Through the routine flow of commerce and day-to-day operations, enterprises accumulate vast troves of data often at a rate outpacing their ability to make sense of it all. So, they rely increasingly on cloud-based networks to distill the information, detect meaningful patterns, and draw actionable insights to improve decision-making and counter disruption.
Recent advances in artificial intelligence (AI), meanwhile, are accelerating the value that businesses reap from the digital networks through which they engage in commerce, collaboration, and coordinated sustainability efforts with trading partners. As AI reshapes traditional procurement and supply chain processes, its emerging capabilities fall into two primary types: generative and predictive.
Generative AI and Business Networks
Generative AI produces text, images, or other media based on the structure and patterns of the information used to train it. Through large language models and other forms of machine learning, cloud-based applications create new content with characteristics resembling that which precedes it. With every successive effort, generative AI sharpens its ability to tailor desired content.
As the world’s largest platform for business-to-business commerce, SAP Business Network presents an ideal setting for generative AI, in that it brings together an extremely large volume of trading partners across hundreds of millions of transactions representing US$4.9 trillion in commerce annually. Starting in 2024, generative AI capabilities in SAP Business Network will help detect errors and present automatic summaries when businesses create invoices. AI will also enable the network to enrich catalog content and discovery posting responses for suppliers. SAP Business Network has significant structured content accumulated over many years to help generate significant insights to enhance all these processes and unlock ongoing value for buyers and suppliers alike.
But what if AI could help businesses to automate their most time-consuming procurement and supply chain activities, not only through generative mechanisms but predictive ones as well?
Predictive AI and Business Networks
How much strategic value, previously tied up in tactical tasks, could enterprises unleash if business-to-business networks could reliably predict certain operational requirements and execute them autonomously? With the rise of cloud-based, intelligent applications, they’re beginning to find out and, in the process, uncovering substantial efficiencies throughout their operations and those of their trading partners.
These technologies are dramatically simplifying some of the most labor-intensive business processes, from sourcing and purchasing to contracting and payments. Through digital transformation, many businesses have streamlined procurement, supply chain, and logistics processes and spurred collaboration among trading partners. That’s because they’re seeing an increasingly clear connection between embracing intelligent spend management solutions – an approach that draws upon immense repositories of operational data to mitigate risk, yield contextual insights, fund innovation through savings, and extend competitive advantage.
Where are AI-enhanced spend management solutions headed in years to come? Toward systems that can carry out end-to-end procurement, supply chain, and logistics processes within parameters strategically set and overseen by humans. These cloud-based solutions are bringing us closer to an era of autonomous functionality, where AI-enabled applications reveal detailed insights, provide prescriptive recommendations, and unshackle talented procurement professionals from endless administrative burdens.
It is important to note that autonomous functionality does not obviate the need for humans. Quite the contrary! There can be no replacement for the carefully considered judgment of a seasoned business leader. But AI-enabled spend management solutions offer an invaluable set of tools through which human professionals can bolster their effectiveness in evaluating complex alternatives and making decisions with confidence.
The trend toward autonomous procurement, supply chain, and logistics processes represents a journey that different industries and personas will pursue from different directions and at varying speeds, embracing AI to reduce human labor for some activities while augmenting it in others. For example, a business network may anticipate the need to replenish inventory levels based on the rate at which they diminish, initiate sourcing events by inferring information drawn from activities transpiring elsewhere in the value chain, or leverage operational analytics to update trading partners with key performance indicators – all without manual intervention. An autonomous system can even notify suppliers when catalog searches turn up empty, suggesting that new goods be made available.
Yet even as humans relinquish various tactical tasks, they remain very much in charge of the strategic ones. Determining an organization’s approach to spend management, in concert with trading partners, is likely to remain in human hands for the foreseeable future. By contrast, deciding how to meet those needs – and then meeting them – presents a very exciting moment for cloud-based AI solutions. In the future, a user may initiate a process, only later to be informed of subsequent actions taken by the system, requiring human approval for only certain types of decisions. Or a user may employ a set of fully trusted, self-learning, analytical applications. Procurement, supply chain, and logistics professionals remain in control, adjusting their level of intervention per the needs of the business.
As emerging AI applications transform the interconnected operational processes among trading partners, manifesting capabilities both generative and predictive, spend management professionals stand poised to usher in a new era of accelerated growth, operational agility, and enduring competitive advantage for their organizations.
Jörn Keller is executive vice president and chief product officer for SAP Business Network.
