Financial Planning and Analysis (FP&A) is at the core of strategic organizational decision-making. Yet, manual processes and reliance on historical data have rendered FP&A ineffective in today’s fast-paced, data-driven business landscape. As McKinsey emphasizes, while many leading finance organizations have increased efficiency – by 39 percent or more – in transactional functions, there are fewer efficiency improvements in more strategic areas such as FP&A.[1]
Clearly, FP&A must evolve. The shift from traditional to next-generation FP&A – leveraging advanced analytics, Artificial Intelligence (AI) and automation – is paramount. This transformation can make finance teams more proactive, helping them capitalize on real-time insights that drive strategic initiatives and fuel growth.
Integrating human intelligence with AI can evolve FP&A into a Center of Intelligence anchored in the four critical pillars of Dynamic Data Ingestion, Intelligent Automation, Predictive and Prescriptive Analytics, and AI-augmented Self-service Reporting.
Figure 1: Human-AI-led Center of Intelligence Framework
This article explores the critical aspects of an FP&A function and outlines how a hybrid intelligence approach can dramatically re-define the FP&A landscape.
Data
Challenge:
Data Consolidation and a Single Source of Truth
Ingesting data from various sources poses a significant challenge in FP&A. FP&A teams must manage and reconcile data from upstream systems, including actuals and budget data. However, these data sources are often disparate, structured / unstructured and in multiple formats, demanding extensive manual processing for accuracy and consistency. The time and effort invested in data cleansing and integration take away from the strategic focus of FP&A professionals.
Solution:
Dynamic Data Ingestion
A finance Center of Intelligence leverages advanced data ingestion techniques to automate the Extraction, Transformation and Loading (ETL) processes for structured and unstructured data, integrating them into cloud-based finance data lakes. AI-powered data pipelines dynamically adapt to changes in data sources, ensuring real-time integration and validation. Efficient data orchestration is vital to connect multiple data centers – including legacy systems, cloud-based tools and data lakes – into a unified, accessible platform. Data orchestration allows data analysis tools to filter, sort and publish complex data within the cloud.
Moreover, these systems can harness Generative AI (Gen AI)-enabled machine learning algorithms to discover, classify and correct in-flight data (critical and sensitive information) for sanitization, anonymization and pseudonymization. This enhances data accuracy and reliability while enabling FP&A professionals to focus on strategic activities such as analysis and strategy development.
Case in Point
A multi-line insurer navigated the challenge of manually preparing over 300 reports from multiple data sources by overhauling its data ingestion process. This entailed understanding all source systems, setting stringent data quality and security standards, and implementing a dynamic data ingestion process. The process aggregated data from various databases, applied business rules for transformation and optimization, and loaded the optimized data into a centralized finance data mart. This laid the foundation for automated data load processes for actuals and budgets.
Process
Challenge:
Manual Processes
Traditional FP&A functions are often characterized by manual processes, from data entry and reconciliation to report generation and variance analysis. These tasks are time-consuming and error-prone and prolong the delivery of critical financial insights. Moreover, their repetitive nature can result in burnout and diminished productivity.
Solution:
Intelligent Automation
Intelligent automation, powered by AI technologies such as Natural Language Processing (NLP) and robotic orchestration layers, can transform FP&A functions. FP&A teams can achieve substantial efficiency gains by automating routine tasks like data reconciliation and report generation. This can enable FP&A teams to focus on making relevant business decisions, engaging in business partnerships and expanding their influence beyond the finance department.
AI algorithms can also improve the accuracy of forecasts and budgets by learning from historical data and identifying patterns not immediately evident to human analysts. This improves the quality of financial insights while accelerating decision-making.
Case in Point
To streamline its reporting process, a multi-line insurer automated the integration and loading of data from various databases after setting up a robust data discovery and anomaly detection process. This automation eliminated time-consuming manual tasks, enhanced report accuracy and timeliness, and introduced self-serve reporting with automated report delivery in various formats. This enabled the enterprise to focus on strategic business reviews rather than routine data management.
Analytics
Challenge:
Historical Analysis
The traditional FP&A approach, which relies on historical data and trend analysis, often fails to accurately predict future performance. The volume of data modern businesses generate necessitates more sophisticated analytical tools to uncover deeper insights and provide actionable recommendations.
Solution:
AI-augmented FP&A Analytics
ML and deep learning algorithms offer robust capabilities for predictive and prescriptive analytics. These algorithms can analyze vast datasets to identify trends, correlations and anomalies, which manual analyses would likely miss. Predictive analytics leverages these insights to forecast future performance, while prescriptive analytics offers recommendations on optimal actions to achieve the desired outcomes.
A Center of Intelligence enables the seamless integration of advanced analytics tools into the FP&A workflow, enabling real-time scenario planning, risk assessment and strategic decision-making. This enhances forecast accuracy while providing a competitive edge by empowering cross-functional business teams to focus on growth opportunities.
Case in Point
A leading US airline needed regular, accurate reports on Revenue Passenger Miles (RPM) and load management to support budgeting and strategic planning. Consequently, it established a comprehensive forecasting process, automating revenue, sales and cost projections. This involved setting up a data lake to integrate internal and external data and building a forecasting model utilizing advanced analytics techniques. The model seamlessly incorporated analysts' insights and market expectations, significantly improving cash flow planning, capacity expansion decisions and overall strategic direction.
Reporting
Challenge:
Static Reporting
Traditional FP&A reporting processes often involve multiple iterations of report preparation, review and approval, delaying the delivery of critical financial information. Moreover, relying on static reports can limit decision-makers' ability to explore data dynamically and extract deeper insights. The growing demand for more interactive, real-time reporting is challenging for many organizations due to a lack of tools and expertise.
Solution:
Gen AI-powered Self-service Reporting
AI-augmented self-service reporting tools empower FP&A teams and business users to generate and customize reports independently without relying on IT support. Gen AI further enhances this capability by providing NLP interfaces, enabling users to query data and generate reports using conversational language. This democratizes access to financial insights, allowing decision-makers at all levels to explore data and uncover insights on demand.
Gen AI-assisted self-serve BI writes complex Structured Query Languages (SQL) based on user requests and selects the respective tables, charts / graphs and intelligent narratives. It provides users with an interface to ask what, why and how to gain better insights from reports. Gen AI, when integrated with automation assistant tools, enables users to request automation, generate personalized content and summarize documents directly within work applications.
A Center of Intelligence enables these self-service tools to be integrated with advanced analytics and dynamic data ingestion capabilities, offering an intuitive user experience. This improves the timeliness and relevance of financial information while fostering a data-driven culture across the organization.
Figure 2: Generative AI-powered Self-serve Reporting
Case in Point
A multi-line insurer transformed its FP&A processes by adopting a Gen AI-augmented self-service reporting solution. This initiative automated data loading and report generation, empowering over 4000 cost center managers with on-demand access to consolidated, accurate management information. The transformation improved productivity by 90 percent and provided a unified view of financial data, revolutionizing the organization's reporting framework.
Figure 3: Self-serve FP&A Reporting
The Path to Creating a Center of Intelligence in FP&A
Transforming FP&A into a Center of Intelligence requires a holistic approach integrating human expertise with advanced AI and ML technologies. This involves several key steps:
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1. Integrate Technology
Investing in robust data integration and automation platforms is vital. These platforms must handle diverse data sources, automating routine tasks and providing advanced analytical capabilities. Cloud-based solutions offer scalability and flexibility, enabling organizations to adapt quickly to changing business needs. These platforms help transform disparate systems into interconnected, streamlined workflows, powering real-time action and data exchange across systems. Pre-built connectors and API packages assist in simplifying the process of automating at scale. Similarly, cloud Integration Platform-as-a-Service (iPaaS) solutions support the execution of automation from enterprise workflow apps, enabling developers to re-use automation assets across the enterprise.
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2. Develop AI Skills
While technology is critical, the human element remains indispensable. FP&A professionals must acquire new skills in data science, analytics and Gen AI to leverage these technologies effectively. Continuous training and development programs are paramount in building a workforce proficient in using advanced tools and interpreting complex data insights.
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3. Manage Change
Transitioning to a Center of Intelligence requires a cultural shift within the organization. Leadership must advocate for adopting new technologies and processes, cultivating a culture of innovation and continuous improvement. Effective change management strategies, including clear communication, stakeholder engagement and phased implementation, can mitigate resistance and ensure a smooth transition.
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4. Ensure Governance and Compliance
As FP&A functions become increasingly data-driven and automated, robust governance and compliance frameworks are essential. This includes ensuring data quality, security and privacy and establishing robust policies and procedures for using AI and analytics. Regular audits and monitoring help maintain the integrity of financial data and ensure compliance with regulatory requirements.
Conclusion
The challenges facing FP&A functions are significant, but so are the opportunities. Organizations can integrate human intelligence and AI technologies to create a Center of Intelligence that transforms FP&A into a strategic powerhouse. Dynamic data ingestion, intelligent automation, advanced predictive and prescriptive analytics, and AI-augmented self-service reporting are key components of this transformation. Together, they enable FP&A teams to deliver more accurate, timely and actionable insights, driving better business outcomes and positioning organizations for long-term growth and success. Leveraging technology, fostering collaboration and communication, and prioritizing talent retention, FP&A teams can consistently deliver value to their organizations and help achieve their strategic objectives.
Talk to our experts to delve deeper into how a human-AI-led Center of Intelligence can transform your FP&A function.