Elevating IT Financial Management with AI

In IT Financial Management (ITFM) there are lots of challenges which make the optimal handling of IT spendings hard to achieve:

• ITFM is complex and data heavy.
• Manual processes are time-consuming and error prone.
• ITFM is reactive.

AI is a great tool to answer the above challenges:

• AI has more capabilities and capacities than the human mind, and it can handle large databases in a fragment of time, especially if all the data is available on the same platform.
• Automation is key, a machine is more suitable for dealing with repetitive tasks.
• AI can make ITFM predictive instead of being reactive.

Forecasting with AI

In case of forecasting there are lots of possibilities and opportunities in using AI, because the forecasting procedure includes a lot of manual work, data analysis, and it works with enormous amount of data. But this is not only about automating the manual works to make it more consistent and less error prone.

It is about the capabilities of AI to find hidden patterns, risky or promising trends in a large dynamic dataset.

Use AI to analyze historical spendings, seasonal trends, and PO data to generate more accurate rolling forecasts:

• AI can adjust the remaining forecast based on over- or underspending in the last 3 months.
• AI is able to flag forecasts as likely inaccurate based on deviation from historical norms (for example, the numbers differ from the same period in the last few years).
• Detect of inconsistencies between budget, forecast and actuals.

Multi-scenario forecasts to be prepared for every possible outcome:

• What-if analysis and modeling to simulate forecast changes based on project delays, contract changes of service scale-up.
• Dynamic scenario scoring and recommendation.

Anomaly detection

Sometimes, in financial matters it is essential to recognize the problem in time. There are always anomalies which are visible signs of a deeper problem. In most of the time the risk and possible loss are directly proportional to the time spent with detecting the core problem.

• Finding overspent and underutilized budgets by showing deviation between actuals and plans.
• Identify patterns across departments or vendors that indicate financial risk or compliance issues: The search can be extended to other working ServiceNow modules like ITSM Incident Management to find and identify potential risks.

 

Natural language interfaces for non-technical stakeholders

Most of the stakeholders are not familiar with the technical side of ITFM in ServiceNow. They only want to know the answers to their questions without the need to take a deep dive into the system. For them an Agentic AI would be a great tool to get the information needed through a simple chat-like conversation.

Generative AI for conversations:

• “Show me the top 3 services over budget this quarter.”
• “What is the current forecast variance for service X?”

Requests for information to be able to make decisions:

• “If project X is delayed by 6 months, what is the impact on Operational Expenses?”
• “Why did the forecast increase for service X in Q2?”

AI Automation

Dealing with finances usually means a lot of manual work with a huge amount of data. And we all know that manual work is exposed to human mistakes, and it takes too much time.

In our current highly competitive economic environment, it is essential to spare time, to achieve the highest possible efficiency.

Automation of general processes is a huge step toward this target:

• Automated collection of siloed financial data through integration.
• Cost allocation and mapping based on historical accuracy.
• Workflows for approvals and financial decisions.
• Notifications for important stakeholders about changes and necessary actions.
• Auto-categorization of financial data, like: Cost buckets based on PO line items, Capitalization based on vendors.
• Data quality and governance automation to identify duplicate or inactive cost centers, services with cost and without owners or gaps in cost mappings.

Conclusion

In summary, AI can make the financial decision-making procedures and the analysis of financial data more fluent and flexible. With its help the platform can provide not only spectacular data visualizations, but also recommendations about what parts of the business should be analyzed deeper, which incidents can cause serious damage to the operations and how they can be solved. The speed of the analysis facilitates the opportunity to create multiple scenarios and compare them to find the optimal one.

And most of all, using AI in finances is something the company feels directly in its wallet.