Perspective:
Perspective
The Business Operations Room
Generative AI in finance: From pilots to performance
Reimagine how finance delivers measurable and sustainable outcomes
Generative artificial intelligence (GenAI) in finance has moved from an experimental novelty to a board-level mandate, leaving chief financial officers (CFOs) with a critical question: How do we drive return on investment (ROI) without eroding controllership? While pilots demonstrated digital potential, the window to prove value is closing. GenAI has become the catalyst for true AI in finance transformation. By embedding these capabilities into workflows, finance teams can rewire execution and elevate internal customer experience. Read on for insights into how you can integrate AI in finance operations to create measurable and sustainable value.
Authors:
Meet Agarwal
Bob Hamzik
- Move beyond pilots.To achieve true AI benefits for finance transformation, CFOs must embed capabilities into workflows to rewire execution and elevate customer experience.
- What can stall enterprise scaling? A stalled finance and accounting transformation often stems from ignoring data readiness, governance and security as strict prerequisites.
- How can you improve order to cash? Utilizing GenAI finance use cases to summarize account history and standardize outreach improves dispute cycle times and collector productivity.
- What is the impact on accounts payable? One <a href="https://myappsplus.com/tech-giants-drive-global-wave-of-layoffs-in-2026/” title=”Tech giants drive global wave of layoffs in 2026″>global beverage producer achieved 92% touchless invoice processing and reduced manual effort by more than 80% using AI in its finance operations.
Why finance is a high-value launchpad for GenAI
Finance sits at the intersection of highly structured transaction data—such as enterprise resource planning (ERP) records, journal entries and invoices—and unstructured, decision-driving content like emails, PDFs, policies and approvals. Traditional automation tools perform best when rules are stable and inputs are consistent, and they often break down in real operating environments marked by variable document formats, incomplete data and context-heavy judgment calls.
Implementing specific GenAI capabilities can help reduce this operational friction by accelerating interpretation, summarization and drafting. The outcome of these GenAI-supported finance use cases isn’t just faster cycle times—it’s less rework and fewer handoffs, which are often two of the largest sources of hidden cost and control risk across a broader finance and accounting transformation.
Where GenAI can deliver measurable impact
The highest-impact GenAI finance use cases are anchored to outcomes finance leaders already own, manage and report, such as cycle time, cost to serve, working capital and control effectiveness. The differentiator is workflow integration: GenAI creates the most value when triggered at the moment of work, not used as an after-the-fact summarizer.
- Record to report
When a reconciliation breaks or an unexpected variance occurs, GenAI finance tools can enhance AI in financial reporting by drafting variance explanations, suggesting reconciliation steps and producing consistent management commentary. With clear reviewer approval and source-linked support, teams can reduce last-mile churn and improve their close processes’ key performance indicators (KPIs) like close cycle time, late corrections and reconciliation aging.
- Invoice to pay
When invoices are ingested with missing purchase order details, mismatches or duplicates, trained GenAI tools can extract, interpret and supplement invoice content; flag issues for review; and triage exceptions to the proper owner. This improves straight-through processing and cycle times while adhering to exception thresholds and producing a clear audit trail.
- Order to cash
When a dispute is opened or a customer inquiry comes in, GenAI capabilities can summarize account history, recommend next-best actions and standardize outreach. With approved templates and guardrails, teams can improve consistency while maintaining customer and compliance controls. Typical KPIs include dispute cycle time, collector productivity and cash outcomes.
- Planning and forecasting
For financial forecasting, trained GenAI tools can accelerate scenario updates, clarify drivers and draft “what changed and why” narratives for leadership when drivers are refreshed or new scenarios are requested. With formally approved assumptions, teams can move faster without weakening confidence in the story behind the numbers. Typical KPIs include forecast cycle time and speed of leadership alignment.
- Controls enablement
When controls are executed or evidence is requested for a prepared-by-client list, GenAI-based finance tools can draft and organize evidence narratives, map evidence to control requirements and flag gaps before they become findings. Designed with traceability and clear human accountability, this can reduce documentation burden while strengthening defensibility. Typical KPIs include evidence collection cycle time, percent of controls with complete evidence and fewer documentation-driven findings.
What must be true to scale value and trust
Moving from a promising pilot to an enterprise capability requires deliberate choices, especially as CFOs seek tangible ROI and clarity on talent, platform direction, data risk and model governance. Six “scale conditions” consistently separate repeatable value from one-off experimentation in GenAI in finance:
- Start with the right tool for the job. Not every problem needs GenAI. Robotic process automation and other AI-driven platforms may better serve rule-based scenarios; GenAI becomes more relevant when nuance is high (for example, identifying fraud risk versus simply extracting invoice fields).
- Treat data readiness as a prerequisite, not a phase-two task. The output quality of GenAI directly ties to the underlying data and the organization’s data maturity—governance, security, quality and volume. Many transformations slow down because financial data is distributed across ERP platforms, workflow tools, shared drives and email trails.
- Build a joint road map anchored to ROI. GenAI capabilities can be costly to implement and maintain (especially during training or model tuning), so investment decisions should align with a practical value roadmap, assessed over short-term (zero to two years) and long-term (three or more years) horizons. Importantly, leaders should also evaluate whether early use cases establish foundational capabilities that can facilitate future innovation and broader finance and accounting transformation.
- Design for risk management and governance from day one. Risk assessment should be explicit: Are you reducing risk or adding it? What’s the probability of achieving benefits? And what mitigation plans are in place? Strong governance—which includes formal policies and procedures, regulatory/compliance risk actions and the right skill sets to validate, test and maintain solutions—can be a scaling lever. Deloitte’s Trustworthy AI™ framework is one tool that can help design ethical safeguards and manage risk while pursuing returns.
- Pilot with intent, then industrialize. A proof of concept is a start, not a finish. The recommendation is to pilot, learn quickly and scale gradually, with budget reserved for practical use cases and a plan to expand enterprisewide.
- Controls by design (make it operational). Translate “governance” into concrete mechanisms, such as approved knowledge sources, source traceability in outputs, prompt/output logging, defined human-in-the-loop approvals, exception thresholds and escalation paths, and ongoing testing/monitoring aligned to finance risk tolerance. This supports resilient AI in finance operations.
From model metrics to measurable operating impact
Realizing tangible business value is rarely won on “model performance” alone. It’s won when GenAI improves operational performance in ways leaders can see in dashboards and monthly reviews: fewer touches, fewer handoffs, fewer late corrections, faster resolution and better consistency. True finance and accounting transformation accelerates when early GenAI finance use cases create reusable assets—curated knowledge bases, standard prompt patterns, and testing playbooks—so each subsequent deployment is faster to industrialize.
A pragmatic path is to start with one or two high-friction moments where outcomes are measurable and workflows are stable, before expanding once governance, monitoring and change adoption are proven.
A tangible example: Transforming accounts payable with Zora AI
Manual, error-prone invoice processing slowed operations and exposed a top global beverage producer to compliance risks. Inefficient communication and accuracy gaps compounded these operational challenges, underscoring the need for a streamlined, AI-driven automated solution. We helped turn that ambition into an execution-ready solution by introducing Deloitte’s Zora AI™ platform. This agentic processing solution uses GenAI, machine learning and self-healing capabilities to improve data quality and overall efficiency across a wide range of enterprise tools. The solution in this case emphasized three practical capabilities finance leaders care about: automated invoice data extraction and validation, a full audit trail and improved process, and robust analytical functions to connect drivers to financial outcomes.
The results of this finance operations use case were measurable. The organization achieved 92% touchless invoice processing, reducing manual effort by more than 80%. Beyond efficiency, the improved accuracy of invoice data strengthened the organization’s ability to forecast cash flow, manage supplier relationships and support advanced analytics.
GenAI offers opportunities to reimagine how finance delivers outcomes end to end across the operating model, rather than just deploying another tool. The strongest GenAI finance use cases come from focusing on a small set of measurable outcomes, embedding capabilities into core workflows and implementing controls by design (governance, monitoring and auditability) so efficiency gains reinforce, not erode, reliability.
Start with a baseline of three to five operational KPIs, selecting one or two high-friction “moments of work”; scale only after controls and adoption prove out. Done well, integrating AI into finance operations becomes a core enterprise capability—one that can accelerate cycle times, improve consistency and strengthen trust in finance outputs and the decisions they enable.
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