• Written by Sonali Subhadarsini, Product Manager

A spreadsheet on the finance team's shared drive, updated monthly, with a tab for variance to budget, is often the first place to look for answers about the financial performance of an energy or utilities company, but it can be slow to yield insights and may not be up to date.

The finance questions that currently wait on a report

The finance team in an energy or utilities company typically has to wait for a report to be generated before they can answer questions about the company's financial performance, such as what the current spend is against a particular budget line, or what the outstanding commitments are for a specific project. This can be frustrating, especially when the report is not available in a timely manner, and the team has to make decisions quickly. The shift supervisor, for example, may need to know the current maintenance spend to plan for upcoming outages, while the category manager may need to track spend against a specific contract to ensure that the company is getting the best value from its suppliers.

These questions are not trivial, and the finance team needs to be able to answer them quickly and accurately in order to support the rest of the business. However, the current process of waiting for a report to be generated can be slow and cumbersome, and may not always provide the most up-to-date information. Most people get wrong the idea that the finance team can simply generate a report on demand, without considering the complexity of the underlying data and the time it takes to generate the report.

The data behind them: general ledger, budget, open commitments and purchase orders, receivables and terms

The data that underlies the finance team's reports is typically stored in a general ledger system, which provides a comprehensive record of all the company's financial transactions. The budget data is also stored in this system, and is used to track spend against specific budget lines. Open commitments and purchase orders are also tracked in this system, and are used to manage the company's procurement process. Receivables and terms are also important, as they affect the company's cash flow and ability to pay its suppliers.

The category manager, for example, may use this data to compare the prices of different suppliers, and to track the company's spend against specific contracts. The shift supervisor may use this data to plan for upcoming outages, and to ensure that the company has the necessary resources to carry out maintenance work. By having access to this data, the finance team can provide more accurate and timely information to support the rest of the business.

Sector constraints: outage planning and regulatory reporting burden

Energy and utilities companies are subject to a range of sector-specific constraints, including the need to plan for outages and to comply with regulatory reporting requirements. Outage planning, for example, requires the company to have a detailed understanding of its maintenance schedule, and to be able to plan for upcoming outages in a way that minimizes disruption to customers. Regulatory reporting requirements, on the other hand, require the company to provide detailed information about its financial performance, and to comply with specific reporting formats and deadlines.

These constraints can make it difficult for the finance team to provide timely and accurate information, as they may have to contend with complex reporting requirements and tight deadlines. However, by using AI for finance, the company can automate many of these processes, and provide more accurate and timely information to support the rest of the business. For more information on how AI can support finance, see our finance page.

What improvement looks like: variance to budget, commitment coverage, days sales outstanding

Improvement in the finance function can take many forms, but some common metrics include variance to budget, commitment coverage, and days sales outstanding. Variance to budget, for example, measures the difference between actual spend and budgeted spend, and can be used to identify areas where the company is overspending. Commitment coverage, on the other hand, measures the extent to which the company's commitments are covered by its budget, and can be used to identify areas where the company needs to reduce its spend.

Days sales outstanding, meanwhile, measures the time it takes for the company to collect payment from its customers, and can be used to identify areas where the company needs to improve its cash flow. By tracking these metrics, the finance team can provide more accurate and timely information to support the rest of the business, and can help the company to make better decisions about its financial performance. The finance team can also use this data to identify areas for improvement, and to develop strategies for reducing costs and improving efficiency.

What this does not do, or when it is the wrong choice

While AI for finance can provide many benefits, it is not a solution for every problem, and there are some cases where it may not be the best choice. For example, if the company's data is not well-organized or is incomplete, AI for finance may not be able to provide accurate results. In these cases, the company may need to invest in data cleaning and organization before it can realize the benefits of AI for finance.

In other cases, the company may have specific requirements that are not well-suited to AI for finance, such as the need for customized reporting or the need to integrate with specific systems. In these cases, the company may need to consider alternative solutions, such as custom development or integration with other systems. For more information on how to get started with AI for finance, or to discuss your specific requirements, please contact us.