Cloud and AI: A Match Made in Business Heaven, But Data Holds the Key

Adya emphasizes the importance of comprehensive data management for cost-effective cloud deployments optimized for AI workloads. Infosys Cobalt offers industry-specific solutions, like "sentient factories" in manufacturing, to address these challenges.

Aanchal Ghatak
New Update
cloud and ai

While Cloud and AI are often seen as a seamless duo, Anant Adya, EVP at Infosys, highlights a hidden challenge: data management. Companies, focused on the exciting world of AI, can overlook the critical role efficient data handling plays in the underlying cloud infrastructure. This can lead to inefficiencies and higher costs.


What are the primary challenges organizations face when integrating cloud and AI technologies, and how does Infosys Cobalt address these challenges?

AI runs on the cloud. In fact, most of the new-age AI is born on cloud.  Therefore, the cloud is integral to AI, and there aren’t any integration challenges involved. However, AI brings a completely different set of challenges.

Comprehensive data management - AI deals with a lot of data. In many cases, these are large data sets that need to be moved around. Most often clients are focused on the AI aspect, and not the underlying cloud part. Inadvertently this makes the underlying cloud inefficient and expensive. Aspects like backup, archival, encryption, de-duplication, etc. which would otherwise be standard practices around data are quickly forgotten.


Could you provide examples of how Infosys Cobalt tailors cloud and AI solutions to meet the specific needs and challenges of different industries?

Infosys Cobalt has consistently focused on Industry Clouds as a mechanism to address the nuances of specific industries. The intention was to avoid broad-brushing cloud implementations and migrations and make them more industry-nuanced. Infosys Cobalt continues to take this concept to AI as well. The underlying cloud is customized to the needs of specific industries. For example:

1. Manufacturing: Building "Sentient" Factories


· Challenge: Manufacturers need to adapt quickly to changing market demands and optimize operations.

· Infosys Cobalt Solution: Infosys leverages cloud-based data analytics and AI to create "sentient" factories. These factories can collect real-time data from sensors and machines, analyze it with AI, and use the insights to adjust production processes automatically. This improves efficiency, reduces downtime, and allows manufacturers to respond faster to market shifts.

2. Financial Services: Reimagining Compliance


· Challenge: Financial institutions face complex regulations and a constant need to improve compliance processes.

· Infosys Cobalt Solution: Infosys offers AI-powered solutions that automate compliance tasks, such as Know Your Customer (KYC) checks and anti-money laundering (AML) processes. This frees up human resources for more strategic work and reduces the risk of errors. Additionally, Infosys Cobalt's secure cloud platform ensures sensitive financial data is protected.

These are just a couple of examples. Infosys Cobalt offers similar tailored solutions for industries like retail, healthcare, and energy, addressing their specific needs and challenges.


How does Infosys Cobalt ensure the security and compliance of cloud and AI solutions, especially in highly regulated sectors like finance and healthcare?

Infosys Cobalt prioritizes security and compliance for cloud and AI solutions, especially in sensitive industries such as finance and healthcare. Here are some key aspects of their approach:

· Compliance Frameworks: Infosys Cobalt follows industry-recognized security and compliance frameworks specific to the specific sector. This could include Payment Card Industry Data Security Standard (PCI DSS) for finance, Health Insurance Portability and Accountability Act (HIPAA) for healthcare, and others depending on the client's needs. These frameworks outline strict data security and privacy controls.


· Secure Cloud Infrastructure: Infosys Cobalt leverages secure cloud infrastructure, potentially using private clouds or adhering to stringent security protocols within public cloud environments. This includes features like data encryption, access controls, and vulnerability management.

· AI Governance: Infosys implements AI governance practices to ensure responsible AI development and deployment, including bias detection, explainability of AI models, and human oversight to mitigate potential risks associated with AI algorithms.

· Threat Detection and Response: Infosys Cobalt offers threat detection and response services to continuously monitor cloud environments and AI applications for suspicious activity, enabling prompt identification and mitigation of security threats.


· Data Privacy and Protection: Data privacy and protection are top priorities for Infosys. They likely employ data anonymization techniques where appropriate and implement robust data access controls to safeguard sensitive information.

· Single-Point Accountability: Infosys serves as a single point of accountability for security outcomes, simplifying the compliance process for clients in highly regulated sectors.

For industries like finance and healthcare, Infosys Cobalt offers specific features that address their unique concerns:

· Financial Services: Integration with existing financial data platforms and strong audit trails for regulatory compliance.

· Healthcare: HIPAA-compliant data storage and access controls, along with solutions for securely managing patient health information.

By combining these security and compliance measures, Infosys Cobalt aims to provide a trustworthy environment for cloud and AI solutions in highly regulated sectors. It's important to note that specific details about their security practices might not be publicly available due to their sensitive nature.

What are the typical return on investment (ROI) metrics or value propositions that organizations can expect from implementing cloud and AI solutions, and how do these vary across industries?

Moving to the cloud can save money on IT costs, and AI can automate tasks for smoother workflows. Cloud solutions can bring in more revenue and AI can assist with personalized marketing and better pricing strategies. Using cloud services provides on-demand resources, and AI can help with automated tasks. Cloud tools offer real-time insights, and AI can find hidden patterns for better decision making. The cloud encourages collaboration and innovation, and AI can handle repetitive tasks.

How are investment patterns evolving in the cloud and AI space? Are there particular areas within this intersection that are attracting more investment?

This era of investment marks a shift from traditional cloud infrastructure to the incorporation of AI capabilities. This extension encompasses AI development tools, training data platforms, and AI-powered applications, denoted as 'AI 1.0', as well as the essential hardware and software, referred to as 'AI 2.0', required for their efficient implementation. Businesses are increasingly leveraging a combination of cloud service providers and on-premises infrastructure. Investment trends are directed towards solutions that adeptly manage these intricate environments. Generative AI exhibits the potential to optimize resources, personalize cloud experiences, and generate training data, thereby drawing substantial investments.

How is generative AI enhancing cloud capabilities, and what is its projected market impact over the coming years?

Generative AI is boosting the capabilities of the cloud by:

· Optimizing resource allocation: AI predicts cloud needs, preventing waste and saving costs.

· Personalizing experiences: AI tailors cloud services and automates tasks for users.

· Creating training data: AI generates realistic data to train AI models directly in the cloud.

· Anonymizing data: AI creates synthetic data for training, safeguarding privacy in cloud-based AI.

Most clients are working on AI-related proof of concepts, and even if a small percentage of them go into production, it could have a significant impact on businesses downstream.

What economic benefits can businesses expect from modernizing their data landscape and adopting multi-cloud environments powered by AI? Are there any case studies or data points you can share?

· Cost Optimization: Cloud migration reduces IT infrastructure and software expenses. AI automates tasks, further streamlining operations and saving costs.

· Revenue Growth: Modern data allows for better customer insights, leading to personalized experiences and the development of new revenue streams through AI-powered products and services.

· Improved Efficiency: Multi-cloud provides flexibility and scalability, while AI automates tasks and streamlines workflows, boosting overall productivity.

· Faster Innovation: Cloud platforms and AI tools accelerate development cycles and time-to-market for new offerings. One example of the value delivered to a customer using the cloud is for a US healthcare company, Molina Healthcare, where Infosys helped the company expedite the integration of a new business.

Infosys Cobalt