Building trust in AI agents: Unlocking their procurement potential through responsible adoption

Agentic AI is transforming procurement, helping teams make faster, smarter decisions. Gartner predicts that by 2028, 33% of software applications will feature agentic AI, up from less than 1% in 2024, enabling 15% of daily work decisions to be made autonomously.

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But to unlock this potential, organizations must trust the technology. In this blog, we explore why trust and transparency are essential for AI adoption in procurement, what responsible AI looks like, and how human-in-the-loop (HITL) systems can ease adoption and reduce risk.

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Why trust and transparency are foundational to successful AI adoption

AI can dramatically improve procurement outcomes, from cutting costs to streamlining supplier selection and automating workflows. But these gains depend on one thing: trust. Procurement teams need to trust that AI recommendations are sound and consistent. Suppliers need to believe they’re being evaluated fairly and transparently. If either group has doubts, adoption slows. Issues like bias in historical data, opaque models, or poor governance can all erode trust. And it’s not just about ethics—regulations like GDPR require explainability and the ability to challenge automated decisions.  

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Responsible AI in procurement: Security, control, and accountability

Responsible AI means designing systems that are ethical, accountable, and transparent, while aligning with your legal and organizational standards. For procurement, this comes down to three pillars: security, control, and accountability.

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Data security: The foundation of responsible AI

Traditional SaaS tools already challenge data security, especially when customer data is processed outside your environment. With agentic AI, the stakes are even higher. These systems rely on continuous access to contracts, supplier data, and transaction histories to deliver results. That’s why strong data security is essential for responsible AI.  

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Leading platforms ensure:

• Strong encryption for data at rest and in transit, preventing unauthorized access during storage or movement.

• Role-based access control, so agents and users only access the data relevant to their permissions.

• Data minimization, limiting AI access to only the information necessary for a given task.

• Comprehensive audit trails, logging every AI action to enable traceability and accountability.

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These safeguards reduce risk, simplify compliance, and build confidence in AI-powered decisions.

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Data residency and control: Beyond sovereignty

While data sovereignty is often linked to the public sector, the need for complete control over where data lives—and who can access it—is just as critical for private organizations. At Procure Ai, we call this data residency and control, which is built into the platform by design through four key features:

• Data Ownership: You keep full control of your data and environment, with the ability to move or terminate access at any time.

• Legal Jurisdiction: You decide where your data is hosted to meet local regulations and compliance needs—whether in the EU, UK, or other regions.

• Exclusive Cloud Control: Your cloud accounts remain yours even on cloud platforms like AWS or Azure. No shared infrastructure, no loss of control. For public sector clients, sovereign or government-hosted clouds provide additional assurance, ensuring sensitive data remains within national jurisdiction.

• Dedicated Environments: Every customer runs in a private, isolated environment with separate infrastructure, ensuring security, privacy, and full compliance.  

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Whether public or private, the common thread is clear:  

Data should be stored, processed, and governed according to your organization’s policies and values, not dictated by your AI vendor.

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AI guardrails and accountability

Trust also depends on putting the right boundaries in place. Guardrails define what AI agents can and can’t do. Key components of AI guardrails include:

• Strategic Boundaries: Agents are limited in what they can do, not only by technical constraints (such as drafting but not signing contracts), but also by the organization’s procurement strategies. Crucially, agents should always operate in accordance with global procurement guidelines and, even more specifically, with the category strategies defined by category managers.  

• Transparency: Every decision is logged and explainable, so both users and auditors can see how and why AI recommendations were made.

• Human-in-the-Loop: Humans review, approve, and can override AI-driven actions at any point.

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Human-in-the-loop: Building confidence and easing adoption

Human-in-the-loop (HITL) systems keep people involved at key decision points, balancing AI efficiency with human judgment. Procurement teams stay in control while the AI supports their workflows. For example:  

• The AI shortlists suppliers; a human approves the list.

• The AI drafts terms; a human reviews and adjusts.

• The AI flags contract risks; a human investigates and responds.

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HITL builds trust by showing how the system works and giving teams control. It’s also ideal for gradual adoption, letting teams build confidence before moving toward more autonomous AI.

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Procure Ai: Connected and secure  

Procure Ai is designed to earn trust through architecture, transparency, and control. Unlike traditional SaaS platforms, your data stays entirely within your environment. It’s never stored or processed externally, which means your organization retains full ownership while meeting internal policy and regional data residency requirements.

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Security and privacy are integrated at every level. Data is encrypted in transit and at rest. Access is controlled using two-factor authentication, IP whitelisting, and role-based permissions, ensuring that only authorized users and agents can interact with sensitive information. Every AI-driven action is logged in tamper-proof audit trails for complete traceability.

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Procure Ai also meets rigorous security standards, including ISO 27001 certification and regular third-party penetration testing. Each customer operates in a dedicated, isolated environment with separate infrastructure and policies. Even if you use cloud platforms like AWS or Azure, you manage your own cloud accounts—there’s no data pooling, shared infrastructure, or vendor lock-in.

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With a zero-data-retention model, clear governance policies, and full control over your environment, Procure Ai doesn’t just promise security and transparency. We deliver it through every layer of its platform.

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Paving the way for responsible AI adoption in procurement

By making trust the foundation of their AI strategy, procurement leaders can unlock transformative value responsibly, ethically, and sustainably. To build trust in AI agents, organizations must be intentional in how they design and deploy this technology. They must embed responsible AI principles from the ground up, make sure to protect data, implement guardrails, maintain oversight, and use HITL systems to build confidence, ensure fairness, and support gradual change.

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Are you ready to start your AI journey? Contact us to learn more about implementing agentic AI with Procure Ai.