65% of CFOs plan to increase their gen-AI investment, while AI usage across finance has grown sixfold year over year, latest research from McKinsey shows; yet 95% of AI pilots fail to deliver meaningful financial impact. The FinTech Baropaper 2025 recently reflected this too: DSO is increasing, invoice accuracy is deteriorating, and data complexity continues to grow. With AI advancing faster than outcomes, what is preventing finance and credit management teams from turning technology into tangible cash-flow improvement?
1. AI can accelerate finance (but only if the foundations are stable)
McKinsey’s research shows a clear pattern across high-performing finance teams:
- They do not start with complex AI pilots
- They first standardise processes, clean data, and integrate systems
- Then they scale AI into forecasting, reporting, and working-capital optimisation
Organisations that skip these steps see the opposite outcome: They deploy AI on top of broken workflows and scattered data.
This is exactly where the Onguard Baropaper shows the industry still struggles:
- Invoice errors keep increasing
- Excel usage is rising again
- CRM/ERP/collections tools are not fully integrated
- Data lives in multiple systems without consistent governance
No AI model can fix DSO if the underlying process still produces incorrect invoices or if approval chains are unclear.
2. DSO is climbing because the basics are still broken
Across the Netherlands and the EU, the pattern is consistent: Payment behaviour is deteriorating.
–DSO is worsening
Only 30% of organisations receive payments within 30 days, down from 42% a year earlier.
–Invoice accuracy is declining
Errors have more than doubled since 2023.
–Data is increasingly scattered
38% of finance professionals cite fragmented data as a major barrier.
–Manual work keeps returning
Excel is used again because teams lack trust in their core systems and need urgent visibility.
These foundations directly block AI impact: AI can analyse patterns, but it cannot fix broken master data, unclear ownership or inconsistent processes. It amplifies what is already there, good or bad.
| The biggest reasons for rising DSO in 2025: -Incorrect or duplicate invoices (21%, up from 9%) -Customer-side delays -Poor internal approval flows -Manual corrections -Fragmented invoicing and credit management systems |
3. What leading organisations do differently
Across all research, five practices consistently separate companies that achieve impact from those that don’t:
A. They start with process clarity, not with AI tools
Leaders simplify processes before automating them: They standardise invoicing, case handling, approval paths and data definitions.
This reduces errors. And it is the #1 new driver of rising DSO in 2025.
B. They integrate systems across the O2C chain
High performers connect ERP + CRM + collections + payment systems. This gives them:
- One source of truth
- Real-time visibility
- Cleaner data for AI
- Faster root-cause insight
Onguard’s data shows CRM and accounting systems now play a bigger role in O2C than ever. This shift to integrated tooling is where impact begins.
C. They choose AI use cases with immediate, visible value
McKinsey’s most effective cases mirror what Onguard sees in the market:
- Payment-behaviour prediction
- Cash-flow forecasting
- Automated reporting drafts
- Intelligent prioritisation of cases
- Invoice-to-contract compliance
- Exception detection
Teams that start with these “low-friction, high-impact” use cases build momentum quickly.
D. They train teams to use insights, not just tools
The Onguard Baropaper shows 41% fear that AI will replace finance roles. The reality is the opposite: AI removes friction; people add judgment. Leaders invest in upskilling so their teams can interpret AI-driven insights and turn them into action
E. They prepare for e-invoicing and data-quality legislation
With EU e-invoicing obligations approaching, more companies are realising that structured, standardised invoices aren’t “nice to have”, they are central to reducing DSO.
Early movers gain a competitive edge because AI thrives on clean, structured input.
4. What this means for credit management in 2025-2027
Across all sources, three shifts are inevitable:
A. Predictive credit management becomes the norm
The shift from “reactive collections” to “predictive prevention” is accelerating. AI will identify late-payment risk before it happens only if the data is clean enough.
B. Data quality becomes the biggest driver of cash-flow performance
- Not automation.
- Not more dashboards.
- Data quality.
C. Integrated systems beat isolated tools
The winning organisations aren’t those with the most technology, but those with the best connected technology.
“AI can improve forecasting, reporting, case prioritisation and cash-flow visibility. But it only works when data is clean, processes are standardised, teams understand the insights and work from the same source of truth. AI is the accelerator, but credit management is the steering wheel. And organisations that invest now in data quality, integration and clear processes will finally see AI translate into lower DSO, faster payments and a healthier cash flow.”
-Adriaan Kom, Managing Director, Onguard.
Curious where your biggest DSO improvement opportunities lie? Contact our team, we’re happy to think along with you.
