Procurement is at a turning point. Over the past year, highly advanced procurement AI agents have emerged, leveraging generative AI and workflow automation to execute complex tasks with minimal human intervention.
This article explores why agentic AI is a defining trend in procurement, how it differs from past AI implementations, and how platforms like Suplari are shaping the future of enterprise procurement with actionable intelligence.
What is agentic AI for procurement?
Agentic AI refers to artificial intelligence systems capable of autonomous action. Unlike traditional AI, which focuses on automating analysis and recommendations, agentic AI can initiate, manage, and complete complex tasks—such as launching an RFP, conducting supplier negotiations, or processing procurement approvals—without requiring human input at every step.
Gartner has listed Agentic AI as one of the top tech trends for 2025, but in reality, very few agentic AI systems have moved beyond the piloting stage in enterprise businesses.
Jeff Gerber, Co-founder and CEO of Suplari, explains the distinction:
"The first wave of AI adoption focused on tactical automation—replacing or augmenting common, repeatable tasks and analysis. Over time, we will see strategic agents driving procurement planning, delegating tasks to tactical agents, and enabling procurement leaders to make better decisions at scale."
How do agentic AI systems work?
Agentic systems have emerged over the past 12 months thanks to advances in generative AI technology, more specifically, advanced large language models (LLMs) that have been augmented with capabilities such as retrieval, access to tools and memory.
Agentic systems consist of two elements:
- Agents - independent systems that use advanced AI techniques to handle complex, multi-stage tasks with minimal human intervention.
- Workflows - pre-defined ways of working or multi-step processes that take a piece of work from initiation to completion.
Agents can be trained to operate independently or function within a team of agents, each responsible for different aspects of procurement. When implemented effectively, agentic AI can:
- Automate complex procurement tasks, reducing manual workload.
- Improve decision-making by providing real-time insights and executing best practices.
- Adapt and self-learn to refine procurement strategies over time.
- Enhance strategic decision-making by automating tactical execution.
In procurement analytics, this means that a typical repetitive workflow—where human professionals investigate supplier options, manually key in data, or review historic spend data—could now be partly or fully automated by AI agents. The impact of these agents, particularly ones that can “use” software tools the way a human might, can reshape procurement by enabling near real-time data analysis, advanced scenario planning, and even frictionless sourcing task automation.
Key elements of an agentic AI platform
An agentic AI platform is built on several foundational components that enable autonomous decision-making, workflow execution, and compliance management in procurement. These elements work together to ensure AI agents can function effectively, adapting to complex enterprise environments while maintaining control and transparency.
Data and analytics
At the core of an agentic AI platform is its ability to process and interpret procurement data. The system integrates information from various enterprise sources, including ERP systems, procurement platforms, and third-party data providers, to create a unified dataset.
AI agents analyze this data in real time, identifying trends, detecting anomalies, and generating insights that drive procurement actions. By maintaining a structured and contextualized data environment, the platform ensures that AI-driven decisions are based on accurate and relevant information.
Orchestration
AI agents do not operate in isolation; they function within a broader system that coordinates procurement activities across multiple workflows. The orchestration layer enables the automation of sourcing, contract negotiations, supplier performance tracking, and spend management, ensuring that AI agents execute tasks efficiently. This layer can also facilitate integration with existing procurement, finance, and supply chain systems, allowing AI-driven processes to align seamlessly with enterprise operations.
Messaging and notification
For AI-driven procurement to be effective, real-time communication between AI agents, procurement professionals, and enterprise systems is key. The messaging and notification component facilitates instant updates on procurement actions, supplier risks, and decision-making events.
AI agents can send alerts, recommend actions, or request human intervention when necessary, ensuring that procurement teams remain informed and in control. This system also supports coordination across departments, enabling smoother collaboration between procurement, finance, and legal teams.
Domain knowledge
AI-driven automation in procurement requires more than just data processing; it needs contextual understanding. The domain knowledge component ensures that AI procurement agents operate with awareness of procurement policies, supplier relationships, contract terms, and category-specific sourcing strategies.
By incorporating historical procurement data and industry best practices, the platform allows AI agents to make informed decisions that align with enterprise objectives. This capability enables AI-driven procurement to extend beyond routine automation, supporting more complex tasks such as strategic supplier negotiations and risk mitigation.
Security and compliance
As procurement becomes increasingly automated, maintaining compliance and data security is critical. The security and compliance framework enforces regulatory requirements, supplier data protection policies, and enterprise procurement guidelines. AI-driven actions are monitored through audit logs, ensuring transparency and accountability.
Role-based access controls restrict sensitive procurement data to authorized users, while governance mechanisms ensure AI agents operate within predefined limits. This layer provides confidence that AI-driven procurement automation adheres to corporate policies and external regulations.
Recap on agentic AI platforms
By integrating data analytics, workflow automation, real-time messaging, contextual knowledge, and compliance safeguards, an agentic AI platform enables procurement teams to automate complex tasks, improve decision-making, and enhance operational efficiency. These core elements create a system where AI agents can function as proactive, autonomous counterparts to human procurement professionals, driving measurable business impact while maintaining oversight and security.
Early examples of agentic AI in procurement
Arkestro – predictive procurement orchestration
What it does: Arkestro uses behavioral science, game theory, and machine learning to optimize procurement decisions and supplier negotiations.
How it goes beyond basic ML: Arkestro proactively guides buyers and suppliers toward mutually beneficial outcomes through automated recommendations and real-time decision-making. It continuously learns from procurement behaviors and outcomes, adjusting its predictions and strategies without human oversight.
Keelvar – intelligent sourcing and event automation
What it does: Keelvar offers AI-driven sourcing optimization, automating complex procurement events such as supplier bidding and reverse auctions.
How it goes beyond basic ML: Keelvar’s agentic AI autonomously designs, launches, and manages sourcing events, evaluates bids in real time, and recommends optimal awards based on business constraints and objectives. It continuously refines its approach based on feedback and outcomes from past sourcing events.
Pactum – autonomous negotiation agent
What it does: Pactum uses conversational AI to conduct automated negotiations with suppliers, aiming to improve terms such as price, payment terms, and delivery schedules.
How it goes beyond basic ML: Pactum operates as an agent by autonomously engaging in dialogue with suppliers, making decisions based on company policies, and finalizing agreements—without human intervention. The system learns from each negotiation, adjusting strategies based on outcomes and supplier responses.
Suplari – automated spend intelligence and risk monitoring
What it does: Suplari provides automated spend intelligence by analyzing procurement data to identify cost-saving opportunities, detect anomalies, and forecast supplier risks. Suplari has had agentic AI functionality since 2018.
How it goes beyond basic ML: Suplari acts autonomously by generating real-time alerts and recommending actions such as renegotiating contracts or consolidating suppliers. Its agentic nature is evident in its ability to automatically monitor procurement patterns, initiate risk assessments, and deliver actionable insights to decision-makers without manual prompting.
Agentic AI’s longer term impact on procurement
While still in the early stages, Agentic AI has the potential to automate procurement workflows, monitor supplier risks in real time, and drive procurement strategy with minimal human intervention.
According to Jeff Gerber,
"Procurement today still involves a lot of emailing spreadsheets, logging into multiple systems, and coordinating tasks manually. AI agents can take over these processes, enabling teams to think more strategically, improve execution, and increase their scope of what they can address."
Here are some high-potential use-cases for AI agents in a typical procurement organization:
- Automated strategic sourcing: Agents monitor supplier performance, identify savings opportunities, and trigger sourcing events.
- Proactive risk management: Agents track supplier disruptions and recommend mitigation steps.
- Contract and compliance oversight: Agents optimize, renew, and renegotiate vendor agreements and ensure spend is kept under management.
- Frictionless collaboration: Finance, legal, and operations teams coordinate complex supplier relationships through a unified control layer.
By integrating AI into procurement operations, organizations can reduce cycle times, improve compliance, and optimize supplier relationships with real-time intelligence and automation.
Product update: See how Suplari's AI agents transform spend analysis
https://www.youtube.com/watch?v=U-Vi9YTi2ao
Why this matters for procurement
With agentic AI, you shift from static dashboards to proactive, real-time spend management. Procurement cycles accelerate, compliance strengthens, and teams collaborate through a single interface. Key benefits include:
- From analysis to action - Agents reduce sourcing cycle times with real-time insights.
- Faster decisions - Instant recommendations drive timely procurement actions.
- Automated compliance - Automated actions align with procurement policies.
- Seamless collaboration - Unified interfaces reduce context switching.
Final thoughts on agentic AI in procurement
Procurement analytics is on the cusp of a major shift, with agentic AI at the forefront. Through Suplari’s AI-driven platform, you gain a partner that has embodied an “AI-first” mindset since its inception—delivering both robust data unification, actionable insights and powerful automation tools.
By embracing these emerging technologies now, you give your organization a decisive advantage in an increasingly competitive market. This goes well beyond what you’d expect from an automated spend analysis solution.
When you’re ready to see how AI agents can be tailored to your procurement workflows, book a 1:1 consultation with Suplari’s experts.
About Suplari
Suplari is a procurement intelligence solution that helps businesses modernize procurement operations using AI. Suplari provides actionable intelligence to manage suppliers, deliver savings and manage compliance beyond the limits of traditional spend analytics. Suplari’s unique AI data management foundation empowers enterprise businesses to modernize procurement operating models with reliable, AI-ready data.
