The role of AI in cloud computing

As Artificial Intelligence (AI) is gaining in popularity, it is now clear that its evolution also complements the growth of cloud computing. Using AI within the cloud can then enhance the performance and efficiency of the cloud as well as drive the digital transformation of organizations.

AI capabilities within the cloud computing environment are a strategic key to make businesses more efficient, strategic, and insight-driven, all the while giving them more flexibility, agility, and cost savings by hosting data and applications in the cloud.

Hence, we have asked experts in the industry to explore the ever-growing role of AI in cloud computing.


What is AI Cloud Computing?

AI Cloud computing essentially means combining artificial intelligence (AI) with cloud computing.

Indeed, according to Dipanshu Shekhar and Swathi Sreekant, AAdvisor Digital Strategy at DXC Technology & Advisor Automation Strategy at DXC Technology respectively, it means that AI tools and AI software are synched with the power of cloud computing, which provides an enhanced value to the existing cloud computing environment and this combination makes enterprises efficient, strategic and insight-driven.

Cloud computing helps enterprises to be more agile and flexible and provide cost benefits by hosting data and application on the cloud. With this new layer of AI, which helps in generating insights from the data, it gives intelligence to existing capabilities and delivers exceptional customer experience. This leads to a powerfully unique combination that enterprises can use for their benefit.

Christopher Patten, Cloud & DevOps leader at Centrica adds that Cloud is a simulation, like a video game. As such, it is inherently observable and like a Tesla electric car, your Cloud kicks out a tremendous amount of operational data and telemetry. Hence, AI Cloud Computing is essentially AI Ops, using algorithms to make sense of all this data and determine the optimal course of action rather than leaving it to people.

In a post-COVID world, Dipanshu and Swathi continue, Cloud-computing has accelerated with spending increasing to 37% to $29 billion in the first quarter of 2020 as compared to the first quarter of 2019 (Source-: Gartner). Therefore, synergizing AI and cloud computing solutions will bring organizations closer to their end customers and improve their operational effectiveness


The role of AI in cloud computing

Cloud computing environment and solutions are enabling enterprises to become more agile, flexible, and cost-effective as this substantially reduces infrastructure management costs for enterprises, Dipanshu and Swathi state. Artificial Intelligence (AI) enables additional flexibility as it helps them manage large data repositories, streamline data, optimize workflows, and produce real-time insights to transform day-to-day operations and re-imagine end customer experience.

Christopher underlines that AI operations allow shifting the operational burden from process and people to engineering and data.

Hence, Dipanshu and Swathi add, AI is improving cloud computing in myriad ways. AI in the cloud is now being utilized effectively via the SaaS route. Many SaaS providers are adding the AI layer to their products which offer exceptional functionality to end-users and customers. This is especially true for CRM software where customer data is being utilized to make personalized actionable insights.

Additionally, AI, as a service, is also one of the ways enterprises are using AI to improve their current cloud setup. AI makes things agile and ensures process efficiencies that help minimize errors and improve productivity.


The benefits of AI in cloud computing

According to Dipanshu and Swathi, there are several benefits of using AI in the cloud:

  • Enhanced data management: We live in a data-driven world with countless data out there. Simply managing that data is a huge challenge that enterprises face. AI tools and applications that run over the cloud that help to manage data effectively by identifying it, updating it, cataloging it, and offer real-time data insights to customers. AI tools also help in detecting fraudulent activities and noticing some patterns in the system that look out of place. Financial institutions and banks are heavy users of this technology, this allows them to stay relevant and secure in very risky environments.
  • Automation: This combined technology of AI and cloud removes the barriers to Intelligent automation and enables enterprise-wide rollout across the organization. AI brings in predictiveness as algorithmic models provide real-time insights based on data pattern, historicity, etc. Leveraging AI and cloud computing solutions can generate forces of hyper-automation for enterprises as it will not only bring in cognitive automation on semi-structured and unstructured documents but also push boundaries for effective infrastructure management thereby ensuring minimum disruption. This leads to cost transformation for enterprises and transformative end customer experience
  • Cost Savings: The adoption of the cloud enables enterprises to only pay as much as they use. This is a huge cost saving over traditional infrastructure costs of setting huge data centers and managing them. The cost saved from this arrangement can be used to set up the more strategic development of AI tools and accelerators that can be further used to generate greater revenue and save fundamental costs for the enterprise.

According to Christopher, AI cloud computing will lead to higher operational quality and lower operational costs.


The challenges

The challenges are that a technical DevOps/SRE mindset and skillset are required, Christopher states. Indeed, this is hugely disruptive to existing operating models built around ITIL with its emphasis on fragmentation by function.

Moreover, Dipanshu and Swathi point out some challenges that can crop up with the integration of these two technologies:

  • Integration: Whenever two disparate technologies come together, there is always a challenge in beginning the integration smoothly. However, this integration is fundamentally dependent on enterprises first moving their applications and technologies to the cloud completely, which itself is a huge task for many enterprises. Only after such a transformational change can enterprises think about adding the AI layer to the cloud. The technology sync hence is too dependent on enterprises working on a concrete digital transformation of their infrastructure.
  • Inadequate data: AI tools work best with large sets of good data. Enterprises need to ensure that data is accessible and clean so that AI can deliver value. This is a huge challenge as we see that many times data is very unstructured, siloed, or incomplete. The quality of data is extremely important for the solution to deliver value.
  • Security and privacy issues: Enterprises use a lot of sensitive and financial information that can be targeted for data breaches by hackers so one needs to be cognizant of ensuring that the privacy breaches do not happen.


AI Cloud Computing strategies & businesses

Dipanshu and Swathi think that businesses should implement AI cloud computing strategies.

However, they continue, there is no one size fits all approach in this regard. Every enterprise must keep in mind their end goal and then slowly and steadily work their way into moving their tech stack to the cloud after which the integration with AI can be made plausible.

Businesses and enterprises must incorporate AI cloud strategy as part of their overall strategic technology roadmap in line with their strategic and operational goals to stay relevant and much ahead of their competition. It is imperative to also note that, this synchronization of AI and cloud requires significant expertise, resources, and cost to effectively translate into something of value for the enterprise.

But once the integration of cloud systems and AI come through successfully, it will allow enterprises access to potent machine learning capabilities such as image recognition tools, natural language processing, and recommendation engines and these toolsets are very integral and disruptive in nature. This will set a precedent for other enterprises to follow suit.

Christopher also adds that as Cloud marches ever onwards, businesses need an operational AI Cloud to look after it, so it is vital that they adopt AI cloud computing strategies.


The future of AI Cloud Computing

Christopher believes that the future of AI Ops is ultimately to be the de facto model for operating the cloud.

Dipanshu and Swathi think similarly. Indeed, they believe AI will add to the already powerful capabilities of the cloud and make it an even more potent technology. Analysis and management of data will completely change with this combination. In this world where we are inundated with massive amounts of data, AI + cloud combination is a game-changer and will provide unmatched value to end-users.

Cloud computing and AI are causing disruption in sectors of all shapes and sizes in the post COVID world and are leading to democratization owing to its wider availability. We are seeing a world where technology has become a reality and occupied its position from operational to strategic priority.

In the pre-COVID scenario, as per the Gartner report in 2019, the AI market was expected to grow at a CAGR of 33.2% from 2019 to 2027. This has increased much more as more sectors have awakened to the reality of a post COVID world.

Most organizations have already doubled their focus on moving to a cloud-enabled world. With the inclusion of AI, the organization expects to solve more visible and new problems and create a new world for its prospective customers


Special thanks to Dipanshu Shekhar, Swathi Sreekant, and Christopher Patten for their insights on the topic.