What is Intelligent Automation?

Intelligent automation has moved beyond mechanical flows. What used to require a custom script or API now runs through AI agents that understand structure and adjust as needed.

Gartner estimates that by 2028, 33% of enterprise software will include agentic AI powering some form of automation, up from less than 1% in 2024.

Traditional automation only works when every step happens exactly as expected. A purchase order with a new layout or a late approval can stop the process in its tracks. Intelligent automation (IA) keeps workflows moving by adapting on the fly.

IA connects to the ERP, CRM, or workflow tools already in use, reads what comes in, decides what to do next, waits when it must, and resumes automatically.

This article examines how intelligent automation functions in real operations, the areas where it delivers the fastest returns, and approaches for piloting it without replacing established systems.

What is intelligent automation?

Intelligent automation, also known as intelligent process automation, combines artificial intelligence with robotic process automation and related tools to run complex workflows.

It uses technologies such as machine learning and natural language processing to read information, interpret it, and act on it inside business systems.

Unlike fixed-step automation, it can adapt while running. It keeps track of what has happened, changes the next action when inputs differ from expectations, and continues until the task is complete.

For example, an AI agent in customer service could:

• Pull the customer’s account from the CRM
  
• Check live delivery status in the logistics system
  
• Escalate to the correct team if a delay is detected
  
• Send an update once the issue is resolved

All of this runs as one continuous process, without stopping for new instructions.

Different Types of Intelligent Automation

Intelligent automation can be implemented at various scales according to the demand. The table below showed the main types of intelligent automation:

Key Benefits of Intelligent Automation

Work keeps moving even when people are busy

In most businesses, orders, invoices, or approvals often sit untouched because someone is out sick or juggling other tasks.

Intelligent automation holds the process open and picks it back up the moment the missing piece arrives. That means fewer “stuck” tasks and customers getting what they need faster.

Lower overhead from constant rework

Every time staff re-enter data or fix mistakes, it adds cost. IA keeps records consistent, so if a customer updates one field mid-order, the rest of the process continues without rework.

More accurate handoffs between teams

Departments often work on different versions of the same data. IA checks the latest values before acting, so handoffs happen cleanly.

The process can be monitored better and understood through simple metrics such as time spent on each query per task and how satisfied are the employees and customers with the resolution.

How to implement intelligent automation?

The best results come from introducing intelligent automation in small, targeted phases before scaling across the business.

Step 1: Identify one process with clear friction

Look for workflows that create recurring delays or manual rework. Examples include:

• Invoice processing with frequent data mismatches
• Purchase order approvals that stall on choke points
• Multiple appointments not being booked due to agent unavailability
• Customer escalations that bounce between departments

Step 2: Layer IA into existing systems

Keep ERP, CRM, and RPA platforms in place. Intelligent automation software and tools can plug directly into the workflows as the controller.

This avoids the risk of replacing core systems outright. Some common intelligent automation tools that can help deploying a strong pilot are Botpress, Langchain, Autogen, CrewAI and Make.

Step 3: Run a controlled pilot

Start small. Test the automation on a limited workflow and track results.

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An example pilot could address something like invoice processing in finance. Run IA alongside your current process for a month.

Track how many invoices are cleared automatically, how many still need human review, and what that does to payment times.

Step 4: Expand to interconnected workflows

Once the pilot succeeds, scale into processes that span multiple systems. At this stage, automation handles human delays, exceptions, and varied inputs with little oversight.

This phased rollout keeps cost under control. Using connected systems and scaling to changing environments, while pilot results provide evidence for further investment.

Top 5 Intelligent Automation Tools

1. Botpress

Best for: Teams building automations that stay active between steps and continue when new input arrives, even if the workflow was triggered earlier.

Pricing:

• Free Plan: Includes core builder, 1 bot, and $5 AI credit
• Plus: $89/month — flow testing, routing, human handoff
• Team: $495/month — SSO, collaboration, shared usage tracking

Botpress is a platform for building AI agents that operate across systems. Each agent runs as a structured flow that can resume from any point by evaluating the task based on its current state on it’s own.

Agents are built using a visual editor or through code. Each step in the flow performs a specific operation — parsing a message, calling an external API, processing a document, waiting for human input, or sending a result downstream.

The agent moves forward based on current data and maintains execution context throughout. The easy drag-and-drop setup can test a prompt, change a condition, or update tool logic while keeping the rest of the workflow stable.

Agents keep track of where a task left off so it can resume later without restarting. If a required value is missing mid-run, the agent can request it directly from the user and continue once it’s provided.

Key features:

• Workflows that hold state and resume after delays or partial input
• Built-in ability to request missing data mid-run
• Structured file and table support for knowledge-based decisions
• External API calls and tool actions inside agent flows

2. LangChain

Best for: Teams building AI agents that need full control over logic, tool usage, and execution behavior, written directly in code.

Pricing:

• Developer: Free – 1 seat, 5,000 traces/month, prompt management, basic tracing tools
• Plus: $39/month per seat – team features, higher trace limits, LangGraph dev deployment
• Enterprise: Custom – self-hosted or hybrid setup, SSO, support, and usage scaling

LangChain is a Python framework for building agents that run logic based on what they observe at runtime. Instead of following predefined steps, the system evaluates context, decides what tool to call, and keeps looping until the task is complete or a stopping condition is met.

By using the framework users define how agents reason, which tools they can use, and how to route decisions based on intermediate results. The agent doesn’t assume a single input or a fixed outcome — it works toward a goal by interacting with external systems and refining its plan step by step.

LangChain works best when automation requires flexible logic. A flow might need to decide which database to query, extract unstructured input from a document, then run multiple retries if the result doesn’t meet a certain threshold. 

Because it’s code-first, it’s not suited for fast prototyping. But it gives full control over tool selection and API behaviour which is essential in complex, high-stakes automation.

Key Features:

• Agent logic defined in code with full control over planning and retries
• Tool usage and memory behavior that adapts at runtime
• Support for structured outputs, custom prompts, and tool chaining
• Native integrations with language models, vector stores, and APIs

3. CrewAI

Best for: Teams structuring automations around multiple AI agents that take on different roles and coordinate tasks through clear, conversational steps.

Pricing: 

• Free: $0/month – 50 executions, 1 live crew, 1 seat
• Basic: $99/month – 100 executions, 2 live crews, 5 seats
• Standard: $500/month – 1,000 executions, 2 live crews, unlimited seats, 2 onboarding hours

CrewAI is a Python framework for building workflows that rely on more than one agent. Each agent is assigned a role and a responsibility — like researcher, writer, reviewer, or controller — and those agents work together to complete the process.

This “crew” model simplifies the logic. Instead of writing one complex agent that handles every tool and condition, user can define a crew that divides the work. Each agent has its own memory, its own tools, and a defined way to speak to others in the system.

CrewAI handles sequencing and communication. Once the flow starts, the agents pass tasks between each other until the goal is met. The process is transparent, and the handoffs are readable, which helps when while debugging or adding new steps.

It’s easy to get started. Roles are defined in a config file, tools are just Python functions, and the coordination pattern makes complex automation feel lighter — especially when things change mid-run.

Key features: 

• Agent roles defined by task, tool access, and communication rules
• Runs as a crew with state passed between agents, not a single chain
• Clear config structure for defining responsibilities and flow logic

4. AutoGen

Best for: Teams building automations where agents need to exchange information mid-run and adapt behavior based on back-and-forth interaction.

AutoGen is a multi-agent framework built around conversation — not just between user and model, but between agents themselves.

It works best when automation requires agents to verify results, double-check assumptions, or decide which tool or action makes sense next.

Much like CrewAI, Autogen let’s the users create an agent group, define their roles, and set up how they interact. Agents can respond to each other with plans, code, intermediate results, or follow-up questions.

This setup is useful when the right answer isn’t known in advance — like choosing between APIs, fixing an error in execution, or rewriting a failed action plan. AutoGen handles all of this through message passing rather than fixed rules.

Key Features:

• Agent-to-agent communication through message loops
• Planning and verification handled inside conversation threads
• Supports code execution, tool calls, and context injection
• Good for automation where follow-up is required at runtime

5. Make

Best for: Teams building structured automations with tool calls, branching paths, and clear visibility into how data moves between steps.

Pricing: 

• Open Source: Free – includes full framework, Apache 2.0 license
• Pro Edition: Free – up to 1,000 conversations/month with Rasa Pro
• Growth: From $35,000/year – includes Rasa Studio, support, and commercial

Make is a no-code automation platform built around visual scenarios. Each scenario consists of modules connected on a canvas, where each module performs a single task — sending data, transforming content, triggering a service, or calling an AI model.

What makes Make relevant to intelligent automation is its ability to manage flows that don’t follow a fixed path. Scenarios can pause, branch, retry, or wait for input without discarding earlier steps. Inputs can be incomplete, arrive out of order, or change mid-run.

The interface presents data movement and step execution clearly. Failures are traceable, inputs are visible at each point, and logic remains editable even after deployment. Scenarios can grow in complexity without becoming opaque.

Make integrates with a wide range of external systems and supports extensions through custom modules. It is suited for workflows where control, flexibility, and traceability are required across multiple tools.

Key Features:

• Visual builder with branching, scheduling, and retries
• See what data moved where
• Built-in error handling for unstable or late-stage inputs

Key Components of Intelligent Automation

Robotic Process Automation (RPA)

Robotic process automation is the execution layer that mimics human actions in a digital interface — clicking buttons, opening a file, entering data, or copying values between systems. 

Many older systems — or those designed only for human use through a screen — do not have this capability. In those cases, RPA works by operating the software the same way a person would, clicking through menus and filling in fields so the task can still be completed.

Large Language Models (LLMs)

When intelligent automation needs to understand instructions, work out the next steps, or explain results, large language models are the component that makes it possible. They add the ability to reason through a process and communicate outcomes in clear language.

In practical terms, LLMs can be responsible for these specific roles in a process:

• Understand and break down a request into smaller steps
  
• Pull the right data or context for each step
  
• Decide which tool or system to use next
  
• Generate clear, human-readable responses or summaries when needed

Finding the best LLMs depends on the setup — factors like data privacy, integration options, and workflow complexity all influence which model will perform best in a given environment.

Machine Learning (ML)

Machine learning models in an intelligent automation pipeline handle more specific, data-driven tasks that improve how automation behaves. They often work in the background to:

• Predict the outcome of a process or classify incoming data
  
• Detect anomalies when a process starts drifting from normal
  
• Monitor system performance over time to maintain accuracy and efficiency

The ML models may not involve LLMs or natural language processing at all. Their role is to give the automation better awareness and decision-making signals through numbers so it can respond appropriately in real time.

Intelligent Document Processing (IDP)

Intelligent document processing is how AI reads unstructured files — from scanned forms to handwritten notes — and converts them into data automation systems can use.

IDP step has historically been one of the most resource-intensive parts of intelligent automation, with each stage of parsing carrying its own complexity and cost.

To give a sense of how document parsing has changed, here’s a quick comparison between what was typical in 2019 and what’s standard in 2025 using LLM-based methods:

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Parsing costs and format support are based on recent benchmarks from LlamaIndex, which tested LLM-based document understanding across scanned inputs, layout-rich files, and retrieval use cases.

API Integrations and Tool Execution

APIs let different software exchange information directly. In intelligent automation, they are used to carry out actions such as submitting forms, scheduling events, creating tickets, or updating records.

The automation decides what to do — often based on parsed documents or RPA-defined steps — and then calls the right API to complete the task. Once the action is taken, the process continues without human input.

Whether the task is simple or dynamic, the core idea is the same: once the automation knows what should happen, it needs a way to act and the API provides a safe and secure way to do it, while maintaining records for future reviews. 

Authorization and Security (OAuth, MCP)

Automation systems act on real accounts, access sensitive tools, make updates in live environments, and most importantly, represent the integrity on the owner’s behalf.

That means every step needs the right level of access, and more importantly, the agent needs to know who (or what) did what.

• OAuth (user-granted access): Used when an automation needs to act on behalf of a human. It provides time-scoped tokens tied to a user’s permissions.
  
• Model Context Protocol-style service identity (machine-to-machine): A way for machines to authenticate with each other directly, like a digital badge, without a human in the loop. 

The exact setup depends on the environment and compliance requirements.

What is the difference between intelligent automation and RPA?

Robotic process automation (RPA) is built for repeatability. It follows set rules to automate tasks like copying data between fields, moving files, or filling out forms. These bots work well when the steps are always the same and the inputs are predictable.

Intelligent automation (IA), instead of following fixed scripts, uses AI to respond dynamically, choosing actions based on context, handling edge cases, and coordinating tools across multiple steps.

Imagine processing an invoice through an enterprise resource planning chatbot.

• An RPA bot pulls totals from fixed fields and drops them into a system. If the format changes, it breaks.
  
• An IA system reads the document, understands what’s there, flags edge cases, and chooses where it goes — even if the layout is new.

The core difference: RPA completes tasks that are already mapped out. IA figures out how to complete them while running.

Add AI Automation to Everyday Tasks

Most businesses already have repeatable routines — approving orders, updating records, moving files around. The problem is those routines only work when every step happens exactly as planned.

AI agents make these workflows flexible. They can wait for missing info, pick up again when something changes, and keep the process moving instead of forcing your team to start over.

You do not need to replace what you already have. AI layers on top of your current tools, stepping in only when needed while the rest of the process keeps running smoothly.

Start building today — it’s free.