Top 7 use cases of Cloud AI – A major fillip to product development

by Shashi Bhargava, on Sep 6, 2022 3:52:35 PM

Estimated reading time: 5 mins

Key takeaways from this blog

  • Cloud and AI have a force multiplier effect.
  • This synergy has numerous applications.
  • This blog shares seven important Cloud AI use cases.

Top 7 use cases of Cloud AI – A major fillip to product development

Cloud Computing and Artificial Intelligence/Machine Learning (AI/ML) have been around for quite some years. Individually, they are two of the most critical technologies that are trending, as highlighted by Gartner®. However, their cumulative synergy has a multiplying effect that offers a tremendous fillip to businesses. AI in Cloud Computing makes sense of voluminous data, accelerates processes of varying complexity, and improves product development by manifolds. AI imparts intelligent sentience to products that get further propelled by the computing power of the Cloud. 

What is the importance of AI/ML?

AI/ML simulates human cognition, such as learning, summarizing, and problem-solving. These traits have given rise to many practical applications, such as recommendation engines, web-search engines, robotic personal assistants, chatbots, etc. The awareness and reinforced learning improve the AI/ML ensemble’s performance in whichever form it uses. The fundamental importance lies in that AI/ML derives actionable insights from voluminous data and has a transformational potential that takes each aspect of the business to the next level. 

What is the importance of Cloud Computing?

Cloud Computing brings hyper-efficiency and productivity to business through the inherent traits of speed, scalability, composability, flexibility, accessibility, collaboration, and security. It enables business enterprises to take future-scoping in their stride because of the platform offered by the Cloud Native environment that makes shifting to different Cloud Service Providers seamless to take advantage of their new offerings. Cloud Computing imparts a high degree of nimbleness and cost-efficiency to the process architecture that otherwise requires heavy IT spending to acquire and maintain the IT infrastructure. 

What is the cumulative importance of AI in Cloud Computing?

AI in Cloud Computing is the ultimate frontier in product development. AI imparts the advantage of deriving actionable insights from voluminous data to the operational scalability, composability, and computing power of applications built in the Cloud Native environment. As a result, it affects the transactions in minutes compared to hours in a similar ensemble in a traditional on-premise setup. AI in Cloud Computing brings forth several use cases that are cutting-edge products by themselves. 

Top 7 use cases of Cloud AI

AI in Cloud Computing offers many scenarios for business automation, which is limited only by the imagination. Each of these use cases is also an AI-enabled product that gives tremendous fillip to the business processes due to the synergy of Cloud and AI. Amazon’s Alexa and Apple’s Siri are just some of the intelligent uses of Cloud AI. Some more are listed here –

  • Contract Management as a Product: Cloud AI allows extraction of the unstructured text in the physical copies of duly vetted and signed contract documents into the digital systems. It enables extracting each clause from the copy along with its summarized versions. It allows comparing similar clauses in between multiple contracts and assigning them a value, which helps to decide the best contract. It helps procurement and legal teams to store the various business contracts and create templates and frameworks to design new ones within minutes. Cloud AI helps with end-to-end Contract Administration and Management, a Cloud-based SaaS product by itself.

  • ESG crawlers: Environment-Social-Governance crawlers need to access data from various public domains and evaluate it. This is not just desirable but also imperative on an ongoing basis for ESG evaluation. The Cloud AI ensemble makes this process both intelligent and easy. From extracting data to assigning a score to the extracted elements based on various ESG parameters to the periodic ESG evaluations for deciding the ranking, it takes nearly a fraction of the time compared to the laborious manual methods. Such ESG crawlers are capable of being launched as SaaS products.

  • Fraud detection / AML: Cloud AI makes it possible to design fraud detection engines, which consume mammoth data, join the dots between seemingly unrelated data points from different domains, and arrive at a bigger picture within minutes, as against months earlier. The engine connects with internal databases and external third-party sites to extract this data. This bigger picture is almost always invisible to the human eye and perception and may require years to correlate if done manually, a time duration wherein the data substratum undergoes a major reshuffle. Cloud AI enables businesses to build Fraud Detection and AML engines and launch them as SaaS products that expedite work.

  • Pre-trained claim adjudication models: Cloud AI helps to build pre-trained claim adjudication models. It helps to build the vital recommendation engine for the claims management lifecycle and captures all the tacit knowledge generated while settling claims on a day-to-day basis. It helps to address the loopholes caused due to employee/decision-maker churn in the insurance claims management and claims adjudication teams. The pre-trained models have a built-in library of insurance and medical fraternity jargon to accelerate the adjudication from days to just a few minutes.

  • Recommendation engines for job sites: Cloud AI helps job generators and the job seekers. It reads the job description submitted by the employer and notes the key skills required. It then scans the database for the relevant skill sets, assigns a relevance score to the resumes, and recommends them to the employer. Similarly, when a job seeker posts a resume, it picks up the key skills, matches them with the jobs posted in the site, and lists down the most suited ones for the job seeker to apply. Cloud AI helps to expedite the process cycles for both the job generators and the job seekers.

  • Omni-channel order management: Cloud-hosted websites/portals use AI for end-to-end order management. These ensembles help to tap the flow of orders received from multiple channels, such as website product pages, product marketing SMS messages, campaign emailers, promotional ads, etc. It helps to pick up the key elements from the order, create a customer record, verify if full payment is received and if not, then what is the credit/EMI arrangement, score it as per predefined logic that also includes the customer’s credit standing, and route it to the packaging/tracking team for dispatch.

  • Customer Insights through Connected Data: Cloud AI helps enterprises generate insights from customer interaction data and product purchase history to create customer persona for thousands and millions of customers. It helps them to define that 20% who generate 80% of the business and personalize their interactions with these customers.

In summary

Products enabled by Cloud AI bring to the table the intelligence and sentience of AI and the extreme computing power offered by the Cloud Native environment. The human imagination is the only limitation to the practical application of this synergy.  

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Topics:CloudArtificial Intelligence / Machine Learning

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