For years, IT departments had a reputation in finance circles: they were the department that said no to things. Every new software request meant another round of security reviews, integration assessments and governance discussions. Finance wanted speed where IT demanded process.
That dynamic is changing. As artificial intelligence moves from analytics labs into operational systems that autonomously decide and execute, IT leaders have shifted from gatekeepers to essential strategic partners. According to the 2025 Sidetrade & PwC Finance & O2C Transformation Study, 85% of organizations are now engaged in finance transformation, with a 145% increase in planned AI investments for 2026. Production AI in finance is an enterprise architecture decision that requires both CFO and CIO involvement from the start.
When Finance AI Becomes an Operational System
Finance teams have grown comfortable with business intelligence dashboards and predictive analytics – tools that offer insights for humans to act upon. Production AI operates differently. Take an AI system that sends collection emails, modifies credit limits or decides which customers to contact first. That’s no longer an analytics tool sitting on the side – it’s making operational decisions that touch revenue, cash flow and how customers experience your business. Gartner predicts 40% of enterprise applications will have these task-specific AI agents embedded by late 2026. Right now, though, that figures is less than 5%.
AI value depends on enterprise data, not isolated datasets. An AI system predicting payment behavior requires access to customer transaction history, ERP data, CRM interactions and external market signals – with real-time data flows instead of monthly exports. This integration depth goes far beyond what traditional finance software demanded.
Here’s the problem: only 13% of companies use their data in predictive models, according to the Sidetrade-PwC study. Most organizations (about two-thirds) still treat financial data as something to report on after the fact. Closing that gap requires data architecture, integration work and real-time orchestration capabilities that finance teams don’t typically have in-house. Whether AI initiatives move forward or get stuck usually comes down to IT’s involvement.
Risk and Accountability in AI-Driven Finance
Every finance decision touches cash flow, credit risk or customer relationships. Add regulatory compliance to that mix. So, when an AI agent escalates a collection case without human review, or adjusts payment terms based on its analysis, or flags a long-standing customer as high-risk – who answers for those decisions?
Auditing a machine learning model isn’t like reviewing a spreadsheet. Explaining to regulators why the AI recommended a specific action soon gets complicated, and the accountability question becomes thornier with each autonomous decision the system makes.
The financial exposure is real. IBM’s 2025 Cost of Data Breach Report shows that shadow AI breaches cost $670,000 more than standard incidents and take longer to detect. Cisco, meanwhile, found nearly half of organizations have already leaked internal data through generative AI tools.
Finance teams know collections and credit inside out. What they don’t always have is the architecture for enterprise security, the frameworks for data governance, or the infrastructure to meet compliance requirements at scale. IT brings that expertise. They know how to build explainability into systems, layer in proper access controls, handle data residency mandates, and set up monitoring that makes autonomous operations trustworthy.
Trying to add governance and accountability after the AI is running? That’s expensive. Finance leaders who bring IT into the planning avoid that retrofit tax.
Preventing the AI Sprawl Problem
Marketing adopts an AI copilot for campaigns. Sales uses an AI assistant for proposals. Customer service implements an AI agent for tickets. Finance experiments with an AI tool for collections. HR trials an AI system for resumes.
Each department delivers local value. Collectively, however, they create an integration problem. The enterprise suddenly has dozens of AI tools, each with different data requirements, security models, API structures and governance frameworks. Some run on public cloud LLMs while others on private models. Finance’s AI needs customer data from CRM, but uses a different data model than the sales AI. Crucial, it is very difficult for one person to explain how these systems interact.
The scale of this problem is striking. Research shows the average enterprise unknowingly hosts 1,200 unofficial applications creating potential vulnerabilities, while a Gartner survey found that 69% of organizations suspect employees are using prohibited public GenAI tools.
Without IT setting standards, you get fragmented AI implementations eating up engineering time, opening security holes and creating data flows nobody can track or control. IT can establish the infrastructure – private cloud environments, unified data lakes and orchestration frameworks – that lets finance deploy AI faster and with less risk.
The Sidetrade-PwC study shows organizations beginning to recognize this: 71% are planning new financial transformation projects for 2026, many requiring joint CFO-CIO approval for AI initiatives.
Deep Integration in Order-to-Cash
Real AI optimization in O2C means the system executes autonomously across the full cycle – generating invoices, assessing credit, orchestrating collections and applying cash. An AI agent analyzing payment patterns might decide to contact certain customers sooner, adjust how it communicates based on past interactions, or route tricky situations to human specialists. Then it learns from what worked.
Making this happen requires touching systems across the enterprise. You need ERP connections for invoice and payment data. CRM links for relationship context. Banking integrations for real-time payment feeds. Access to every communication channel customers use. Finally, you need data orchestration that keeps everything current.
The technical challenge is significant. API management, event streaming, data quality checks, identity controls, audit logs, rollback capabilities… these are all important tasks. IT has to design these data flows, nail down integration patterns, implement security properly, and build monitoring that ensures the autonomous execution works reliably.
When finance and IT co-design these systems, AI can optimize decisions and automate steps across O2C with proper governance. When finance attempts this alone, the result could be a point solution which is disconnected from enterprise architecture – delivering limited value and creating future technical debt.
The Partnership That Enables AI-Driven Finance
IT leaders have moved from gatekeepers to enablers of AI-driven transformation. They bring architecture, governance expertise and integration capabilities that make safe, scalable AI possible. The Sidetrade-PwC study identifies the CIO/CTO as a co-economic decision maker alongside the CFO for AI-driven financial operations, reflecting that production AI in finance is both a business transformation and a technology architecture decision.
Finance leaders should involve IT early in AI planning, before procurement reviews. What gets decided about architecture at the start shapes everything that comes after. Setting enterprise AI standards before departments start building their own solutions prevents the kind of fragmentation that costs serious money to fix later. IT partnership on governance helps balance moving fast with maintaining proper controls.
For IT leaders, there’s a real opportunity here. Finance transformation shows clear, measurable returns – faster cash collection, better risk management and leaner operations. By bringing robust architecture and governance to finance AI initiatives, IT can demonstrate clear business value while establishing patterns that benefit the entire organization.
Organizations that treat AI in finance as a joint initiative between finance and IT will transform faster and more safely than those attempting finance-only implementations. The partnership between financial domain expertise and IT architectural excellence has become the foundation on which AI-driven financial operations should be built.