its culture. Procurement leaders must lead this transformation by placing people at the centre, promoting collaboration, and encouraging agile experimentation. The inevitable adoption of AI is going to be a journey, and its success depends as much, if not more, on people and culture as it does on technology. The procurement function that can embrace this balance will be best positioned to thrive in an AI-driven future.
Joe Gibson is Director of Digital Innovation at 4C Associates.
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