How Finance Teams Use ChatGPT, Gemini, Grok & Perplexity—And Why It Still Starts With Kepion

Generative AI tools like ChatGPT, Grok by xAI, Gemini by Google, and Perplexity AI (whose flagship search engine launched in June) are fundamentally changing how finance teams operate. But here’s the catch: while these assistants make it easier to ask better questions, they don’t replace the structured decision-making, governance, and collaboration that tools like Kepion bring to Financial Planning & Analysis (FP&A).

The meaning of FP&A (Financial Planning & Analysis) lies in its role as a core business function responsible for budgeting, forecasting, and supporting strategic decision-making, making it essential for effective financial management and long-term business success.

This article explores how to actually use AI in finance—and why enterprise-grade FP&A platforms remain mission-critical for forecasting, budgeting, and scenario modeling at scale. FP&A tools can help finance professionals provide fast and accurate financial analysis and advice to business leaders, making them indispensable in modern financial operations.

AI is Shifting FP&A From Manual to Strategic

Most finance teams still waste hours on manual data consolidation and low-value tasks instead of driving strategy. Generative AI helps by:

• Summarizing trends (e.g., “Why did ARR drop $20K this month?”)
• Recommending actions based on anomalies (e.g., churn spike, margin compression)
• Highlighting forecast vs plan discrepancies instantly

There are a lot of opportunities and benefits that AI brings to FP&A, including improved efficiency, automation, and enhanced decision-making. Financial planning and analysis tools augmented with AI and machine learning dramatically improve the accuracy of financial forecasts, enabling teams to make more informed decisions.

But just because AI can identify the “what” doesn’t mean it can manage the “how”—that’s where platforms like Kepion play a foundational role, offering advanced functionality to integrate accounting, risk management, and analytics, and transforming the way finance teams achieve more accurate and efficient financial planning. For example, scenario modeling or trading strategies such as limit buys can be supported and analyzed, allowing teams to evaluate different purchase order approaches. Automation in FP&A processes can speed up processing times and reduce errors, further enhancing the value of these platforms.

What Finance Teams Ask AI (and Where It Falls Short)

AI assistants are already being prompted with queries like:

• “Create a rolling forecast model for a SaaS company.”
• “What are current AI chip revenue projections for Nvidia?”
• “Help me explain why our cash flow missed plan.”
• “Compare Kepion and Anaplan for CapEx modeling.”

These are excellent use cases for ideation, research, and assumption validation—but not for managing actual data structures, team workflows, or planning cycles. Finance professionals are often asked to provide proactive, data-driven insights that support strategic decision-making in a rapidly changing business environment. Users of AI assistants expect intuitive experiences and actionable results when interacting with these tools, making user engagement and satisfaction key considerations. Prompt training is not the solution, you still need structured accountability, source-of-truth data, and operational context .

Best Practices for Implementing AI in Finance

Implementing AI in finance is more than just adopting the latest technology—it’s about building a foundation that ensures accuracy, security, and actionable insights in a rapidly changing financial world. As companies look to respond to evolving financial times, following best practices can help maximize the value of AI-powered solutions like Gemini and Perplexity, while safeguarding sensitive financial data and supporting users at every step.

1. Integrate Financial Data from Multiple Sources Start by ensuring all relevant financial data is centralized and accessible to your AI models. Pull in information from trusted sources such as Bloomberg, Reuters, and internal systems to give your AI the context it needs for accurate analysis. Integrated data enables models to provide a complete, up-to-date view of your company’s financial position and the broader market.

2. Prioritize Security and Access Control Protecting financial data is non-negotiable. Implement robust security protocols and strict access controls so only authorized users can interact with AI-powered services. This is especially critical for companies handling large volumes of sensitive data, such as those using Gemini or Perplexity, to maintain trust and compliance.

3. Plan for User Support and Ongoing Maintenance Successful AI adoption requires more than just technology—it needs people. Provide comprehensive training and ongoing support to help users learn how to use new AI solutions effectively. Regularly update and maintain your models to ensure they continue to deliver the expected functionality and accuracy as your business and the financial world evolve.

4. Understand Model Functionality and Limitations Know what your AI models can—and can’t—do. While AI can analyze large datasets and surface trends, it’s important to recognize its limits and validate its outputs. This understanding helps companies set realistic expectations and use AI as a support tool, not a replacement for expert judgment.

5. Take a Step-by-Step Approach Roll out AI solutions in phases. Start with pilot projects to test functionality and learn from real-world use, then scale up as you gain confidence. This approach allows you to adapt quickly, remove obstacles, and make changes based on user feedback and evolving needs.

6. Focus on Actionable Insights, Not Just Data AI should do more than deliver raw data—it should provide clear, actionable recommendations that drive financial planning and decision-making. Look for solutions that help users move from information to action, improving efficiency and supporting better outcomes.

7. Leverage Multiple Information Sources Stay informed by combining data from a variety of reputable sources, including financial news and market data from Bloomberg and Reuters. This multi-source approach ensures your AI models are powered by the most current and comprehensive information available.

8. Embrace Change and Adaptation The financial world is always changing. Be ready to update your AI models and processes as new data, regulations, or market trends emerge. Flexibility is key to staying ahead and maintaining a competitive edge.

9. Ensure AI Can Answer Complex Questions Choose AI solutions that can handle both large-scale data analysis and nuanced financial questions. The ability to provide clear explanations and support for recommendations builds trust and helps users make informed decisions.

10. Scale Solutions for All Business Sizes Whether you’re a large enterprise or a small business, look for AI-powered finance solutions that can scale to your needs. The right tools should offer value to companies of all sizes, helping everyone sign off on smarter, data-driven financial decisions.

By following these best practices, finance teams can unlock the full potential of AI—improving efficiency, accuracy, and the ability to respond to the demands of modern financial times. In a world where every day brings new challenges and opportunities, companies that leverage integrated, secure, and adaptable AI solutions will be best positioned to thrive.

Where Kepion Complements AI in FP&A

Think of Kepion as your orchestration layer for FP&A. while AI assistants are your creative co-pilots. Kepion and AI assistants each offer unique advantages for FP&A teams, providing different features and benefits that can enhance planning and analysis processes. Here’s how they work together:

FP&A Use Case	AI Assistants (ChatGPT, Gemini, etc.)	Kepion
Research assumptions	✔️
Generate scenario templates	✔️	✔️
Store & manage structured models		✔️
Run what-if analysis with driver logic	Partial	✔️
Real-time collaboration with ops teams		✔️
Enterprise-grade auditability & compliance		✔️

Finance teams need to find the right combination of tools that offer the flexibility, automation, and security required to meet their specific planning and analysis needs.

AI-Powered FP&A Starts with Clean, Centralized Data

The importance of a central model repository is to avoid conflicting versions of truth. Accurate, timely information is essential for effective financial planning and analysis, ensuring that decisions are based on reliable data. Without a platform like Kepion, it’s too easy for AI-generated forecasts and insights to become disconnected from actuals and operational plans. Cloud-based solutions offer access to more Big Data sources and are scalable and cost-effective, making them a critical component of modern FP&A strategies. A centralized platform like Kepion also helps remove barriers to collaboration within and outside the finance team, streamlining planning processes.

Kepion ensures:

If you’d like to learn more or have any questions, feel free to contact us.

• Version control and structured model governance
• Live actuals vs forecast comparisons
• Integrated planning across finance, sales, and operations
• Microsoft Fabric & Power BI compatibility for real-time visibility
• Strong security features with compliance to industry standards such as ISO/IEC 27001:2013 and SOC certifications, ensuring robust information security and data protection. Account protection is prioritized, with hardware security keys offering the strongest level of account protection for users, including support across mobile devices.

Final Thoughts

Generative AI is helping finance teams move faster, but platforms like Kepion ensure they move in the right direction. As AI continues to augment FP&A, the companies that succeed will be those that blend:

• Strategic automation
• Strong planning infrastructure
• AI-enhanced decision support

To learn more about how AI tools and strategies can benefit finance teams, explore our educational resources and guides.

🧠 Use ChatGPT, Grok, Gemini, and Perplexity to generate insights—use Kepion to operationalize them.