Did you miss Ardent Partners’ recent webinar, Procurement 2025: BIG Trends and Predictions, that delivered a series of insightful predictions designed to help procurement teams in all industries and regions focus on and prepare for what is important while remaining proactive and agile?
The session featured Ardent Partners’ Founder and Chief Research Officer, Andrew Bartolini, and Senior Vice President of Research, Christopher Dwyer, as well as experts from Ivalua and Beeline, as they explored trends and predictions that procurement teams in all sectors and regions can leverage to better prepare and strategize for what lies ahead in 2025.
As part of the webcast, Christopher Dwyer spoke about the trends and predictions within the Future of Work space.
In this two-part series, we provide some webcast highlights of that discussion and a link to the full event.
The Role of Data in Supplier Management and AI Transformation
With the increased focus on supplier management, the role of data has become more crucial than ever. Organizations are leveraging artificial intelligence (AI) to improve risk management, supplier performance, and overall procurement efficiency. However, AI is only as effective as the data it processes. A fundamental challenge that persists in supplier data management is the inconsistency and fragmentation of data across multiple systems. Organizations must prioritize creating a consolidated and accurate data foundation to maximize AI’s potential in supplier management.
Data and AI Workforce Optimization
AI’s effectiveness in procurement and supplier management is largely dependent on the availability of high-quality data. While AI has the capability to optimize risk assessment and performance management, its success hinges on having a centralized, reliable data source. Many organizations delay the establishment of a “golden record” of supplier data, viewing it as a future priority rather than an immediate necessity. However, to truly harness AI’s capabilities, organizations should prioritize data consolidation early in their AI journey. A strong data foundation enhances AI’s ability to analyze supplier usage, transaction patterns, and performance metrics, ultimately leading to more informed decision-making.
A specialized area where data plays an essential role is contingent labor management. Unlike purchasing standard goods like office supplies, managing contingent labor involves complex variables such as job roles, contract terms, and compliance requirements. The common adage, “you can’t manage what you can’t see,” holds particularly true in this domain. Without access to comprehensive and well-structured data, organizations struggle to answer even basic questions about their contingent workforce, making strategic decision-making nearly impossible.
Beyond procurement, AI has broader implications in workforce management and hiring practices. One of the emerging trends is skills-based hiring, where AI helps organizations identify and match candidates based on their unique skill sets. Traditional hiring methods often overlook hidden talents within an applicant’s experience, but AI can analyze years of professional history to extract relevant skills that may not be explicitly listed. This shift toward skills-based hiring highlights the need for accurate data to support AI-driven talent acquisition strategies.
Avoid Pitfalls with Data Governance
However, the adoption of AI is not without its challenges. Organizations must be cautious about AI’s limitations and potential pitfalls, particularly concerning data integrity. AI models trained on incomplete or biased data may generate inaccurate recommendations, leading to poor decision-making. Therefore, companies must ensure that AI-driven solutions are built on robust datasets to maintain reliability and accuracy. Additionally, there is a growing recognition of the need for governance structures to oversee AI implementation. The black-box nature of AI algorithms necessitates transparency and compliance measures to prevent decision-making biases and ensure ethical AI usage.
As AI adoption accelerates, companies are increasingly investing in specialized roles such as Chief AI Officers and data scientists to drive AI transformation. These professionals play a critical role in overseeing AI integration, ensuring data governance, and optimizing digital transformation efforts. Within procurement and HR, organizations are recognizing the importance of data analytics and compliance expertise to mitigate AI risks and enhance operational efficiencies. The conversation around AI governance and ethical considerations will only grow more significant as AI continues to reshape the business landscape.
AI’s success in procurement, contingent labor management, and hiring is intrinsically tied to data quality. Organizations must proactively address data fragmentation issues and establish governance frameworks to maximize AI’s effectiveness. As AI continues to evolve, companies that invest in high-quality data and AI expertise will be better positioned to leverage the full potential of AI-driven decision-making. By embracing data-driven strategies, businesses can enhance their supplier management processes and workforce optimization, paving the way for a more efficient and intelligent future.
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