Our last post, 5 Reasons You Shouldn’t Fragment Your AI Rollout, focused on avoiding a fragmented rollout of artificial intelligence (AI). We talked about the ways a fragmented AI rollout can happen, why it can limit the value, and how to avoid it. In this second part of our three-post series on how cross-functional collaboration compounds the value AI brings to any company, we’ll dig into collaboration and how a cross-functional approach with clear use cases can unlock the full potential.
The adoption of AI across multiple business functions is rapidly accelerating, ushering in a new era of organizational efficiency and innovation. According to McKinsey’s State of AI report from early 2024, organizations are increasingly leveraging AI across multiple departments with companies implementing AI across business functions and areas.
More and more companies are taking this cross-functional approach because they recognize it’s an effective way to unlock AI’s potential to drive value. In spend management, AI can be applied across procurement, finance, supply chain, and IT to yield exponential benefits.
By breaking down traditional silos and fostering a collaborative approach to AI implementation, organizations can:
- Enhance decision-making through a more holistic view of spending patterns and risks.
- Streamline processes that span multiple departments, reducing inefficiencies and bottlenecks.
- Leverage diverse expertise to develop more robust and effective AI solutions.
- Maximize return on AI investments by identifying use cases that benefit multiple stakeholders.
Learn more about how procurement and IT leadership teams can collaborate more effectively.
Let’s delve deeper into the AI multiplier effect in spend management and explore how this cross-functional approach can amplify value and drive transformative outcomes for organizations.
High-value AI use cases across departments
AI is revolutionizing spend management by offering powerful solutions that span multiple departments, creating an efficiency and savings multiplier effect. Below are some high-impact use cases across procurement, finance, and supply chain. They highlight where these applications overlap and amplify benefits across stakeholders.
Procurement collaboration use cases
Procurement leaders are looking for AI solutions that have efficiency and savings impact in regard to supplier risk and management, contract analysis and optimization, and spend classification and categorization. However, other teams also have a shared interest in many of these same goals so it is important to develop processes where teams can share visibility and insights.
| Procurement Working Across Departments | |
| Procurement + Supply Chain Teams | Supplier Risk Assessment and Management
AI-driven risk assessment tools analyze vast amounts of data to provide real-time insights into supplier stability, compliance, and performance.
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| Procurement + Legal + Finance Teams | Automated Contract Assessment and Optimization
AI-powered contract analysis tools can rapidly review and extract key information from complex agreements.
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| Procurement + Finance Teams | Intelligent Spend Classification and Categorization
Machine learning algorithms can automatically classify and categorize spend data with high accuracy.
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Finance collaboration use cases
Finance leaders and supporting teams are looking for AI solutions that give visibility into cash flow, enabling an accurate prediction of cash needs, automated invoice processing and reconciliation, and automated fraud detection and prevention.
| Finance Working Across Departments | |
| Finance + Procurement + Executive Leadership | Predictive Cash Flow Forecasting
AI models can analyze historical data and market trends to provide more accurate cash flow predictions.
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| Finance + Procurement Teams | Automated Invoice Processing and Reconciliation
AI-powered optical character recognition (OCR) and natural language processing (NLP) can automate invoice processing and reconciliation.
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| Finance + Procurement + Compliance | Fraud Detection and Prevention
Machine learning algorithms can identify unusual patterns or anomalies in financial transactions, helping to detect and prevent fraud.
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Supply chain collaboration use cases
Supply chain leaders and supporting teams are looking for AI solutions that help mitigate risk and support inventory optimization based on demand and real-time visibility into the supply chain.
| Supply Chain Working Across Departments | |
| Supply Chain + Procurement + Finance Teams | Demand Forecasting and Inventory Optimization
AI-driven demand forecasting models can analyze multiple data sources to predict future demand more accurately.
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| Supply Chain + Procurement + Executive Leadership | Real-Time Supply Chain Visibility and Risk Assessment
AI-powered supply chain visibility tools can provide real-time insights into the movement of goods and potential disruptions.
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Implementing these high-value AI use cases across departments, creates a synergistic effect that amplifies the benefits of AI throughout the entire spend management process. The key to success lies in fostering collaboration between departments and leveraging AI solutions that can integrate and analyze data from multiple sources to provide comprehensive, actionable insights. For a deeper understanding of fostering this collaboration see our white paper on Unleashing Productivity and Growth with AI.
Coupa AI Value Amplification Use Case Example
Coupa’s AI-native Total Spend Management platform demonstrates the power of cross-functional AI use cases. For instance, community-generated AI leverages anonymized data from more than $7 trillion in global cumulative spend — real-time transactions on the platform — to provide benchmarking and insights that benefit multiple stakeholders:
• Procurement teams gain visibility into market pricing and supplier performance.
• Finance departments identify potential savings opportunities and optimize working capital.
• Supply chain managers assess supplier risk and identify alternative sources.
By combining these insights with Coupa’s AI recommendations, organizations can make more informed decisions that amplify value across departments, creating a true multiplier effect in spend management.
IT has a crucial role in AI success
In the journey to leverage AI for spend management, IT plays a pivotal role in working with all departments to ensure successful implementation, integration, and ongoing management of AI solutions. Let’s look at the key areas where IT leadership is essential and how it contributes to the overall success of AI initiatives.
IT leadership is critical
IT’s leadership can ensure that AI initiatives in spend management are implemented securely, efficiently, and in alignment with overall business objectives. This approach not only maximizes the value of AI investments but also minimizes risks associated with data security and compliance.
| IT Leadership Roles & Contributions | |
| Crucial Tasks | Ensure scalability and interoperability:
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| Key Tasks | Maintain data integrity and compliance:
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| Core Tasks | Provide technical expertise and support:
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| Consider Ethics | Implement Responsible, Purpose-Built AI
IT should lead in ensuring that AI implementations are not only effective but also ethical and responsible:
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| Support Innovation | Establish sandboxes for experimentation:
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| Establish Robust Testing | Develop robust testing and deployment processes:
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| Evaluate and Select Vendors | Evaluating and Selecting AI Spend Management Vendors
In addition to these critical responsibilities, IT plays a crucial role in evaluating and selecting AI-powered spend management vendors. When assessing potential solutions, IT leaders should ask pointed questions to distinguish between substance and hype in AI offerings:
By thoroughly vetting AI vendors with these questions, IT can help ensure that the chosen solution aligns with the organization’s technical requirements, security standards, and business objectives. This diligence is crucial in selecting an AI-powered spend management platform that delivers real value and avoids the pitfalls of overhyped but underperforming solutions. |
What’s next?
In this series we’ve discovered how to avoid a fragmented AI rollout and looked at specific use case examples outlining a strategic vision for implementing AI across procurement, finance, supply chain, and IT to create valuable efficiencies and visibility.
The final installment of this series focuses on how to choose a platform that delivers results so you can realize the best ROI and benefits from your implementation. When evaluating an AI platform, it’s important the solution implements AI across source-to-pay (S2P), including addressing specific S2P challenges, applying the right tools that fit existing workflows, ongoing refinement of AI models, and strategic alignment. Stay tuned for “Choosing A Platform for Cross-Functional AI Success” coming soon.
*McKinsey & Company, McKinsey Global Surveys on the state of AI, 2021-2024, 12 Mar 2025.
