AI enables continuous analysis of 100% of journal entries, not just samples.
Key takeaways
A unified Microsoft Fabric data foundation powers enterprise-wide AI insights.
Enterprise intelligence can cut transaction review time by up to 80% and accelerate ROI.
Enterprise data is one of the most valuable assets a business holds, yet it remains one of the most underutilized. Information often sits locked across enterprise resource planning (ERP), customer relationship management (CRM) and other systems of record, making it difficult to track meaningful business outcomes, detect risks and maximize returns on artificial intelligence investments.
As AI adoption accelerates, middle market organizations are moving beyond chatbots to embed AI directly into enterprise workflows through enterprise operational intelligence, driving measurable return on investment (ROI), expanding review capacity and delivering more timely decision intelligence. This growing demand for intelligence at scale is driving the next wave of AI adoption, helping organizations expand visibility, improve operational performance and accelerate ROI.
One of the clearest examples of driving value with enterprise intelligence is the ability to analyze 100% of journal entries rather than relying on traditional sampling approaches. AI-powered anomaly detection can continuously review transactions, identify unusual patterns, prioritize investigations and strengthen financial oversight in near real time.
More importantly, the same AI-ready data foundation can be leveraged far beyond risk monitoring, powering forecasting, key performance indicator (KPI) tracking, operational analytics, performance reporting and executive decision support. The result is a scalable intelligence platform that not only strengthens controls but also creates ongoing business value across the enterprise.
Unlock the value of enterprise data
View RSM's on-demand webinar, Enterprise operational intelligence: Smarter decisions through AI-driven insights, where our AI advisors share detailed practical strategies to modernize financial monitoring, detect anomalies and support data-driven decision making through dashboards and reporting tools.
Common challenges limiting business value
Middle market organizations often struggle to unlock the power of enterprise data. They struggle to leverage it to drive value and often have trouble feeding it to AI to accelerate meaningful outcomes.
Key issues include:
Siloed data: Enterprise data remains isolated across ERP, CRM and other solutions such as warehouse management systems. This limits pattern detection, cross-functional analysis and AI-driven decision making.
Manual processes: Teams rely on exported spreadsheets and reports to combine data from multiple systems, creating inefficiencies and increasing the risk of errors.
Limited visibility: Without a unified view of enterprise data, leadership often makes critical decisions using incomplete information.
Organizations that cannot effectively assess and analyze enterprise data are more likely to face lost revenue opportunities, higher operating costs and greater business risk.
Five common business domains of AI enterprise operational intelligence
Enterprise operational intelligence is the practice of continuously collecting, connecting and analyzing data across business operations to surface risks, detect anomalies and deliver timely insights to decision makers.
Key areas supported by enterprise operational intelligence include:
- Operations: Improve inventory insight and analysis, and streamline receiving, invoicing and reconciliation
- Finance and accounting: Identify anomalies and outliers by connecting general ledger, journal entry and transactional data to strengthen audit readiness, improve days sales outstanding and monitor vendor and expense risk
- Human resources and workforce: Detect timesheet and hours discrepancies, particularly in workforces with large numbers of contractors, support termination compliance and uncover ghost employees
- IT and security: Monitor unusual system access, spot data leakage, track privileged accounts and strengthen authorization controls
- Marketing: Target customers and identify opportunities to cultivate relationships to improve topline revenue
These use cases demonstrate how enterprise operational intelligence helps organizations unlock value and insights from data that could otherwise remain hidden.
Why continuous monitoring matters in financial operational intelligence
Many internal teams face increased complexity and require deeper insight to address evolving challenges. For example, finance leaders face growing pressure as auditors expand their use of AI to analyze transactions beyond traditional small sample-based audits. As audit coverage expands, organizations without readily available supporting documentation often face longer remediation efforts and greater operational disruption.
Key drivers include:
- AI-driven audits: Continuous transaction reviews using AI increase the need to detect anomalies in real time before they become audit findings.
- Manual processes: Spreadsheet-based analysis becomes difficult to scale as transaction volumes and business complexity grow.
- Legacy tools: Traditional approaches capture only a subset of anomalies and overlook complex relationships within enterprise data.
Connecting finance data and operations with natural language
To elevate insight and performance, finance leaders seek to utilize natural language conversation, as well as enhanced data visibility and reporting within the tools they use every day. As AI solutions continue to develop, users have new opportunities to interact with data in real time through advanced natural language processing to accelerate decision making.
The Microsoft AI platform is increasingly acting as critical connective tissue between several point solutions in the finance organization. Companies can connect data and processes through agentic workflows, agents and Copilot to streamline operations that typically require manual work and approvals.
By bringing data together in a more intuitive, cohesive platform, organizations can identify anomalies, monitor transactions, investigate findings, document outcomes and remediate issues within a single workflow.
Finance intelligence built on Microsoft Fabric
As a modern, comprehensive data and analytics platform, Microsoft Fabric connects directly to an ERP system and gives organizations a unified foundation to identify, prioritize and investigate financial risks with AI.
Key capabilities include:
- Connecting financial data: Merge general ledger, journal entry and transactional data from enterprise systems into a centralized analytics platform
- Prioritizing risk: Apply business rules and AI to detect anomalies, score outliers and surface high-priority transactions
- Accelerating investigations: Access transaction details and related account information in a single workspace, eliminating the need to navigate multiple systems, reports and spreadsheets
- Self-improving AI: Document investigation findings and feed the results back into the model to improve future risk detection and analysis
When we help an accounting team, we focus on the highest-risk areas of the business that may be hidden within the financial data. By bringing that data into one place to run business rules, apply AI models and surface the results in a dashboard, users can gain immediate insights into where they should focus and the different risk levels associated with those areas, all at their fingertips.
Jim Milam, Principal, RSM
A solution to optimize operational intelligence
Organizations can take advantage of enterprise operational intelligence solutions built on Microsoft Fabric to establish a foundation for more informed decision making. An effective platform should follow a four-stage workflow that transforms operational data into actionable insights:
- Ingest: The platform consolidates 100% of operational transactions in near real time, creating a unified data foundation for analytics, forecasting and operational use cases.
- Detect: AI models analyze every transaction, assign risk scores and trigger audit flags, prioritizing transactions so teams can focus on the highest-risk items.
- Review: Teams investigate prioritized transactions through a guided workflow, document findings and label transactions, creating a feedback loop that continuously sharpens AI model accuracy.
- Insights: Executive dashboards provide visibility into transaction trends, operational patterns and potential risks, helping leadership identify opportunities for improvement.
Key issues include:
Foundational configuration: Establishes the AI-ready foundation for transaction analysis and risk scoring
Feedback loop: Incorporates reviewer decisions to continuously refine AI models and strengthen audit documentation
Executive insights: Delivers dashboards and reporting that improve operational visibility and support governance
Practical use cases
A centralized dashboard can prioritize journal entries using AI-generated risk scores. Teams can use dashboard KPIs to filter high-, medium- and low-risk journal entries and quickly identify the audit flags behind each score.
In addition, selecting a journal entry can provide a detailed view of the underlying transactions, including posting date, accounts, business units, departments, debit and credit amounts, transaction owners, and associated audit flags, eliminating the need to navigate multiple systems.
During the review process, teams can document their findings within the platform by:
- Classifying transactions as unusual or expected
- Categorizing issues such as late journal entries, period-end adjustments, manual corrections or data issues
- Recording investigation notes
- Defining additional audit rules as business requirements evolve
- Assigning, escalating or closing reviews
Each investigation can automatically create a complete audit trail, capturing reviewer actions, timestamps and review status.
With this solution, companies can continuously create an audit trail from the moment data enters the system in near real time and not have to wait for the end of the month to get the bottleneck sorted.
Joy Cruz, Principal, RSM
Key business outcomes and value metrics
An enterprise operational intelligence platform extends beyond anomaly detection to support planning and forecasting, delivering significant ROI on AI investments, including:
- 100% journal entry coverage with near real-time visibility
- Significant annual software cost savings by reducing reliance on multiple point solutions
- Two to four times more journal entries reviewed per hour without increasing headcount
- Up to 80% time savings for transaction reviews and pre-audit preparation
- Up to five times faster journal entry investigations through centralized access to transaction details
- 50% to 80% faster implementation using prebuilt accelerators for data ingestion, AI models, audit rules, dashboards, labeling workflows and analytics
In addition, prebuilt accelerators can help organizations realize value in weeks rather than months and establish a scalable foundation for future AI initiatives.
While AI coding tools can rapidly create proof-of-concept solutions, production-ready enterprise applications require extensive data engineering, business rules and governance. Integrating AI with complex enterprise systems requires processing millions of records across thousands of data tables, making scalability, accuracy and cost management critical considerations.
Even if we're able to mock up a user interface that somebody could interact with, the ability to get the right data, feed it to AI and identify outliers and anomalies is extremely relevant to achieving accurate outcomes. There are a lot of different moving pieces that are very difficult, and even impossible, to obtain through vibe coding, including multiple security risks.
Jim Milam, Principal, RSM
Frequently asked questions
AI continuously analyzes journal entries and financial transactions to identify anomalies, prioritize high-risk activity and support faster investigations.
No. A solution should use a capacity-based pricing model with predictable monthly costs, helping organizations avoid unexpected token consumption charges.
Turning financial intelligence into action
As organizations accelerate AI adoption across finance and accounting, success depends on more than deploying new technology. Establishing a unified data foundation enables decision makers to strengthen financial oversight, improve risk detection and expand AI-driven operational intelligence across the enterprise.
To maximize value, it is critical to prioritize focused actions such as consolidating enterprise data, modernizing financial monitoring and strengthening AI governance to help scale across business functions.
As AI strategies mature, many finance leaders are seeking experienced advisors to assess readiness, define governance frameworks and implement enterprise AI solutions that deliver measurable business outcomes and drive long-term growth.
Ready to get started? RSM's experienced advisors can help develop a practical AI roadmap, modernize financial intelligence and build a scalable foundation for enterprise operational intelligence. Contact our team to learn how RSM can help accelerate your AI transformation.
RSM contributors
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Jim MilamPrincipal
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