Buyers 2.0

Purchasing processes are experiencing a period of great change and evolution due to heavy investments in digitization and the dynamics of transformation towards greater sustainability. The Covid19 pandemic and the war in Ukraine have also forced companies to redesign supply chains and review relationships with suppliers.

In this historical moment of great discontinuity, Chief Procurement Officers (CPOs) have taken up the various challenges by researching new solutions to support the purchasing processes. In particular, many companies have focused on adopting technologies to increase the productivity of procurement managers and the end-to-end visibility of supply chains, exploiting the value of data and the potential of Artificial Intelligence. From a research conducted in 2021 by the SDA Bocconi Procurement Lab which interviewed procurement managers of 132 medium-large companies, 37% of the sample had active projects in the AI ​​field and 16% had already adopted the tool on a large scale.

The application of AI to purchasing processes can in fact provide an important contribution to the ability to process historical and contextual data, improving the buyer's ability to possess a more complete vision of the business scenario and to make more solid and rational decisions. Recent research has identified various applications on processes of Vendor Management, eSourcing & Tender Management, Contract Management and Spending Analysis.

In recent years, numerous companies have devoted economic resources and people to AI, starting a constructive dialogue with external subjects such as consultancy firms and software vendors and, in some cases, even with specialized start-ups, as highlighted by the research project.

The objective was first of all to make the operating machine of the purchasing processes more efficient, introducing algorithms capable of automating and governing time-consuming activities for buyers and to support them in the various phases of the purchasing process. Technologies such as basic data analysis and visualization, Robotic Process Automation (RPA), Natural Language Processing (NLP)/Text Analytics and Optical Character Recognition (OCR) have proven to be particularly popular in this area.
Secondly, as a consequent evolution, there has been a focus on the application of AI in complex areas, i.e. in support of a better reading and analysis of the competitive context. Algorithms such as Advanced & Predictive/Prescriptive Analytics, Machine Learning and Deep Learning are being used.

In this case, the effectiveness of the algorithms in analyzing and processing the data depends both on the availability of quality data (produced by the company, shared by the supply chain or provided by third parties), and on learning systems based on automatic feedback or returns from the reference buyer. To date, the output of the AI system, i.e. the final decision, is rarely left completely to the autonomy of the machine (only 4% of the projects analyzed). This aspect not only means that the buyer remains ultimate decision maker, but also highlights the need for full man-algorithm collaboration.

Large companies, early adopters to date, state very positive results from the introduction of these solutions (51% of projects), in several cases even exceeding expectations (10%). At the same time, they reveal how the critical issues for projects are linked to organizational aspects such as corporate culture, people's skills and the ability to re-engineer processes.
We are still in an early-adopter phase, but both technologies and the experience accumulated by companies and service providers are contributing to a strong acceleration of the phenomenon. Looking to the future, two investment directions can be identified.
For companies not yet engaged in AI projects, the advice is to work on corporate strategies and culture in order to ingrain greater openness to the technologies themselves, focusing on more proactive leadership towards external collaborations.

For organizations that have ongoing and already operational projects, there is a need to develop training plans for people to strengthen the skills acquired and develop new qualified skills, also through greater integration and collaboration with external parties. Furthermore, attracting young resources, more inclined to use and evaluate these technologies, can be of enormous support, especially if associated with the enhancement of experience and the ability to identify the critical points of processes brought by more senior buyers.