written by Lorena Pérez Sandroni, Treasury Masterminds board member
Treasury technology projects often fail due to not starting with a structured approach from the beginning—one that prioritizes collaboration, thorough assessment, and most importantly, adaptability throughout the implementation process.
Key Factors Behind Failure
- Insufficient planning: Lack of clear objectives and misalignment with organizational needs.
- Resistance to change: Inadequate training leaves users ill-equipped to leverage new systems.
- Integration complexity: Failure to account for scalability and legacy systems leads to technical setbacks.
- Project misalignment: Treasury teams were not consulted sufficiently, resulting in systems that don’t meet operational needs.
Real-World Examples of Failure
- Global TMS Implementation Gone Wrong
A company invested in a best-in-class TMS to improve global cash visibility and automate payments. However, due to poor stakeholder engagement and a lack of training, the system failed to meet treasury’s needs for bank connectivity, forecasting, and liquidity management. Manual processes persisted, and ROI was never realized.
- Payment Platform Integration Chaos
A new payment processing platform was introduced without considering the complexity of integrating with outdated systems. Frequent customizations were needed, leading to delays and technical glitches that disrupted daily operations.
- Debt Management Misfire
A debt module was purchased without a proper assessment. It couldn’t handle all debt types, forcing teams to manually calculate and manage exceptions—defeating the purpose of automation.
- The Data Monster
From a technical standpoint, the project was a success. But beneath the surface, daily operations lacked the controls and discipline needed to maintain data integrity:
What Went Wrong After Go-Live:
- No standardized process for data entry: Regional teams entered bank account names, payment types, and entity codes using inconsistent formats.
- No validation rules or approval workflows: Forecasting inputs and cash positions were uploaded manually with no checks, leading to outdated or incorrect data.
- No ownership of data quality: Treasury assumed IT would manage it, while IT expected business users to maintain it. The result? No one took responsibility.
- No audit trail or exception reporting: Errors went unnoticed for weeks, and reconciliations became manual again.
The Consequences:
- Reports showed conflicting cash positions across regions
- Forecasting accuracy dropped, eroding trust in the system
- Treasury reverted to spreadsheets for critical decisions
- The TMS became a “data monster”—technically sound, but functionally unreliable
Key Takeaway:
Even the best TMS can become a liability if data governance isn’t embedded into daily operations. Success isn’t just about implementation—it’s about sustaining accuracy, ownership, and trust.
Turning It Around
- Engage stakeholders early: Treasury, IT, and finance must co-own the project.
- Define clear objectives: Align tech capabilities with business outcomes.
- Invest in training: Empower users to adopt and optimize the system.
- Prioritize data governance: Clean, validate, and standardize data before and during implementation.
- Plan for scalability: Ensure the syscan grow with your business.
- Establish clear data ownership, implement validation rules, and embed regular controls into daily workflows to prevent the system from becoming unreliable over time.
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- Bridging Old and New: Citi Steps Into Stablecoins and Crypto ETF Custody
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