In 2025, manufacturing companies navigated a landscape shaped by regulatory changes, shifting trade policies, and ongoing geopolitical uncertainty. These factors heightened the need for real-time visibility into cash flows, greater predictability of payments and receipts, and enhanced agility in managing working capital. Are manufacturers turning to artificial intelligence (AI) to strengthen their treasury operations? And what will help companies improve efficiency and resilience in an unpredictable economic environment?
The Cash Management in an AI World survey explores these very questions. In addition to manufacturing, the survey gathered responses from over 250 treasury professionals across industries, including commercial real estate, healthcare, higher education, warehousing, transportation, and logistics. This blog post focuses on the survey results of respondents practicing treasury at manufacturing companies.
Manufacturing companies have changed how they identify and manage risk exposures, adjust supply chain strategies, and experience cost increases (see Figure 1).
How are these changes affecting cash management?
In 2025, the impacts of economic volatility on cash management included an increase in both Days Sales Outstanding (DSO) and Days Payable Outstanding (DPO). (See Figures 2 and 3). This suggests that making data-driven decisions around cash movements is critical for treasury teams.
Figure 2: Days Sales Outstanding trends in manufacturing treasury, 2025.
Figure 3: Days Payable Outstanding trends in manufacturing treasury, 2025.
Figure 4: Manufacturing’s Current Use of AI in Cash Management
Manufacturing companies are using AI across many areas of cash management. At least 49% of companies surveyed apply AI in cash forecasting (62%), accounts payable (63%), accounts receivable (49%), and/or fraud detection (51%). The adoption of AI in cash management beyond cash forecasting aligns with the fact that many treasury departments have direct control over accounts payable (AP) and accounts receivable (AR). Forecasting success depends on understanding and predicting cash inflows and outflows, so leveraging AI across cash forecasting, AP, and AR makes sense.
Why are respondents in manufacturing turning to AI in treasury? Identifying the top cash management challenges faced by treasury teams can help in answering this question.
Treasury teams face common cash management challenges, including lack of visibility into bank account activity, lack of forecasting accuracy, regulatory changes, and too much time spent on non-value-add activities. AI-driven tools for fraud detection, AP, AR, and cash forecasting can help companies mitigate these issues.
AI Adoption in Manufacturing Treasury
- Collaboration between treasury, AP, and AR is less of a challenge for manufacturing than for commercial real estate, healthcare, or higher education.
- Silos between AR and treasury are more common in manufacturing than in commercial real estate or higher education.
- Manufacturing treasury teams often have less incentive to collaborate internally and externally compared to those in commercial real estate, healthcare, or higher education.
Survey Insights: Key Takeaways
Unprecedented global market dynamics in 2025 have increased DSO and DPO at companies of all sizes across diverse industries.
- Top cash management challenges include unanticipated regulatory changes, limited cash visibility, and lack of collaboration between treasury, AP, and AR teams.
- To manage cash more effectively, treasury teams should identify and mitigate cash management silos within their own teams, and between AP/AR and treasury.
- AI adoption in treasury is common in areas such as cash forecasting, fraud prevention, AP, and AR.
- Key objectives for adopting treasury technology in 2025 and 2026 include improving security and control around financial processes, gaining better data insight for smarter decision-making, and automating manual/repetitive tasks.
- Companies are investing in technology training, business partnering, and upgrades to cash management, data management, and data analytics tools.
- Despite AI adoption, companies plan to add treasury staff in 2026.
Next Steps for Treasury Leaders
- Conduct an internal review to identify and address silos and foster greater collaboration and information sharing.
- Prioritize AI adoption in key areas like cash forecasting, AP, AR, and fraud prevention.
- Invest in technology that improves real-time cash position access and forecasting accuracy, leveraging AI tools for more agile, data-driven decisions.
Download the full white paper for benchmarks and actionable strategies.