In the modern digital
landscape, artificial intelligence (AI) and automation are two
crucial technologies driving innovation. While these terms are often used
interchangeably, they serve different purposes and offer distinct advantages.
Understanding the difference between automated
intelligence vs artificial intelligence is key to leveraging the right
technology for your needs.
This blog by Emergenteck explores Intelligent
Automation training by breaking
down the differences between AI and automation, focusing on their
functionalities, applications, flexibility, and impact on industries.
Definition: What Are AI and
Automation?
Automatization refers
to the use of machinery or software to complete activities that require little
or no decision-making input from a human operator. It functions by setting
programs or instructions and will complete assigned work plans in a line of
work for example data inputting, sending emails, or assembly line duties.
In contrast,
Artificial Intelligence (AI) describes the creation of a machine that can learn
from experience and perform human intelligence. They can also be used to scan
large amounts of information, analyze them, and make decisions on the data on
their own. AI is not limited to a rigid set of directions rather it progresses
concerning the new information.
Functionality: Rule-Based vs.
Learning-Based
The primary
distinction between AI and automation is in what it can do.
Automation is squarely
routine-based in that each activity is clearly defined and performed within a
particular pattern or set of directions. This makes automation ideal for
processes such as Invoice processing or completion of forms.
Whereas, Artificial
Intelligence (AI) or intelligent systems work on learning-based algorithms like
Machine Learning (ML). Any AI system can benefit from new data and the
invention of new solutions and strategies to complete given tasks and address
complex, unstructured issues and inquiries.
Applications: Where Are They Used?
Major industries that
incorporate automation incorporate manufacturing, finance, and IT since most
processes in those fields are routine. RPA for instance acts as a tool to
automate customer support queries, data migration as well as other related
functions in workflow.
In turn, AI is applied
in other, more difficult, tasks involving understanding, reasoning, and
learning. Machine learning is an increasingly commonplace technology in use for
fraud detection, medical diagnosis, offering recommendations, and chatbots that
imitate human conversations.
Automated by
definition RPA does not distinguish between RPA vs AI vs ML because while RPA
is just used for the automation of forms and processes, AI and ML are used for analysis, data
learning, and decision making.
Flexibility: Static
vs. Adaptive Systems
Automated systems are
usually fixed to do one job and cannot be altered to do anything different
apart from being reprogrammed. This makes automation extremely credible but
restrictive in regards to versatility.
AI, on the other hand,
is adaptive. The AI systems can easily adapt to changes in data or other
circumstances that exist that do not require reprogramming. For instance, an
AI-based customer service chatbot can read past conversations and become better
at the responses within a period.
Human Involvement
Automation is best
carried out with very little influence from the actual human user, as it is
meant to work without too much supervision. Nevertheless, the use of the
automated system requires a human to install and closely supervise it.
AI can work on its own
when it is programmed but it needs human interaction to be produced or for
feedback to produce its models. For instance, AI in machine learning performs
analysis based on the data provided and labeled by humans, besides, humans
correct the models as the system develops.
Examples of Automation vs. AI
Automation Example:
A manufacturing line
features occasions of Automation since it involves the production of products
using machinery. The machines execute set procedures where they need to carry
out functions such as cutting, welding, or packaging.
AI Example:
Some of the
applications of AI programs are in the calculation of mortgage and credit
risks, and the identification of fake transactions. In addition to that, the AI
system can understand past transactions and identify suspected fraudulent
transactions.
Benefits and Limitations
The advantages of
automating involve-
· Efficiency
· Reduction of human intervention
· Increase in speed
However, there is an
associated disadvantage in that they are not all-round programmer and cannot
handle more than a preprogrammed task.
AI also has some advantages-
· Higher flexibility
· Analysis unstructured data
However, AI systems
are costlier, need large amounts of data to operate well, and may have issues
with transparent decision-making.
Cost and Complexity
Automated systems are
much cheaper than AI because the application and maintenance of the automated
systems are easier. However, automation is more structured and is mostly
guideline base hence cannot be used in all, unlike AI.
AI systems while more
sophisticated and costly are more malleable in the long run because of their
learning attributes.
Real-World Impact
Technology has brought
about automation processes in industries where employees are not expected to
exercise their skills in repetitive work hence improving efficiency and
reducing costs greatly. It is more and more popular in manufacturing, customer
service departments, and IT departments.
AI is more significant
in organizations whose decisions are crucial for organizational success,
especially in healthcare, finance, and processes involving predictions such as
marketing. Hyping of AI has been a reality because it has analyzed and
processed large sets of data that were unimaginable to industries and made
breakthroughs in areas such as medical research and autonomous vehicles.
If one has to choose
between AI and automation, then consideration of the complexity of the
activities to be performed should be made. As for repetitive tasks that may
involve the application of rules, automation is a cheap solution that produces
immediate positive effects. However, the tasks implying data analysis, decision-making,
or flexibility are performed better by AI than by humans.
In conclusion, automation
is good at dealing with routine and procedural work, which follows clear,
algorithmic logic, On the other hand, AI embodies cognitive possibilities
capable of self-learning from the data they receive. Deciding on the right
course of action is in line with the capacity of the organization, the nature
of the tasks involved, etc.
