AI Business Process Automation – Benefits, Use Cases, and Best Practices

Artificial Intelligence (AI) in processes automation stands out for its flexibility and ability to work with literally hundreds of cases. With advanced AI capabilities, such as computer vision and machine learning, companies can optimize their work at multiple levels, which results in reduced operational costs and better decisions that improve overall productivity. So, what is AI-driven automation and in which ways does it make the difference? Here, we got you covered.

What is AI business process automation? The basics

AI business process automation means using AI development techniques to automate and optimize workflows that typically require human involvement, such as accomplishing repetitive tasks and making decisions based on data.

What are the key elements of AI-driven business process automation?

• Generative AI and Large Language Models (LLMs) are indispensable parts of an AI-powered automated system that create new content, including images and code.

• Natural Language Processing (NLP) allows AI-powered BPA systems to read, understand, and respond to human language not only in text-based conversations but also in spoken and handwritten communication.

• Computer Vision (CV) and Optical Character Recognition (OCR) make it possible for AI software to recognize and interpret visual information, such as images, photos, and camera footage.

• Machine Learning (ML) lets AI systems classify information and identify patterns for making predictions based on historical and real-time data.

• Workflow orchestration engines unite all the processes across an AI system to make them work according to established logic and business process management strategy.

AI-driven automation vs. Traditional business process automation

AI-powered automation, unlike standard business process automation (BPA), involves the power of advanced AI techniques to execute far more complex tasks than were possible before. This automation method involves not just following a strict algorithm but reasoning and making decisions based on data and prior model training.

To understand the difference between traditional BPA and AI BPA more deeply, let’s explore the most widespread BPA types.

• Task automation: It’s the simplest range of tools that focuses on dealing with only one repetitive task. As an example of such a tool, consider a script for sending automated emails.

• Workflow automation: This type of automation works with a sequence of tasks considered as a single process. Zapier, Make, and n8n are well-known examples of workflow automation tools that let you create chains of tasks executed by different platforms, such as CRMs, messengers, email providers, and more. These tools typically work through APIs, connecting to the required service or software.

• Robotic process automation: RPA is the next level of automation that mimics human behavior using bots, interacting with software, especially one without an API (otherwise, it’s easier to handle requests through workflow automation). Such bots can fill in the forms, manage accounts, and handle customer feedback, working across multiple systems.

So, how does AI for business processes differ, and where to use it?

How AI-powered automation enhances traditional business process automation

Business process automation with AI goes far beyond out-of-the-box automation solutions or RPA since it makes conclusions from the data you provide it with, and learns over time to make better decisions. To understand how AI in process automation enhances traditional approaches, let’s examine the limitations of each BPA type and how AI addresses these gaps.

• While standard task automation fails in handling unstructured data, such as PDFs, and may commit errors if rules change, AI-driven automation works with any data type, and it can adapt to changes and new patterns over time.

• When it comes to workflow automation tools, they typically cannot handle exceptions within the task chain and require human reasoning for making decisions. AI business process automation, in turn, can make real-time decisions and suggest action for exceptions. On top of that, such a system can use predictive analytics to optimize the workflow for better performance.

• Robotic process automation is a fully rule-based approach that sometimes lacks flexibility, especially when the system changes. AI software adapts to changes more easily based on its self-learning capabilities.

So, is there any reason to use traditional automation methods in the era of AI? Yes, since they still can handle a lot of rule-based processes, plus, many services adapt to changes and implement their own AI-based solutions.

However, the best option is to combine AI techniques with traditional methods to make them work as a whole, and such a combination is called Intelligent Process Automation (IPA). IPA combines RPA and other automation methods with AI capabilities, such as machine learning and predictive analytics, to automate processes that previously were problematic to handle. This way, it works within a broader automation framework that ensures an end-to-end business process.

Use cases of business process automation with AI

AI is applicable across multiple industries and at different levels of business operations, from accounting to sales and marketing. Let’s reveal some of the examples of how to use AI for process automation in a real-world business.

Finance management

You can implement AI for a business automation solution in accounting to reduce manual effort in extracting data from invoices, such as vendor names and invoice amounts, and entering this information into your ERP or CRM. Another way to utilize AI in this field is to classify expenses for enhanced financial management.

Customer service

AI chatbot development is the most obvious way to streamline customer request processing. However, you can also utilize AI for sorting support tickets by department and scanning emails and incoming messages for the level of urgency to reduce churn rates. Explore how the DigitalSuits team created an AI assistant for streamlined property management.

Human resources (HR)

AI-powered automation can improve HR processes at every step, from hiring and onboarding to performance management. At the resume screening stage, AI tools reduce manual work by matching CVs with the company's requirements, ranking them by skill relevancy. At the onboarding step, AI automation can ensure account setup, offer training courses, and provide answers to frequently asked questions. Finally, you can use AI to evaluate candidates based on their achievements and predict which employees are at risk of leaving based on specific parameters.

Check the case study of our client – a technical staffing agency – that reveals the process of implementing AI for recruitment automation. We developed a dashboard with multiple AI-powered automation tools to streamline the processes of matching candidates with companies, CV screening, and much more.

Supply chain management

In retail, AI enables the automatic adjustment of inventory levels based on predictions of product demand, resulting in significant cost savings. For faster and cost-effective logistics, AI can find the optimal delivery routes by analyzing the weather, traffic, and other factors familiar to delivery companies. When it comes to manufacturing, AI allows for predictive machine maintenance based on data received from sensors.

Sales and marketing

AI-powered recommendation engines in ecommerce are no longer optional for boosting customer satisfaction and sales. However, AI automation goes further, analyzing the best channels to promote a particular product and choosing the best times to send a promotional email. On top of that, the sales teams can leverage AI for lead scoring based on actions to focus on ‘hot’ prospects first.

Development and security

AI streamlines software development processes in multiple directions, from analyzing code and identifying bugs to generating comments on code lines and updating documentation. In the field of cybersecurity, AI can automatically monitor systems, find anomalies, and flag suspicious activities.

This is not an exhaustive list of AI automation use cases, as real-world cases are unique and development teams adapt to company requirements to build tailored AI solutions. However, this compilation gives you an idea of how broad AI capabilities can be.

Best practices of AI business processes automation

Next, we explore how companies should approach AI development so that delivered solutions meet their expectations.

Identify the right use cases

The first step is identifying the processes where AI adds real value. It could be the most time-consuming processes that regularly occur and require much manual effort and resources. At the same time, if you’re considering custom development, make sure that traditional BPA can’t handle your tasks. As an example, you can consider using AI if your tasks require interpreting unstructured data or utilizing natural language processing.

Start small, then scale

Begin with solving just one pain point using AI, and once your solution proves to be efficient and shows positive ROI, scale it to handle a broader scope of tasks. For example, you can build an AI chatbot to automate FAQ and scale its work for order management once you get an expected outcome.

Combine AI with other automation tools

AI automation works best when combined with other efficient techniques. Plus, such a combination allows you to cut expenses without compromising quality. For example, you can use RPA to collect data while supplementing it with AI to check for fraud patterns. In the same way, AI automation doesn’t replace ERPs and CRMs but rather enhances insights, improving productivity.

Ensure high-quality data

You need to clean and unify data across the channels you’re planning to use for AI automation to get correct results. Your development team also needs to set up pipelines for effective data improvement over time.

Keep humans involved

Currently, you still need to manually control AI automation to compare the outcome with your expectations. Instead of blindly following AI decisions, it’s much better to involve humans in inspecting responses, especially those that influence business decisions and performance. For example, even if AI flags some accounts as suspicious, to not lose your audience, it would be better if your compliance officer reviews the accounts before blocking.

Check for compliance

Ensure that you use AI for task automation in a way that doesn’t violate customer privacy regulations, such as GDPR, and other government regulations relevant to your business. To achieve this, you must thoroughly document the functionality of your AI software and be transparent about its operations with regulators.

Improve continuously

After AI integration, your team still needs to monitor your software performance and update workflows in line with your business changes. Tracking the KPIs will also help you evaluate the effectiveness of your solution and its growth areas.

Challenges of implementing AI for business automation and how to solve them

To reap the benefits of AI for business automation, it is essential to be aware of the core challenges that may arise and how to overcome them.

• Inconsistent data. If your business data is incomplete or fragmented, this could lead to inaccurate predictions and automation errors. To solve this problem, you should conduct a data audit and use data enrichment techniques to fill in the gaps.

• Integration breakdowns. AI solutions are often designed to integrate with other services, such as CRM systems or legacy applications. However, not every integration works smoothly. If you encounter challenges here, consider using middleware systems, such as RPA or an integration platform-as-a-service (iPaaS).

• Lack of qualified specialists. Since AI process automation is a relatively new field, you may find it challenging to find skilled developers with proven expertise. To hire the right team for your project, look for professionals with a portfolio of accomplished AI projects and honest customer feedback. Check out the success stories of DigitalSuits clients, which reveal the process of creating AI solutions. If it resonates with your vision, contact us to explore potential cooperation.

• ROI uncertainty. Complex AI-driven systems are expensive, and it may be unclear what profit they can bring. To prevent budget losses, begin with small and measurable projects, clearly define KPIs, and once your goal is achieved, consider scaling.

• Staff resistance. Once you have decided to improve your team's productivity with AI, you may find that your employees are resistant to change. To solve this problem, provide your staff with training sessions and, even more, involve them in optimizing the workflow.

The bottom line – benefits of AI in automation

To summarize, let’s pay attention to the main benefits of AI in business process automation.

• AI BPA ensures better productivity since processing of repetitive tasks takes minutes, not days, as with manual work.

• AI, if trained and appropriately validated, allows for fewer mistakes, especially when it comes to calculations.

• With thorough planning, AI integration leads to reduced costs and new business opportunities that allow for higher revenue.

• Thanks to advanced AI capabilities, you have access to smart decision-making that improves outcomes across different fields.

• AI systems are highly scalable, allowing you to handle growing workloads and adapt to changing environments.

If you consider implementing AI technologies into your business, don’t hesitate to contact the DigitalSuits team – proven professionals in AI development. We’re here to help you find the right solutions for your business and implement them for long-term success.