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Companies use many AI and automation software tools. Each tool has a different purpose but often, these tools work together to increase efficiency. In this blog, we’ll explore the differences between AI and automation, how they can complement each other, and how they can combine to become intelligent automation. We'll also cover four real-world examples of how they work together and highlight the benefits of using AI in business process management.
AI vs. automation—What’s the difference?
Let’s start by clarifying the difference between artificial intelligence and automation.
The International Society of Automation defines automation as “the creation and application of technology to monitor and control the production and delivery of products and services.” This includes many technologies like scheduling, expert- or rule-based systems, and programmed conditional logic. These technologies perform repetitive tasks without human help. These tasks may be rule-based, such as using robotic process automation (RPA) or business rules. Automation can also occur at the task-level or be part of a broader process that passes work between automation technologies and humans. Its primary goals are to save time, reduce risk of human error, and free up workers for more high-value tasks.
AI refers to systems that mimic human intelligence and includes specific types of automation called “cognitive automation.” Artificial intelligence analyzes unstructured data, learns from it, and makes decisions without being explicitly programmed for every scenario. AI technologies can adapt to new situations by learning patterns and improving performance over time. It’s worth noting that artificial intelligence can come in several subtypes such as:
Generative AI, which creates new content like text or photos.
Natural language processing, which understands and interprets human language.
Machine learning, which improves predictions over time based on patterns.
In short, business process automation tools handle everything from routine, rule-based tasks to more complex problems that require learning and decision-making.
Automation using AI, and the results it unlocks
AI plus automation is a strong combination that helps businesses automate not only repetitive tasks but also tasks that require some level of analysis, decision-making, and learning.
Embedding AI within a process puts essential guardrails for automation technologies in place, making them more powerful, more efficient, and with a lower risk of error. It provides structure by giving bots and AI agents clear goals, defined workflows, and established escalation paths to ensure smooth operations. And it allows you to measure ROI, refine performance, and stay in control.
Here’s a simple breakdown of AI workflow automation:
Automation: Performs routine tasks and maps processes with known inputs, variables, and outcomes.
AI: Learns from data and makes decisions based on limited information.
AI + automation: Combines AI and automation tools like RPA to automate and streamline more complex tasks and processes that involve decision-making and learning.
How can AI can be used in business automation? Four real-world examples.
Let’s dive into some real-world examples where artificial intelligence and automation are working together to streamline processes.
1. Customer onboarding and KYC
Know-your-customer (KYC) checks often stall onboarding, and data extraction is the culprit. Pulling from identity documents, ownership records, and news coverage about the applicant all adds up. For instance, assigning data retrieval to automations and OCR, and the comparison task to AI with thresholds or keywords to flag for a human – is a highly effective approach. Banks reduce time processing applicants while maintaining a clear audit trail for regulators.
2. Accounts payable exception handling
When an invoice fails to match its purchase order and receiving record, the process snags. Someone has to pull multiple documents, review vendor history, and identify the root cause. By comparison, an AI agent can do that research, identify the likely cause, and draft the query to the vendor – so a human simply reviews and pushes forward. Fringe cases like this shouldn’t consume the team’s time and expertise when AI can manage the administrative tasks.
3. Application intake and eligibility
Caseworkers can spend hours sorting benefits application paperwork before they evaluate a single case. Many of these steps can be automated traditionally: request missing items from the applicant, route complete cases into queues. AI takes it a step further, checking each packet for completeness, extracting the eligibility data, and flagging missing or conflicting documents.
Eligibility decisions remain with caseworkers, but they start from clean, verified files.
4. AI for legacy system automation
Many core systems like mainframes and older ERP applications have no API. Bots reach them by replaying recorded steps on screen. But if a screen or interface changes, the process grinds to a halt. A ticket is generated for someone to rewrite the bot’s script.
Agentic RPA gives the bot a fallback at the steps most likely to break. When a bot can’t navigate a screen, an AI model reads the new screen and picks the next action. Processes on those systems keep moving while teams lose fewer hours to fixing bots and the delays that follow.
What are the business benefits of AI automation?
There are several key benefits to integrating AI with other business process automation technologies.
Increased efficiency. AI processes vast amounts of data quickly, identifying patterns and optimizing workflows. This leads to faster, more accurate results. Automating with AI frees up employees to focus on more value-added tasks and improves employee experiences. It also reduces errors by cutting down on copy-paste chaos during data entry, so less rework is needed.
Cost reduction. By reducing human error, streamlining processes, and minimizing downtime, AI-driven automation helps businesses cut costs. Predictive maintenance, for example, significantly reduces the cost of unplanned machinery breakdowns. By adding AI to compliance processes, banks reduce the risk of being fined by regulators.
Scalability. AI-powered automation can scale with your business. Whether you're handling customer service, finance, or HR, AI-driven automation handles increasing amounts of data without compromising accuracy or speed.
Reduced risk of error. AI automation cuts down on handoffs to humans for input and streamlines workflows. With repeatable and reliable guardrails already in place, AI automation gets it right the first time and avoids costly errors.
In-process governance. AI inside a process provides structure by giving AI agents clear goals, a defined workflow, and the established governance required to meet critical regulatory standards.
Audability by design. The right AI operations don’t compromise on governance. Processes, applications, and agents with built in controls ensure compliance and auditability.
The future of AI automation
Artificial intelligence and broader automation tools like bots and document extraction tools work best when combined with intelligent automation. By leveraging AI’s ability to learn, adapt, reason, and create, businesses are making their automated processes smarter and more efficient. Whether it’s in customer service, HR, or finance, intelligent automation offers enterprises a way to streamline processes, cut down on busy work, reduce costs, and improve decision-making.
As artificial intelligence grows, AI process automation will offer more possibilities. AI agents are now acting as digital workers, making decisions within a set of boundaries to perform tasks with speed and accuracy. Businesses that embrace the combination of artificial intelligence and automation are well-positioned to thrive in an increasingly competitive world.
