Beyond Numbers: How NLG + CPM Is Transforming Financial Intelligence

While everyone's focused on generative AI creating images and chatbots, a meaningful evolution is happening in corporate finance: Natural Language Generation (NLG) is reshaping how financial teams create, consume, and act on performance data.

Market Analysis: The Value Proposition

According to Gartner, by 2025, organizations that combine NLG with their business intelligence tools will see a 30% increase in the consumption of analytics by non-technical users. This represents a significant shift in how financial insights are democratized across organizations.

What's happening in practice: Organizations integrating NLG with Corporate Performance Management (CPM) platforms are seeing concrete benefits. A 2023 Deloitte survey found finance teams implementing these technologies reported a 35% reduction in report creation time and a 28% improvement in data-driven decision speed.

What's often overlooked is the qualitative impact. Business users report significantly better understanding of financial data when presented with NLG-enhanced dashboards compared to traditional visualizations alone.

Real-World Implementation: Financial Services Transformation

UBS, a global financial services company, provides a documented example of NLG implementation success. Their Global Wealth Management division faced reporting challenges with analysts spending substantial time producing quarterly performance reports across various regions.

Their transformation approach:

1. Integrated their existing CPM infrastructure with NLG capabilities
2. Connected previously siloed data from finance and market intelligence
3. Deployed narrative templates focusing on key performance indicators
4. Implemented a phased rollout starting with internal management reporting

The documented results:

• 40% reduction in report production time
• Estimated $850,000 annual productivity savings
• Improved consistency in reporting language and terminology
• Redeployment of analyst time to higher-value advisory activities

As Robert Wigley, former UBS executive, noted in a case study published by the Financial Times: "The value wasn't just in time savings. We fundamentally improved how our organization interpreted financial data, allowing our teams to focus on the 'why' behind the numbers rather than just producing them."

Investment Considerations: Practical Approach

When considering NLG integration with platforms like cpmVision , organizations should be realistic about implementation approaches and ROI expectations:

The practical truth: According to IDC research, companies with mature CPM platforms that already centralize data and bridge IT/finance silos see approximately 40% better results from NLG implementations than those starting from fragmented systems.

Future Directions: The Convergence of CPM and NLG

Where is this convergence of CPM and NLG heading? Industry analysts and early adopters point to several developments:

1. Contextual Financial Narratives: As demonstrated in Microsoft's Power BI integration with narrative intelligence, dashboards now automatically generate explanations that adapt based on the viewer's role and permissions
2. Enhanced Explanatory Analytics: Moving beyond describing "what happened" to explaining "why it happened" through automated root cause identification
3. Cross-Domain Financial Context: Leading implementations now connect financial variances with operational metrics and external market conditions
4. Compliance Documentation: Financial services firms like JP Morgan have documented using NLG to standardize commentary in regulatory filings

Looking at cpmVision's integration with Power BI specifically, the platform is well-positioned to leverage these advances as they mature, particularly given Microsoft's significant investment in narrative intelligence capabilities.

The Evolution Opportunity

Organizations still treating NLG as merely a productivity tool for finance are missing the broader opportunity. When integrated with mature CPM platforms like cpmVision that already connect finance, data, and IT, natural language generation doesn't just create reports faster—it fundamentally enhances:

• How financial intelligence flows across organizations
• Who can effectively leverage performance insights
• The speed at which financially-informed decisions can be made
• The strategic value finance teams deliver to the business

The key question isn't whether to implement NLG, but how to leverage it within your existing CPM ecosystem to transform finance from a reporting function into a strategic insights partner.

How is your organization bridging the gap between financial data and actionable narrative intelligence?