Unleashing the Power of AI in Strategic Finance

Competitive advantage through AI-supported strategic finance

Leveraging data and advanced technologies for strategic planning will be a game-changer for companies worldwide. They enable companies’ finance functions to contribute to strategic decisions more effectively and streamline the corresponding processes. We call this data and AI-supported way of gaining strategic insights as Intelligent Finance Strategy.

The transformation towards an Intelligent Finance Strategy can be observed when companies are harnessing unstructured and external data and AI when shaping their strategy. AI algorithms can now analyse vast volumes of data, including social media sentiment, customer reviews, news articles, and industry reports, providing invaluable insights into market trends, consumer behaviour, and competitive dynamics. AI can tirelessly with high diligence identify patterns, trends, and anomalies that humans easily miss. As a result, AI enables companies to make more accurate (long-term) forecasts, identify potential risks and opportunities, and streamline decision-making.

Companies that are embedding these types of input in their strategy work are better positioned to face the future, as they gain a competitive edge in today's rapidly evolving business landscape. Finance teams have always had an important role in supporting business strategy, but with the addition of heterogeneous data and AI, this role has become even more significant. The CSRD (Corporate Sustainability Reporting Directive) requires companies to disclose forward-looking perspectives for up to 10 years, which also puts pressure on the external reporting perspective. 

Examples of Intelligent Finance Strategy

 There are many practical examples of how AI could be used to transform strategic planning. Here are a few examples:

• Strategic foresight and scenarios: AI can be used to generate different scenarios for the future, based on a variety of factors. This can help companies to identify potential risks and opportunities, and to develop contingency plans. For example, companies could use AI to simulate the impact of a new product launch on sales, costs, and revenue projections. This would allow them to make more informed decisions about the launch strategy and enable them to identify potential risks and opportunities.
   
• Competitive intelligence & benchmarking: AI can be used to track the activities of competitors, identify new market entrants, and benchmark performance against industry peers. This information can be used to inform strategic decisions such as pricing, product development, and marketing campaigns. For example, companies could use AI to identify which products were underperforming compared to competitors and make data-driven decisions to improve topline growth.
   
• Innovation portfolio management: AI can be used to track the progress of innovation projects, identify potential risks, and make recommendations for improvement. This can help companies to improve the efficiency of their innovation process and to improve the return on investment. For example, companies could use AI to analyse product data and identify emerging trends, allowing them to gain data-driven insights into which innovative products to invest in.
   
• Risk & compliance monitoring: AI can be used to monitor compliance with regulations and to identify potential risks. This information can be used to take corrective action and to avoid penalties. For example, companies could use AI to identify potential AML (anti-money laundering) red flags, such as unusual transactions or suspicious patterns of behavior. This information was used to investigate potential breaches and to take corrective action.

The biggest hurdles to be tackled

While the benefits of Intelligent Finance Strategy are undeniable, several challenges must be overcome to fully capitalize on its potential. Although the same challenges apply here as in all AI adoption; some aspects are especially relevant in strategic finance.

One of the biggest hurdles to be tackled is the concern over data availability and quality. Although company internal data may have some issues, utilizing external data is normally a much bigger challenge. That is, relevant external data may be hard to come by and they can come from a variety of sources and in a variety of formats. Since the data are used in a strategic context, it is mandatory to ensure their reliability and uniformity.

The adoption of AI and advanced technologies requires also a skilled workforce capable of understanding, implementing, and using these tools effectively. Companies must invest in upskilling their finance teams, fostering a culture of continuous learning, and attracting top talent with expertise in data analytics and AI.

Finally, introducing AI into a traditional finance function can pose a significant change management challenge. Finance teams will need to adapt to new ways of working and embrace data-driven insights that are based on the output of an algorithm. This can be a daunting task for some teams, which are used to work on detailed spreadsheets and rule-based logic. Change management is also required when traditional corporate silos are outrun with new ways of working.

 

Contact us for more information:

Heli Moilanen
Director, BearingPoint Finland

Jussi Ahola
Senior Technology Advisor, BearingPoint Finland