Since AI is becoming the first choice of most leading businesses in every vertical to take the advantage of the growing technology in recent times. Check out, all the benefits of AI in cloud computing along with the stated downside of using the same.

1. AI in Cloud Computing – Blend of
Two Growing Technologies
In order for a business to scale in today’s world, a tech-driven approach is required. The
combination of two popular technologies, Cloud and AI, has proven to be a potent source of
opportunity for companies looking to improve their IT operations.
In a nutshell, combining AI and cloud computing creates a large network capable of storing
massive quantities of data while also learning and improving.
According to MarketsAndMarkets, the cloud computing market size is estimated to reach $947
billion by 2026, double the size of the current market. While the AI market is expected to
increase more than fivefold to $309 billion, as per the study. The combination of cloud
computing with AI will enable users to not only store data, but also analyze and draw
conclusions from it.

2. Advantages of AI in Cloud Computing
The incorporation of artificial intelligence and machine learning capabilities in the cloud has
completely altered the cloud environment. Using machine learning algorithms, the cloud is
evolving into an intelligent cloud that can accomplish a lot of useful work effectively. Here are
some of the most significant advantages brought about by the combination of AI with cloud
computing:
More Economical
The elimination of costs associated with on-site data centers, such as hardware and
maintenance, is a significant benefit of cloud computing. With AI projects, those initial expenses
can be prohibitive. With the help of the cloud, organizations can quickly access these
technologies for a monthly subscription, making research and development costs more
reasonable. Furthermore, AI systems can extract insights from data and analyze it without
human participation.
Boost Productivity
Software management, developing production, and testing environment are a few of the tasks
for which the algorithms based on AI need significant time and effort. It gets eliminated by
using a centrally managed hybrid cloud, or a public cloud, allowing IT employees to focus on
routine activities.
Impactful Analytical Data
Analyzing a vast data set for evaluating customer trends and patterns is accomplished
effectively with AI. It compares past data to the most recent data, providing IT teams with well-
informed, data-backed information.
Furthermore, AI systems can do data analysis quickly, allowing businesses to respond to client
requests and issues immediately and efficiently. AI capabilities provide observations and vital
advice, resulting in faster and more accurate results.
Automation Intelligence
Integrating AI right into the cloud ecosystem can help in automating repetitive processes and
simplify work. AI tools are used in a hybrid cloud system to monitor, manage, and self-heal
individual public and private cloud components.
Advance Data Management

3. We can clearly see that AI plays a core role in data processing, management, and structuring.
Using reliable real-time data, we can improve marketing, customer service, and supply chain
data management. AI solutions simplify the process of ingesting, modifying, and managing data.
Downsides of AI in Cloud Computing
While the benefits are substantial, it is critical to have a thorough grasp of the subject before
making a judgment. As with every coin, there are two sides to every coin, and merging AI with
cloud computing can have certain drawbacks. For example, deploying AI may drastically reduce
costs, which is true, but there is a catch. AI is a complicated technology, and businesses will
require well-trained people to make the most use of such cutting-edge technology. Which will
eventually cost more money in terms of providing enough training and knowledge.
Here are some challenges that companies can face while using artificial intelligence in a cloud
computing environment:
Connectivity Crisis
The constant flow of internet access is imperative for cloud-based machine learning systems to
function smoothly. IT departments use the internet to deliver raw data to cloud services and
recover processed data. Poor internet connectivity can limit the benefits of cloud-based
machine learning algorithms.
While cloud computing is faster than traditional computing, there is a significant delay between
sending data to the cloud and obtaining results. It is a primary issue when utilizing machine
learning algorithms for cloud servers since prediction speed is a widespread concern.
Data Privacy Concerns
SaaS technology’s pay-as-you-go model enables thousands of businesses globally to make sense
of data, identify efficiencies in daily procedures, develop new products, and even grow into
other verticals.
As a result, companies run their customer, vendor, and market data through cloud applications
with little or no understanding of the public cloud’s security dangers. When AI processes data
provided into a SaaS service in a public cloud setting, it exponentially compounds these
hazards. When the processes and perimeters for AI algorithms are not established in a clear
manner, the chances of sensitive data getting exposed to a security breach increases.
To avoid such circumstances, enterprises need to focus on creating privacy policies and
protecting all the sensitive data while utilizing AI in a cloud computing environment.

4. Conclusion
AI has already made a dominating start in the tech world and is present in every industry. On
the other hand, almost every technology now includes cloud backup services. As a result, the
demand for cloud computing services appears to be increasing in the next years.
Although the usage of AI and cloud computing together is in its initial phases and few of the
leading companies take charge of investing huge amounts in AI-based cloud testing services.
We need to understand that both are growing technology that has a lot of potential to grow
and evolve more in the coming years.