The role of artificial intelligence (AI) in corporate treasury is steadily evolving, with a recent Citi report outlining how generative AI is poised to reshape treasury operations by 2030. AI’s potential to enhance efficiency, optimize liquidity, and streamline decision-making processes is already being realized, although many corporate treasuries are still in the experimental phase. According to Citi’s findings, the future of treasury operations lies in AI-powered platforms that will turn financial management into an intelligent, real-time system.
AI in Treasury: From Experimentation to Full Integration
Citi’s report highlights a clear trajectory for AI adoption within corporate treasury functions. Currently, 82% of treasury teams are still in the early stages of generative AI experimentation, with only 3% having scaled it across their operations. However, by 2030, AI is expected to become the new treasury operating system, revolutionizing the sector from a control center into a dynamic financial hub.
Citi’s 4-Stage AI Maturity Model
The report introduces a four-stage AI maturity model that outlines the journey from initial AI use case identification to full-scale adoption. The stages are:
- Identification: Identifying potential AI use cases.
- Exploration: Testing AI applications in a limited scope.
- Transformation: Overhauling treasury operations with AI, supported by human oversight.
- Optimization: Fine-tuning AI systems for continuous improvement.
AI Use Cases: Early Adoption and Practical Applications
While most treasury teams are still experimenting, nearly 60% of respondents reported identifying at least one practical use case for generative AI. The most common applications include:
- Liquidity Forecasting: Predicting cash positions and optimizing cash flow.
- Reconciliation: Streamlining the matching of financial records.
- Report Generation: Automating the creation of financial statements and reports.
A smaller percentage of respondents are exploring AI for more complex tasks, including variance analysis and narrative creation in management reports. 40% of treasurers plan to increase their AI investments over the next two years, signaling strong growth potential for AI in treasury functions.
| AI Use Cases in Corporate Treasury | Percentage of Treasurers Using AI |
|---|---|
| Liquidity Forecasting | 70% |
| Reconciliation | 60% |
| Report Generation | 50% |
| Variance Analysis | 20% |
| Narrative Creation for Management | 15% |
Challenges to AI Adoption: Data Quality and Human Oversight
Despite the promising outlook for AI in treasury, data quality remains a significant barrier. Over 70% of respondents cited fragmented or incomplete data as a major challenge to AI adoption. As AI algorithms rely heavily on structured data, inconsistent or incomplete datasets could lead to errors, undermining trust in AI-driven solutions.
To overcome these obstacles, Citi recommends:
- Building a centralized data lake for consistent and accurate data.
- Establishing API connections to integrate enterprise systems.
- Defining clear data ownership to ensure accountability for data accuracy.
Treasurers also raised concerns about the level of trust in AI-generated outputs. Joseph Neu, Founder and CEO of NeuGroup, emphasized the importance of 100% trust in AI results. Generative AI’s credibility hinges on clear governance, explainability, and audit trails to ensure its outputs are reliable and actionable.
Building Human Readiness for AI Adoption
As Alexander Reijrink, Global Head of Corporate Finance at Philips, shared, employee training and a mindset shift are key steps in successfully adopting AI. Building human readiness alongside AI technology is essential for fostering trust in the system and ensuring smooth AI integration.
The Road to AI-Driven Treasury Systems: Real-Time Operations and Automation
Citi’s vision for the future of treasury operations involves a connected treasury ecosystem, where AI, cloud technologies, and data integration work seamlessly to enable real-time decision-making. This vision is already taking shape in parts of the financial ecosystem.
For example, Bank of America’s CashPro platform provides treasurers with real-time visibility into global cash positions, allowing them to forecast and manage liquidity more effectively. Similarly, Citi’s Treasury and Trade Solutions group is extending tokenization and programmable money capabilities to corporate clients, facilitating instant cross-border liquidity and more automated cash management.
| Key Features of AI-Driven Treasury Systems | Benefits |
|---|---|
| Real-Time Visibility | Quick, data-driven decision making |
| Automated Cash Management | Enhanced liquidity management |
| Tokenization & Programmable Money | Instant cross-border transactions |
| Data Integration | Seamless connection to enterprise systems |
Expanding the Role of Treasury in Business Strategy
The role of corporate treasurers is evolving beyond traditional finance functions. As AI and cybersecurity risks converge, treasury teams are becoming integral to strategic enterprise planning, overseeing not just liquidity, but also data quality, payments infrastructure, and digital resilience.
Citi’s findings align with this shift, showing that treasurers who collaborate with tech and data teams early are better positioned to move from experimentation to full-scale AI integration. This proactive approach ensures that treasuries can handle the complexities of the new AI-driven financial landscape.
Treasurers as Strategic Participants
As AI becomes embedded in treasury operations, treasurers are increasingly seen as strategic participants in shaping a company’s financial future. By collaborating with other departments early on, they can help businesses navigate the transition to AI-driven financial management systems.
FAQs
1. What is the role of AI in corporate treasury?
AI is transforming corporate treasury by automating tasks such as liquidity forecasting, reconciliation, and report generation, making treasury operations more efficient and data-driven.
2. What are the challenges to AI adoption in treasury?
Data quality is a major barrier to AI adoption in treasury, with many treasuries citing fragmented or incomplete data. Ensuring data integrity and human oversight is crucial for success.
3. How will AI impact treasury operations by 2030?
AI is expected to become the new treasury operating system by 2030, automating decision-making and real-time cash management, ultimately transforming treasury departments into intelligent financial hubs.
4. What are the key benefits of AI in treasury?
The key benefits of AI in treasury include real-time visibility into cash positions, automated cash management, and the ability to make data-driven decisions for liquidity optimization.
5. How can treasury teams prepare for AI adoption?
Treasury teams should focus on data integration, training employees, and collaborating with tech teams to ensure a smooth transition to AI-driven treasury operations.