From pilots to production: Why AI in finance often stalls—and how to find the solution

AI continues to dominate boardroom conversations, especially in finance. Yet despite growing urgency, many enterprises are still stuck in a familiar loop: promising pilots that don’t scale. What’s standing in the way of meaningful, enterprise-grade impact? And more importantly, how are leading organizations breaking through?

Drawing on field experience and a recent discussion with HFS Research, here are four practical insights into what it takes to turn AI ambition into operational results.

Too often, AI initiatives begin with a promising tool or use case, but fail to address an immediate, measurable business problem. The most successful finance functions start small—but deliberately—with real client data and concerns. Whether it’s improving query turnaround times, accelerating dispute resolution or streamlining reconciliations, the goal is to build trust quickly through visible outcomes. These early wins build confidence—and more importantly, the momentum needed to scale. 

For example, a global building materials manufacturer engaged IBM Consulting® to tackle a backlog of over 1.2 million customer queries annually. Using real operational data, we implemented a coordinated set of AI-powered agents to triage queries, assess financial risk and automate enterprise resource planning (ERP) updates. The result was a 60% improvement in query resolution efficiency, faster deliveries and measurable cash flow gains. This approach helped the client to reduce the number of days sales outstanding (DSO) within the same fiscal year. 

For a global telecom provider, we integrated AI-powered analytics agents into their billing operations. These agents matched billing data, flagged discrepancies and guided collectors on their next actions. The agents improved international collections performance and generated hundreds of millions in value.

AI in finance isn’t just about automating repetitive tasks. It’s about orchestrating intelligence across fragmented processes to improve both the speed and quality of outcomes.

This approach often includes:
•    Routing and triaging incoming queries
•    Assessing financial risk or creditworthiness
•    Triggering workflows in ERP and financial systems
•    Generating insights and narratives for decision support

The recent IBM Institute for Business Value (IBV) report on agentic AI for intelligent business operations highlights how autonomous agents are reshaping finance by learning, adapting and optimizing in real time. These agents don’t just follow rules—they pursue outcomes, anticipate challenges and personalize experiences across the finance value chain. By interconnecting AI agents across the order-to-cash and record-to-report cycles, organizations can move from isolated improvements to full-scale transformation.

Rather than replacing finance professionals, AI is elevating them.

A leading consumer goods company in the UK worked with IBM Consulting to streamline its monthly reporting across 52 markets. AI now consolidates data, surfaces trends and drafts narrative insights—cutting reporting time from 11–15 hours per market to just 2–3 hours. 

Controllers still review and refine the output, but the shift from manual compilation to intelligent oversight frees up capacity for higher-value work such as business partnering and strategic analysis. This evolution is a key element of the IBM Consulting approach to AI-driven finance transformation, which blends digital capabilities with human judgment.

The technology is ready. The challenge is aligning your organization around it. To successfully scale AI in finance, leaders must address:

•    Data quality and access: Ensuring clean, structured data is available for AI to work effectively
•    Systems integration: Simplifying or connecting earlier platforms and ERPs
•    Change management: Helping teams embrace new ways of working through training, trust building and process clarity

In each of the previous examples, success was not just a result of the AI capabilities—it was the organization’s readiness to adopt them that made the difference.

IBM’s recognition as a leader in the HFS Horizons: F&A Service Providers 2023 report underscores its ability to drive enterprise-wide transformation through AI-powered finance solutions. AI adoption isn't a leap of faith—it’s a structured journey. Enterprises that invest in foundational readiness are the first to unlock compounding value. AI is no longer just a tool—it’s a strategic enabler. From credit scoring and fraud detection to predictive analytics and compliance, AI is redefining how finance operates.

AI is already transforming finance operations—but only for those professionals and enterprises that shift from experimentation to execution. By solving real problems, orchestrating AI across processes and empowering teams with the right tools and support, leading organizations are building a more intelligent, agile and resilient finance function.

Want to dive deeper into this conversation? Listen to Khalid Siddiqui’s interview with Saurabh Gupta of HFS Research to hear how real clients are moving from proof of concept to enterprise-grade AI in finance operations.