Agentic AI Meets Generative AI in Procurement 

Explore how Generative AI and Agentic AI complement each other in procurement, from drafting reports to autonomous execution of tasks 

Generative AI (GenAI) and Agentic AI are different types of artificial intelligence, but there is some overlap, and they perform complementary tasks, not least in procurement. In this article we’ll examine how these two technologies can take the heavy lifting out of procurement tasks to optimize workflow execution, decision-making, compliance and risk management. 

Understanding Generative AI and Agentic AI: Key differences and synergies  

Let’s start with two definitions. 

Generative AI is a subset of deep learning techniques that enables computers to create new content using previously created content, such as text, audio, video, images and code. 

Typical applications of GenAI in procurement include the drafting of RFPs and reports, revising contracts, summarizing information, and simulating scenarios. 

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.  

Typical applications for Agentic AI in procurement include supplier selection and autonomous sourcing of goods and services.  

Note the key distinction. The term Agentic AI does not apply to any system that relies on human prompts to do anything significant. Agentic AI runs autonomously, it is goal-driven, and it does its own reasoning. Generative AI by contrast will only do anything when prompted by a human or by an autonomous AI agent (hence the overlap). 

Intelligent procurement will leverage the synergies between the two, combining GenAI’s data processing and creativity with Agentic AI’s autonomous decision-making to maximize value and optimize outcomes. 

Use Case: How GenAI and Agentic AI work together in procurement   

Let’s take a real-world use case in which an Agentic AI system generates supplier risk reports through Generative AI, then autonomously acts on the findings, such as adjusting supplier contracts or notifying relevant stakeholders. This is achieved in three steps: 

1. Continuous data ingestion and monitoring 

Process: AI agents constantly ingest data from internal systems (including ERP, and procurement/ S2P platforms) and external sources (news feeds, ESG databases, supplier filings etc.) In this way the system maintains a live view of supplier performance, geopolitical risks, regulatory shifts, and ESG/cybersecurity concerns. 

Benefits: The system eliminates the need for manual data gathering while increasing accuracy, since the agent accesses real-time data updates, reducing reliance on outdated or siloed data. 

2. Generative AI creates a supplier risk report 

Process: When triggered by a performance deviation or scheduled review, or other pre-programmed trigger, GenAI composes a tailored supplier risk report. This could cover risks such as tariff exposure, ESG violations, financial instability, contract obligations, tier-N exposure. 

Benefits: Tailored reports such as executive-ready summaries or deep-dive dashboards, depending on the recipient. Professional and consistent style of output. Reports are available in minutes, fully referenced, with no need for manual write-up. 

3. Agentic AI interprets the report and determines actions 

Process: Agentic AI reviews the findings and autonomously initiates predefined actions or recommends next steps. Such actions could include adjusting contractual terms (e.g., revise SLAs or exit clauses), triggering re-sourcing workflows to identify alternate suppliers, flagging the supplier for closer monitoring, or sending alerts to legal, compliance, or the CPO/CFO’s office. 

Benefits: Timely response to events without the need for human intervention. 

Possible disadvantage: Danger of false flags and inappropriate actions, so human oversight recommended. 

4. Stakeholder notification and workflow orchestration 

Process: Relevant stakeholders are notified with AI-generated reports when human oversight or approval is needed (e.g. for high-risk contractual changes). The Agentic AI integrates with collaboration tools and approval workflows. 

Benefits: Ensures humans remain in the loop for high-impact decisions while removing them from routine checks. Decision cycles are significantly reduced, possibly from days to hours or minutes. 

5. Autonomous Learning and Refinement

Process: Based on feedback (e.g. supplier performance post-intervention or human approval patterns), the AI updates its models and thresholds. 

Benefits: The system becomes smarter and more aligned with organizational risk appetite over time. 

Benefits of combining Generative AI and Agentic AI in procurement  

Such collaboration between Agentic AI and GenAI delivers significant benefits. It reduces human workload. There is no need for manual report writing, risk identification, or chasing stakeholders. Human intervention is needed only in exceptional circumstances. Procurement analysts and other stakeholders can focus on interpretation and strategy, not data wrangling. 

Consequently, combining GenAI and Agentic AI improves procurement timelines by automating both decision-making and reporting. 

Other benefits include the reduction of human bias and error in risk scoring and contract assessments, as well as faster response times to disruptions. Risks that are pre-empted can be addressed before they turn into actual disruptions, for example through contract renegotiation or sourcing from alternate suppliers. 

Decision-making accuracy is enhanced thanks to GenAI’s ability to synthesize vast amounts of data and Agentic AI’s autonomous execution based on that data. 

Overcoming challenges in integrating GenAI and Agentic AI for procurement 

However, such integration is not without its challenges. Clean integration between Generative AI and Agentic AI in procurement scenarios such as supplier risk management poses several technical obstacles, even if the vision is compelling. They include: 

Data interoperability and structure: Generative AI thrives on unstructured or semi-structured data, while Agentic AI requires structured, contextualised data to reason and act. A GenAI-generated supplier risk report may be eloquent, but if it lacks structured tags, categories, or machine-readable conclusions, the agentic system cannot interpret and act on it. Systems must be designed and implemented with a shared schema or use output parsing (e.g. structured JSON generation) so Agentic AI can extract intent, risk levels, or recommended actions reliably. 

Semantic misalignment: Generative AI may phrase outputs in ways that lack precision or consistency, causing Agentic AI to misinterpret the meaning or confidence level. For example, GenAI might report, “The supplier may be facing liquidity concerns.” This is too vague for an agentic trigger. It needs some kind of metric such as “45% probability of financial collapse.” The developers need to create output guardrails using prompt engineering or post-processing layers that translate free-text into agentic intents or actions. 

Actionability of insights: Generative AI can surface insights or scenarios that sound plausible but aren’t actionable within the scope of what Agentic AI is authorised or able to do. GenAI might suggest, “exclude this supplier from the approved list,” but the agentic system can’t execute such an action without legal review or stakeholder approval. Agentic systems must have clear governance boundaries and fallback protocols when GenAI oversteps. In this case, it would be better to issue an alert than to execute based on the suggestion. 

Governance, traceability and auditability: In regulated industries any action taken autonomously must be explainable and traceable. GenAI lacks inherent traceability. It may hallucinate or omit sources. Agentic AI, by contrast, must rely on verifiable input to justify actions. One solution to this challenge is to integrate Explainable AI (XAI) frameworks or restrict GenAI outputs to summarising previously validated data (for example, from supplier audits and ESG databases). 

Orchestration engine maturity: It is essential to implement a robust orchestration layer capable of sequencing GenAI output > agentic intent > system action > feedback loop. Without this, the AI stack becomes a fragmented patchwork. GenAI acts as a siloed “copilot,” not part of a closed-loop system. The issue is soluble by deploying adequate workflow engines. 

Quality control and feedback integration: There’s no standard mechanism for automatically validating GenAI output before acting on it. If, for example, GenAI misclassifies a temporary supply delay as a long-term risk, Agentic AI might wrongly initiate re-sourcing. This is another example of where human-in-the-loop (HITL) validation may be needed. Alternatively multi-agent review (such as fact-checker and risk classifier agents) can be required before a final action is taken. 

Latency & performance: GenAI is not real-time. It uses historic data. Agentic AI workflows, particularly those monitoring supply chain events, may need responses in seconds, based on real-time data. The system should therefore have fast-acting risk filters and classifiers to prevent actions being taken based on out-of-date information. 

Security and confidentiality: GenAI services often rely on cloud APIs or large models that may pose a risk if they process sensitive procurement data. This can be overcome by using local/private LLM deployments with strict access controls, redaction filters, and zero-data-retention policies. 

Future potential: how the fusion of Gen AI and Agentic AI will shape procurement in the next 3–5 years 

We are not there yet, but we are not far off either. It is highly likely that many of the benefits from integrating Generative AI and Agentic AI in procurement, such as reduced workload, faster decision-making, and increased accuracy, will be achievable at scale within the next five years, especially in digitally mature organizations. However, the full vision of seamless, autonomous orchestration still depends on overcoming the technical hurdles described above, as well as organizational challenges.  

Moreover, progress may be hindered by the lack of a clear regulatory framework.  Generative models are not yet legally robust or reliable for unsupervised contractual decision, for example. Consequently the deployment of AI will be limited to low-value or standardised contracts under strict rules. 

Transparency beyond Tier 2 remains elusive due to data availability and trust. Progress will be driven by ESG pressure, but wide adoption remains a stretch. 

Key Takeaways: harnessing the power of GenAI and Agentic AI in procurement 

Generative AI and Agentic AI are different forms of artificial intelligence. They complement each other strongly, so the integration of the two in procurement will bring enormous benefits. Nevertheless, there are some technical obstacles that need to be overcome. Pilot projects are already underway in areas such as the combination of GenAI risk summaries and agentic action on structured triggers. In other areas such as autonomous contract adaptation the scope is limited, given the complexity of contracts in some categories and the current lack of legal clarity. Real-time GenAI-Agentic AI interplay will probably have to wait beyond the five-year time horizon due to latency and accuracy issues. 

Change management will be a challenge. Procurement leaders should therefore accelerate adoption by securing C-suite sponsorship to put the right governance structures in place and make the appropriate adjustments to human resources. New skills and talent will be needed.  

These technologies cannot exist in standalone form so procurement leaders should work to embed AI in an existing robust procurement platform such as JAGGAER One. They should also work to standardize all AI outputs for interoperability across systems.