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AI opportunities in procurement

Hadley Baldwin

Artificial intelligence (AI) is transforming how organizations operate across almost all major business functions. The sourcing, purchasing and management of goods that form the primary responsibilities of an organization’s procurement function is no exception to this trend.

AI is already helping procurement leaders reduce costs, optimize their processes and make more informed decisions. However, with AI technology advancing rapidly, how can you be sure which use cases best suit your organization?

AI implementation

Our recommended approach

Step 1

Define the AI vision and guiding principles

Step 2

Identify AI use cases and define a strategic blueprint

Step 3

Prioritize pilots and define an AI roadmap

Identifying potential AI use cases within procurement

Not all AI initiatives are equally appropriate for every organization. Before researching the specific applications that AI can have within a function, we recommend that an organization defines key guiding principles and a high-level AI strategy to ensure initiatives remain aligned with overall goals.

With these in place, it is useful to consider the relative ease of implementation compared to the potential business benefit any initiative can deliver. This will help you narrow down the list of appropriate use cases. Considering the specific procurement ‘pain points’ or ‘opportunity areas’ you want to address should help you identify a small number of initiatives that are the right fit for you.

Promising AI developments within procurement

Evaluating feasibility and implementation risks

While the outlined use cases are becoming more popular and sophisticated by the day, feasibility limitations may still hinder deployment and user adoption. Before implementing your use cases, you should always consider any applicable technical and organizational limitations.

Some AI use cases are more straightforward to implement than others, and balancing business benefit with ease of use is critical to ensuring success. For example, while AI-powered, data-driven decision-making is already a reality, AI lacks the human intuition and judgment required for complex decision-making based on more subjective factors.

Common technical feasibility challenges for AI in procurement

Data availability and quality

Any decision-making tool relies on large, diverse data sets that are representative of the area it is attempting to analyse. Procurement data can often be fragmented, inconsistent, or simply false. Fake reviews are a particular threat to procurement decision-making and could easily skew an algorithm’s recommendation. The data cleanse activity required to produce good quality data may often be more challenging than implementing an AI tool itself. 

Risk of biased decision-making

AI tools may introduce biases or risks that affect the fairness and transparency of the procurement process. Recommendations for appropriate vendors may be subject to bias based on individual vendor characteristics. It can also be challenging to explain why an algorithm made a particular recommendation, leading to potential audit challenges in future. 

Common organizational feasibility challenges for AI in procurement

Resistance from users

Procurement professionals may express scepticism towards AI tools when they already have well-embedded processes and decision-making approaches. Explaining the drivers for change and managing employee expectations throughout the implementation is critical to a successful roll-out.

Aligning to wider AI goals

The AI use case you want to trial may address a specific procurement problem but does not align with the wider organizational AI strategy. This can result in strong opposition from leadership or potentially lead the function down the wrong path.

Lack of internal AI skills

The AI market is constantly changing, so developing the internal technical skills required to run or make changes to your initiative following its deployment can be challenging. 

Organizational structure

You may need to consider changes to the overall organizational structure to make best use of AI benefits, e.g. freeing capacity within a role may result in requirements for new role definitions or appropriate changes to team structures. 

Calculating the return on AI investment

Aside from potential feasibility factors, AI tooling often requires significant up-front investment. Procurement departments need to carefully evaluate the potential costs versus the benefits and build an appropriate case for change. 

It is easy to be swept up in the current excitement over AI, but investment decisions should always be based on solid foundations. We recommend comparing the return on investment with other alternative investments or baseline scenarios to determine which AI tooling is worth pursuing. 

Key benefit categories for a potential AI procurement business case

It is worth noting that most major procurement software vendors are starting to develop AI add-ons to their existing systems. This may offer a more cost effective, and less risk adverse approach than implementing a brand-new tool. It would also help to mitigate the challenge of integrating AI tools into your existing technology landscape which can otherwise require significant effort. 

A pragmatic approach for AI in procurement

New and existing AI use cases offer significant potential benefits to procurement functions, but a pragmatic approach to assessing and implementing them should always be adopted.

Consider the cost, compatibility, scalability, security and organizational context of your proposed initiatives before committing to significant investment. Trialling different use cases on a small scale e.g. procuring one category of products, or deploying tools within one region, may be an appropriate starting point for leaders looking to embark on their AI journey.

A risk averse approach can also be adopted when implementing your desired use cases. For example, use AI-assisted procurement approaches rather than opting for fully autonomous buyer selection, a prospect which many procurement professionals may be cautious to adopt.

As the technology matures in this space, becoming more accessible and affordable, various other use cases are certain to emerge.

Procurement leaders should make sure they stay informed of the latest developments to maintain a competitive edge and unlock the transformative potential that AI tooling looks set to bring.