Procurement has long been burdened by complexity – slow contract cycles, compliance risks and manual handoffs that eat away at time and efficiency.
As AI matures beyond basic chat interfaces and static outputs, procurement leaders are beginning to witness the transition from manual inefficiency to automated agility.
What makes agentic AI different?
Most professionals are now familiar with generative AI – tools like ChatGPT that can create content based on training data. But while these tools can produce impressive outputs, they largely stop at providing suggestions.
Enter agentic AI – the next evolutionary leap. AI agents can work independently or collaboratively, performing entire parts of the contract lifecycle – like drafting terms, flagging risks or routing for approval – either autonomously or alongside human teams. Think of it as a digital team member that doesn’t just suggest but executes.
Why procurement teams are taking notice
According to a recent KPMG study, procurement leaders named AI as the most transformative technology for the next 18 months. Here’s why Agentic AI stands out:
- Efficiency gains: Automates complex processes, freeing up human teams to focus on strategic tasks.
- Better decisions: AI surfaces insights and options that might be missed otherwise.
- Scalability: Expands capacity without requiring additional staff.
For example, during contract lifecycle management (CLM) editing and negotiation, an AI agent can suggest appropriate clauses based on prior agreements. During review, it can scan for deviations from company standards, identify potential risks and flag non-compliant clauses – without adding delays
Modernizing CLM with agentic AI
Whether you’re running on a managed cloud or your own infrastructure, agentic AI doesn’t operate in a vacuum. To work well, it needs a foundation – a platform that supports accuracy, usability, governance and ethical compliance. That’s where a business orchestration platform like SAS comes in.
I like to think of this like an iceberg. The AI agent is the visible tip. But beneath the surface is everything that makes it possible: trustworthy AI, data, governance, analytics, models. It’s the weight under the waterline that keeps the system stable and effective.
Using SAS, agentic AI becomes an achievable goal since the underlying “below the waterline” architecture is cloud-native and positions an Agentic AI implementation as a “solutions approach” instead of a “niche tool” standalone project. A solutions approach encourages interoperability, automation and operationalization so you can use and augment within your existing IT investments. It also helps organizations deploy new systems rapidly which translates into a quicker time to value.
New global research reveals the key component for AI success
How to build a smart agentic AI architecture
If you’re just getting started, here’s a simple recommendation: start small. Small Language Models (SLMs) are easier to train with your own data, more affordable to deploy and generally more predictable than large language models (LLMs). They’re also better suited for focused, task-specific work, which is exactly what contract management needs.
A well built agentic AI architecture includes a few components:
- Building blocks: Data sources, tools and a prompt repository to drive AI responses.
- Design: Task specific agents that work within defined workflows.
- Deployment: The control center that manages usability for streaming, API and batch services, workflows, agents and quality checks.
- Interaction: Interfaces built for actual business users, not just developers.
- Governance and trust: The foundation that ensures everything works safely and reliably.
This is the model SAS® Viya® follows – an approach built for real-world use, not theoretical demos.
Laying the groundwork: Practical steps for success
My SAS colleague Manisha Khanna’s blog “Starting your AI journey: The groundwork” has some great words of advice
- Commit to making a change: “Using AI is now a clear mandate for any organization hoping to stay competitive, serve customers and expand in any environment.”
- Identify the business problem: “Instead of asking, “How can we get ourselves ready for AI?” it’s more effective to ask, “What are the problems we need to solve? What are our opportunities and threats?” This approach ensures that AI is used purposefully to address real business needs.”
- Update your development plan: “To update your development plan for your AI initiative, it’s essential to start with the stakeholders. Begin by accounting for the individuals, roles, and groups that will be affected by your organization’s AI initiative. This ensures that all relevant perspectives are considered and that the initiative aligns with the overall business strategy.”
- Start small, think big: “The first three steps help you set the stage for a successful AI program, both immediately and in the long term. You don’t need to overhaul your entire operation overnight. Start with a clear goal, build internal support, and invest in your people.”
Why it all comes back to your platform
In procurement, success often depends on speed, accuracy and compliance – all at once. That’s why a strong, unified CLM platform isn’t just helpful for agentic AI, it’s essential.
The right platform ensures data consistency, policy enforcement and end-to-end visibility across every contract stage. It gives AI something solid to stand on, so it can deliver real results without creating risk or complexity.
Final thoughts
Agentic AI is already transforming procurement teams across industries. While the hype is real, so is the opportunity. The key is a strategic, thoughtful rollout that balances innovation with trust, agility and security.
If you’re still waiting for the “right time” to start, know this: the organizations that move now will shape the benchmarks of tomorrow.
