In the age of uncertainty, the past is no longer an accurate guide to the future. Fast-developing geopolitical issues, hyperinflation, the coronavirus pandemic and extreme weather events have all rocked the global economy in recent years – often catching finance off guard.
“Everyone is talking about growing complexity, increasing speed, more disruption – also driven by technology – which expands the scope of what finance has to cover,” says Rolf Gegenmantel, chief product officer at Jedox, an enterprise performance management platform that helps businesses with planning, analysis, reporting and data consolidation.
Despite the highly volatile nature of today’s markets, many financial planning and analysis (FP&A) teams are still looking in the rearview mirror. This means they fail to provide the detailed and accurate plans, forecasts and budgets organisations need, or the kind of granular variance analysis that identifies important deviations.
Instead of identifying key drivers and developing dynamic forecasts, for instance, they base their insights and recommendations on historical data that is all too rapidly out-of-date. “It’s important to let go of the classical ‘I have to do my budget and this is the plan for the next year’ approach, because at the end of the day, once you hit the first of January the budget is already obsolete,” says Kyanusch Kay, VP of FP&A and performance at Jedox.
At the same time, there’s a real desire for finance to become a more strategic partner that works closely with business units and leaders. Indeed, 62% of CFOs are seeing more demand for insights from financial data, according to Accenture, but 53% worry that finance is too reactive.
Reactive to strategic
To shift from ‘reactive’ to ‘strategic’ finance needs the right data and a good handle on key business drivers. “It’s essential to know what drives the business and define those KPIs. That way, when a factor influencing a key driver shifts, you can adjust accordingly to maintain a clearer view of what’s ahead,” Kay explains.
At its core, strategic finance is about foresight - leveraging real-time insights to craft dynamic, rolling forecasts that empower businesses to navigate uncertainty with confidence and precision. Essentially, evolving into digital business partners through communication and collaboration across the wider business.
Existing systems and manual processes for prepping data are a significant barrier to achieving this, along with limited budgets. Before adopting Jedox, one customer handled data collection with a staggering four-digit number of Excel sheets, for example. This represented a significant strain on the financial teams’ time and also caused data quality to suffer.
Data sprawl can take a variety of forms. “Finance data is typically more or less okay from a quality perspective because it’s coming from ERP systems,” says Gegenmantel. “But there are sometimes multiple systems in a large organisation and you need to get the data harmonised. Also, the more data sources you have, the more difficult it is to make them match.”
Because data is often stored in a range of formats, with inconsistent definitions and classifications, it can be hard to combine and refine. “As you move closer to operations, the volume of data grows exponentially. This presents challenges such as missing classifications, varying definitions and different formats, making the process of cleaning the data incredibly time-consuming and complex,” says Gegenmantel.
Complex and inconsistent data is a common issue for companies that rely solely on Excel for finance operations. Jedox, on the other hand, can quickly bring together raw data and transform it into actionable information, enabling the kind of dynamic, error-free reporting and forecasting that supports growth and future readiness.
Unleashing AI
The analytical tools built into Jedox allow financial teams to understand trends, patterns and drivers within their data – an essential capability as businesses scale. Crucially, internal data can be augmented with third-party data sets to refine predictions based on commodity fluctuation, political risk, interest rates, seasonality, weather forecasts and other key information.
AI can process and review all this data at a much faster pace than any human worker, supporting faster, easily adaptable forecasts and shorter planning cycles. Numerous data streams can be added to increase forecast detail, identify patterns at speed and gain back time to focus on insight generation. Finance and business leaders alike can then make more confident decisions about capital expenditures, investments, headcount or expansion plans.
Another advantage of AI is its ability to provide more granular insights into specific business lines, geographies, or customer churn risk – a valuable capability in a volatile world. For example, the pest control company Terminix used Jedox AI to analyse a data set with a variety of customer drivers and use it to predict customer loyalty. These faster predictions helped them to make changes to their business processes and see rapid results in customer retention.
“There are patterns behind large datasets, like sales transactions, that reveal which customer groups or product combinations correlate with success,” says Gegenmantel. “Identifying these patterns and forecasting future trends based on your optimisation goals is where AI can help you achieve things that were previously impossible.”
When it comes to variance analysis, the extra detail enabled by AI also makes it easier to explain deviations and refine assumptions in financial models before the next iteration. Automation, meanwhile, addresses many of the pain points of transforming, cleaning and preparing data. “You can therefore analyse and uncover insights you might have missed previously while preparing the data. This unlocks an opportunity to add significant value to the business,” adds Kay.
Smarter scenario planning
With AI-enabled tools like Jedox, finance teams can move beyond simple best-case and worst-case scenarios. It’s now possible to create multiple examples that factor in changes in tariffs, inflation, supply chain disruptions and other external data. “In just a matter of minutes, you can visualise four or five different scenarios, discuss them with the business, and plan ahead based on the most likely outcome,” says Kay.
Drawing upon greater volumes of external data also makes for more detailed scenarios than may previously have been possible. What’s more, with prescriptive analytics financial teams can identify where to allocate resources to capitalise on new growth opportunities – a vital benefit in challenging market conditions.
“You can add value to the business by simulating situations that were not possible in the past because of lack of time and information,” says Kay. “Alternatively, if you have the right resources and strategies in place, you can approach new market entries with a fresh perspective, uncovering opportunities you might not have considered before.”
Fast ad-hoc reports, self-service AI-generated summaries, and dashboards that can easily be drilled into – these can all open up new conversations with business leaders. “AI can act as the interface between the data and the information that’s in it, making solutions more accessible,” says Gegenmantel.
These elements lay the foundation for finance to become a valuable business partner, embodying a more strategic role within the organisation. As Gegenmantel puts it, “Finance has the potential to be a crucial business coach and advisor, guiding others to understand what needs to be done to achieve the desired financial results - which are, after all, at the core of any business.”
Ultimately, a platform like Jedox is not just a technological upgrade for finance teams wrestling with Excel spreadsheets. It’s the catalyst for transforming finance from a reactive, rear-view mirror function into a predictive, future-focused one that can help the business successfully navigate today’s challenging market conditions.
Strategic takeaways for finance leaders
To navigate today’s volatility, finance must move beyond historical data and embrace AI-driven insights for more accurate, dynamic forecasting. This shift requires transparency in decision-making, seamless data integration and user-friendly tools that empower teams. The following key takeaways provide actionable insights that finance leaders can harness to drive business agility and strategic decision-making.
Transparency and clarity in AI-driven decision-making processes are non-negotiable. Gegenmantel emphasises the need for ‘explainable AI’, where the logic behind the results is clear and it’s not just a ‘black box’ that can’t explain how a conclusion was reached. This explainability is crucial in finance.
Using a platform that provides traceable and transparent results enables everyone to trust the forecasts and scenarios AI has played a role in creating. The shift may also require finance professionals to develop new skills so that they can understand the most complex algorithms and interpret their results effectively.
When it comes to using data effectively, an ecosystem mindset is key. “Not all the necessary information will reside in a single system. Therefore, it’s important to combine it with external data or information from other sources, and to make that process as seamless as possible,” says Gegenmantel.
The AI-enabled finance tools that an organisation invests in should be easy to use without the involvement of IT. For instance, users should be able to ask questions and get insights from data using plain language. “Employees should have the ability to produce a simple analysis without any delays. Then, the results should be ready to discuss with relevant departments without needing to debate the consistency of the data,” says Kay.
The technology landscape is also constantly changing, as are business needs, so AI solutions – as well as approaches to using the technology – must be adaptable. “We don’t know what the next development is and how quickly it’s coming up. Organisations need the flexibility to change gear or go a different route,” says Gegenmantel.
Training and communication are key to making people comfortable with AI. “You have to get your stakeholders aboard when you train an AI model so they can better understand it and relate to what the outcome is,” says Kay. “Then it’s a question of doing simulations so they can see whether the results align with their gut feelings.”
For more information on strategic finance visit jedox.com
In the age of uncertainty, the past is no longer an accurate guide to the future. Fast-developing geopolitical issues, hyperinflation, the coronavirus pandemic and extreme weather events have all rocked the global economy in recent years – often catching finance off guard.
“Everyone is talking about growing complexity, increasing speed, more disruption – also driven by technology – which expands the scope of what finance has to cover,” says Rolf Gegenmantel, chief product officer at Jedox, an enterprise performance management platform that helps businesses with planning, analysis, reporting and data consolidation.
Despite the highly volatile nature of today’s markets, many financial planning and analysis (FP&A) teams are still looking in the rearview mirror. This means they fail to provide the detailed and accurate plans, forecasts and budgets organisations need, or the kind of granular variance analysis that identifies important deviations.
Instead of identifying key drivers and developing dynamic forecasts, for instance, they base their insights and recommendations on historical data that is all too rapidly out-of-date. “It's important to let go of the classical ‘I have to do my budget and this is the plan for the next year’ approach, because at the end of the day, once you hit the first of January the budget is already obsolete,” says Kyanusch Kay, VP of FP&A and performance at Jedox.
At the same time, there’s a real desire for finance to become a more strategic partner that works closely with business units and leaders. Indeed, 62% of CFOs are seeing more demand for insights from financial data, according to Accenture, but 53% worry that finance is too reactive.
