The Impact of Data, Analytics and AI on Enterprise Performance Management

At this LinkedIn Live event we discussed the transformative impact of data analytics and AI in enterprise performance management (EPM). It was an enlightening conversation with experts Gary Cokins, Prashanth Southekal and Tobias Zwingmann, where we explored the challenges CFOs face and the opportunities that emerge with the integration of data analytics and AI in their organizations.

Host, Steve Rosvold, Founder of CFO.University sets things up be highlighting the key challenges CFOs are facing in today’s economic landscape.

These challenges include:

• Pressure on profitability and cash flow due to higher inflation and interest rates.

• Difficulty in funding and reduced capex capacity from bank failures and higher interest rates that are increasing debt service payments.

• A talent shortage which is is driving up the cost of labor, restricting growth in addition to bludgeoning the bottom line.

• Leadership challenges as many finance leaders face economic headwinds they have never experienced during their career.

• Requirements to drive more strategic insights while ESG and other compliance activities grow are creating more new demands on CFOs, may of whom already have a full plate.


View the summary of this event in our newsletter, Future of Finance Leadership, The Impact of Data, Analytics and AI on Enterprise Performance Management

Gary Cokins, an expert in EPM, emphasizes the importance of integrating different methods of performance management seamlessly. These methods include strategy management, cost and profitability analysis, enterprise risk management, process improvement, budgeting, and driver-based budgeting. Gary also highlighted the benefits of embedding analytics, such as regression, correlation, clustering, association, and segmentation, into each of these methods. He stressed the need for individuals with specific skill sets and competencies in these areas.

Prashanth Southekal discusses the relationship between decisions, insights, and data. He explained that decisions are derived from insights, which are derived from data. Statistical techniques are applied to the data to derive insights, which can be performance insights or actionable insights. Prashanth highlighted the potential of AI to automate or augment decisions once rules are defined from the insights. He also emphasized the importance of understanding AI’s capabilities today in order to identify areas where it can help improve business processes.

Tobias Zwingmann emphasizes the importance of gaining hands-on experience with AI, rather than just focusing on acquiring AI knowledge. With AI technology rapidly developing, even the leading experts admit to not fully understanding the inner workings of certain AI models. Therefore, it becomes crucial for CFOs, and anyone interested in AI, to actively engage in experiential learning. Tobias compares starting your AI journey with learning to drive. You wouldn’t jump straight into a high-speed race without prior experience, would you? Similarly, it’s important to start in a safe and controlled environment, exploring AI for personal use or within a specific context. By doing so, you’ll not only understand what AI can do but also its limitations.

Overall, these experts provided insights and recommendations on how CFOs can leverage data analytics, AI, and EPM to overcome challenges, enhance decision-making processes, and drive performance improvement in their organizations. The discussion emphasized the need for practical steps, hands-on experience, and a balanced approach to data protection and democratization.