Businesses are enthusiastically adopting artificial intelligence, with McKinsey reporting that 78% of companies now use at least one AI tool. Not surprisingly, the adoption rate is even higher in tech companies.
With this information in mind, it is only natural that AI automation is a much sought-after innovation.
Of course, you might want to understand it better before you decide you need it for your operations. We are here to help you with that.
What is AI automation?
AI automation is the process of using advanced technologies to make standard automated operations smarter and context-aware. It reduces the manual effort your team needs to put in by using AI and intelligent automation for tasks such as data analysis, report generation, and workflow optimization.
AI automation uses a mix of software, data, and decision logic to quickly and automatically execute tasks that normally require time and human judgment.
The specific technologies involved depend entirely on the business case: sometimes it’s simple rules, sometimes it’s document processing, and in certain cases it can include machine-learning or NLP models. The goal is always to make a workflow faster, more consistent, and less manually intensive.
Instead of predicting the “right” action on its own, AI supports the decisions that have already been defined. It can classify information, extract meaning from text or images, or surface relevant insights so that the system can follow the workflow the business has set.
When conditions change, the model can adjust its behavior within those boundaries, which makes it more flexible than fixed, step-by-step scripting.
As the system is used, its components can be refined through controlled cycles of periodic updates or retraining based on new data.
In short, AI automation expands what traditional automation can do by helping systems understand inputs and choose the right path in a defined workflow, all within clear business rules and oversight.
Since the process is still responsive, AI automation can be very useful in areas where you want constant monitoring. In cybersecurity, it can spot suspicious behavior and trigger alerts instantly. In fraud detection, it can analyze transactions in real time to prevent losses.
Similarly, it can also be very useful for initial screening tasks, where the technology can decide who to send the problem to. For example, AI can be used to scan medical test reports and X-rays to make initial diagnoses. They then forward it to the specialists who verify it and recommend treatment.
This makes the process of getting healthcare quicker for patients and frees up healthcare providers to focus on the people who need them most.
Curious as to how technology professionals use AI? Here are the five ways we use AI tools at Infinum.
How is AI automation different from traditional automation?
Traditional Automation, without AI, is a mechanical process that follows fixed and predefined rules. For example, a conveyor belt is automated: You flip a switch, and it starts moving. It might be programmed to stop if there is no weight on it, but it cannot react to a situation for which it was not explicitly programmed.
In contrast, AI-driven process automation can adapt its behavior based on input: it can interpret unstructured text, classify information, or extract relevant details, allowing the workflow to respond appropriately without manual intervention. The automation still follows defined steps, but the AI components enable the system to process a wider range of human input, formats, and real-world variability.
A simple example is a smart chatbot. The user enters a query using natural language, and the tool interprets the intent to create an appropriate response accordingly. It hasn’t been told what to say, but it understands and adapts.
At a slightly more advanced level, AI agents take this a step further. Agentic AI can autonomously or semi-autonomously automate entire processes rather than individual tasks. They can plan, reason, and even collaborate with other systems or humans to achieve their goals.
Intelligent automation: Taking AI automation further
Intelligent automation (IA) is the next level of AI automation. Here, instead of tasks or processes, entire ecosystems are automated. They can run, adapt, and improve over time, all on their own.
There are three key technologies that power these digital workers:
Robotic process automation (RPA)
This component is made up of software, not hardware, “robots” that automate repetitive, rule-based digital tasks. For example, they may be used to copy data between systems, generate reports, or update customer records without requiring any creativity or strategy. In these cases, RPA mimics human actions to complete these jobs, while saving you time and reducing manual errors.
Business process management (BPM)
BPM tools help visualize processes so you can identify bottlenecks and orchestrate work between humans, bots, and systems. This visualization helps you define how tasks and data flow across your organization, reducing complex workflows that slow productivity.
Artificial intelligence
This is the layer that adds reasoning, learning, and adaptability. AI analyzes data to identify patterns and make decisions based on context. AI includes technologies such as ML, NLP, and computer vision.
With these components working together, you can move beyond automating isolated tasks. Intelligent automation can handle end-to-end workflows. It can detect issues, trigger corrective actions, and even enhance business outcomes.
Essentially, while AI automation makes individual processes smarter, intelligent automation transforms how entire business operations function.
How AI automation works
To create AI automation, you need to integrate processes with artificial intelligence. The processes create the framework around which algorithms are built.
These algorithms use the same decision-making logic that a person would, and are trained on business data. This informs them of the type of information and the patterns to expect.
The logic built into them allows them to use new data to spot these patterns and make predictions. Meanwhile, ML allows them to learn from it, so the systems continuously refine and improve their results.
Here is a quick overview of the parts that come together to make this technology work:
Foundational models and cloud services
This infrastructure enables AI automation to function and scale effectively, with foundational models serving as the “thinking” component and cloud services as the delivery mechanism.
Data collection and processing
Data is the fuel that powers your system. Both structured and unstructured data need to be collected and made ready to be used in AI training.
AI model training
The AI-ready data is used to “teach” the model how to complete its assigned tasks, using the following techniques:
- Machine learning algorithms: While not all automation solutions will include ML elements, some will use it to further optimize their operations and enable additional features, as needed. In these cases, automation solutions will be empowered with ML algorithms, including:
- Supervised learning: The model is given explicitly labeled data, so it learns how to categorize it.
- Unsupervised learning: The training data is unlabeled, and the model must find patterns and meaning on its own.
- Reinforcement learning: The model learns from the feedback it receives while interacting with its environment.
- Deep learning: A subset of ML that uses neural networks with many layers to automatically discover features and patterns in large volumes of data.
- Natural language processing: The ability of a model to understand and interpret the way people talk and respond in a similar manner.
Execution
The model is deployed into the workflow, where it uses the decision engine to make predictions, and applies these to determine how to action the next step.
Continuous learning
Also known as lifelong or incremental learning, this allows the model to perpetually refine algorithms and improve results based on the new data it receives.
Benefits of AI automation in your business
We already know that AI automation can free up your human workers by taking over routine tasks. Let us take a look at how this can benefit your business:
Fewer errors, more accuracy
Unlike people, AI does not get bored or distracted. Once a system has been trained, it will carry out tasks consistently, without losing focus or requiring much human intervention. As a result, you will see fewer or even zero mistakes. Automation with AI can empower your workers to deliver better, faster results, especially when tasks are repetitive.
Faster operations
AI automation can complete even the most complex tasks in a matter of seconds. It helps your team deliver more work, but without a corresponding increase in mistakes. This improves efficiency across the board and eliminates redundant work, boosting your employees’ morale.
Real-time responsiveness
AI can identify the new inputs and process them in the blink of an eye. This allows your business to adapt to changes and issues in real time. As a result, your operations stay agile, and any potential problems are caught by automated systems before they escalate.
Scalable growth
As your business grows, you need more hands on deck to complete the workload manually. Automating processes with AI, however, means you can handle more complex data, interactions, and transactions without hiring more staff. You might need to buy or rent more resources, but that will only be required if the growth is exponential.
Data-led decision-making
Artificial intelligence can analyze large data sets to find trends and enable predictive analytics. It helps you extract actionable insights from your business information. Most importantly, it does so quickly, often in real time. As a result, you can make informed decisions based on hard facts and numbers.
More time for human creativity
Not everything can be replaced with technology, but you can automate tedious work so that your human resources are free to focus on strategic or creative endeavors. It helps your people innovate and solve complex problems without worrying about “busy work.” That helps your business productivity and boosts employee morale and engagement.
AI automation use cases
Reading about benefits in an abstract sense does not paint a very clear picture. Here’s how businesses across industries are applying AI automation to solve their challenges:
Finance and accounting
The finance industry is heavily regulated, so accuracy and compliance are a priority. You also need to make quick, data-driven decisions. AI automation is the ideal solution here, reducing manual work and human error in data-heavy tasks.
One of its most important uses in financial services is to flag anomalies and detect potential fraud in real time. However, it can also reconcile transactions and automate expense reporting and invoice processing to save teams hours of administrative work.
The result is cleaner data, faster reporting, and improved financial visibility.
Healthcare and life sciences
Automated AI systems scan X-rays, lab results, and medical records to provide initial diagnoses. Urgent cases are prioritized and flagged for specialists to look at, while routine cases can be automatically given treatment plans. This reduces waiting times for patients and allows healthcare professionals to focus on direct care.
AI tools also streamline the administrative side, by automating appointment scheduling, patient record updates, and discharge documentation to save time and reduce paperwork.
Manufacturing and logistics
In manufacturing and logistics, automation technologies predict problems before they occur, to keep operations running smoothly. They monitor equipment performance to detect maintenance needs and automatically adjust production schedules.
These technologies can reroute shipments to optimize delivery times based on real-time conditions. The result: fewer delays, lower costs, and stronger supply chain resilience.
Marketing and sales
AI marketing automation personalizes engagement at scale. It segments audiences based on behavior and demographics to create tailored messages that deliver more engagement and results, improving customer experience.
AI-powered automation analyzes customer data to predict purchasing intent and suggest the best next action for each lead to improve conversion rates and help teams focus on high-value prospects.
Human resources
Automated tools can screen resumes to match candidates to roles. They can even handle initial communication. Once employees are onboarded, AI-driven HR systems can manage routine queries and update records automatically.
This leaves HR professionals free to focus on talent development and culture-building.
Customer service and support
AI automation tools streamline customer interactions. Chatbots and virtual assistants powered by generative AI provide round-the-clock support. They handle common questions and route complex issues to the right human agent.
Meanwhile, customer service teams can spend their time on the conversations that need a human touch.
We helped the surgeons at Mount Sinai Hospital to identify prosthetic implants with an AI-powered app. Read more about it in our case study.
Challenges of implementing AI automation
Implementing AI workflow automation solutions can be a great way to optimize your operations. However, the process requires careful planning, or it will not increase efficiency.
Here are some caveats to consider:
Data quality and accessibility
AI and automation systems are only as good as the data that powers them. If your data is outdated, inconsistent, or siloed across departments, your models will not be able to deliver accurate results. The problem is: Clean, structured, and accessible data is often a bigger hurdle than the technology itself.
Integration with existing systems
It is highly likely that your business runs on a combination of legacy platforms and custom software. You could struggle to integrate new AI tools into such an environment.
Without proper integration, you will find it hard to ensure that AI delivers insights and actions across your entire ecosystem. If you aren’t careful, automation efforts could potentially create new silos instead of eliminating them.
Skills and expertise gaps
You need more than coding knowledge to implement AI automation. You also need expertise in data engineering, process mapping, and user experience design, along with a clear understanding of your business goals.
Most teams don’t have all these capabilities in-house, which can make it hard to move from pilot projects to production-ready systems.
Ethics, compliance, and governance
AI systems must handle data responsibly. Bias, privacy concerns, and regulatory requirements can create significant risks if not addressed early.
Establishing strong governance practices, from data management to model transparency, is essential to ensure automation remains ethical and compliant.
Automate your business with the right partner
Several of the above challenges can be mitigated by working with the right technology partner, such as Infinum.
We have the experience that allows us to develop solutions tailored to your needs, so they can integrate easily with your existing technology and workflows.
Most importantly, our methods, reporting, and internal processes meet strict, internationally recognised security standards. With Infinum, the security of your AI automation solution will be in safe hands.
We have a large team that can help you navigate strategy development, data engineering, model building, deployment and integration, as well as ongoing monitoring and maintenance.
Interested in learning more about how we can help you with your AI automation journey? Talk to us!
