AI & Automation in Spend Management: Reducing Unmanaged Spend with Data 

Explore Understand the difference between spend and expense management and how the two can be brought into harmony to deliver strategic control across all categories of spend.  

Introduction: The Rise of AI in Spend Management  

Both push and pull factors have driven the rapid rise of artificial intelligence in spend management in recent years. The push factors are external or internal forces compelling organizations to adopt AI in spend management. They include economic volatility and cost pressures, with CFOs and CPOs under pressure to safeguard margins amid inflation, tariffs, and supply chain shocks; geopolitical and supply chain risk, with global disruptions such as trade wars, conflicts and sanctions exposing overdependence on single suppliers; regulatory and ESG compliance; talent shortages; manual inefficiencies; and fragmented systems with a lack of visibility. For all these reasons organizations, which have to deal with vastly expanding volumes of data, are being pushed to use artificial intelligence to deliver a single and reliable version of the truth to provide a basis for effective spend management. 

The pull factors toward AI adoption for spend automation are attractions or incentives that draw buying organizations towards a new state. They include a variety of attractive benefits and enablers such as efficiency and ROI gains through reduced manual processing time. AI also enables predictive analytics and smarter decision-making with machine learning models forecasting demand, predicting supplier risks, and identifying hidden savings. AI-powered insights support more informed negotiations and transparent, collaborative supplier relationships, which reduce disruption costs. AI also facilitates the seamless integration of sourcing, contracting, and payments, creating end-to-end visibility and control over spend. 

In short: procurement is adopting AI both because it is necessary (push factors) and because it is highly desirable (pull factors). 

But for early adopters of AI in spend management the benefits can go even further. They demonstrate financial discipline and innovation, signaling agility to investors and other stakeholders. Gartner, in its “Gen AI for Procurement” press release (July 2025), states that “early adopters are positioned to gain a strategic edge over competitors” by using Gen AI to improve procurement productivity, cost efficiency and supplier workflows. Forrester Research, in its blog “Introducing Forrester’s IT Spend Management Framework” (June 2025), argues that high-performing organizations build spend-management on three capabilities: visibility, control, and optimization, which enable cost savings and then reinvestment into innovation such as AI. 

The Role of Artificial Intelligence in Procurement  

Artificial intelligence technologies are increasingly deployed to automate, enhance, and optimize various tasks within procurement, ultimately improving efficiency, accuracy, and decision-making. AI-powered tools can analyze data, predict market trends, streamline RFx events, and automate tasks like contract management, invoice processing, and supplier risk management. But the absolute bedrock of procurement in the age of digital transformation is spend management. You cannot move forward effectively unless you know in granular detail where you are spending your money, with whom, at what cost and at what risk. Spend management is the sine qua non of proactive and strategic procurement and in today’s fast-moving world, you will only manage spend effectively if you deploy artificial intelligence. 

How AI Helps Reduce Unmanaged Spend 

If we consider spend management in particular, AI can help increase the percentage of spend under management (SUM) compared with traditional processes. The calculation of SUM is straightforward: divide managed spend by total organizational spend, then multiply by 100. If an organization spends €100 million annually and procurement manages €70 million under approved contracts through official channels then its SUM is 70%. As discussed in previous articles, low SUM means fragmented buying power, missed savings opportunities, inefficiencies, and strategic weakness. High SUM provides a factual basis for negotiations with suppliers, reduces risk, and provides spend visibility needed to optimize supply chain strategies. 

Procurement AI technology can be deployed to eliminate the barriers to raising spend under management. Instead of analyzing spend data for months on end using manual or semi-automated methods, procurement teams can use AI to identify opportunities, target areas of off-contract spend, track progress, and optimize purchasing strategies. The technology transforms spend management from a reactive compliance exercise into proactive value creation. 

Identifying Leakage and Off-Contract Purchases 

Hidden inefficiencies, duplicate payments, and maverick spend are difficult to identify manually. But today’s ML models detect anomalies and outliers in transaction data: purchases outside contract, sudden supplier price changes, and duplicate invoices for example. Predictive algorithms flag potential fraud or policy non-compliance in real time. Such anomaly detection leads to reduced leakage, tighter policy enforcement, and immediate savings. 

Data Collection and Classification 

Procurement data is often fragmented across ERPs, source-to-pay systems, and spreadsheets. Machine learning (ML) algorithms automatically cleanse, deduplicate, and classify spend data using natural language processing (NLP) and pattern recognition. Category taxonomies (such as UNSPSC) are applied automatically, improving spend visibility by up to 95%. AI thus not only reduces manual effort but also provides a reliable data foundation for analytics, budget forecasting, and supplier consolidation. 

Key Benefits of AI for Spend Optimization 

AI is increasingly central to spend optimization, i.e., the continuous process of analyzing, controlling, and improving how an organization allocates and manages its expenditure to maximize value and minimize cost and risk. Here are some of the main benefits of using AI in spend management. 

Predictive Demand and Price Forecasting 

Procurement teams often rely on historical averages rather than forward-looking indicators. But AI-powered predictive analytics use external data (commodity prices, FX rates, logistics indices) to forecast cost trends and optimize timing of purchases. demand forecasting models help align procurement with production schedules, reducing overstock or urgent spot buys. Benefits include better cost predictability and improved working capital management. 

Supplier Performance and Risk Scoring 

Using manual or semi-automated techniques, supplier risk evaluation is slow and reactive. Modern AI software now aggregates and analyzes structured and unstructured data (financial reports, news, ESG ratings, social media, logistics data etc.) to create real-time supplier risk scores. These models can predict potential disruptions such as bankruptcy risk or late deliveries before they happen. The benefits include reduced cost of failure and business continuity. 

Category and Contract Optimization 

Manual category management misses cross-supplier savings opportunities. Generative AI and optimization algorithms, by contrast, simulate “what-if” scenarios (for example, to examine the impact of switching suppliers or changing order volumes) to find the best total cost of ownership (TCO). Natural language processing (NLP) models analyze contract language to detect unfavorable terms, renewal deadlines, or pricing clauses. As a result, organizations can pursue smarter negotiation strategies, reduce total cost, and improve contract compliance. 

Continuous Spend Optimization through Recommendation Engines 

With traditional spend management approaches insights are often static and periodic. But recommendation engines similar to those used in e-commerce continuously monitor data to suggest new savings opportunities, such as supplier consolidation, contract bundling, or alternative materials. These insights can be integrated into dashboards for category managers who can act on them, creating a continuous improvement culture and sustained ROI on procurement technology. 

Optimized Spend Management and ESG 

Sustainability and compliance goals can conflict with cost reduction, but AI can help bring them into harmony. Models integrate carbon, social, and governance metrics into spend decisions, balancing price with ESG impact. “Carbon-aware sourcing” algorithms help identify low-emission suppliers or logistics options that minimize Scope 3 emissions. By aligning financial and sustainability targets, which are increasingly important for investor confidence, organizations can boost their ability to attract fresh capital 

Practical Steps to Implement AI in Spend Management 

The benefits of deploying AI technologies in spend management and spend optimization are clear. But how should an organization go about it? Here is a suggested best-practice approach to AI-driven spend optimization projects that combine multiple perspectives: not just procurement but also finance, data science, and change management. 

Readiness, Audit & Vision Statement 

It is essential to secure executive sponsorship from the start and a cross-functional steering group. The project kicks off with an assessment of current spend management maturity (data, processes, governance, and technology). The steering group defines measurable objectives linked to procurement and financial KPIs (cost savings, working capital, risk mitigation, and ESG compliance, among others). It can then present a clear business case and ROI targets to the sponsors, who should then endorse the roadmap. 

Data Discovery & Integration 

The team identifies all relevant data sources (S2P, ERP, CLM, supplier portals, financial systems, and external risk/commodity data feeds). The data scientists cleanse, deduplicate, and classify spend data using ML-assisted tools and establish a data lake or unified repository. This creates a “single version of the truth” for spend data with a high level of data quality. 

Use-case Prioritization 

You cannot do everything at once, so the team must rank AI use cases by value and feasibility. Candidates include spend classification, anomaly detection, demand/price forecasting, spend optimization, supplier risk scoring, contract optimization, and ESG spend tracking. Some of these will be quick-wins, ideal for pilot projects, while others will be long-term strategic enablers. Make your best estimates of ROI per use case. 

Platform and Tool Selection 

Depending on your current digital maturity in procurement, you should evaluate whether to extend the existing source-to-pay and ERP environment with specialized AI/analytics modules or implement a completely new platform. In doing so you’ll need to assess integration capability (APIs, data pipelines, governance), ensure auditability and compliance with corporate data policy, and select platform(s) and toolsets aligned with your organization’s IT and business strategy. 

Pilot Project Implementation 

It is highly advisable to deploy AI models on a small scale at first and in a controlled environment (for example, one spend category or one business unit). Measure results against baseline KPIs such as savings realized, anomaly detection accuracy, and time-to-insight. Collect qualitative feedback from procurement and finance users and provide validated proof of concept with quantifiable benefits. 

Change Management, Training & Documentation 

New skills are needed among end-users so you must train procurement, finance, and category managers, in particular in the art of interpreting AI-generated insights. Workflows, governance, and exception handling must be clearly defined. Communicate early wins to sustain sponsorship. 

Scale-Up & Enterprise Roll-Out 

Now you can expand to additional categories, regions, and supplier tiers and automate data ingestion and analytics refresh cycles. Integrate AI insights into dashboards and decision-support tools for CPO/CFO visibility. AI can then be embedded in day-to-day operations across the organization and in regular spend review cycles. 

Continuous Improvement & Model Refinement 

Once the AI applications are up and running, you should review data pipelines and model performance periodically. The scope can be extended, for example by adding external data (ESG ratings, logistics data, and inflation indices). Then iterate based on user feedback and new business objectives. Over time, machine learning will deliver ongoing accuracy improvements with sustained cost and efficiency gains. 

Future Trends: AI-Driven Procurement and its Limits 

With agentic AI we are moving towards increasingly autonomous procurement, with adaptive procurement workflows, predictive sourcing, real-time supplier analytics, and automated decision-making. Generative AI and AI copilots will increasingly streamline processes such as RFP generation and contract management.  

This does not mean the elimination of human agency. On the contrary, while areas such as transactional procurement and routine sourcing are increasingly moving towards autonomous or semi-autonomous, machine-driven decisions, functions such as spend management and spend analytics inherently require a high degree of human agency and strategic oversight.  

AI can aggregate and analyze spend data, but determining what that data means in a business context – how spend patterns align with financial goals, risk tolerance, or sustainability targets – calls for human judgement. Supplier collaboration, diversification, and resilience depend on trust, negotiation, and long-term partnership. These qualitative aspects are difficult, if not impossible, to codify into autonomous decision systems. Moreover, spend management connects procurement with finance, operations, and ESG compliance. Strategic trade-offs require human deliberation and alignment with corporate objectives. 

Ethical and governance considerations also come into play. AI can surface hidden savings or risks, but decisions about supplier exclusion, workforce impact, or ESG compliance must remain accountable to human leaders under corporate governance frameworks. 

Closing Recommendations 

In conclusion, the introduction of AI in spend management is becoming a business imperative, but it will pay to get good advice and approach projects in a systematic manner with high-level sponsorship and cross-functional teams.  

You also need to work with external partners who can provide reliable and proven skills in project management and technology implementation, as well as the needs of your vertical sector. 

Drawing on unparalleled supplier intelligence and an integrated set of source-to-pay applications, JAGGAER One’s AI-powered solutions provide the most robust option for spend management. Our single solution for all spend  including spend not currently under management, puts you in control. Machine learning speeds up classification while automated algorithms accelerate decision making. Analysts find faster data insights, managers track and optimize KPls, and executives get dashboards providing insights and monitoring progress.