2023 Budget & Planning in Uncertain Times
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Rose Report: Issue 59
By Ted Rose, President and CEO, Rose Financial Solutions
It’s that time of the year when company leaders develop their annual budgets to project income, profits, and returns on investments and control expenses for the coming year. While this yearly exercise of creating a roadmap to manage financial expectations and goals for the coming year is challenging, it will be even trickier this year due to the current economic downturn, unpredictable workforce trends, and supply chain challenges.
How is the unpredictable impact of the pandemic affecting corporate budgeting decisions? To explore current budgeting trends across industries, Forrester’s Budget Pulse Survey, 2022, polled 382 U.S. business and technology decision-makers to assess current expenditure projections for 2023. According to the survey:
- Most decision-makers expect at least some increase in overall spending in the next 12 months; less than 10% expect a decrease.
- On average, 60% of those surveyed expect to increase spending on personnel, and 62% expect to increase spending on external services.
- Of those surveyed, 67% said their technology investments would continue to grow.
As these survey results reveal, many business decision-makers have a positive outlook for 2023. While optimism is essential to growing a company, you must establish realistic growth expectations that align with your company’s goals and financial standing. When preparing your 2023 budget, it’s essential to keep the following in mind.
- Make sure you create a cost structure that matches your growth expectations based on the market, the economic outlook, and your previous year’s budget.
- Confirm that staffing levels correspond to revenue levels. To maintain an accurate forecast, you must monitor income growth and other expenses, such as staffing. It is expensive and risky to make hiring decisions or other investments based on unrealistic projections.
- Often, billing occurs at the end of the month; however, you’ll have costs you’ll need to pay throughout the month. In anticipation of your cash needs, make sure to project the amount of cash necessary to maintain operations before you receive revenue. Plan for at least 45-60 days of money to cover your expenses. This is best accomplished by projecting your balance sheets and cash flow statements for the upcoming year.
- Make sure to include your senior leadership and managers in your budget planning. Operating managers should calculate their divisions’ projected sales revenues, operating costs, and gross profits. Giving them the responsibility to build out their plans motivates them and helps you hold them accountable for meeting those numbers or raising their hands early when unexpected results occur. While it’s wise to encourage them to take reasonable risks from the bottom up, the view from the top down should be more conservative. Remember that setting overly ambitious goals for your entire company can throw off budget planning for each department.
- Monitor your annual budget regularly—either monthly or quarterly, to see if your forecasts are on track with actual performance. Prepare and share monthly reports illustrating the projections and actual results. By analyzing your numbers, your team can adjust as needed to keep their goals on track.
As we head into 2023, it is more important than ever to have financial clarity to make sound business decisions. During uncertain times, it’s critical to have a financial planning system that enables decision-makers to recognize when budgeted projections are veering off course so they can act to pivot rapidly and get back on track.
Schedule an introductory virtual meeting to find out how RFS can provide you with the financial clarity needed to succeed.
This content is for information purposes only and should not be considered legal, accounting or tax advice, or a substitute for obtaining such advice specific to your business.
The post 2023 Budget & Planning in Uncertain Times appeared first on Rose Financial Solutions.
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Issue 72 - Data Driven and AI Enablement Strategies for 2024
Recent findings from Pigment’s Office of the CFO 2024 survey highlight a critical issue for business leaders – the prevalent use of inaccurate data in their decision-making processes. The survey reveals that a staggering 89% of finance leaders are basing their decisions on incomplete or faulty data. The foundation of successful business strategies depends on the quality and accuracy of the decisions made. As businesses navigate expansion and heightened competition, the reliance on data-driven insights has become critical. Harnessing the transformative power of accurate, reliable data enables informed and effective decision-making. Businesses with financial clarity will outpace companies that struggle with flawed data. Financial visibility will help businesses avoid common pitfalls while shaping a future oriented strategic vision. Why Is Most Financial Data Flawed? Financial Data often ends up flawed due to several factors. Disparate systems and fragmented processes within an organization can cause increased inaccuracies over time. The lack of standardization of data within an organization introduces complexities and leads to inconsistencies in data handling. Nomenclature and connectivity issues further compound the problem, making it challenging to establish a framework for data organization. When these issues persist, they pave the way for flawed data, hindering accurate analysis and decision-making. Improving Financial Data with a “Single Source of Truth” Addressing the complexity of inaccurate financial data requires a strategic approach. Streamlining systems and processes and implementing standardized, data-oriented procedures across departments can mitigate inaccuracies stemming from disparate systems and fragmented processes. Moreover, establishing a unified nomenclature and resolving connectivity issues are pivotal to ensuring data integrity. By instituting a cohesive framework for data organization and management, businesses can tackle the root causes of flawed financial data. Establishing a single source of truth consolidates data into a single data structure. This allows for the streamlining of processes, reduction of complexity, standardization of nomenclature and improved connectivity. In essence, a single source of truth reduces errors by ensuring everyone in an organization refers to the same accurate information. This unified data hub speeds up decision making and lays the groundwork for integrating AI into future financial operations. Enter Easby, a system of engagement that standardizes financial activities and data while improving data integrity. As a CFO Co-Pilot, Easby streamlines data handling and reporting, allowing leaders to make better decision based on better information. Easby reduces administrative activity and promotes data-accuracy, improving decision-making and driving companies toward success in our competitive business environment. Easby connects with your accounting system of record to become a “single source of truth”, centralizing data and refining processes. By streamlining data collection and reporting, Easby empowers leaders to refocus their efforts on strategic growth initiatives. To discover how Easby can become your CFO Co-Pilot while fortifying the future of your organization, we invite you to schedule an introductory call with Rose Financial Solutions (ROSE). Schedule an Introductory Call
Technology, Data and Automation are transforming decision-making, especially with the democratization of Artificial Intelligence (AI). This transformation is especially pronounced within finance, where AI's emergence is influencing financial system strategies, placing a premium on structured data for AI-driven initiatives. However, the ability to utilize AI effectively heavily relies on data organization and security. Organizing data includes data consolidation, categorization, and tokenization. This organization can help establish the groundwork for your company to benefit from the full potential of a wide-range of AI-driven use-cases. Consolidating Diverse Data for Unified Insights Data consolidation includes merging and unifying diverse data sets from multiple sources into a single source of truth. Let’s consider a corporation that operates across various states. Each division might maintain financial and operational records, such as sales figures, payroll, operational expenses, and inventory in disparate systems. Data consolidation in this scenario involves merging these diverse datasets from different divisions into a singular, centralized system. For instance, combining sales data from different regions, integrating it with payroll and inventory records, and aligning financial reports across divisions creates a comprehensive overview of the company's overall performance. This consolidated data allows for better analysis of revenue streams, cost optimization strategies, and more accurate forecasting across the entire organization, aiding in strategic decision-making for the whole company. Enhancing AI Precision through Categorization Categorization involves sorting data into specific items or categories based on various parameters or attributes. It's about organizing and labeling data in a structured manner. For example, in accounting, data categorization refers to sorting expenses into a variety of dimensions, such as general ledger codes, department codes, project codes, etc. These codes are normally broken down into logical categories that help users and AI understand that certain vendors are related to travel and others are related to office supplies, or utilities. In AI-driven strategies, categorization is paramount for contextualizing and organizing information effectively. By classifying data into relevant categories or items, AI systems can understand the nuances of different data sets. This categorization allows for more precise analysis, facilitating the extraction of actionable insights and comparisons that are crucial for decision-making. Tokenization for Advanced Data Efficiency and Security Tokenization is the segmentation of complex data into smaller, more manageable units known as tokens, each representing individual pieces of data or information. This process primarily focuses on maintaining confidentiality when inputting data into AI systems. Its core objective is safeguarding sensitive data by substituting identifying information with distinct tokens or representations. By implementing tokenization, organizations create a protective barrier around sensitive information, like personal or financial data, thwarting AI from associating the data from a specific entity. Tokenization ensures that AI algorithms work with transformed data. For instance, tokenization involves converting sensitive data, like vendor names, into random tokens in financial transactions. This not only enhances security by safeguarding sensitive information but also streamlines data analysis by reducing the complexity of the dataset. In AI strategies, tokenization is a critical step. By segmenting data into tokens, AI algorithms can more effectively identify patterns, trends, and correlations within the information, ultimately enabling more accurate predictions and insights, all without providing the AI with sensitive information. Leveraging Integration Opportunities with AI Consider a company working to streamline its accounting processes. The organization creates a unified database through data consolidation and tokenization. The integration of AI technology allows for the use of machine learning to automate transaction coding, a move that significantly reduces manual workload while improving processing accuracy. Other examples of AI integration include automating graphic analysis and categorization creation. For instance, AI-driven tools can autonomously generate visual representations of complex datasets. Moreover, within categorization, AI systems excel at continuously refining and automating the sorting of diverse data sets into specific categories or segments, ensuring accuracy and efficiency in data handling. Finally, AI-driven tools leverage historical patterns to track and analyze financial behaviors. For instance, by examining past expenditures, these systems identify trends, anomalies, and potential cost-saving opportunities. This level of insight allows businesses to make more informed decisions regarding budget allocation, identifying areas for optimization and possible financial risks. Scaling Efficiently Through AI-Driven Strategies By merging AI-driven strategies with data management, businesses gain adaptability. This agility powers informed decisions, intelligent resource allocation, and proactive risk management. This approach isn't just about navigating competition; it's about efficient scaling and strategic growth, representing a shift towards growth while benefiting from financial clarity. This strategic combination empowers businesses to thrive, evolve, and seize opportunities in a constantly changing business environment. Schedule an introductory call with us today to explore how optimizing your data strategy can enhance your adaptability, drive informed decisions, and propel your business towards scalable growth. Schedule an Introductory Call
