3 Ways AI Moves B2B Tech From Reactionary to Anticipatory

Across B2B payments and technology, artificial intelligence is emerging as more than just another transformative technology.

It’s becoming a compass for the future.

For decades, the B2B ecosystem has operated under the confines of a traditionally reactive framework, where processes like payments, compliance and risk management were structured to respond to challenges after they occurred.

But with recent examples, such as the news Tuesday (Nov. 26) that AI-powered business solutions provider Esker now enables real-time integration of its Esker Accounts Payable with two Microsoft cloud-enabled enterprise resource planning (ERP) systems, it is becoming clear that AI is increasingly shifting legacy B2B paradigms by enabling businesses to anticipate issues, seize opportunities and recalibrate traditional buyer-supplier dynamics.

As businesses grapple with the challenges of a volatile global economy, AI is helping turn clunky, manual-heavy processes into streamlined and automated ones. The result is faster transactions, fewer headaches and more control over cash flow.

Read also: AI in Commerce: 5 Essential Use Cases for B2B Operations

Strengthening Risk Management and Predictive Cybersecurity

Risk is a focal point of B2B operations, but the sophistication of today’s challenges — ranging from geopolitical instability to cyber threats — demands an advanced response. AI is redefining how businesses identify, assess and mitigate risks.

“Technology, especially AI, has accelerated the success of finance teams by providing real-time access to data and insights,” Brian Unruh, chief financial officer of global AI company ABBYY, told PYMNTS in June. “This allows us to proactively address issues before they become significant problems, rather than just reporting on them after the fact.”

The shift from reactionary to anticipatory risk management is most evident in cybersecurity. Traditional defenses are designed to detect and respond to attacks, but AI allows for a preemptive approach. By analyzing datasets — including network traffic, user behavior and past incidents — AI can identify anomalies and predict potential breaches before they occur. This not only protects sensitive financial data but also ensures business continuity amid escalating cyber threats.

AI’s ability to synthesize information in real time transforms risk management from a static process into a dynamic, forward-looking strategy.

Regulatory environments vary by region, making compliance a perennial headache for multinational firms. AI systems equipped with machine learning algorithms can monitor regulatory updates, flag potential risks and automate reporting to ensure businesses remain compliant across jurisdictions.

See also: AI Makes Impact in Supplier Enablement and Predictive Analytics

Revolutionizing Payment Workflows With Dynamic Automation

Accounts payable (AP) and accounts receivable (AR) have long been the cornerstone of B2B payments, yet their traditional processes often lack the fluidity needed to support modern business demands. AI’s integration into these workflows is turning routine automation into a differentiator.

AI helps enable businesses to go beyond simple task automation by making payment workflows adaptive and intelligent. For instance, machine learning models can analyze vendor payment histories and cash flow data to recommend optimal payment terms, balancing liquidity needs with supplier satisfaction.

“AI has the potential to really move the needle for so much of the industry in terms of their ability to better understand what’s going on within their payments environments and the ability to be more proactive with their buyers or suppliers,” Nick Izquierdo, executive vice president of payments at Billtrust, told PYMNTS last year. “And alongside that, the productivity AI brings is really driving more success satisfaction out of both sides of the equation.”

By analyzing payment trends, supply chain data and market conditions, AI can offer insights that help suppliers tailor their offerings to buyers’ needs, creating a more collaborative and mutually beneficial ecosystem.

“To truly unlock the power of AI, especially in a B2B world, you really need to have tremendous amounts of real-world business data to train the AI,” Rajiv Ramachandran, senior vice president of product strategy and management at Coupa, told PYMNTS this month.

Read also: AI in the Chat: Generative AI’s Growing Role in Customer Service

Redefining Global Expansion With AI-Powered Precision

The globalization of B2B commerce has introduced complexities that demand more than traditional workflows may be able to deliver. International expansion often involves managing currency fluctuations, reconciling cross-border transactions, and navigating language barriers in contracts and procurement processes.

The PYMNTS Intelligence report “Cross-Border Sales and the Challenge of Failed Payments” found that faulty cross-border payments cost U.S. merchants at least $3.8 billion in sales last year. Increasingly, these once-daunting challenges are being reimagined through AI.

AI-driven tools now enable businesses to manage currency exchange rates in real time, reducing exposure to unfavorable fluctuations and ensuring seamless cross-border reconciliations. For example, by analyzing historical data and market trends, AI can predict optimal times for currency conversion, safeguarding revenue streams and maintaining predictability in cash flow.

On the cutting-edge of AI applications, advanced natural language processing models are breaking down language barriers, ensuring that buyer-supplier agreements are understood and adhered to on both sides of the table. AI-powered localization not only helps enhance transparency but also can support the speeding up of procurement cycles, allowing businesses to scale globally while avoiding historical bottlenecks.

Ultimately, whether through streamlining global expansion, fortifying risk management or transforming payment workflows, AI is reshaping the future of B2B commerce.

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