P2P

How AI Disrupts the Procure to Pay Process 

Arnaud Malardé, Product Marketing – P2P solutions expert, Ivalua

Which Procure-to-Pay (P2P) manager has not dreamt of optimizing processes into a single standardized process that could apply to all purchasing categories, all his business units and all his countries?

For many years, Procurement departments have tried to unify, standardize and optimize the number of processes while they were centralizing their activities. The motto was less processes, less specifications, less suppliers.

Blinded for the sake of optimization, many have tried to severely cut off the number of existing processes to only remain with a few process and increased efficiency. Or so they believed. Indeed, the cost of maintaining a single process is far lower than having many exceptions. In addition, employees can easily remember one process.

Nonetheless, standardization and centralization have their limits.

After 2008’s financial crisis, regulations tended to converge. This is not the case anymore. The rising local regulation requirements and increasing legislation demands have made a simple process like procure-to-pay, complex. Think about the impact of General Data Protection Regulation (GDPR) in Europe or the many states’ tax enforcement acts worldwide. We are getting into an increasingly more complex world. This often makes it difficult to meet the simplification objectives.

On the other hand, not abiding by local specific needs in the long run creates much dissatisfaction among process stakeholders. Subsequently, there comes a point where employees look for any means to avoid such a rigid process. Compliance would be at its lowest again.

Shall we then allow a different process for any specific requirement?

Doing so will multiply processes and sub-processes to the point nobody would ever remember them, not even experts. Specific processes cost more time to go through and more resources to maintain. Thus, procure-to-pay managers seek for a compromise between a reduced number of processes and a need for specific requirements. Which number allows this compromise? Having more than three ways of doing things is already too challenging for most people, whose primary job is not to purchase through a maze of processes. But for any global procure-to-pay team, specific requirements will surely demand tens of exceptional processes.

This compromise that procure-to-pay managers have to make too often tends to go beyond stakeholders’ capacity to remember processes. Process simplicity is then sacrificed due to specific requirements built in. This time, compliance will be impaired by employees being lost in the maze of processes.

Informing by any means has been the first response Procurement departments have thought of to solve this problem. Newsletters, blogs, banners, leaflets, process libraries and repositories have multiplied in every company. Procurement has become marketing savvy. However, only the most curious employees would read through this content and only the most frequent users of the process would actually remember it.

The second solution that Procurement departments have implemented is to train people. As a result, the procurement training program blossomed. But, classrooms are most of the time empty! Or only the same people keep coming every year to these training sessions. Again, this format may work for heavy process users but most will ask “Why do we need a training to buy something within a company while any newbie can shop online pretty easily?”.

So, are we fighting a useless war? A puzzle without solution?

The answer might have been YES for the last couples of years. The answer has been switching to NO these days. How? Here is how the power of Artificial Intelligence (AI) comes into play. “What can AI do about that?” you ask. Well, a number of things.

First, AI’s memory is far greater than ours. It can remember all the various processes. But, one could argue that this is no difference with a traditional computer technology which also memorizes our data. This is where the second point comes into play: AI is able to retrieve the information in a wide variety of formats and locations that could not have been done before. But what is the greatest advantage of AI is its superior user interface capabilities. It is able to translate both ways: human language into machine actions or machine language into meaningful content for us.

Let us imagine together the future of procure-to-pay with AI. In this future, a random user will think about the product or the service he needs to buy. He will not think about the process to get this product or service. He will directly address this need to the AI interface from his procurement platform, either by typing on his keyboard or by speaking his need. AI will then interact with the user through relevant questions to qualify the need (volumes, product specifications, budget, deadline) to refine the answer it will provide to the user. Eventually, this answer might be an order to a preferred supplier in a case of a mere commodity purchase. It might alternatively turn into a sourcing request which would go to a relevant Sourcing Category Manager because no preferred supplier is found and a budget threshold is passed. Determining the appropriate process to a user need will be AI’s job. Providing guidance as to the process to follow, the right goods or services to buy, the right suppliers, etc. In the end, a user will not need to know the process. He only cares about when and for how much he will get his need satisfied.

Obviously, AI will not do everything.

First, Procurement departments will need to map up their processes, translate them into rules that would be understood by AI. The machine needs to be properly fed upfront to be able to run afterwards. Second, they will need some maintenance with the business and process evolution. On this side, we can hope that machine-learning capabilities will reduce this maintenance work.

As a result, AI will empower true guided buying. Maverick spend, non-compliance or user dissatisfaction with processes will be a thing of the past.

 

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