Artificial intelligence (AI) is changing the way businesses operate, especially when it comes to automation. AI automation takes traditional, rule-based automation a step further by allowing systems to learn, adapt, and make decisions on their own. In this guide, we’ll explain what AI automation is, how it works, and how businesses are using it to save time, reduce costs, and improve accuracy across operations.
What is AI automation?
AI automation uses artificial intelligence to perform tasks that normally rely on human judgment, like decision-making, problem-solving, and analysis. Unlike traditional automation, which depends on preset rules, AI automation learns from data, adapts to new information, and handles uncertainty. By applying technologies such as machine learning and natural language processing, it helps businesses streamline operations, boost efficiency, and make smarter decisions.
How AI automation works
AI automation combines data, intelligence, and action to streamline processes that would otherwise be completed manually by humans.
For example, procurement AI tools can help identify suitable vendors, while AI automation for digital payments can handle tasks like fraud detection and payment processing.
Naturally, it's a complex technology, but here’s how it works at a high level:
- Data input: The AI automation system gathers information from different sources, like invoices, receipts, or customer records.
- AI interpretation: Leveraging technologies such as machine learning and natural language processing (NLP), the AI system analyzes the data, identifies patterns, and determines the next steps to take.
- Automated action: Once the system makes a decision, the action is carried out (such as categorizing an expense, setting a reminder, or updating an accounting record.
- Continuous improvement: A key part of AI automation is ongoing learning. Each new data point and outcome is fed back into the system, helping it refine accuracy and adapt to changing scenarios.
Within this cycle, AI brings specific capabilities that make automation more powerful. These capabilities include:
- Triggering workflows when a defined event occurs, such as a payment being due or an invoice being received
- Understanding and processing data from both structured and unstructured sources
- Making decisions by weighing information against learned patterns or business rules that you have specified
- Interacting with systems by updating databases, notifying stakeholders, or initiating approvals across platforms (or raising approval requests where necessary)
These advanced capabilities turn traditional, rule-based automation into intelligent workflows that can handle both routine and complex tasks.
Traditional automation vs AI automation
Both traditional automation and AI automation aim to reduce manual work, but they differ in how they operate and what they can achieve.
AI essentially takes traditional automation to another level, providing greater sophistication across four areas:
- Rules vs. learning: Traditional automation is rule-based, following strict, pre-programmed workflows. AI automation learns from data and can adapt workflows and rules as conditions change.
- Data handling: Traditional systems handle structured, predictable inputs but struggle with unstructured data. AI can process unstructured information, such as scanned documents, emails, or natural language.
- Decision-making: Traditional automation is built to execute tasks. It can’t decide what to do next. AI automation, on the other hand, analyzes patterns and can make context-driven decisions.
- Flexibility: Traditional automation breaks when workflows shift or new variables appear. AI automation adjusts to new scenarios and improves over time, meaning it's much more flexible to changing circumstances.
Given these major differences, traditional and AI-powered automation are implemented in very different situations.
While traditional automation is best for repetitive, stable processes that need to be handled at scale, AI automation is a better fit for managing complex, dynamic workflows that require judgment and adaptability.
Examples of AI automation
To understand the full impact of AI automation, it's helpful to examine some examples of its use in practice.
AI automation is being applied across industries to improve efficiency, accuracy, and customer experience. Common examples include:
- Customer services: AI chatbots and virtual assistants can understand text and voice messages, resolve common issues, escalate more complex cases, and provide around-the-clock support to international audiences.
- Content creation and marketing: AI content creation tools can perform tasks like drafting reports, generating personalized emails, optimizing ad and landing page copy, and suggesting ideas for social media content.
- Finance and operations: From invoice matching and expense approvals to compliance checks and fraud monitoring, AI automation streamlines processes that once required hours of manual work.
- Data processing and analysis: AI systems can analyze documents or other forms of unstructured data sets to extract details, classify information, and generate insights to speed up otherwise manual tasks like financial forecasting, trend analysis, and risk detection.
- Human resources and workforce management: AI systems can scan resumes, schedule interviews, and automate onboarding paperwork, helping HR teams focus on people instead of processes.
Benefits of AI automation for businesses
AI automation can create a measurable impact across a company's operations. While the extent of that impact depends on where and how it is implemented, most companies see the following benefits:
- Increased efficiency and productivity, with routine tasks being completed faster and teams being freed up to focus on more strategic work
- Reduce costs and resource optimization as a result of fewer errors and the ability to scale more rapidly without proportional staffing increases
- Enhanced decision-making through analysis of large datasets and AI’s ability to surface trends and actionable insights
- Better customer experience where AI automation is used to provide personalized interactions and round-the-clock service.
Together, these benefits allow businesses to operate more efficiently, adapt to changing conditions, and stay competitive in demanding markets.
Key applications of AI automation
As AI automation becomes more and more common, it's continuing to be adopted in various parts of different kinds of businesses.
Some of the most common and valuable applications include:
- Streamlining financial processes such as invoice matching, payment approvals, expense categorization, and forecasting
- Improving customer service with AI chatbots and virtual assistants that allow support teams to focus on higher-value or more challenging interactions
- Automating marketing campaigns with personalized emails, tailored recommendations based on customer behavior, and optimized ad targeting
- Monitoring transactions and detecting anomalies to protect against fraud and maintain legislative compliance
How to implement AI automation in your business
Let’s now turn our attention to how you can leverage AI automation in your own organization, starting with a manageable five-step process.
1. Define your goal
Begin by clarifying what it is you’re actually looking to achieve by implementing and investing in AI automation.
For example, you may want to:
- Cut costs
- Leverage accounting AI to reduce manual errors
- Improve support response times
- Free up accounts payable employee time for more strategic work
2. Identify suitable processes for automation
Not every workflow is a good fit for AI automation.
Look for processes that are:
- Repetitive
- High-volume
- Easy to apply rules to (though AI can work without strict rules)
- Data-heavy
3. Choose the right AI automation tools
As you implement AI automation, you’ll likely need to onboard some new software tools or replace some old ones.
When evaluating options, consider these criteria:
- Integration: Ensure the platform connects seamlessly with your existing systems, such as an AI accounts payable tool that integrates with your ERP, CRM, and accounting software.
- Scalability: Choose tools that can handle increased data volume and user demand as your business grows.
- Transparency and control: Look for audit trails, explainable decision-making, and options for human review when confidence is low.
- Security and compliance: Confirm the vendor follows strict data protection standards, especially for sensitive financial or customer data.
- Ease of use: Select platforms your team can adopt quickly without needing extensive technical training.
4. Pilot on a small scale
Don’t try and automate at once.
Instead, start with one high-impact process or begin with a single department. For instance, you might start by deploying AI automation in accounts receivable before expanding into AP and accounting.
Running a pilot alongside your current workflow lets you measure results, identify issues, and build internal confidence before scaling further.
5. Monitor and refine
AI automation is rapidly improving, meaning you’ll need to pay close attention not only to the workflow performance but to new capabilities that you can leverage.
Track KPIs such as accuracy rates, exception volumes, and cycle times. Use this data to fine-tune rules, retrain AI models, or adjust workflows for better outcomes.
Future trends in AI automation
AI automation is rapidly advancing, and its influence on business will continue to grow.
We’ll likely see capabilities in natural language processing and generative AI continue to develop, and predictive capabilities should continue to improve as AI models learn from their own successes and failures.
Compliance and governance will also be a key focus as AI develops and is embedded into critical systems. Transparency, auditability, and clarity around ethical practices will be essential to building consumer trust and maintaining compliance.
Individual industries will differ in how they develop and adopt AI automation:
- In finance, AI will drive faster reconciliations, real-time cash flow forecasting, and automated compliance reporting
- In customer service, more advanced chatbots will resolve a wider range of issues
- In marketing, AI will eventually orchestrate end-to-end campaigns with minimal human input
Beyond efficiency, AI automation will become central to decision-making. Businesses will use it to uncover opportunities, predict risks, and design more agile strategies. Rather than being an add-on, AI will become a core driver of how organizations compete and grow.
Discover how AI is used in financial operations
AI is at the heart of the future of automation. It transforms simple, rule-based automation into smart, adaptable, flexible workflows that support decision-making and reduce administrative burden.
BILL's AI for finance is built to support:
- Faster payment processing
- Streamlined expense management
- Improve protection against forward
