Key Takeaways
- Nearly 80% of European buyers already use AI in purchasing or payments workflows.
- AI adoption varies significantly by country and company size.
- The biggest impact comes from decision intelligence, fraud prevention and automation of A/R processes.
- Many organisations remain in the tactical automation phase, rather than using AI strategically.
- The next wave of B2B commerce will be defined by AI integrated across payments and order-to-cash processes.
Nearly 80% of buyers already use AI in purchasing & payments
Artificial intelligence is rapidly becoming embedded in the B2B purchasing environment.
TreviPay commissioned Censuswide to survey 550 B2B buyers across the UK, France, Germany and Spain to understand how payment preferences influence supplier choice. The research found that a large majority of European buyers already use AI in some capacity when managing procurement and payments.
The research found that nearly 80% of European buyers already use AI in purchasing or payments workflows, signaling how quickly AI has become part of day-to-day operations.
However, adoption does not necessarily mean transformation.
While many buyers report frequent use of AI, adoption varies by market and is often focused on improving decision-making, managing risk and reducing manual work rather than replacing human judgment.
In many organisations, AI is still used primarily to automate routine tasks rather than to deliver deeper strategic insights.
“AI adoption in B2B finance is already widespread, but the real opportunity lies in how organisations use it to improve decision-making rather than simply automate existing workflows.”
Where AI is delivering the most measurable impact
While AI applications vary widely, several use cases are emerging as particularly valuable for finance and operations teams.
Decision intelligence
AI can analyse purchasing behaviour, supplier performance and payment trends to provide insights that help organisations make better commercial decisions.
This shift reflects a broader move from reactive reporting to proactive insight, where transactional data is used to identify opportunities, flag early risk signals and guide action across the buyer lifecycle.
Advanced analytics allow finance teams to detect anomalies and potential fraud earlier, strengthening financial oversight.
As buyers and suppliers operate across increasingly complex environments, these capabilities help balance stronger risk controls with the need for seamless purchasing experiences.
Reduction of manual A/R workflows
AI and automation reduce manual work in accounts receivable processes, improving efficiency while allowing teams to focus on higher-value activities.
When applied across receivables, AI not only streamlines workflows but also improves visibility into buyer behavior, enabling teams to move from managing transactions to actively supporting revenue growth.
AI should improve outcomes, not just efficiency. Intelligent automation drives visibility, reduces risk and delivers scalable A/R performance.
AI adoption varies across market & company size
The research also highlights important differences in AI maturity across Europe. While adoption is widespread, the report shows meaningful variation by geography and organization size, with different priorities shaping how AI is applied.
AI maturity by market
These differences reflect broader market dynamics, including regulatory complexity, operational priorities and varying levels of investment in digital infrastructure.
| Country | AI Adoption Level | Primary AI Use Case | Attitude Toward AI |
|---|---|---|---|
| United Kingdom | Moderate to high adoption as organizations expand digital procurement and finance automation. | Improving operational efficiency and integrating AI into purchasing and payment workflows. | Buyers generally view AI as a tool to streamline operations while maintaining strong governance and oversight. |
| Germany | Growing adoption, often tied to enterprise technology investment and structured operational environments. | Automation, analytics and operational decision support within finance and procurement systems. | German organizations tend to adopt AI carefully, prioritizing reliability, governance and integration with existing systems. |
| France | Moderate adoption shaped by regulatory considerations and organizational governance frameworks. | Data analysis, operational reporting and process optimization in finance functions. | Organizations balance innovation with compliance and risk management when adopting AI technologies. |
| Spain | Emerging but accelerating adoption as companies invest in digital tools to support growth and operational efficiency. | Automation of routine processes and improving purchasing and payment workflows. | Companies show openness to AI innovation while ensuring it aligns with operational needs and governance standards. |
AI maturity by company size
Larger enterprises are more likely to scale AI across complex environments, while mid-sized organizations tend to apply AI more selectively to reduce manual work and improve flexibility.
| Company size | Primary AI focus | Strategic Priority |
|---|---|---|
| Large Enterprises | Using AI for automation, analytics and operational insight across finance, procurement and payments processes. | Scaling AI across complex operational environments to improve decision-making and efficiency. |
| Mid-sized organizations | Applying AI selectively to streamline workflows and reduce manual tasks. | Balancing innovation with cost efficiency and flexibility while evaluating broader AI adoption. |
“Regulatory environments, risk tolerance and data governance all influence how quickly organisations adopt AI capabilities.”
The tactical vs strategic AI gap
A clear maturity gap exists between organisations that use AI tactically and those that integrate it strategically across the order-to-cash lifecycle.
Tactical automation vs full O2C integration
Many organisations use AI to automate specific tasks such as invoice processing or reconciliation. Fewer have integrated AI across the full order-to-cash process.
This reflects a broader trend where AI is often implemented at the workflow level rather than embedded across end-to-end financial processes.
Why skills gaps and compliance slow adoption
AI adoption often requires specialised expertise and robust governance frameworks. Organisations that lack these capabilities may move more cautiously.
Where AI delivers the most measurable ROI
The greatest returns typically occur when AI is integrated with broader financial workflows, enabling predictive insights and automated decision support.
This is where AI begins to shift from efficiency to growth, helping organizations identify at-risk buyers, uncover expansion opportunities and act earlier to improve outcomes.
“The difference between automation and transformation lies in how AI is integrated into the broader financial ecosystem.”
The convergence of AI, payments & A/R automation
Looking ahead, AI will increasingly shape how payments and receivables processes operate.
This is particularly important as 57% of buyers report issues with payment options, highlighting how gaps in payment experience continue to create friction in the purchasing process.
When integrated with payment infrastructure and A/R automation, AI can deliver:
- improved visibility into buyer behaviour
- earlier identification of risk
- more scalable financial operations
By combining payment data, behavioral signals and predictive analytics, organizations can move from reactive program management to more proactive engagement with buyers.
“The convergence of AI, payments and automation will redefine how suppliers manage revenue and buyer relationships.”
AI will define the next generation of B2B commerce (conclusion)
AI is already influencing purchasing behaviour across Europe. The organisations that benefit most will be those that integrate AI across their financial and commercial processes.
At the same time, 82% of buyers say invoice customization is important when choosing a supplier, reinforcing how closely payments, invoicing and AI-driven insights are tied to long-term loyalty.
By combining AI insights with payment flexibility and automation, suppliers can identify opportunities earlier, strengthen buyer relationships and create more predictable revenue streams.
Download the report for more on how to align AI to meet rising buyer expectations.
FAQs
How are European B2B buyers using AI today?
Many buyers use AI for decision analysis, fraud detection and automation of purchasing or accounts receivable processes.
Does AI adoption vary by country?
Yes. Regulatory environments, technology investment and organisational culture influence adoption rates.
What are the top AI use cases in B2B payments?
Fraud detection, predictive analytics and automation of financial workflows are among the most common.
Is AI replacing finance teams?
No. AI is typically used to enhance decision-making and reduce manual work rather than replace finance professionals.
