How to Incorporate AI Agents into Your Automation Strategy

Summary

Everyone’s talking about AI agents, and with good reason. The promise of AI agents comes down to their ability to act independently and even make decisions based on complex reasoning.
 
Using AI agents in your automation strategy seems like a natural step, but if they’re not properly integrated into your end-to-end processes, they may not work as well as you’d like them to—or at all. The goal should be to make your processes more efficient and/or minimize repetitive human tasks.
 
However, implementing AI without seamless integration can do just the opposite—think siloed, disjointed workflows that reduce efficiency and degrade customer and employee experiences. Accenture reports that AI is now a leading contributor to tech debt alongside enterprise applications. This issue is expected to intensify, with 52% of organizations planning to increase their generative AI budgets in 2025.
 
Let’s explore how AI agents work, some of the challenges and risks and how to operationalize AI within your existing automation strategy.

How AI agents work in end-to-end automation

Business processes are made up of a series of steps, often executed across a wide variety of endpoints. These endpoints can be people, systems (including AI) and/or devices. Process orchestration ensures that each step happens as intended, and that the process flows seamlessly across endpoints.
 
The challenge is that AI agents are a bit of a wild card. Some processes follow a pre-defined logic, making it easy to predict what will happen next. With an AI-enhanced process, some steps in the process may still follow a planned, logical flow, while others are executed using a large language model (LLM). LLM outputs are more difficult to predict, but at the same time can be beneficial for dynamic, variable processes that don’t need to follow a specific structure.
 
Take for example, insurance claims and customer complaint handling. Automated processes in these areas might include some predefined steps before a handoff to a qualified person to look at the case, figure out what needs to be done, and sometimes do the work. Taking advantage of AI agents can increase the level of automation in these types of hard-to-define processes, freeing up knowledge workers’ time.

Challenges and risks of AI agents

The wild card factor of AI agents can make them especially risky for certain regulated industries that require a high level of auditability and governance over their processes. Today, 84% of organizations say a lack of transparency in applying AI applications within business processes is causing regulatory compliance issues.
 
That doesn’t mean a regulated industry like financial services wouldn't be able to use AI—or AI agents for that matter. Process orchestration provides a design for end-to-end processes, so enterprises can stay in compliance with industry-specific regulations, data privacy laws and emerging AI laws.
 
End-to-end orchestrated processes maintain a visual audit trail, so teams can verify decisions and simplify compliance reporting at any time. Depending on the level of regulation, organizations can scale their use of AI agents up or down, or implement additional human-in-the-loop checks to have more fine-grained control over AI outputs.

Operationalizing AI agents and other types of AI

As described above, process orchestration ensures that end-to-end business processes operate seamlessly, even when they involve AI. This is particularly important because automation impacts so much of the employee and customer experience. If a process is broken or disjointed, it can impact productivity, customer loyalty, and the bottom line.
 
Fortunately, there’s a lot of flexibility in how you define your process logic and process orchestration approach. In cases where you need control, it’s better to follow a pre-defined logic. In cases where there’s more room for variability, you can rely more heavily on agents or blend a pre-defined process logic with AI-powered processes and human checks.
 
The bottom line? You should be able to define how and where AI agents work within your end-to-end processes, audit and trace their decisions, and implement human interventions when you need them. Process orchestration allows you to do all of these things, effectively operationalizing AI within your automation strategy without technical debt or unintended consequences for your business.