AI in Budgeting: From Data to Decisions

Welcome to the second issue of Balanced AI Insights. In this edition, we dive deep into the world of AI-enhanced financial forecasting and explore a data-driven approach to budgeting. As we navigate the AI revolution in finance, our goal remains to find the perfect balance between cutting-edge technology and human expertise.

Upcoming Webinar: AI-Powered Budgeting: A Practical Guide

Don't miss my upcoming free webinar TOMORROW September 25, where we'll dive deeper into practical strategies for integrating AI into the budgeting process.

Register now!

The Balanced View: Human-AI Collaboration in Financial Forecasting

In today's rapidly evolving financial landscape, AI has emerged as a powerful tool for enhancing forecasting accuracy. However, the key to success lies not in replacing human expertise, but in forging a symbiotic relationship between AI capabilities and human insight.

AI excels at:

• Processing vast amounts of data

• Identifying complex patterns and trends

• Generating rapid, data-driven predictions

Humans shine in:

• Context and nuanced understanding of market dynamics

• Ability to factor in qualitative information

• Judgment in handling unprecedented situations

To achieve the right balance:

1. Use AI as a decision support tool, not a replacement for human judgment.

2. Regularly validate AI predictions against real-world outcomes and adjust as needed. Remember, AI learns and improves!

3. Invest in upskilling your team to interpret and leverage AI-generated insights effectively.

4. Maintain transparency in how AI forecasts are generated to build trust and understanding.

By embracing this collaborative approach, finance teams can significantly enhance their forecasting accuracy while maintaining the critical human element that drives strategic decision-making.

Poll: What's Your Budget Season Persona?

As AI transforms the budgeting landscape, let's have some fun with our roles. CFOs, which budget season persona do you relate to most? Cast your vote:

1. 🧐 The Challenger-in-Chief

2. 🕊️ Peacemaking Mediator

3. 🎩 AI Magician

4. 🎪 ROI Ringmaster

Vote by replying with your choice number or on my LinkedIn page!

Remember, whether you're juggling AI insights or orchestrating a dazzling display of returns, it's the CFO's unique blend of skills that turns budget season into the greatest show in finance.

Case Study: A Data-Driven Approach to Budgeting

Traditional budgeting methods typically fall into two categories: top-down (where upper management sets targets) or bottom-up (where departments propose their budgets). In my experience, these approaches often lead to a frustrating disconnect from reality. Top-down budgets stretch divisions unrealistically, while bottom-up forecasts tend to be overly conservative. As a result, budget meetings often feel more like political negotiations than planning sessions.

This year, I had the opportunity to help a company shake up its budgeting process. We introduced a data-driven forecast to complement their traditional bottom-up method. Before I share the approach, it's important to mention:

1. This method requires sufficient historical data to identify meaningful trends.

2. It's crucial to involve team members who deeply understand the market and the company in the process.

3. The company didn't have to buy any specialized software for this, we only used ChatGPT and Excel.

The approach:

1. Historical Data Analysis: the company leveraged AI to analyze over three years of data, identifying trends and patterns in revenue across product groups and locations. This analysis revealed clear patterns that were validated by the team.

2. Predictive Modeling: Using the discovered patterns and dependencies, we built an AI-powered model. The model was meticulously reviewed with internal stakeholders to ensure its accuracy and relevance.

3. Model Refinement and Forecasting: AI then refined the model based on the business leaders' feedback. Once satisfied, we used AI to generate forecasts based on the model and their agreed-upon assumptions.

4. Comparative Analysis: we compared the data-driven forecasts with the traditional bottom-up inputs. This comparison sparked insightful discussions about discrepancies and their underlying causes.

While this budgeting process is still ongoing, we've already seen two significant outcomes:

A. AI streamlined data analytics, allowing the production of complex, data-driven models in a surprisingly short time.

B. The data-driven scenario has significantly enhanced the budget conversations. It's helping identify blind spots and reduce the political maneuvering that we often see in traditional budgeting processes.

In conclusion, the company considers this approach a win. It's bringing more objectivity and insight into their planning process while AI simplifies and democratizes data processing and model preparation. I strongly encourage fellow finance leaders to experiment with data-driven forecasting in their current budgeting cycle. The insights gained and the quality of discussions that follow make it well worth the effort!

Below is the graph of one of the first revenue forecasts generated by AI. It reflects clear seasonal and market trends identified in historical data.

I can also say that it is one of my most satisfying projects as an AI-driven fractional CFO where I could leverage both my CFO and AI expertise. If you're interested in exploring how a data-driven approach could transform your budgeting process, don't hesitate to reach out for a consultation!

Ask the AI CFO

Q: "What skills should finance professionals develop to stay relevant in the AI era?"

A: As AI continues to transform finance, professionals need to evolve their skill sets. Here are key areas to focus on:

1. Data Literacy: Understand how to interpret and leverage data insights.

2. AI/ML Basics: Grasp fundamental concepts to effectively collaborate with data scientists.

3. Strategic Thinking: Develop the ability to translate AI insights into business strategy.

4. Ethical AI: Understand the ethical implications of AI in finance.

5. Soft Skills: Enhance communication, leadership, and adaptability.

6. Business Acumen: Maintain a deep understanding of your industry and business models.

7. Continuous Learning: Stay updated with the latest AI trends and applications in finance.

Remember, the goal is not to become an AI engineer, but to be an effective translator between AI capabilities and business needs.

AI News of the Week: Microsoft's GRIN-MoE

Just as we were starting to experiment with OpenAI's ChatGPT o1, Microsoft has introduced GRIN-MoE (Gradient-Informed Mixture-of-Experts), a new AI model with some interesting implications for finance professionals. GRIN-MoE's standout feature is its computational efficiency: it activates only 6.6 billion of its 16x3.8 billion parameters during operation. In practical terms, this means powerful AI capabilities without overwhelming existing IT infrastructure – a crucial factor for enterprise adoption.

GRIN-MoE shows promising capabilities in areas crucial to finance. It scored 90.4 on the GSM-8K math test, demonstrating strong potential for complex financial modeling. Additionally, its score of 74.4 on HumanEval indicates advanced coding capabilities, which could streamline financial software development and automate reporting processes. These strengths open up possibilities for

• rapid scenario analysis,

• enhanced risk assessment,

• more accurate financial forecasting

• efficient report generation.

However, like all AI models, GRIN-MoE has its limitations. It performs best with English-language inputs and may not be as adept at conversational tasks. This highlights an important point: as AI models develop, it's becoming increasingly crucial for finance professionals not only to learn how to use AI but also to understand the differences between various models and how to best apply them to specific tasks.

This new layer of AI literacy is becoming a critical skill in finance. Different models have distinct strengths and weaknesses – one might excel at natural language processing for market sentiment analysis, while another might be better suited for mathematical modeling in risk assessment. Understanding these nuances will be key to effectively leveraging AI in finance and could become a significant competitive advantage.

As we continue to navigate the evolving landscape of AI in finance, staying informed about these developments is crucial. We'll keep monitoring the implications of new AI models for the finance sector, helping you understand not just what's new, but how it can be applied practically in your work. In the world of AI-enhanced finance, the ability to choose and use the right AI tool for each task is becoming as important as traditional financial acumen.

Ethics Corner: Addressing Bias in AI-Driven Financial Forecasting

As we increasingly rely on AI for financial forecasting and budgeting, it's crucial to address potential biases that can skew results:

1. Data Bias: Ensure your historical data is representative and doesn't perpetuate past inequities.

2. Algorithm Bias: Regularly audit your AI models for unintended biases in their predictions. Simple trick: ask your AI model to give you detailed assumptions it used to form the prediction. Then, challenge the assumptions, not the prediction!

3. Interpretation Bias: Train your team to critically evaluate AI-generated forecasts, questioning assumptions and potential blind spots.

Mitigation Strategies:

• Diverse Teams: Include diverse perspectives in your AI development and implementation process.

• Regular Audits: Implement ongoing checks for bias in your AI systems.

• Transparency: Maintain clear documentation of AI decision-making processes.

• Human Oversight: Always have human experts reviewing and validating AI-generated forecasts.

Closing Thoughts

As we've seen in this issue, the power of AI in finance lies not in replacing human expertise, but in augmenting it. By embracing a balanced approach that combines the best of both worlds, we can unlock new levels of accuracy and efficiency in our financial processes. Remember, the future of finance is not about AI vs. humans, but AI with humans.

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Until next Tuesday, keep balancing!

Anna Tiomina 
CFO & AI Enthusiast