Published by Jessica Weisman-Pitts
Posted on September 6, 2024
8 min readLast updated: January 29, 2026
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By Pat Bermingham, CEO of B2B digital payment specialist,
By Pat Bermingham, CEO of B2B digital payment specialist, Adflex
Visit any social media newsfeed and countless posts will tell you that AI means “nothing will ever be the same again” or even that “you’re doing AI wrong”. The sheer volume of hyperbolic opinions being pushed out makes it almost impossible for businesses to decipher between the hype and reality.
This is an issue the European Union’s ‘AI Act’ (the Act), which came into force on 1 August 2024, aims to address. The Act is the world’s first regulation on artificial intelligence, setting out how to govern the deployment and use of AI systems. The Act recognises the transformative potential AI can have for financial services, while also acknowledging its limitations and risks.
Within the ongoing debate about AI in financial services, B2B payment processes have been identified as an area where AI has huge potential to accelerate digital innovation. Today, I will do my best to go beyond the hype to provide a true perspective on what AI really means for B2B payments specifically.
Understanding what artificial intelligence is, and what it isn’t
In a nutshell, AI is a system or systems that can perform tasks that normally require human intelligence. It incorporates machine learning (ML), which has been used by developers for years to give computers the ability to learn without being explicitly programmed. In other words, the system can look at data and analyse it to refine functions and outcomes.
A newer part of this is ‘deep learning’, which leverages multi-layered neural networks to simulate the complex decision-making power of our brains. The deep learning benefits outlined later in this article are based on Large Language Models (LLM), that are pre-trained on representative data (such as payment/transaction/tender data). Deep learning AI does not just look at and learn patterns of behaviour from the data, it is becoming capable of making informed decisions based on this data.
Before I explore what this could mean for B2B payments, I want to make one caveat clear: human supervision is still needed to ensure the smooth running of operations. AI is a supporting tool, not a single answer to every question. The technology is still maturing, so you cannot hand over the keys to your B2B payments process quite yet. Manual processes will retain their place in B2B payments today, but AI tools will help you learn, adapt and improve more quickly and at scale.
The AI Act – what you need to know
The Act attempts to categorise different AI systems based on potential impact and risk. The two key risk categories include:
The most widely used form of AI currently, ‘generative AI’ (think ChatGPT, Copilot and Gemini), won’t be classified as high-risk, but will have to comply with transparency requirements and EU copyright law.
High-impact general-purpose AI models that might pose systemic risk, such as GPT-4o, will have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.
The Act aims to become fully applicable by May 2026, following consultations, amendments and the creation of ‘oversight agencies’ in each EU member state. Though, as early as November, the EU will start banning ‘unacceptable risk’ AI systems and by February 2025 the ‘codes of practise’ will be applied.
So, with the Act in mind, how can AI be used in a risk-free manner to optimise B2B payments?
AI will transform payment data analysis
Today’s B2B payment platforms are not one-size-fits-all solutions; instead, they provide a toolkit for businesses to customise their payment interactions.
AI-based language models and machine learning can be used by payment providers to rapidly understand and interpret the extensive data they have access to (such as invoices or receipts). By doing this, we gain insights into trends, buyer behaviour, risk analysis and anomaly detection. Without AI, this is a manual, time consuming task.
One tangible benefit of this data analysis for businesses comes from combining the extensive payment data available, with knowledge of a wide range of vendors’ skills, products and/or services. AI could then, for example, identify when an existing supplier is able to supply something that is currently being sourced elsewhere. By using one supplier for both products/services, the business saves through economies of scale.
Another benefit of data analysis comes from payment technology experts. Ours have been training one service to extract data from a purchase order or invoice, to flow level 3 data, which is tax evident in some territories. This automatically provides the buyer with more details of the transaction, including relevant tax information, invoice number, cost centre, and a breakdown of the products or service supplied. This makes it easy and straightforward to manage tax reporting and remittance, purchase control and reconciliation.
AI-driven data analysis isn’t just a time and money-saver, however. It also adds new value by enabling providers to use the data to create hyper-personalised payment experiences for each buyer or supplier. For example, AI and ML tools could look out for buying and selling opportunities, and perform a ‘matchmaking supplier enablement service’ that recommends the best payment methods – and the best rates – for different accounts or transactions. The more personalised a payment experience is, the happier the buyer and more likely they are to (re)purchase.
Efficient data flows mean stronger cash flows
Another practical application of AI is to help optimise cash management for buyers. This is done by using the data to determine who is strategically important and when to pay them. It could even recommend grouping certain invoices together for the same supplier, consolidating them into one payment per supplier, reducing interchange fees and driving down the cost of card acceptance.
AI can also perform predictive analysis for cash flow management, rapidly analysing historical payment data to predict cash flow trends, allowing businesses to anticipate and address potential challenges proactively. This is particularly valuable in the current economic climate where cashflow is utterly vital.
By extracting value-added, tax evident data from a purchase order or invoice, AI can rapidly analyse invoices and receipts to enable efficient, accurate automation of the VAT reclaims process. Imagine: the time comes for your finance team to reclaim VAT on recent invoices and receipts, but they don’t have to manually go through every receipt or invoices and categorise them into a reclaim pile or not reclaimable. It sounds like a dream but it will be the reality for business everywhere: AI does the heavy lifting and humans verify it, saving significant time and resources.
Quicker, more accurate invoice reconciliation
The third significant benefit of AI is automated invoice reconciliation. By identifying key information from an invoice and recognising regular payees, AI can streamline and automate the review process. This has the potential to significantly speed up transactions and enable more efficient payment orchestration.
Binding together all supporting paperwork, such as shipping, customs, routes, and JIT (just-in-time) requirements can also be done by AI, and it’s likely to be less prone to human error.
This provides an amazing opportunity to make B2B payments faster, reduce costs and increase efficiency. Businesses know this: 44% of mid-sized firms anticipate cost savings and enhanced cash flow as a direct result of implementing further automation within the next three years. According to American Express, 48% of mid-sized firms expect to see payment processes accelerate, with more reliable payments and a broader range of payment options emerging.
When. Not if.
There are significant opportunities to leverage AI in B2B payment processes, making it do the heavy lifting. It is, however, essential to view these opportunities with a balanced understanding of the limitations of AI.
While all the opportunities for AI in B2B payments outlined today are based on relatively low-risk AI systems, human oversight of these systems is still essential. Having said this, with all the freed-up time and resource achieved through the implementation of AI, this issue can be avoided.
AI in B2B payments is not an if, but a when. The question is, when will you make the jump, hand in hand with technology, rather than either fearing it or passing full control over to it.
In order to grow, it is essential for users to see the tangible benefits. For example, by enhancing efficiencies in account payable (AP), businesses can reallocate time and resource previously spent in AP to other areas of the business. Early adopters are starting to test the water but only time will tell how much of an impact AI will make.
Most businesses will likely wait for the early adopters to fail, learn and progress. As we know, if something goes wrong in B2B payments, it can have a huge impact on individuals, businesses and economises. Only when the risk is clearly defined and manageable will AI truly become the gamechanger in B2B payments that all the adverts claim.
Artificial Intelligence (AI) refers to systems or machines that can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
The AI Act is a regulation by the European Union that governs the use and deployment of AI systems, categorizing them based on risk levels and ensuring compliance with safety standards.
Cash management involves the collection, handling, and usage of cash, ensuring that a business has sufficient liquidity to meet its obligations while optimizing its cash flow.
Invoice reconciliation is the process of matching invoices to their corresponding payments and ensuring that all financial records are accurate and up-to-date.
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