Maria Homann
AI and automation. These two terms are often used interchangeably, due to the fact that they serve similar purposes. Both help businesses optimize their operations to work smarter and more efficiently. In this blog, we give you a deeper understanding of the two terms and how they are different from each other.
Skip to:
- What is automation?
- What is AI?
- Automation vs AI: key differences
- How is AI used in automation?
- How can you get started with AI-based automation?
Many companies have already adopted automation due to its ability to effectively perform tasks at high speed and accuracy. The next step for these companies is to adopt AI within their automation.
Adding AI to automation adds another dimension to productivity and efficiency, letting these companies do so much more. It brings them towards hyperautomation, which Gartner declares is “an unavoidable market state.”
AI has become such a buzzword in the last few years, but what is AI actually (in simple terms)? And, how can businesses use it in their automation efforts to achieve this next level of automation?
In this guide, we'll give you a basic understanding of how AI works within automation, so that you can utilize its capabilities and elevate your automation efforts.
If you're interested in learning more about AI and automation, make sure to also download our report, AI and Software Quality: Trends and Executive Insights, to gain a comprehensive understanding of how AI is reshaping software quality:
What is automation?
Automation boils down to getting robots to follow orders; “if I say 'A' the robot does 'B'. We, the humans, define the rules, and the robots perform them. That's the essence of automation.
The point is to free humans from highly repetitive tasks that are tedious and error-prone.
When us humans perform repetitive, and sometimes menial tasks, we tend to not only get bored but also make mistakes. Robots don't (unless they are set up incorrectly) and can also perform these tasks faster and more consistently. They also don't get sick or take holidays, which is of great convenience to employers.
Of course, not all tasks are performed better by robots, which is why we shouldn't fear being replaced by them anytime soon. Instead, we should view automation as something that supports us and frees up our time, so that we can do other types of tasks that require critical and creative thinking.
"If I say A, the robot does B.” We, the humans, define the rules, and the robots perform them. That's the essence of automation.
When robots perform the jobs robots are best at, humans can perform the jobs they are best at. The result is then more efficient workforces and happier employees. It's really quite the win-win situation.
So how does automation work in practice? Well, to set up automation, you have to find a way to tell the robot what to do. In order to do that, you have to speak robot.
There are different approaches to this, depending on what you're automating. If you're automating tests, you can either code your way through it in a free, open-source tool such as Selenium, or you can automate without code with an automation tool such as Leapwork. More on this later.
What is Artificial Intelligence (AI)?
The idea behind AI (Artificial Intelligence) is to build systems that simulate certain aspects of human intelligence and cognition, such as learning and reasoning, thereby enabling robots to think, speak, and even act like human beings.
Essentially, AI enables machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, solving problems, and learning from this experience.
While AI systems can perform specific tasks very effectively, they do not possess general intelligence or human-like awareness. This isn't to undermine their capabilities; they are very capable at performing the tasks we humans assign them to do, but they are not at the level of human cognition. What we have today is a junior version of intelligence, also called Narrow Artificial Intelligence (NAI).
You can place AI in two broad categories:
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Narrow AI: Also known as "weak AI," this type of AI is designed to perform a narrow task (e.g., facial recognition, internet searches, driving a car). Narrow AI is programmed to perform specific tasks and does not possess consciousness, genuine understanding, or the broader cognitive abilities of humans. It operates under a limited set of constraints and contexts.
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General AI: Also known as "strong AI," this is the type of artificial intelligence that can understand, learn, and apply knowledge in a way that is indistinguishable from that of a human. This form of AI does not yet exist and would be capable of performing any intellectual task that a human being can do.
Siri is an example of Narrow AI because it is designed to perform specific tasks like answering questions, setting reminders, or providing weather updates, without possessing general intelligence or self-awareness.
Another example is ChatGPT. Here's how ChatGPT defined itself when we asked it:
"ChatGPT, like me, is an example of narrow AI. It is specifically designed to generate human-like text based on the input it receives. It does this by processing and analyzing large amounts of text data and learning how to predict the most likely subsequent word in a sentence, based on the words that came before." - ChatGPT
You have also likely come across the term Generative AI. This is another subset of Narrow AI, where the focus is on creating new content, whether it be text, images, music, video, or other forms of media.
It's important to know that NAIs aren't able to do much more than what they've been trained to do. They don't have a conscience, they don't have feelings, and they can't think outside the box. In that sense, they're still very far from replicating humans on a cognitive level.
A better way to describe AIs today is perhaps that it's a bit like a dog learning certain commands. You can teach it certain things, and at some point, it will understand how to do those things on its own. What AIs do to learn is that they seek patterns, and based on those patterns, they 'learn' how to perform certain actions.
When explained in this way, it sounds quite simple, but in reality, it is a rather complex technology, and it's instrumental when used in combination with automation.
Automation vs. AI: What's the difference?
This brings us to our central question. What is AI vs. automation?
As mentioned, AI and automation are often discussed together because they both involve using technology to perform tasks traditionally done by humans. However, while they do overlap, they also have distinct characteristics and applications.
The point of AI isn't to perform repetitive tasks like automation. Instead, AI is designed to mimic humans at a more intellectual level, and to perform tasks they learn by observing patterns and past outcomes.
In that sense, you could say that AI is a more advanced form of automation. It introduces an element of intelligence to automation, allowing it to perform a broader range of tasks and adapt to these over time based on new data and experiences. Automation, however, remains focused on consistency and reliability in performing repetitive tasks.
Where automation is about setting up robots to follow a set of pre-defined rules, AI is about setting up robots to make their own decisions (though still based on human input and existing patterns).
In other words, they're designed to learn and act on what they learn, rather than merely take orders.
This is why some people see a potential threat in AIs taking over jobs and even becoming so intelligent that they can outsmart humans (like we saw in this sci-fi action film featuring Will Smith).
However, AIs are still very far from world domination.
The point of AI isn't to perform repetitive tasks like automation. Instead, AI is designed to mimic humans at a more intellectual level, and to perform tasks they learn by observing patterns and past outcomes.
Still confused?
We asked ChatGPT once again to help us define and provide some clarity on the differences and similarities between AI and automation. Here's what ChatGPT said:
Similarities between AI and Automation:
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Efficiency and Productivity: Both AI and automation are used to increase efficiency and productivity in various industries. They reduce the need for human labor for repetitive or easily codified tasks, thus speeding up processes and reducing errors.
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Task Execution: At a fundamental level, both AI and automation involve machines carrying out tasks. Whether these are physical tasks (like in manufacturing) or cognitive tasks (like data analysis), the goal is to reduce the human workload.
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Technological Progress: Both are results of advances in technology and are crucial in the ongoing transformation in industries such as manufacturing, services, and business processes.
Differences between AI and Automation:
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Complexity and Adaptability: Automation is typically rule-based and designed to perform a highly specific, repetitive task without variation. It doesn't "learn" from its experiences but rather follows pre-set instructions. In contrast, AI involves a level of complexity and adaptability; it can learn from data, improve over time, and make decisions based on its learning. AI can handle a wider variety of tasks and adapt to new situations that it wasn't explicitly programmed for.
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Scope of Application: Automation is generally applied to tasks that require consistency and precision but do not necessarily require adaptation or decision-making based on unknown variables. AI, particularly with the inclusion of machine learning, is applied to areas requiring decision-making that mimics human judgment, such as recognizing speech, interpreting complex data, or predicting trends.
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Technological Foundations: While automation can be as simple as a mechanical device designed to multiply human effort (like a lever or pulley system), modern automation often involves more complex machinery and software. AI, on the other hand, relies on sophisticated algorithms and computational theories like neural networks, natural language processing, and more, to perform tasks that require cognitive abilities.
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Goal Orientation: Automation's primary goal is to perform a task exactly and repeatedly with high reliability. AI's goals can be more aligned with mimicking human behaviors and performing tasks in a way that is perceived as intelligent or thoughtful.
Here's a side-by-side comparison of AI and automation to provide a clearer overview of their similarities and differences (and yes, you guessed it - ChatGPT also helped us here):
| Artificial Intelligence (AI) | Automation | |
|---|---|---|
| Purpose | To mimic human cognitive functions and learn from data. | To perform repetitive tasks efficiently without variation. |
| Complexity | High; involves learning and decision-making capabilities. | Lower; operates based on predefined rules and sequences. |
| Adaptability | Highly adaptable; can improve and adjust over time. | Static; does not adapt unless reprogrammed. |
| Scope of Tasks | Broad; can handle a variety of tasks and scenarios. | Narrow; designed for specific, repetitive tasks. |
| Learning | Capable of learning and evolving from data. | Does not learn; performs tasks as programmed. |
| Technological Base | Based on advanced algorithms, neural networks, etc. | Can range from simple mechanical systems to complex software. |
| Applications | Diverse, including data analysis, natural language processing, etc. | Common in manufacturing, data entry, repetitive office tasks, etc. |
| Goal Orientation | Perform tasks in an intelligent, context-aware manner. | Execute tasks exactly and reliably without deviation. |
How is AI used in automation?
In many ways, AI perfectly complements automation. As an example, an automation tool could transfer data from A to B, while the AI capability could interpret that data and respond to it.
For many businesses, AI is a brilliant way to augment their automation robots' abilities. To understand how it is used, it might be helpful to look at a few different types of AI:
Types of AI in automation
- Machine Learning (ML): Enhances predictive modeling and decision-making in systems like maintenance forecasting and production optimization.
- Natural Language Processing (NLP): Powers automated customer service tools like chatbots for natural language interactions and sentiment analysis.
- Optical Character Recognition (OCR): Translates images of typed, handwritten, or printed text into machine-encoded text, used in document automation and data extraction tasks.
- Computer vision: Applied in quality control for defect detection and in surveillance systems for automated monitoring.
- Robotics: Combines AI with physical robots to perform complex, adaptable tasks in manufacturing and hazardous environments.
- Expert systems: Uses rule-based AI to simulate the decision-making of human experts, which is useful in diagnostics and problem-solving applications.
- Predictive analytics: Employs statistical and machine learning techniques to forecast future outcomes, crucial for logistics and supply chain management.
- Speech recognition: Converts spoken language into a computer-readable format for use in automated voice response systems.
An example of AI and automation
Imagine an enterprise that has a customer service center.
Every day, thousands of emails are received by the customer service center. So many, in fact, that they physically cannot respond to those emails within a 24-hour time span with their existing resources.
Without adding more human resources, management wants to find a way that customers can be served right away, and either resolve their issue immediately through email or being put in contact with a customer service representative.
To do this, the company automates the email classification process. Based on keywords that the automation robot finds in the emails, the robot sorts them into different folders and assigns them to the right person.
This helps speed up the sorting process, but it still doesn't allow the customer to retrieve an answer to their request right away. For this, AI is needed.
AI technology, more specifically Natural Language Processing (NLP) in this case, can be used to interpret the intent of the request in the email. Based on that interpretation, the AI robot can then send out a response right away that immediately resolves the customer's problem, especially if this problem occurs frequently or is easy to fix. This also means that time is freed up for the customer service team to focus on more complex or unique problems.
How can you get started with AI-based automation?
As AI is becoming increasingly central to core processes, many businesses are wondering how they too can get started with the technology and implement it as a part of their digital transformation and application modernization efforts. In fact, there is a growing consensus that businesses that fail to adapt to AI and adopt AI, will fall behind the rest.
Yet the path to adoption isn't about following the hype cycle or simply adding AI. It's about making strategic, business-aligned choices that solve your real problems. The critical challenge for these companies lies in finding the right business cases for AI use. Here's what you can do to identify your business case for AI:
1. Identify your business need
The first step is not to look at AI technologies. It's to identify what it is that they cannot solve with their current set of tools and pinpoint the bottleneck. After examining your business operations with a critical eye, ask yourself:
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Where are the inefficiencies in our workflows?
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Which processes are time-consuming, error-prone, or scale poorly?
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Where do customers or internal users consistently report friction or dissatisfaction?
You should be investigating pain points that current tools can’t address efficiently. Instead of saying, “everyone is using chatbots, should we too?” ask your customers for feedback on your services and investigate what is the best technology for solving that problem.
Are they saying the response time is too slow? Then a chatbot using NLP might help you speed up responses. Or are they saying the quality of responses is low? Then robotic responses are probably not your best bet.
Only when you've uncovered the real customer pains should you start looking at the technologies.
Customer feedback, employee insights, and data analytics can all help you isolate the true problem, and only when you've uncovered the real pain points should you start looking at the technologies to solve it.
2. Research available technologies
Once you’ve identified your business need, it's time to research what kind of AI can help, and what the limitations are. This is not just about choosing between machine learning and chatbots. You’ll want to evaluate not just the capabilities but also the maturity, risks, and explainability of different AI technologies. Here, you might want to bring in expert help as AI is a highly complex field with many more sub-technologies than those we've mentioned above. These experts can guide you in finding the correct technology fit and feasibility assessments.
3. Select your tool
Once you've decided on your technology, then comes another critical research phase: finding the right tool vendor. No matter what your needs are, it will pay off in the long run to select a tool that will allow you to work across technologies and enable you to integrate AI capabilities into your existing tool or IT landscape.
You also need to ask the question: build or buy?
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If you have an in-house AI/data science team, you might develop custom models or frameworks tailored to your environment.
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If not, you’ll likely look to vendors or platforms that provide off-the-shelf solutions with pre-trained models, workflow integration, and low-code capabilities.
In either case, focus on tools that are both scalable and secure. Look for platforms that:
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Support integration with your existing IT ecosystem
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Offer explainability and governance features
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Allow modular expansion (so you can test and scale responsibly)
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Provide user-friendly interfaces for non-technical users (especially important for business-driven automation)
4. Implement
The last phase is implementation. In this phase, it can be good to test your new technologies internally first. Start small, ideally in a low-risk environment, such as a pilot program with a single department or team, as employees are usually more forgiving than customers.
In this pilot, focus on:
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Accuracy and performance benchmarks
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User adoption and feedback
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Integration pain points
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Operational impacts
Start with the small problem areas and learn to solve those efficiently. Once you've aced this, you're ready to scale it up.
Remember, AI is not a one-off project. AI's capabilities are ever evolving, and you need to ensure you're keeping up. Building internal literacy, establishing governance frameworks, and maintaining a feedback loop are essential for your long-term success.
The future: Agentic AI and automation
So what comes next? Well, the next big leap within the AI space is Agentic AI. This subset of AI moves beyond reactive tools toward more autonomous and goal-directed systems that can plan, execute, and adapt in dynamic environments. Unlike traditional AI, which operates within narrow parameters and requires human prompting or oversight, agentic AI systems are designed to take initiative.
These systems can break down high-level objectives into sub-tasks, orchestrate actions across multiple tools or systems, monitor progress, and course-correct based on outcomes, all with minimal human intervention. This will bring new possibilities to complex workflows such as continuous software testing, autonomous customer service, and intelligent business process management.
Despite their autonomy, Agentic AI systems will still rely on human oversight for setting strategic goals, ensuring ethical alignment, and managing edge cases or ambiguous scenarios. And, of course, human guidance will be needed to gauge accuracy and to guide, supervise, and continuously refine these agents, ensuring they operate safely and in alignment with business values.
How will this impact testing? According to Forrester, Agentic AI “enables self-updating, self-healing, and continuous testing with human orchestration”. These autonomous agents can independently discover, generate, and execute tests, adapting to application changes in real time.
Agentic AI helps reduce test flakiness, accelerate feedback loops, and support shift-left and shift-right strategies, and by doing so, is redefining the role of testers and the architecture of testing platforms.
Continue learning
Here are a few more resources you might find useful for understanding automation and AI:
Last, make sure to download our report, AI and Software Quality: Trends and Executive Insights, to gain a comprehensive understanding of how AI is reshaping software quality. This report offers key insights and actionable solutions to help your business adapt, scale, and consistently deliver exceptional user and customer experiences.
