What Finance Tasks Suit AI Agents Best?

This week’s edition is a bit more theoretical—but that’s intentional. To effectively adopt new technologies like AI agents, we need to first master the underlying concepts. Taking the time to learn and stay up to date in this rapidly evolving field is crucial for making informed decisions and leading with confidence.

I’ve also put together recommendations for a course, an article, and a YouTube video. So, if you’ve set aside time to expand your knowledge, I have some great ideas for how you can spend it!

In this edition, we’ll explore a practical framework to match the right AI tools to your workflows while ensuring cost efficiency and team buy-in.

Let’s dive in!

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The Balanced View: Types of AI Agents and Finance Tasks They Can Handle

AI agents have the potential to revolutionize workflows in the CFO office. But, understanding the types of AI agents available and matching them to the right tasks is critical for maximizing their value.

AI agents can be broadly categorized based on their complexity and capabilities. Each type offers unique strengths and is suited to different kinds of finance tasks. Understanding their specific use cases is essential for maximizing their value in the CFO office.

1. Simple Reflex Agents

These are the most basic AI agents, designed to operate solely on predefined rules and conditions. They excel at reacting to specific triggers with precision and consistency, making them highly efficient for tasks that follow clear, repetitive patterns. Simple reflex agents rely on preprogrammed instructions and do not have memory or learning capabilities.

Examples of Finance Tasks:

• Automating responses to common internal finance queries (e.g., “How do I submit an expense report?”).

• Classifying transactions based on pre-set rules, such as expense categories.

• Automating invoice matching or simple reconciliations.

Key Advantage: Immediate efficiency gains with minimal complexity.

Limitations: Cannot handle tasks requiring learning, adaptation, or context awareness.

Use When: Tasks involve straightforward, repetitive rules without variability.

Don’t Use When: Tasks require adaptation to new conditions or involve unstructured data.

2. Model-Based Agents

Model-based agents incorporate an internal representation of their environment, enabling them to make informed and context-aware decisions. These agents analyze past states, identify patterns, and use contextual data to predict future outcomes, which makes them highly effective for dynamic, data-driven tasks. Unlike simple reflex agents, model-based agents can adjust their actions based on historical insights, allowing them to manage variability and adapt to changing conditions.

Examples of Finance Tasks:

• Predicting cash flow based on historical trends and current inputs.

• Providing suggestions for resource reallocation during budget reviews.

• Identifying anomalies in financial data, such as unusual spending patterns.

Key Advantage: Increased accuracy and adaptability for moderately complex tasks.

Limitations: Relies on high-quality data and may require periodic updates to the underlying model.

Use When: Tasks require historical analysis and context-based predictions.

Don’t Use When: Data is inconsistent, incomplete, or the task requires significant creativity or flexibility.

3. Goal-Based Agents

These agents operate with a specific goal in mind, meticulously planning their actions to achieve the desired outcome. They are capable of evaluating multiple strategies and assessing trade-offs to determine the optimal path forward. Unlike reactive agents, goal-based agents anticipate potential scenarios and proactively adjust their approach, ensuring alignment with the overarching objectives. This strategic planning capability makes them invaluable for workflows where achieving precise, measurable results is critical.

Examples of Finance Tasks:

• Optimizing working capital by determining the best payment schedules for suppliers.

• Automating the creation of financial forecasts based on scenario planning.

• Monitoring and maintaining key performance indicators (KPIs) for financial health.

Key Advantage: Strategic problem-solving capabilities tailored to achieving defined outcomes.

Limitations: May require significant upfront effort to define goals and metrics clearly.

Use When: Clear goals are defined, and the task involves achieving measurable outcomes.

Don’t Use When: Goals are vague, or the environment is too unpredictable for predefined planning.

4. Utility-Based Agents

Utility-based agents are designed to make decisions that maximize overall value by evaluating the utility or benefits of various possible outcomes. Unlike goal-based agents that work toward a single predefined objective, utility-based agents operate with multiple competing priorities, balancing trade-offs to arrive at the optimal solution. This makes them particularly suited for tasks requiring prioritization, resource allocation, and real-time adaptability to changes in input data.

Examples of Finance Tasks:

• Prioritizing investment opportunities by analyzing potential returns under different scenarios.

• Allocating resources dynamically across departments to maximize organizational efficiency.

• Managing complex financial portfolios with real-time adjustments based on market conditions.

Key Advantage: Flexibility and the ability to optimize across competing priorities.

Limitations: Requires a well-defined utility function and significant computational resources.

Use When: Tasks involve competing objectives or require prioritization across multiple variables.

Don’t Use When: Objectives are unclear, or trade-offs are not easily quantifiable.

5. Learning-Based Agents

Learning-based agents are designed to continuously improve their performance by analyzing their own actions and incorporating feedback from the environment. These agents excel in tasks where conditions evolve regularly, requiring adaptability and refinement over time. Unlike static models, learning-based agents leverage machine learning techniques to identify patterns, adjust their approaches, and predict outcomes with increasing accuracy. This capability makes them particularly valuable in dynamic environments where workflows need to respond to new data or changing conditions.

Examples of Finance Tasks:

• Refining credit risk models based on evolving borrower data.

• Enhancing fraud detection systems through continuous learning from flagged cases.

• Improving revenue forecasts by incorporating external economic indicators.

Key Advantage: Continuous improvement and adaptability to changing conditions.

Limitations: Requires careful monitoring to prevent unintended biases or errors from compounding.

Use When: Tasks require continuous learning and adaptation to evolving conditions.

Don’t Use When: Data is scarce, or frequent retraining is impractical due to resource constraints.

6. Hierarchical Agents

Hierarchical agents consist of multiple interconnected layers, each designed to specialize in distinct aspects of a task. High-level agents function as managers, overseeing the broader objectives and breaking them down into smaller, more manageable goals. These sub-tasks are then assigned to lower-level agents, who execute them with precision and report their progress back to the higher levels. Additionally, hierarchical agents facilitate collaboration between levels, ensuring that tasks are completed cohesively and synchronized, even in environments with high complexity and interdependencies.

Examples of Finance Tasks:

• Coordinating the month-end close process with different agents managing reconciliations, variance analysis, and report generation.

• Overseeing multi-entity financial consolidations across regions or departments.

• Managing dynamic budgeting processes by delegating tasks to specialized sub-agents.

Key Advantage: Scalability and specialization for handling intricate workflows.

Limitations: Significant effort is required to design and integrate the hierarchy effectively.

Use When: Tasks involve multi-step, interdependent processes requiring coordination across teams or systems.

Don’t Use When: Tasks are simple or standalone, where hierarchical complexity would add unnecessary overhead.

Steps to Match Agents to Tasks

1. Understand Task Requirements: Begin by analyzing the task in detail. Consider its frequency, complexity, and data needs.

2. Evaluate Risk and Sensitivity: Assess the task’s impact on the organization. High-risk tasks, such as compliance monitoring, may require advanced agents with built-in accountability mechanisms.

3. Match Tasks to Agent Types: Use the framework below.

4. For each task, use the simplest AI agent possible. Avoid overcomplicating solutions, as AI systems consume significant computational resources. 

Begin by deploying more basic agents, such as simple reflex or model-based agents, to handle individual tasks. Experiment with these simpler agents to understand their effectiveness and thoroughly validate their outputs. Once their performance is consistent and reliable, they can be integrated under the supervision of a hierarchical agent, which will coordinate and optimize the workflow across multiple interconnected tasks.

Additional Resources: Articles, Courses, and Videos

To further explore the world of AI agents, here are some excellent resources to deepen your understanding:

1. Anthropic's Post: "Building Effective Agents"

   This post is widely recognized as a must-read guide on AI agents. Everyone seems to quote it for its practical advice on where agents truly excel and, more importantly, where they don’t. It’s a great reference if you want to start on the right foot.

2. LinkedIn Learning Video: "Transforming Business with AI Agents: Autonomous Efficiency and Decision-Making"

   I took this course myself and found it incredibly valuable. The structure is clear, the explanations are spot-on, and the content is highly relevant.

3. YouTube Video: "This 20+ AI Agent Team Automates ALL Your Work"

   This video truly inspired me. The creator has built a structured team of AI agents, with a Manager Agent at the top coordinating a whole department of specialized AI agents. They’re not handling highly complex tasks yet, but this example clearly shows the direction we’re heading.

Closing Thoughts

AI agents hold transformative potential for the CFO office, but their success hinges on selecting the right type for the task at hand. By understanding the strengths and limitations of each type of agent and using a structured framework to align them with finance workflows, organizations can unlock new levels of efficiency, accuracy, and strategic insight.

In the next edition, we’ll expand on this discussion, exploring how to deploy and scale AI agents effectively, including addressing challenges and mitigating risks. Stay tuned!

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO