Getting started: A practical roadmap for intelligent procurement decision-making

The final chapter of Beroe’s Future Vision report, “Artificial Intelligence: Real Decisions”, lays out a practical roadmap to help procurement teams turn AI ambition into action. It’s not about reinventing the wheel; it’s about starting smart, learning fast, and scaling with confidence. 

Here, we offer a grounded, step-by-step guide for procurement leaders ready to move from AI theory to AI implementation. It begins with identifying challenges where AI can make a tangible difference – emphasizing the importance of data readiness, selecting low-barrier, high-value use cases, and involving end-users early to build trust and adoption. The roadmap highlights how to scale responsibly, with governance and performance monitoring built in from the start. The message is clear: intelligent procurement doesn’t have to be a leap of faith. It should be a series of smart, strategic steps that build momentum and deliver measurable value. 

A practical roadmap for intelligent procurement decision-making 

The most successful implementations begin with clear priorities, focused use cases, and a readiness to experiment and learn. 

Here’s how to get started. 

1. Start with a real decision problem 

Successful AI initiatives begin with real-world challenges, not with the technology. Anchor efforts to decisions that are frequent, high-impact, and currently difficult to make with confidence. 

Examples: 

• “Which suppliers are emerging risks to my critical categories?” 
• “How should I time my commodity buys based on predicted price shifts?” 
• “Which sourcing strategy gives me the best balance of cost and ESG compliance?” 

If the answer isn’t clear today, that’s a strong candidate for intelligent augmentation. 

2. Assess data readiness and close gaps 

AI requires more than data; it needs the right data in the right shape. A readiness audit can identify whether your organization has the structure, standards, and sources needed to support AI. Many AI use cases can begin with partial datasets and improve through iteration. 

Key questions: 

• Are our internal datasets (e.g., spend, supplier records, contracts) clean and unified? 
• Do we have access to trusted external data (e.g., risk feeds, market indices, supplier intel)? 
• Can we link data across systems (e.g., P2P, SRM, risk) for a 360° view? 

3. Choose high-value, low-barrier use cases first 

Avoid the trap of trying to automate everything at once. Instead, focus on use cases that: 

• Solve a clear business problem 
• Have well-defined workflows 
• Can deliver early wins and build confidence 

Ideal early candidates: 

• Supplier risk alerting and monitoring 
• Conversational insights for category managers 
• Personalized market briefings for executive stakeholders 
• Autonomous negotiations are a high-value, low-barrier use case  

4. Involve the end-users early and often 

Change management is as important as model accuracy. Procurement professionals need to trust the system, understand how it works, and see it as a help not a threat. 

Best practices: 

• Pilot tools with real teams on real tasks 
• Provide visibility into how recommendations are generated 
• Train for adoption, not just usage 

5. Scale with confidence 

Once early wins are delivered and adoption is secured, the roadmap can expand:  

• Broaden from one category to many 
• Extend from advisory tools to agent-based automation 
• Integrate AI insights into strategic planning, budgeting, and supplier management 

Throughout, governance should scale in parallel, including model auditing, bias mitigation, and performance monitoring. 

“Artificial Intelligence: Real Decisions” explores how leading procurement teams are using AI today to strengthen decision-making across key domains spanning insight generation and risk forecasting to supplier engagement and strategic sourcing.