Introduction
Choosing the right method for automating business processes—whether using AI agents or rule-based systems—can shape how effectively an organization meets its goals. For example, a financial firm deciding between these options might weigh the need for AI-driven adaptability against the predictability of rule-based solutions, ensuring their choice aligns with both current demands and long-term strategy. For instance, McKinsey estimates that up to 70% of tasks in financial services are still performed manually, where automation could significantly improve efficiency and reduce errors (McKinsey Global Institute, 2022). For instance, a financial services firm might choose an AI agent to rapidly analyze complex legal documentation and recommend compliance frameworks, or a rule-based system to ensure accurate reporting based on fixed regulatory thresholds. Each approach has unique strengths, and the choice depends on factors such as the nature of the task, the environment, and the available expertise.
Rule-based systems, built on predefined logic, excel in structured environments with clear workflows, offering reliability and predictability. AI agents, however, bring adaptability and the ability to learn, making them valuable in dynamic settings. Their implementation, though, requires not only technical expertise but also a deep understanding of the processes they support. Organizations must ensure they have the skills to evaluate and monitor AI systems effectively, as these agents often operate in ways that differ significantly from traditional methods.
Moreover, integrating AI agents into business workflows takes teamwork between technical teams and domain experts. Technical teams focus on building and fine-tuning AI models, while domain experts outline the operational goals and evaluate the outcomes of these systems. For example, technical teams could handle the development and tuning of AI models, while domain experts define operational requirements and evaluate outcomes. A practical structure might include regular review meetings where domain experts provide feedback on AI performance, and technical teams adjust the system accordingly, ensuring alignment with business goals. Troubleshooting and interpreting AI decisions, such as why a portfolio adjustment was recommended, require insights into both the system’s architecture and the business context. Without this dual expertise, it can be challenging to align AI outputs with organizational goals. This paper explores the differences between rule-based systems and AI agents, highlights their applications, and advocates for a hybrid approach to maximize the benefits of both.
Dynamic Environments
AI agents excel in dynamic environments where conditions and requirements frequently change. These systems use advanced learning techniques to adapt and respond to new information, making them ideal for tasks involving human generated content ranging from voice, chat or documents or unpredictable patterns. For instance, an AI agent could analyse marketing reports and documents and match them towards an investement style checking for suitability and compliance.
Static Environments
Rule-based systems shine in static environments with predictable and repetitive tasks. These systems follow clearly defined rules to deliver consistent results, making them well-suited for machine-to-machine interactions such as checking adherance to threshholds or data validation. Their precision and reliability are invaluable for tasks requiring strict adherence to predefined workflows. Many business processes today are predominantly static, such as compliance checks or routine financial reporting. For instance, reconciling transactions with predefined thresholds or generating regulatory reports follows predictable patterns. However, it is essential to evaluate each process individually to determine whether it requires adaptability or strict rule adherence.
Why Not Always AI?
Despite the growing enthusiasm for AI, it is not the best solution for every problem. AI systems can be costly and unnecessary for tasks that do not require adaptability or learning. For instance, a financial services company once implemented an AI system to automate client reporting. However, the system’s complexity far exceeded the needs of the task, resulting in high development costs, ongoing maintenance challenges, and user dissatisfaction. In such cases, a simpler rule-based approach would have been more cost-effective and reliable. For repetitive, straightforward work, a rule-based system is often simpler, more reliable, and cost-effective. Businesses should evaluate task requirements carefully to avoid implementing AI purely for its novelty.
Combining AI and Rule-Based Systems
Using both AI agents and rule-based systems allows organizations to harness the strengths of each approach. AI agents can adapt to changes and update rules dynamically, while rule-based systems ensure stability and precision in routine tasks. For example, in fund administration, AI can interpret new regulatory requirements and modify workflows, while rule-based systems ensure accuracy in execution.
Understanding and Monitoring AI Agents
AI agents require a deep understanding of the tasks they perform to ensure their effectiveness and reliability. Unlike rule-based systems, which operate on predefined logic, AI agents adapt to data and evolve their behavior based on learning models. This flexibility introduces a layer of complexity in evaluating their performance.
For instance, troubleshooting why an AI agent made a specific decision in a portfolio adjustment scenario requires insights into the agent’s learning process and data inputs. This often demands a skill set distinct from those who use the system, such as portfolio managers or compliance officers.
Moreover, like rule-based systems, AI agents need to scale to justify their investment. Scaling these systems, however, depends not only on increasing computational resources but also on ensuring their outputs remain interpretable and aligned with business objectives. Without proper monitoring, scaling AI could lead to unintended outcomes that undermine trust and effectiveness.
To bridge this gap, organizations must establish effective collaboration between technical teams who understand AI architecture and business users who rely on its outputs. This collaboration ensures the system’s behavior aligns with operational goals and regulatory standards.
Questions to Guide Your Decision
When deciding between AI agents and rule-based systems, consider these key questions:
- Is the environment predictable or changing?
- Example leaning towards static: The monthly reconciliation of fund accounts with custodian banks typically involves standardized formats and clear workflows. Rule-based systems excel here, ensuring accuracy and adherence to predefined steps.
- Example leaning towards dynamic: A portfolio management team adjusting asset allocation strategies in response to real-time market data benefits from AI agents, which can analyze trends and provide adaptive recommendations.
- How complex is the task?
- Example leaning towards static: Generating standard reports for regulatory compliance, such as Solvency II filings, follows a clear template and rules, making it ideal for rule-based systems.
- Example leaning towards dynamic: Analyzing and detecting unusual trading patterns across multiple markets requires AI agents to identify potential risks and anomalies dynamically.
- What resources and expertise are available?
- Example leaning towards static: A mid-sized asset management firm with a small IT team might prefer rule-based systems for automating client onboarding, as these systems are easier to maintain and require less specialized expertise.
- Example leaning towards dynamic: A global investment firm with access to a skilled AI development team may implement AI agents to analyze market data and predict trends, leveraging advanced resources to gain a competitive edge.
- Will the system need to grow or adapt?
- Example leaning towards static: Managing internal approval processes for executing contracts and checking their uses are stable processes that do not require frequent updates, making rule-based systems suitable.
- Example leaning towards dynamic: AI agents can play a role in detecting insider trading by analyzing transactional and communication data to uncover hidden patterns indicative of suspicious activity.
- Is the interaction human-to-machine or machine-to-machine?
- Example leaning towards static: A system that processes trades between the asset manager and custodian banks benefits from the predictability of rule-based automation.
- Example leaning towards dynamic: Supporting a team in filing documents after reading them and comparing them to other existing information will allow an agent to propose metainformation for a more consistend filing methodology.
Answering these questions with specific examples helps businesses determine whether AI, rule-based systems, or a combination is the right choice.
Conclusion
Choosing between AI agents and rule-based systems requires a clear understanding of the task and its environment. Visualizing the suitability of tasks for each approach can further clarify decision-making. For instance, dynamic tasks like client communication or fraud detection align well with AI agents, while static tasks like regulatory reporting or data reconciliation fit rule-based systems. See the accompanying chart for a breakdown of task suitability.
Conclusion Detailed
AI agents provide adaptability for complex and evolving tasks, while rule-based systems deliver reliability and consistency for structured processes. A hybrid approach integrates these strengths, addressing a spectrum of business needs. By continuously reassessing their processes and leveraging visuals or data-driven analyses, businesses can make informed automation decisions that align with their strategic goals and regulatory requirements. AI agents provide adaptability for complex and changing tasks, while rule-based systems deliver reliability for structured processes. A hybrid approach combines these strengths and is often a good, second step when remodeling processes.
Business processes often involve multiple steps, each with unique requirements. Some steps may be better suited to rule-based systems, while others benefit from agent-based approaches. Imagine a financial services firm launching a new product line: a rule-based system could handle static processes like automating threshold checks for compliance, while an AI agent could streamline onboarding by analyzing client needs and proposing tailored investment portfolios based on real-time data. For example, a fund accounting company outsourcing its core accounting functions to a third-party provider must still perform regulatory compliance tests, such as NAV validation. A rule-based system could efficiently handle threshold checks on financial data, while an AI agent might analyze legal documentation to recommend static rulesets for onboarding new products, enabling quicker deployment of services. For example, in client relationship management, a rule-based system could automatically schedule reminders for client meetings based on predefined intervals, ensuring regular touchpoints. Meanwhile, an AI agent could analyze client interaction history and market data to suggest personalized investment opportunities, creating deeper engagement and value. For example, in monitoring investment compliance, a rule-based system can efficiently evaluate whether specific numbers fall within defined thresholds in a machine-to-machine interaction. However, an AI agent could operate in the same hybrid environment by analyzing legal documentation and proposing static rulesets for implementation, significantly accelerating the onboarding process for new projects.
