You're a budgeting and forecasting professional. How can you use automation to make your work more efficient?

Leveraging automation can significantly enhance efficiency. Automating data collection and entry processes can save time and reduce errors, allowing for real-time data analysis. Implementing software that integrates with existing systems to automatically update financial models ensures accuracy and speeds up the forecasting process. Utilise machine learning algorithms to analyse historical data, identify trends, and make predictive analyses more accurate and less time-consuming.

Automation can significantly enhance efficiency. Process Streamlining: Automate repetitive tasks like data collection, aggregation, and report generation. This frees up time for strategic analysis. Scenario Modeling: Use automation to run “what if” scenarios swiftly. Adjust variables and assess their impact on budgets and forecasts. Self-Service Reporting: Implement self-service dashboards and reports. Employees can access real-time data, reducing dependency on manual requests. Accuracy and Speed: Automated processes ensure accuracy and timely updates. This aids in quicker decision-making. Focus on Value-Add: With mundane tasks handled by automation, professionals can concentrate on strategic initiatives and competitive edge.

Automating can assist you in the following, 1. Data Collection and Entry: Automate the process of collecting and entering data by using tools that can pull data from various sources directly into your budgeting software, reducing manual errors. 2. Analysis and Reporting: Utilize automated algorithms to analyze historical data and generate forecasts quickly and accurately. 3. Alerts and Notifications: Set up automated alerts for significant budget variations or deviations from forecasts. This will help you identify issues early on and take necessary actions promptly. 4. Communication: Automate the distribution of budget reports and updates to stakeholders, ensuring that everyone has real-time access to relevant information

The biggest error companies make - with many ERP implementations - is that the time for blueprinting of the processes is highly underestimated. "Just implement something" becomes very costly in the end, then taking the time up front to really understand what output you (dept heads, mgmt, etc) require and then work backwards to determine data input and the value stream to be set up. The same goes with any FP&A tools: ensure all stakeholders - and not just finance! - have a chance to provide input and feedback. What is the focus - BS & PL development? Sales & asset utilization (production)? Cashflow? Inventory? etc...

Technology can help to automate many things for preparation of budget and forecast. A system with a blend information storage that we can consolidate the SOPs and base assumptions for forecast which help us predict forecast numbers considered inflation of cost can help. Either a system or worksheets can help it. It will be helpful if we can have automated charts and presumptions before we start based on historical and actual data.

Automation tools can also facilitate scenario planning by quickly adjusting variables to see potential outcomes, aiding in strategic decision-making. This is super useful in the current climate where quick and accurate decisions are required. Moreover, automating report generation and distribution streamlines communication across departments, ensuring stakeholders have timely access to relevant financial insights. By freeing up time from manual tasks, you can focus on strategic analysis and decision support, adding greater value to your organisation.

The use of AI and Machine Learning in Budgeting & Forecasting is still in its infancy. When using automation tools in the Budget & Forecast process; the user has to consider and analyse what the predicted outcomes are telling them, and whether, based on the data they have available to them, the result makes sense. History is not necessarily an indication of future trends, so using AI solely to analyse and forecast based on the historical data, then failing to validate the forecast; could result in unnecessary business risks being taken and the company falling short of their financial and operational goals.