From rapid technological advancements to fluctuating economic policies, disrupted supply chains and rising capital costs, financial operations and B2B payments are undergoing a profound transformation. The future of financial flow is up for grabs—and businesses that can leverage AI to increase capacity, sharpen accuracy and gain velocity for strategic decision-making will remain competitive while those clinging to manual processes, static data and fragmented systems will fall behind.
For middle market companies, though, how to best access and apply AI is not an obvious path. A recent survey found that 78% of middle market executives are using AI in some capacity, yet only 20% feel they’ve fully integrated it into their organizations. Even more telling: 67% of those using generative AI say they need external support to maximize its value. This gap signals that while AI appears to be everywhere—many businesses are still at the starting line, seeking trusted guides to help evaluate use cases and invest in fit-for-purpose applications of emerging technology.
Many of these elements are not new. Consulting is a century-old, formidable industry. Individuals and businesses have been adopting software, automating financial decisions and trusting digital money for decades. So, what is so overwhelming about AI? Despite its seemingly low barrier to entry and obvious path to mainstream status, it’s not a silver bullet. It is an activating agent for when the circumstances are right—precise but abundant ingredients and thoughtful guardrails to replicate meaningful outcomes at scale.
The need for data
In the Fourth Industrial Revolution, data is an undeniable asset. Data informs our understanding of market needs, unlocks business opportunities, drives innovation, and—most importantly—it powers AI models. But for many companies the necessary data is difficult to access—within static spreadsheets, incompatible tools, even in people’s notes or personal devices, resulting in incomplete insights and underperforming models. In some cases, data is being shared insecurely, or without the user’s awareness. In others, particularly in less digital heavy verticals like manufacturing, construction and distribution, data analysis as a tradecraft is simply not common.
To fully realize AI’s potential, business leaders need to treat data not just as a resource, but as a responsibility. Data must be collected, stored, and used with intention and integrity—and may require partnerships to access exactly what you need to increase predictability, spot patterns and design sound business logic.
Think of data for AI like fuel for a high-performance vehicle — it powers the machine. If the data is flawed or insufficient, the engine does not perform optimally, whereas with clean, comprehensive data, organizations can surge ahead. This is especially true in financial operations; in a recent survey of 1,000 mid-market C-level executives, 82% stated that their company has lost work due to miscommunication in the payment process. Inaccurate data, like a poorly designed product, can directly impact cash flow and access to working capital.
The need for new interfaces and integrations
For finance teams, this means there’s no time to wait in creating high-quality digital data density, preparing for systems to handle AI-driven automation in payments, collections, credit management, customer insights, and compliance. Additionally, AI is not only changing how humans manage money — it’s changing who manages it. While early players like Wealthfront kicked off autonomous finance nearly 20 years ago, leveraging robo-agents to challenge the status quo in mutual fund investment decisioning, we are entering an era of agentic finance, where AI agents act on behalf of businesses to handle everything from collections to reconciliation.
With the rise of instant payment rails like FedNow and RTP, we are starting to operate in a 24/7 cycle with game-changing, dynamic experiences like predictive risk management with AI models analyzing historical behavior and external signals to identify at-risk accounts early, prompting proactive customer outreach; or Intelligent collections in which AI agents can automate outreach based on risk profiles, past interactions and payment behavior, improving resolution rates.
Imagine a supplier’s Accounts Receivable (AR) bot negotiating directly with a buyer’s Accounts Payable (AP) bot, agreeing to a dynamic early-payment discount based on liquidity preferences. Or a smart contract that executes payments upon delivery confirmation, eliminating dispute cycles entirely. These advances won’t just speed things up — they will redefine the interfaces for finance teams and place high demand on interoperability and accessibility across key business systems like ERPs, CRMs, bank accounts, and other data and payment gateways.
The need for financial flow
One you’re operating at the next level of the connected economy, this new model unlocks 24/7 operations without increasing headcount—suddenly time takes a backseat to data, with automation and AI manufacturing newfound hours and energy for employees.
Leading finance teams are already exploring how autonomous agents could negotiate payment terms or apply micro-incentives for early payments based on real-time cash needs. AI-powered agents can engage customers in real time, adapt to market shifts instantly, and execute complex tasks like applying cash or resolving disputes, all while preserving transparency and auditability.
Looking further ahead, AI will also support smart payment routing, selecting the best payment rail or method based on cost, speed, and buyer preferences. It can also strengthen fraud prevention and compliance by constantly monitoring transactions against risk thresholds — collecting and reconciling B2B payments will not just be automated, but truly intelligent. It’s hard to imagine sending an invoice and being told “the check is in the mail” ever again.
Globally, trillions of dollars including both trade receivables from B2B transactions and outstanding invoices across public and private sectors are locked up AR at any time. Middle-market businesses — which power a third of U.S. jobs and 40% of GDP and often operate on thin margins — stand to regain massive control by modernizing this working capital lever. But AI-driven digital transformation won’t succeed without data—the same way your cash flow can’t thrive when faced with invisible payment delays; operational chaos; and inaccurate financial records.
