Introduction
The advent of AI made organizations take account of their manual inefficiencies
and set into action a process to implement and revert these obsolete systems
that slow down & hinder business growth. We keep guessing how AI will or won’t
affect the workforce, their customers, etc.
Quick fact: A study by McKinsey shows that a total of 8% of enterprises will
remain successful if their respective industries digitize and automate at the
current rate. (Contract.fit)
What is AI-powered automation
Performing manual tasks that are repetitive and require a lot of resources
through machines like robots or cognitive technology is called automation. It
helps in the simplification of lengthy processes along with improved efficiency
in organization-wide operations.
In the pre-AI world, automation had been mostly associated with robots or
machines called Robotic
Process Automation (RPA). With the rapid advancements in AI and
businesses looking to leverage AI and integrate it within their systems, it
becomes a necessity that we understand AI-powered systems clearly to minimize
human errors and inefficiencies.
AI & Automation together
For an enterprise to gain a competitive edge in the market you must automate all
the manual, redundant & time-consuming processes through robotic automation.
AI and automation are two different entities that come together to give rise to
what is called Cognitive
Automation.
| Artificial Intelligence |
Automation |
| AI gives results by collecting information and experiences. |
Automation is a set of pre-decided guidelines to perform redundant
tasks. |
| AI includes learning and evolving from information. |
Automation does not include learning & evolving. |
| AI systems understand data & make decisions independently. |
Automation only follows the instructions fed into the machine. |
Automation thus completes a set of predefined tasks and cannot go beyond the
parameters of set guidelines predetermined by humans.
It is when AI is further integrated with RPA that we get a system that can go
beyond the human guidelines and perform tasks in whichever way they perceive to
be the most efficient.
This amalgamation is what we call Intelligent Automation & it has three key
components to it. Let’s discuss.
Intelligent Automation. The three components of Intelligent Automation
AI along with Robotic Process Automation (RPA) and a third component Business
Process Management (BPM) is grouped under the umbrella term Intelligent
Automation (IA).
Intelligent automation or IA helps enterprises gain a competitive and lasting
edge over their competitors in the market. This is especially true for
medium-sized organizations to streamline their operations and scale their
business. Below we’ve discussed the three pillars of Intelligent automation.
1. Artificial Intelligence (AI):
AI is perhaps the most crucial among all the three components of Intelligent
Automation. The cognitive abilities of AI models emulate human intelligence that
analyzes, and segregates structured and unstructured data using Machine learning
and other complex algorithms. According to IBM, this helps enterprises develop a
knowledge base and formulate further predictions. IBM terms AI to be “the
decision engine of IA.”
2. Business Process Management (BPM):
Business process management is also called business workflow automation. It is
the set of technologies that streamline and automate workflows for improved
agility and better consistency for a chosen business. BPM is employed across
domains to improve interactions and engagement.
3. Robotic Process Automation (RPA):
Robotic process automation uses software bots to complete back-office tasks like
data extraction or automatically filling forms. These robots fit well by the
insights provided with AI to increase the use cases of automation and undertake
even more complex tasks.
Implementing AI-powered automation
AI automation allows businesses to make data-driven decisions that would not have
been possible otherwise. Two factors that need to be considered while implementing
automation into workflows are discussed below
1. Identifying use cases and choosing tools: It’s recommended
that you choose from repetitive & data-driven tasks that can benefit from
automation. Choose AI tools and platforms that align with your business needs.
2. Sanitizing data & integration: Ensure that your data is
clean, structured, and accessible, and Integrate AI models into your existing
systems and workflows.
3. Democratizing the automation model: This model involves
empowering individual teams and employees to develop automation, while a central
automation team provides support, training, and guidance.
Use Cases & Benefits
AI-powered automation can have large-scale implementations from speeding up
automobile production to boosting insurance processes and addressing regulatory
& compliance needs. It can help replace legacy systems that were otherwise
resource-intensive, costly, and prone to human error to become aligned to
scaling businesses efficiently.
1. Supply Chain Management
Use Case: AI can optimize supply chain processes by predicting demand,
identifying potential disruptions, and recommending the most efficient routes
for transportation and delivery.
Benefits:Lower inventory carrying costs, reduced stockouts and overstocks,
improved delivery rates on time, and increased overall supply chain efficiency.
2. Automotive manufacturing
Use Case: AI can optimize supply chain processes by predicting demand,
identifying potential disruptions, and recommending the most efficient routes
for transportation and delivery.
Benefits: Lower inventory carrying costs, reduced stockouts and
overstocks, improved delivery rates on time, and increased overall supply chain
efficiency.
3. Healthcare
Use Case: AI algorithms can analyze medical images such as X-rays, MRIs,
and CT scans to assist radiologists in detecting abnormalities and diagnosing
diseases more accurately.
Benefits: Faster diagnosis, early detection of diseases, improved patient
outcomes, and more efficient use of radiologist's time.
4. Finance & Accounting
Use Case: AI-powered algorithms can analyze financial data and market
trends to automate trading decisions, optimize investment portfolios, and
execute trades at the best possible times.
Benefits: Improved investment returns, reduced risk through data-driven
decision-making, and faster trade execution.
5. Customer Service
Use Case: AI-powered chatbots can handle customer inquiries and support
tickets, resolving common issues and answering questions without human
intervention.
Benefits: Reduced response times, 24/7 availability, scalability to handle
large volumes of inquiries, and cost savings by minimizing the need for customer
service representatives.
Business Impact
AI-powered automation or Intelligent Automation is a revolutionary step towards
transforming the way enterprises do business. It streamlines workflows
end-to-end and helps organizations, especially mid-sized enterprises scale even
further. Let’s then have a look at the overall business impact:
- Efficient Resource Utilization
- Innovation-centered growth
- Improve Bottomline
- Competitive edge in niche technologies
At MetricDust we are adept at weaving together the intricacies of AI-powered
automation into a functioning and efficient system for your organization, that
helps you improve your topline while also regulating the bottom line in tandem.
This is done by streamlining all functional activities for cost-cutting and
savings.
