Embracing Agentic AI in Procurement: Bridging the Data-to-Action Gap

Written by Varvara Kharitonova and Marius Ciobanu, with contributions by Erkki Seikkanen.

Big data, NLP, Machine Learning, GenAI, and now…Agentic AI? Is this the new buzzword of the week or the result of true innovation in AI models? We think the latter.  

In our experience, the average procurement team spends around 90% of their time gathering and cleaning data, leaving only 10% for strategic analysis and decision-making. This leads to delayed responses due to the time needed to gather and analyze data, which can result in missed opportunities and compliance risks. 

Procurement professionals often find themselves buried under mountains of data scattered across their organizations. This fragmentation makes it difficult to see the big picture and make informed decisions. Even when data is consolidated, there remains a pressing need for robust analytics to guide strategic focus. The key to effective procurement lies in having constant access to the most up-to-date data, ensuring decisions are timely and relevant.

While traditional AI and automation tools can accelerate transactional tasks, they often struggle to deliver nuanced insights that drive transformative procurement strategies.

Agentic AI is a more autonomous form of artificial intelligence designed to analyze data holistically, offer proactive recommendations, and even automate decision-making when appropriate.

In this blog, we’ll cover everything about Agentic AI and convince you why you shouldn’t ignore it. With special attention to risks, human-AI collaboration, and how to implement it, this guide will offer you a starting point for launching your Agentic AI journey.

Key Takeaways

• Agentic AI represents a fundamental shift from passive analytics to proactive, autonomous intelligence that can transform procurement operations.
• Agentic AI is bringing about a new era of decision and process orchestration.
• The technology bridges critical gaps in data utilization, strategic decision-making, and risk management that traditional AI cannot address.
• Successful implementation requires strong data governance, cross-functional collaboration, and balanced human oversight.

The Data-to-Action Gap in Modern Procurement

Procurement teams today face a critical dilemma: despite massive investments in data and analytics, they struggle to convert insights into strategic action. According to Gartner research, while 68% of Chief Procurement Officers are investing in AI technologies, nearly half still struggle with data accuracy and utilization.

The result? Procurement professionals spend most of their valuable time on data management rather than strategic decision-making. This disconnect isn't merely a technology problem—it's a fundamental gap between information and execution that costs organizations millions in missed savings opportunities and unaddressed risks.

Agentic AI represents a paradigm shift in procurement intelligence—moving from passive reporting to proactive guidance and autonomous action. Unlike conventional AI systems that simply process and present data, Agentic AI can:

• Predict needs before they arise by analyzing patterns across multiple data streams.
• Interpret complex situations by considering market conditions, supplier performance, and business objectives simultaneously.
• Adapt to changing requirements without requiring manual reconfiguration.
• Take action when appropriate, from initiating negotiations to flagging compliance risks.

How Agentic AI Differs from Traditional AI

Procurement leaders recognize the strategic importance of data but frequently encounter stumbling blocks in transforming raw information into meaningful action. Traditional tools for procurement analytics generally provide rearward-facing reports. 

They may track historical spend and categorize suppliers, but they struggle to integrate real-time data streams or predict future disruptions. It works well with structured tasks like invoice matching, spend classification, and basic reporting. However, these systems can be rigid, forcing procurement staff to sift through multiple dashboards and conduct manual data extractions. 

These tools predominantly automate mechanical tasks rather than thinking strategically about supplier performance, risk, or contract nuances. As a result, the potential to drive efficiency and savings stalls short of transformational change. This fragmentation complicates the linking of procurement decisions to corporate objectives, such as sustainability initiatives or risk mitigation strategies.

Agentic AI stands out by proactively delivering insights and recommendations. Rather than requiring analysts to piece together data from various modules, it provides contextual guidance in real-time. Unlike Chatbots or GenAI, Agentic AI is designed to undertake actions rather than provide just conversational support.

Agentic AI shifts from reactive to proactive models designed to predict needs and provide actionable insights that assist in decision-making while continuously learning and adapting. This capability addresses a significant gap in proactive decision-making, enabling procurement teams to act swiftly and strategically.

Agentic AI aims to predict, interpret, and adapt to changing procurement needs. This capability frees procurement professionals to focus on higher-value activities such as relationship-building, innovation, and long-term planning. By analyzing market conditions, historical spend, and supplier performance simultaneously, it can initiate negotiations, pre-empt supply interruptions and risks, or generate fresh ideas for cost savings.

Traditional AI vs. Agentic AI in Procurement

How Agentic AI Will Change Procurement

Agentic AI introduces notable advantages to procurement. It eliminates many manual steps associated with data retrieval and analysis, delivering insights in plain language or via one-click actions.

This accessibility democratizes advanced analytics, allowing a broader set of stakeholders—or even non-technical users—to engage with and act on procurement data.

The technology also anticipates challenges rather than merely reacting to them. Enabled by real-time data feeds, it can flag early warnings about potential supplier disruptions, commodity price fluctuations, or contract compliance issues. Proactive risk management becomes more achievable. By freeing buyers from routine tasks, Agentic AI also allows them to devote more time to strategic activities such as supplier development, innovation scouting, or cross-functional collaboration.

Agentic AI is not about replacing human roles but about enhancing efficiency. It serves as a supportive copilot, allowing procurement professionals to focus on strategic tasks while the AI handles routine data management. This synergy empowers teams to achieve more with less effort.

This innovation represents the most promising solution to procurement's persistent data-to-action gap. Combining advanced analytics with autonomous capabilities it enables procurement teams to:

• Extract meaningful insights without specialized data science expertise
• Convert those insights into concrete actions and measurable outcomes
• Shift focus from transactional tasks to strategic initiatives
• Respond more quickly to market changes and emerging risks

The Importance of Data Maturity

The necessity for high data quality and maturity to effectively implement Agentic AI cannot be overstated. Poor data governance can lead to unreliable AI outputs. Despite widespread interest in AI, procurement teams continue to wrestle with fragmented data, inconsistent standards, and low analytics maturity. Poor data governance, siloed systems, and imperfect integration hamper the ability of AI to produce reliable decisions. This lack of data maturity often results in missed opportunities, flawed recommendations, or an over-reliance on static dashboards.

This low data maturity manifests in several critical challenges:

• Fragmented data across disparate systems and departments
• Inconsistent standards for data collection and classification
• Poor governance leading to quality and reliability issues
• Limited integration between procurement and other business functions

Implementing Agentic AI effectively requires high data quality and maturity. Poor data governance can lead to unreliable AI outputs. Fortunately, tools like Sievo play a crucial role in data cleansing and consolidation, preparing organizations to adopt Agentic AI more effectively.

Complete our Procurement Data & Maturity Assessment to learn your current state and areas for improvement

Use Cases of Agentic AI in Procurement

Agentic AI delivers concrete value across the procurement lifecycle:

Spend Analytics

• Proactively identify savings opportunities by analyzing spend patterns across categories and suppliers.
• Understand the main drivers of a certain change, and help in building the analytics and visualizations to understand what is going on.
• Surface issues with suppliers and suggest strategic actions to take, such as drafting an email–and, if needed, execute the action for you.

Supplier Management

• Integrate with various data providers that help identify sustainability hotspots or financial risks.
• Alert teams to emerging risks before they impact operations.
• Suggest alternative suppliers when performance or risk thresholds are breached.

Contract Management

• Draft initial contract terms based on historical agreements and market conditions.
• Automate routine negotiation tasks, including outreach emails and amendment suggestions.
• Flag compliance issues and recommend remediation steps.

The better your AI agents understand the context of your work, the better. That’s why Sievo’s Copilot tool stands out – by analyzing 2% of Global GDP annually, it’s specifically trained for the needs of global procurement organizations. 

Read our AI in Procurement Guide for a full overview of AI use cases in Procurement

Risk, Ethics, and Oversight

A major challenge of implementing Agentic AI involves maintaining trustworthy, unbiased, and compliant operations. Poor data governance can bias the AI’s conclusions or lead to breaches of sensitive information. Establishing clear governance ownership and consistent data standards is a non-negotiable step in responsible AI usage.

Ethical considerations, including regulating algorithmic biases and protecting data privacy, are equally critical. Procurement leaders often work with confidential supplier terms, sensitive cost structures, and proprietary pricing. Any data leak or misuse of information by an automated system could have significant legal or reputational repercussions. Organizations should form cross-functional committees that include legal, compliance, and IT experts to set transparency standards and build safe processes around AI deployment.

While Agentic AI offers unprecedented automation capabilities, human expertise remains essential. The most effective implementations create a balanced partnership where:

• AI handles data processing, pattern recognition, and routine execution
• Humans provide strategic direction, relationship management, and ethical oversight

This partnership addresses one of procurement's persistent challenges: the shortage of skilled professionals who can blend domain expertise with data literacy. By automating routine tasks, Agentic AI allows procurement teams to focus on higher-value activities that truly require human judgment.

Here are the key traits of successful human oversight in an Agentic AI-driven procurement function:

• Complementing Automated Decision-Making: Automated decision-making is expected to complement, not completely replace, human decision-making. The primary role of analytics is still to help humans make better decisions, and this will continue to be crucial.
• Focus on Strategic Activities: AI agents will automate traditional procurement activities, freeing up human staff to focus on higher-value creative activities. Human support will be needed in roles requiring decisions and actions based on limited data, such as strategic planning, stakeholder relationship management, and complex analysis.
• New Competencies for Procurement Staff: Procurement staff will need to acquire strategic competencies like insight generation, communication, and influence. They will be expected to drive strategy throughout the organization while AI handles tactical execution.
• Data Quality and Governance: Humans are needed to ensure AI is used ethically and legally within procurement processes. This includes developing protocols to address potential risks like data loss, privacy breaches, and algorithmic biases, as well as ensuring compliance with regulatory standards for AI applications.
• Risk Mitigation: If AI assesses a higher risk exposure, human intervention is required to enforce risk mitigation measures.
• Strategic Oversight and Governance: A steering committee is needed to oversee the tool strategically, define its vision and objectives, manage resources, and make strategic decisions to optimize utilization.
• User Support and Feedback: Human roles include providing user support, gathering feedback, resolving issues, ensuring data accuracy, and conducting training sessions.
• Ethical Standards and Legal Regulations: Ensuring compliance with ethical standards and legal regulations related to the use of AI in procurement is a key area of human oversight.
• Training and Understanding: Providing training and resources to improve the understanding of Agentic AI. Without that understanding, AI cannot be effectively managed or utilized.

How to Get Started with Agentic AI

Achieving optimal results with Agentic AI begins with aligning AI initiatives to the organization’s core values and strategic goals. Collaboration across departments is essential, as procurement rarely owns all the data it needs. Involving other stakeholders—especially from finance, legal, and risk management—can help unify data sources just as effectively as it ensures compliance.

A robust data infrastructure strategy should include data quality metrics, consistent cleaning and enrichment protocols, and scalable systems capable of supporting AI workloads. Change management plays a pivotal role: short pilot programs can demonstrate AI’s value and gather feedback, while training sessions upskill employees to interpret and act on AI-generated insights. Regular performance checks and iterative improvements also keep the solution aligned with evolving business needs.

Successful Agentic AI implementation follows a structured approach:

1. Start with a clear problem statement rather than implementing AI for its own sake. Identify specific use cases where the data-to-action gap is most costly.
2. Implement a crawl-walk-run approach by beginning with decision support before advancing to autonomous action. For example:
   • Crawl: AI analyzes spend data and suggests savings opportunities
   • Walk: AI drafts communications to suppliers based on those opportunities
   • Run: AI negotiates directly with supplier systems within defined parameters
3. Establish clear governance frameworks that define:
   • Decision boundaries for autonomous action
   • Escalation protocols for exceptions
   • Audit trails for AI-driven decisions
   • Human oversight responsibilities
4. Invest in procurement team upskilling to focus on:
   • Strategic oversight of AI systems
   • Exception handling and complex negotiations
   • Continuous improvement of AI parameters
   • Translating business objectives into AI guidance

Conclusion: The Impact of Agentic AI in Procurement

Agentic AI is poised to redefine procurement. It eliminates reliance on manual dashboards, integrates risk management more effectively, and allows procurement teams to harness advanced analytics without requiring specialized AI or data science backgrounds. By proactively interpreting data and driving actions, it shifts procurement away from purely reactive tasks toward a more strategic, future-facing function.

As AI agents become more adept at autonomous execution, procurement professionals will see their roles evolve toward strategic orchestration and stakeholder management. This shift demands new skill sets encompassing data analytics, advanced negotiation, and cross-functional leadership. Procurement teams equipped with these capabilities can use AI to explore untapped cost efficiencies, champion sustainability initiatives, and elevate the overall performance of the supply base.

AI reasoning combined with multimodal capabilities will continue to transform procurement over the coming years, leading to end-to-end automation in tasks like supplier selection, contract management, and performance tracking. Professionals must guide strategic objectives, define acceptable thresholds for risk, and manage ethical or compliance concerns. In other words, AI can do the heavy lifting, but humans still hold the reins—especially when qualitative judgment or stakeholder relationships come into play.

Sievo’s Agentic AI Copilot

Sievo’s Agentic AI Copilot demonstrates how AI can simplify data analysis, allowing users to extract actionable insights without extensive analytics expertise. The transition to Agentic AI signals a pivotal evolution in procurement analytics—one that addresses longstanding data challenges while empowering teams to become more agile and innovative.

Sievo is committed to helping procurement teams bridge the data-to-action gap through innovative technology and proven methodologies. Schedule a demo with our experts to discuss your specific procurement challenges and how Agentic AI can address them.