AI in procurement: What it takes to succeed

In chapter six of Beroe’s latest report from its Future Vision series, “Artificial Intelligence: Real Decisions”, we look at what it really takes to make AI work in a procurement setting; and not just from a technical standpoint, but from a practical one. From cleaning up data to building trust in probabilistic outputs, successfully deploying AI in procurement depends on more than just smart algorithms. It’s about embedding it into workflows, aligning it with human judgment, and creating a culture where intelligent systems support decision-makers, rather than replacing them. Prioritizing a Human-in-the-Loop approach at the core, this chapter explores how procurement leaders can turn AI from a useful tool into a dependable partner. 

AI in procurement: What it takes to succeed 

To embed AI effectively, procurement leaders must address a set of foundational challenges and build an environment where intelligent systems can deliver 

sustainable value. 

1. Data sufficiency and readiness 

AI’s output is only as good as the data it’s trained on and fed with. Gaps in supplier data, outdated market intelligence, and disconnected systems are among the most common blockers. 

What to do: 

• Prioritize data consolidation and standardization across platforms 
• Enrich internal data with trusted external sources 
• Build feedback loops to continuously improve data quality 

2. Probabilistic nature of AI vs. deterministic expectations 

Some AI technologies can work with probabilities, rather than certainties. They rank options, assesses likelihoods, and suggests courses of action. This is fundamentally different from the deterministic logic of traditional systems, and often unsettling to users accustomed to black-and-white answers. 

What to do: 

• Set the right expectations: AI suggests, humans decide 
• Use confidence scores and explainable outputs to increase trust 
• Introduce AI in assistance / augmentation modes first, before moving to automation 

3. Bias, explainability and governance 

AI can inadvertently learn and reinforce biases, especially if trained on skewed or incomplete data. In procurement, this can lead to unfair supplier recommendations or flawed risk signals. 

What to do: 

• Implement explainability-by-design: users should always be able to ask, “Why this recommendation?” 
• Use diverse training datasets and audit for bias regularly 
• Establish AI governance structures involving procurement, data science, and compliance 

4. Change management and digital fluency 

Even the most advanced AI system will fail if users don’t trust it or don’t know how to use it. Procurement professionals may view AI as a threat or simply revert to old habits when insight delivery feels too complex. 

 What to do: 

• Position AI as a capability enhancer, not a job replacer 
• Provide hands-on training and pilots with high-trust, high-frequency use cases 
• Celebrate early wins to build momentum and buy-in 

5. Integration into existing workflows 

AI won’t succeed as a standalone tool. For maximum adoption, it must fit seamlessly into existing procurement platforms, routines, and interfaces. 

What to do: 

• Focus on frictionless delivery: embed insights where work already happens 
• Use APIs to connect AI outputs with sourcing, contract, or risk management systems 
• Design with simplicity in mind: the best AI often feels invisible 

What Success Looks Like 

When these barriers are addressed, AI becomes more than a tool, it becomes a decision partner.  

This means: 

• Sourcing cycles shorten as AI narrows the supplier universe and pre-qualifies candidates 
• Category strategies improve as market signals are interpreted in real time 
• Risk exposure shrinks as early-warning systems surface potential threats 
• Procurement becomes a growth enabler, not just a cost controller 

But most importantly, human decision-makers stay in control, only now they’re equipped with better foresight, sharper options, and more time to focus on what matters. 

“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.