We recently connected with Baxter Lanius, CEO and founder of Alternative Payments, to learn about how AI is modernizing B2B payment infrastructure, specifically in blue-collar industries that have long been overlooked by fintech.
For background, Alternative Payments is focused on modernizing payment systems for managed service providers and other underserved industries.
Its autonomous platform replaces outdated accounts receivable, payable, and financing tools with a streamlined, automated solution.
Baxter has commented the specific pain points that blue-collar industries face in B2B payments, and how can AI directly address those challenges.
He has also touched on how AI is helping automate manual invoicing, reconciliation, and approval workflows in sectors like construction, logistics, or manufacturing.
Our discussion with Baxter Lanius is shared below.
Crowdfund Insider: What specific pain points do blue-collar industries face in B2B payments, and how can AI directly address those challenges?
Baxter Lanius: The reality is that blue-collar industries get squeezed from both directions on payments. Take a general contractor – they’re waiting 45-60 days to get paid by property developers on the accounts receivable side, while still needing to pay subcontractors weekly or bi-weekly on the accounts payable side to keep projects moving.
So they face collection challenges on incoming payments and cash flow pressure on outgoing payments simultaneously, creating a working capital crunch.
These pressures create three core operational pain points. First, cash flow unpredictability is magnified. That same contractor might complete a $50,000 job but wait two months for payment while covering $15,000 in weekly payroll and material costs. With construction margins typically 3-8%, there’s zero room for error when working capital gets tied up on both ends.
Second, administrative overhead doesn’t scale with field operations. These businesses are built around getting work done, not managing paperwork. I’ve seen electrical contractors spend 8-10 hours weekly just on invoice processing and payment reconciliation across both their receivables and payables – that’s 20% of their operational capacity consumed by back-office work.
Third, compliance complexity that traditional payment systems ignore. Construction has lien laws, manufacturing has supply chain auditing requirements, logistics has freight payment regulations. Generic payment platforms force these businesses to layer on manual processes to stay compliant on both their incoming and outgoing payment flows.
Here’s where AI changes everything. Instead of treating all B2B payments the same, AI can learn industry-specific patterns and automate accordingly. For construction, AI can track project milestones and automatically trigger progress payments when certain conditions are met, while simultaneously managing subcontractor payment schedules based on completion milestones. For manufacturing, it can reconcile purchase orders against delivery confirmations and quality control reports before releasing payment, while also automating customer invoicing based on shipment confirmations.
The breakthrough is contextual automation. AI doesn’t just process payments faster – it understands that a plumbing contractor’s NET-15 terms mean something different than a law firm’s NET-30, because of cash flow realities and industry dynamics on both sides of their payment equation.
Crowdfund Insider: How is AI helping automate manual invoicing, reconciliation, and approval workflows in sectors like construction, logistics, or manufacturing?
Baxter Lanius: The magic happens when AI learns how these businesses actually operate versus how they’re supposed to operate on paper. Take a mid-size manufacturing operation we work with. Their purchase orders reference part numbers, but their receiving reports use different SKU formats, and their quality control system uses yet another numbering scheme. Traditionally, someone has to manually cross-reference these three systems before approving payment.
AI pattern recognition solves this by learning the relationships between these different data formats. It can automatically match a purchase order for “Motor Assembly 4B-2X” with a receiving report for “MTR-ASM-4B2X” and a QC report referencing “Assembly Unit 4B/2X.” What used to require 15-20 minutes of manual verification now happens in seconds.
In logistics, the complexity multiplies because you’re dealing with freight bills, delivery confirmations, detention charges, and fuel surcharges that change constantly. AI can automatically validate that delivery windows were met, calculate detention fees based on actual timestamps, and adjust fuel surcharges against current market rates. The result is that freight payment cycles drop from 15-20 days to 3-5 days.
Construction presents the most interesting automation opportunities because everything is project-based and milestone-driven. AI can automatically trigger payments when certain conditions are met – photos uploaded showing completion, materials delivered and signed for, inspection reports filed. Instead of waiting for someone to manually review and approve each milestone, the system learns what constitutes valid completion criteria.
The reconciliation piece is where AI really shines. These industries generate massive amounts of unstructured data – photos, delivery receipts, inspection reports, timesheets. AI can extract relevant payment information from all these sources and automatically reconcile against invoices. A concrete contractor’s invoice for “40 yards delivered Tuesday” gets automatically matched against delivery receipts, timestamp data, and project photos without human intervention.
What’s clicking for the businesses crushing it with AI automation is they’re not trying to change their field operations to match their payment systems. They’re letting AI adapt the payment processing to match how work actually gets done.
Crowdfund Insider: Why has fintech historically underserved blue-collar industries, and what role does AI play in finally closing that gap?
Baxter Lanius: Fintech has a Silicon Valley problem. Most fintech founders come from tech or finance backgrounds, so they build payment solutions for businesses that operate like tech companies – subscription models, digital transactions, predictable cash flows. Blue-collar industries got left behind because their payment patterns don’t fit standard SaaS templates.
The numbers tell the story. Construction represents $2.1 trillion in annual volume, manufacturing adds another $2.9 trillion, but these sectors received less than 4% of fintech investment over the past decade. Venture capital flows toward markets that scale easily, and blue-collar businesses were perceived as too fragmented and operationally complex.
Traditional payment platforms failed these industries because they required businesses to change their operations to fit the technology. A general contractor would have to train subcontractors on new invoicing systems, modify their project management workflows, and standardize processes across multiple job sites. The switching costs were enormous relative to the benefits.
AI changes the equation by meeting businesses where they are. Instead of requiring a roofing contractor to learn a new system, AI learns how that contractor operates. It adapts to their existing project management tools, integrates with their field reporting processes, and automates payments based on their established workflows.
The breakthrough is that AI can handle the operational complexity that scared off traditional fintech. Construction projects with 20 different subcontractors, each with unique payment terms and compliance requirements? AI can manage that. Manufacturing operations with complex supply chains and quality control gates? AI learns those patterns and automates accordingly.
But here’s what’s really driving adoption: AI doesn’t just automate payments, it provides intelligence that these businesses never had access to. A logistics company can now see real-time cash flow projections based on delivery schedules and payment terms. A construction firm can identify which subcontractors consistently deliver on time and should get priority payment status.
The gap is closing because AI makes it economically viable to serve these markets. Previously, the operational complexity meant you needed human intervention for every edge case. AI handles the edge cases automatically, which makes it profitable to serve smaller, more fragmented industries.
Crowdfund Insider: Can you share real-world examples of how AI is improving cash flow management, fraud detection, or credit decisioning in B2B transactions?
Baxter Lanius: The cash flow transformation we’re seeing is nothing short of epic. We work with a regional electrical contractor who was running 47-day average collection cycles. Their biggest challenge wasn’t getting paid – it was predicting when payment would hit their account so they could manage payroll and material orders.
AI changed everything by analyzing payment patterns across their customer base. It learned that municipal projects pay exactly 45 days after invoice approval, but approval timing varies by department. Private developers typically pay 21-28 days, but always faster in Q4 when they’re managing year-end budgets. Armed with this intelligence, the contractor can now predict cash flow 60 days out with 94% accuracy.
The fraud detection piece is fascinating because blue-collar industries face unique risks. In construction, duplicate invoicing is common because multiple parties might bill for the same materials or labor. AI can detect when a concrete pour shows up on both the general contractor’s invoice and the concrete supplier’s bill, flagging potential double-billing before payment.
We’ve seen AI catch invoice manipulation that humans miss. A subcontractor was gradually inflating material costs by 3-5% across multiple line items – small enough to avoid scrutiny but adding up to significant overcharges. AI detected the pattern by comparing invoice data against historical pricing and market rates.
Credit decisioning is where AI really demonstrates its value. Traditional credit scoring doesn’t work for project-based businesses because their cash flow is lumpy and seasonal. A roofing contractor might have zero revenue in January but $200,000 in May after storm season. Standard credit models see this as instability, but AI understands it’s normal seasonality.
AI credit models look at operational indicators that traditional banking ignores. How quickly does a contractor complete projects? What’s their material supplier payment history? Do they maintain consistent crews or constantly hire new workers? These operational metrics predict payment behavior better than traditional financial ratios.
One manufacturing client saw their approval rates increase 23% after switching to AI-driven credit decisioning, not because standards were lowered, but because AI could identify good credit risks that traditional models rejected due to non-standard cash flow patterns.
The real breakthrough is predictive cash flow management. AI can analyze accounts receivable aging, project completion schedules, and historical payment patterns to predict exactly when cash will hit the account. This lets businesses optimize their own payment timing and working capital management.
Crowdfund Insider: What should companies in these industries consider when adopting AI-driven payment tools to ensure long-term scalability and trust?
Baxter Lanius: The biggest mistake I see is companies trying to implement AI payment tools the same way they’d roll out any other software. AI systems need data to learn from, which means you need 3-6 months of parallel processing before you can fully automate workflows. Plan for this learning period and don’t expect immediate ROI.
Data quality determines everything. If your current invoice processing relies on manual interpretation – someone looking at a delivery receipt and manually entering quantities – AI will struggle with inconsistent data formats. Spend time standardizing your data inputs before expecting AI to automate your outputs.
Integration architecture matters more than features. The best AI payment platform is useless if it can’t connect with your project management software, accounting system, and field reporting tools. Focus on platforms that offer robust API connectivity rather than impressive demo features.
Trust builds through transparency, not black box algorithms. Your team needs to understand why AI made certain decisions, especially around payment approvals or fraud detection. Look for systems that provide clear audit trails and explanation capabilities.
Compliance requirements don’t disappear with automation – they become more critical. Construction lien laws, manufacturing audit requirements, logistics regulations all still apply when AI processes payments. Make sure your AI platform understands industry-specific compliance requirements and can generate necessary documentation automatically.
The scalability question comes down to learning capacity. AI systems that work well for 50 transactions might break down at 500 transactions if they’re not designed to handle volume. Test AI platforms under realistic load conditions before committing.
The businesses crushing it with AI payment adoption start small and scale gradually. They pick one specific workflow – like progress payment automation for construction milestones – and perfect that before expanding to other use cases. This builds internal confidence and allows the AI system to learn their specific operational patterns before handling more complex scenarios.