How Agentic AI Can Transform Sourcing and Supplier Management 

Discover how Agentic AI enhances sourcing and supplier management with autonomous RFx, vendor evaluation, and negotiation support. 

It is no exaggeration to say that the sourcing and supplier management function is on the brink of a revolution. But note the words, “on the brink.” We’re not there yet, but we are already on our way. Agentic AI offers tremendous opportunities to automate tasks, provide proactive risk management, and enable data-driven decision-making, ultimately leading to cost savings, improved efficiency, and stronger supplier relationships.  

But let’s first remind ourselves what agentic AI is and, equally important, what it is not, so we can avoid false claims. Patrick Reyman, Research Director for Procurement and Enterprise Applications at IDC, recently offered a neat definition at our user conference, REV. “Agentic AI is a subset of machine learning and deep learning techniques that enables computer systems to exhibit agency: set goals, make decisions and take actions through a perception, reasoning and action loop. This rules out any system that relies human prompts to do anything significant. Agentic AI runs autonomously, it is goal-driven, and it does its own reasoning.” 

Sourcing and supplier management are resource-heavy, complex, and prone to human bias, so they are ripe for transformation. If you work in these domains, AI agents should, over time, make your life easier and more interesting. You’ll be able to focus on the strategic activities and human-to-human professional relationships.  

In this article we look at how agentic AI can address some specific tasks and ask if and when this can become reality. 

Autonomous RFx Creation and Customization 

Agentic AI will enable autonomous RFx (Request for [Information, Proposal, Quotation]) creation and customization by combining advanced automation with intelligent decision-making capabilities. Let’s break this down into a series of steps. 

First, agentic AI can be harnessed to identify the need, shifting process initiation from a manual task to an autonomous response to business signals. It could do this, for example, by parsing emails, internal chats, or planning documents and recognizing when a new project, supply gap, or cost-saving opportunity triggers sourcing needs. 

Next, agents select the appropriate template (RFI, RFP or RFQ) and customize it based on the procurement context, for example supplier category, risk level, budget constraints, delivery timeline, and compliance needs. In this way, AI reduces the risk of error and increases supplier engagement. 

The system draws on and integrates data in real time from various sources: ERP and spend analytics tools for budget and demand forecasts; supplier databases for incumbent performance and risk scores; and contract repositories to reuse relevant clauses. In so doing, agentic AI will eliminate a lot of tedious manual effort. 

The system will ensure that the RFx fully embeds all the relevant company policies, regulatory requirements etc. 

The AI then autonomously identifies and shortlists suppliers based on historical data and scoring models, sends out RFx packages and tracks engagement. In so doing it compresses cycle times and scales outreach without human bottlenecks. As supplier responses come in, the AI can suggest follow-up questions or negotiation strategies and adapt criteria weights or timelines based on emerging priorities or constraints. This introduces a feedback loop and turns the RFx process into a living, responsive workflow. 

Ultimately, agentic AI will transform RFx management from a form-filling exercise into a smart, adaptive, and value-generating process. But how soon? Autonomous RFx creation and customization via agentic AI is partially possible today and likely to become fully operational within 2–4 years. However, some aspects still lean toward the “science fiction” end of the spectrum.  

JAGGAER can already auto-fill RFx templates based on category, spend level, or past projects. It also enables smart supplier selection. Nevertheless, this is still largely generative or rules-based AI, so it can be described as agentic AI-ready, but not yet autonomous. 

Intelligent Supplier Evaluation and Scoring  

Agentic AI can transform supplier evaluation and scoring by acting not just as a data processor, but as an autonomous orchestrator of structured, multi-dimensional, and goal-aligned decision-making.  

Unlike conventional tools, agentic AI does not wait to be fed data but rather seeks it out autonomously, updates it, and flags anomalies. Agents pull structured and unstructured data from internal systems (such as the JAGGAER One platform and ERP systems) and external sources (such as third-party risk and ESG databases, and news feeds). In doing so, the agentic software standardizes disparate formats and fills any data gaps using inference, for example by estimating supplier risk where data is missing. 

Agentic AI will also optimize for outcomes rather than adhering to static templates.  It adjusts scoring weightings dynamically based on enterprise goals (such as carbon reduction, on-time delivery and diversity targets) and recommends trade-offs such as risk versus cost savings. 

Supplier selection is prone to human bias so the ability to decompose subjective ratings into structured sub-criteria and flag inconsistent ratings by users (for example, when two evaluators score the same supplier very differently without justification) will be a major benefit of agentic AI. It will be able to offer outputs that meet audit and compliance requirements and can therefore be justified based on objective criteria. 

Rather than creating static scorecards, agentic AI will enable multidimensional optimization across a wide range of criteria and nominate the best suppliers across changing scenarios (for example, in some cases the priority is fast ramp-up, in others, cost savings or ESG compliance). These choices may require some human intervention, but the agent can be programmed to recognize the scenarios when this must be prompted. 

Finally, agentic AI will be able to monitor performance and use this to improve. Such an autonomous feedback loop is a hallmark of agentic behavior. 

How soon is this coming? While scorecard auto-fills are already possible, and there is some progress on scenario-based ranking, it will be a couple of years before we see other features of agentic AI-based supplier evaluation.  

AI-Supported Negotiation Planning and Strategy  

Agentic AI will significantly enhance negotiation planning and strategy in procurement by automating tasks, providing data-driven insights, and enabling proactive decision-making.  

The AI will analyze different negotiation scenarios and suggest optimal tactics based on the specific context, including potential trade-offs and concession strategies. By analyzing past negotiations and market trends, it will be used to create standardized negotiation playbooks that ensure consistency and compliance across contracts. 

Agentic AI has the potential to reduce cycle times and accelerate the overall procurement process, conducting negotiation rounds autonomously. It will identify opportunities for cost optimization and potential risks, adjusting to supplier responses and market data in real time, thereby ensuring procurement teams always negotiate from a position of strength.  

Of course, truly autonomous negotiation between buyers and suppliers using agentic AI would require both parties (buyers and suppliers) to be digitally equipped. We’re not there yet, but closer than you might expect, at least for limited-scope negotiations. JAGGAER already supports automated or semi-automated negotiations in defined areas. However, this is still human-in-the-loop automation, using generative AI or optimization engines, not fully agentic systems. In the near future, as agentic AI matures, we can expect to see autonomous proposal evaluation, iterative, multi-round counteroffers and the simulation of supplier responses. In the medium term (five-year horizon) we can expect mutual agentic AI negotiation; this will require interoperability and the creation of trust frameworks. But widespread adoption, requiring a critical mass of suppliers to implement agentic systems, is going to take longer. 

Post-Award Supplier Monitoring and Risk Detection 

Post-contract agentic AI applications are much closer to mainstream deployment than autonomous negotiation. This is where agentic AI can already deliver clear, actionable value — especially in areas like contract compliance monitoring, supplier performance management, and proactive risk alerts. 

Agentic AI can continuously monitor performance metrics such as delivery timelines, defect rates, and invoice accuracy, integrating internal systems such as ERP and quality management with external sources such as EcoVadis ESG scores, sanctions lists and even social media signals. It excels at triggering alerts when contractual KPIs are not met and identifying compliance drift. By flagging underperformance based on pre-set thresholds or evolving norms agentic AI will enable teams to respond before issues become problems. 

In future, advanced agentic systems will not only identify such issues but suggest corrective actions autonomously (for example, shift order volume to an alternate supplier, or conduct a review of contract terms). They will coordinate escalation procedures and workflows, such as notifying legal teams of breaches of contract, and learn over time what response work best. 

The reasons that agentic AI is already quite mature in this area is the existence of a clear KPI structure in most large organizations and an abundance of historical data. It’s also a low-risk entry point, as monitoring is less risky than (for example) contract negotiations. Moreover, there is mutual benefit — both buyer and supplier benefit from meeting KPIs and avoiding surprises. 

Agentic AI can scan vast databases and market conditions in real time to identify potential suppliers and evaluate their suitability based on predefined criteria.  

Key Benefits of Using Agentic AI in Sourcing & SRM 

As we have noted, it will be some time before we see fully autonomous processes in sourcing and supplier relationship management. Nevertheless, it is not too early to point to huge benefits in terms of automating tasks, enhancing decision-making, and improving efficiency. Agentic AI will accelerate sourcing processes, optimize supplier selection while reducing bias, enhance compliance and auditability, and proactively manage risks, ultimately leading to cost savings and increased resilience. 

With regard to supplier relationship management, agentic AI will provide real-time data on supplier performance, risks, and market trends, providing greater transparency for both parties. AI-driven evaluations foster open, data-backed communication between businesses and suppliers, improving collaboration and accountability. 

In addition to cost savings and efficiency gains, agentic AI has the potential to make organizations more agile and resilient. It can adapt to changing market conditions and unexpected disruptions, enabling businesses to respond quickly and maintain operations. Organizations will be better placed to identify fraud and maverick spend, and they will improve their audit-readiness. 

By automating tasks such as contract management, invoice processing, and performance reporting, agentic AI will free up human resources to focus on strategic SRM activities and those that demand face-to-face contact. 

Final Thoughts: Toward Autonomous Strategic Procurement 

In summary, agentic AI will allow procurement teams to shift from tactical execution to strategic leadership. While not replacing humans, it will become a powerful copilot. As adoption grows, teams that embrace autonomy will outperform reactive peers. Agentic AI will thus make sourcing and supplier management a more attractive career. 

That said, there are some important caveats. Obstacles to the implementation of agentic AI include fragmented data and systems, low digital maturity, and the need for systems robust and scalable enough to deal with the complexity of the supplier ecosystem and potential cybersecurity risks. 

In addition, there are more human challenges. Agentic AI raises concerns about accountability, especially in high-stakes supplier decisions, requiring strong oversight mechanisms. A governance framework is needed, and it will never be possible to take humans entirely out of the loop. There is likely to be resistance to change from existing employees, and it will be a challenge to find suitable new tech-savvy talent. Finally, it is also essential that companies do not allow themselves to rush into investments in solutions that are unproven. 

So long as organizations are aware of these obstacles, they can realize huge competitive advantages by embracing this exciting technology.