Process Automation with AI: Faster. Fewer Errors. Smarter Decisions.

Businesses around the world are under immense pressure to make their processes more efficient, scalable, and error-free. Intelligent process automation – the combination of artificial intelligence (AI) and modern workflow automation – holds enormous potential for all businesses. One example is automated invoice processing using an AI model that recognises invoices, matches data, and forwards them for approval. The result: 50% time savings and significantly fewer errors. That’s why, according to the same study, 44% of mid-sized companies plan to adopt automation and AI within the next one to two years to further boost employee productivity. How can this be achieved? Find out in this blog post.

AI & Processes – The Basics of Smart Automation

Artificial Intelligence is no longer a topic of the future – it’s the driving force behind modern process automation. But how does AI actually work? And how does AI-powered automation differ from traditional methods?

How AI Works – Simply Explained

AI systems learn patterns from large volumes of data. They identify relationships and make decisions independently – much like the human brain, but digitally. Instead of relying on rigid rules, AI uses flexible algorithms that evolve with every new input. This makes processes not only faster, but also smarter and more adaptable.

Traditional Process Automation vs. AI-Powered Automation

Traditional automation follows fixed rules: if A happens, do B. It’s effective, but inflexible, and quickly reaches its limits when faced with complex or unpredictable tasks. AI-powered automation, on the other hand, can handle uncertainty, learns from experience, and continuously adapts to new situations. This makes it significantly more agile and powerful.

Processes That Particularly Benefit from AI

AI is especially well-suited to processes involving large volumes of data, pattern recognition, or complex decision-making. Examples include:

• Invoice verification: Automatically detects errors and anomalies in invoices, even in unstructured formats
• Customer enquiries: Intelligent chatbots understand natural language and resolve issues without human intervention
• Document management: Automatically classifies and extracts relevant information from texts
• Quality control: Visual inspection of products using AI-powered image analysis

What Potential Do Automated AI Workflows Offer?

Automated AI workflows take over tedious, repetitive tasks, boosting everyday efficiency. They analyse data in an instant and provide fast, well-founded decisions. Perfect when time is tight – and a real stress-saver. Best of all: AI tailors your workflows to your specific needs by learning continuously. This makes your processes smarter and more personalised.

Efficiency Boost for Businesses of All Sizes

This allows you to allocate resources more strategically and focus on what truly matters. Even today, examples like automated customer service or intelligent document analysis show how AI can ease your workload and free up your time. In the future, such systems will become even better at independently managing complex operations – a real efficiency driver for businesses of any size.

Challenges in Using AI for Process Automation

Artificial intelligence offers tremendous potential for automating business processes, but it also comes with its own set of challenges. One of the key hurdles is the data foundation: AI requires structured, high-quality information – yet in many organisations, data is still incomplete, fragmented or inconsistent.

In addition, AI-driven decisions are often difficult to trace, which can be problematic, especially in rule-based or compliance-relevant processes. Organisationally, much also changes: processes need to be rethought, roles adapted, and employees specifically trained. Ethical and legal considerations also play a crucial role – for example, when it comes to protecting personal data or ensuring transparency in automated decisions. Those who actively address these challenges can lay the groundwork for future-ready, intelligent process automation.

From Traditional Automation to AI Workflows

Traditional process automation relies on clearly defined, rule-based procedures. These are efficient for straightforward, repetitive tasks but quickly reach their limits as complexity increases. This is where AI workflows come into play: they combine proven workflow engines like Camunda or UiPath with intelligent, learning systems.

AI Detects Patterns, Makes Predictions and Takes Decisions

Workflow software continues to manage process logic and sequencing. Meanwhile, AI modules independently detect patterns, generate forecasts and make decisions. In this way, machine learning components enhance classic BPMN processes – for example, through automated document analysis, dynamic prioritisation or intelligent error detection.

Automation That Thinks Ahead and Continuously Improves

The result: processes become more flexible, adaptable and significantly more efficient. Businesses benefit from automation that doesn’t just follow rules blindly, but thinks ahead and continuously improves.

Implementing AI in Workflow Engines

Integrating AI into existing workflow engines enhances process management – and it means more than just automation. Thanks to open APIs and flexible data pipelines, modern AI models can be embedded directly into established process management tools. This turns static workflow processing in workflow management into dynamic, intelligent control.

Data Pipelines Keep Information Flowing

To unlock AI’s full potential, a solid infrastructure is essential: efficient data pipelines ensure a steady flow of up-to-date information, while APIs enable seamless communication between AI and workflow systems. Monitoring tools track the performance of AI modules in real time, ensuring reliability and transparency.

Smarter, More Agile and Future-Proof Process Control with AI

The biggest advantage? Automated decision logic. It dynamically adjusts and optimises processes based on real-time data. As a result, workflows become not only faster and more efficient, but also more flexible and resilient. AI makes your process control smarter, more agile and future-proof.

From AI-Powered Workflows to AI Agents

AI agents represent the future of process automation: they act autonomously, learn independently, and adapt workflows in real time. They analyse data, make decisions, and manage complex processes without the need for constant human intervention. Today, AI agents are already in use – in chatbots, production control systems, and financial tools. In the coming years, they will become even more intelligent thanks to technologies like Auto-GPT and multimodal capabilities. They will process text, images and more, orchestrate complex operations independently, and take automation to new heights. In short: AI agents are the digital assistants of tomorrow – reshaping workflows and making businesses more efficient.

AI Workflows with d.velop process studio and d.velop pilot

In modern process automation, traditional digitalisation is no longer enough. What’s needed, are systems which think ahead. This is precisely where the d.velop process studio, in combination with the d.velop pilot, comes into play. Together, they form a powerful duo for AI-driven automation. With the d.velop process studio, processes can be intuitively modelled, automated and adapted – all without deep IT expertise. And with the d.velop pilot, d.velop’s AI solution, true artificial intelligence enters the picture – intelligence that understands data. Decisions are made based on context and content.

More Than Automation: How AI Is Transforming Processes – An Interview with Andre Thesker, Product Manager of d.velop process studio

Question: What role does AI play in workflows compared to traditional process automation?

Andre Thesker: Artificial intelligence enhances traditional process automation by enabling the efficient handling of complex, non-linear tasks. While conventional automation is mainly used for clearly structured, rule-based processes, AI can respond flexibly to unstructured data and extract valuable insights from it.

A major advantage lies in the reusability of prompt templates, which allow high-quality results to be generated consistently. AI can take on a wide range of tasks, including:

• Document classification
• Data extraction and analysis
• Forecasting and risk assessment
• Process optimisation
• Text generation and summarisation
• Anomaly detection

Question: Which processes are particularly well-suited for AI automation – and which are not?

Andre Thesker: AI is especially effective for processes that are recurring, data-driven and high in volume. Examples include:

• Individual, complex tasks with repetitive elements, such as verifying whether an invoice is covered by an insurance policy
• Summaries that provide users with a quick overview
• Typical use cases:
  • Invoice processing, contract analysis, email classification
  • Credit checks, applicant pre-screening (with appropriate caution)
  • Support ticket categorisation
  • Processes with clear goals and decision criteria
  • Processing of unstructured data such as text recognition, image classification or speech transcription

Less suitable are processes that occur infrequently, are highly context-dependent or carry a high risk of error – especially when data is incomplete or contradictory. Examples include:

• Implementation of a new ERP system
• Conflict resolution in HR
• Contract negotiations with partners or clients
• Legal contract interpretation with legal implications
• Medical diagnoses without clinical oversight

The clearer, more data-driven and standardised a process is, the better suited it is for AI. The more individual, social or context-sensitive a process is, the more difficult it is to automate with AI.

Question: How is the role of employees changing through intelligent process automation?

Andre Thesker: AI primarily takes over repetitive tasks and provides decision support. The final decision – especially in critical matters – remains with the human. This allows employees to focus more on value-adding activities and reduces their workload. Their role doesn’t fundamentally change, but it becomes more effective and strategic.

Question: What prerequisites must companies meet to integrate AI meaningfully into their processes?

Andre Thesker: Several factors are key to successful integration:

• Acceptance: AI is a tool – not magic, and not a replacement for people, but a support system
• Digital information sources: These are essential for training models like RAG or building indexes for context-based responses
• Know-how: Companies need internal or external expertise in AI
• Clear objectives: AI should be applied to specific, well-defined use cases
• Digitalised processes: Only processes that are already digitally mapped can be meaningfully enhanced with AI

Question: What level of data quality is required for AI-supported processes to function reliably?

Andre Thesker: Data should be as complete and consistent as possible. Only then can AI make sound decisions and deliver reliable results.

Use Case: Intelligent Document Processing (IDP)

One particularly exciting use case is Intelligent Document Processing (IDP). In this scenario, AI takes over the time-consuming task of information analysis: documents are automatically classified, relevant content is extracted, and seamlessly transferred to the next process step. The integration within the d.velop process studio makes automating these IDP workflows easier than ever before.

Automation with AI: Building a Successful Digital Workplace

Intelligent process automation powered by AI is a key pillar in creating a future-ready digital workplace. It relieves employees of repetitive tasks and creates space for value-adding activities. With solutions like d.velop process studio and d.velop pilot, processes can be designed not just digitally – but intelligently.

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FAQ about Intelligent Process Automation with AI

What is process automation?
Process automation refers to the use of technology to automate business processes. Intelligent process automation and AI-powered automation go a step further by analysing, managing and optimising workflows efficiently. Using AI agents, AI workflows and AI models, intelligent processes can be structured, data flows understood, predictions made, and systems scaled and monitored – all contributing to maximum efficiency, improved system integration and measurable cost savings.

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What types of process automation exist?
There are several types of process automation, including: Traditional workflow automation, Business process optimisation, and Intelligent process automation, where AI-based solutions identify and optimise complex workflows. By leveraging AI models for process optimisation, organisations can develop intelligent processes that lead to significant cost savings and increased operational intelligence.

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What is workflow automation?
Workflow automation is the digital control and execution of recurring tasks within an organisation, where activities are carried out automatically based on predefined rules. Through intelligent process automation with artificial intelligence (AI), businesses can create smart workflows that enable efficient business process automation, precise AI-driven optimisation, and future-ready, AI-supported operations.