Mr. Ajith Pai
VP and Global Delivery Head - TME,
Tech Mahindra
Dr. Anshu Premchand
Group Function Head – Multi Cloud and Digital Services,
Tech Mahindra

Constructive interaction between AI (particularly GenAI) and multi-cloud is rapidly transforming the landscape of hi-tech organizations. By leveraging the distributed computing power and flexibility of multi-cloud, hi-tech organizations like Microsoft, Apple, Samsung, Cisco, Google, Amazon and Applied Materials are unlocking the immense potential of GenAI, accelerating innovation, optimizing operations, and gaining a competitive edge.

AI & cloud present a powerful opportunity to unlock new frontiers. Here, we delve into some specific use cases in hi-tech industries where GenAI & cloud can be a game changer in five specific areas viz., product development & innovation, supply chain and logistics, customer experience and engagement, security, and fraud prevention and finally data management and analytics. We applied three principles to arrive at these use cases as areas of interest – innovation acceleration, operational optimization, and competitive advantage. Other than these principles, we believe that data security & privacy, interoperability & data portability and cost optimization will continue to be key multi-cloud considerations. 

Let us dive into some of these use cases:

Product Development & Innovation

  • GenAI-powered design tools: Imagine GenAI assistants suggesting optimal circuit layouts, streamlining chip design in chip manufacturing & material engineering companies. Imagine phone manufacturers utilizing GenAI to design phones that seamlessly adapt to diverse user preferences or software product companies employing it to craft intuitive interfaces for their software product suites. Think about GenAI-driven tools like Microsoft's Bonsai, Google’s Vertex AI or AWS’s Bedrock assisting industrial designers at personal product companies in optimizing product form and function and the impact these can have on timeline of new product development. Such design tools can significantly reduce design timeimprove product quality, and foster breakthroughs.
  • Predictive analytics for market trends: AI can analyse vast datasets of user behaviour, social media sentiment, and competitor activity to predict future market trends. This can empower manufacturers to pre-emptively tailor product features and release schedules, maximizing market impact. Gaining real-time insights into market trends, customer preferences, and competitor activities through AI could empower technology companies to stay ahead of the curve in the cloud computing market.
  • Automated quality control and testing: Manufacturing companies can leverage AI to automate quality control processes, ensuring product consistency and reducing error rates. Additionally, AI can analyse complex test data to predict potential failures, enhancing product reliability. 

Supply Chain & Logistics

  • GenAI-powered demand forecasting: Machine learning algorithms can analyse historical sales data, market trends, and external factors to accurately predict future demand for components and finished goods. This can enable IT product companies to optimize inventory levels, reduce costs, and avoid stockouts. Predictive pricing and revenue optimization using GenAI driven pricing strategies, demand forecasting and competitor analysis are other use cases of interest. 
  • Intelligent logistics optimization: AI algorithms can analyse traffic patterns, weather conditions and resource availability to optimize delivery routes and logistics networks. Such AI is leveraged by web mapping platforms that leverage satellite imagery as well. Logistics efficiency can be enhanced through AI-powered route planning, fleet management, and automated warehouse operations. 
  • Predictive maintenance: AI can analyse sensor data from equipment to predict potential malfunctions, enabling manufacturing companies to schedule preventive maintenance, minimize downtime, and extend equipment lifespan. For example, AI-powered network monitoring could predict and prevent outages, ensuring seamless connectivity.

Customer Experience & Engagement

  • Personalized product recommendations: AI can analyse customer purchase history, preferences, and browsing behaviour to recommend personalized products and services, enhancing customer satisfaction and loyalty. 
  • AI-powered chatbots and virtual assistants: Conversational AI can provide 24/7 customer support, answer complex questions, and resolve issues efficiently. This frees up human agents for more complex tasks and improves customer satisfaction.
  • Sentiment analysis and feedback monitoring: AI can analyse customer reviews, social media mentions, and email feedback to understand sentiment and identify areas for improvement. This could help address customer concerns and enhance overall brand perception.

Security & Fraud Prevention

  • Anomaly detection and threat identification: AI can analyse network traffic patterns, user behaviour, and system logs to detect anomalies and identify potential security threats. This enables mitigation of cyberattacks and data protection.
  • Fraudulent transaction detection: AI can analyse real-time financial transactions to identify suspicious patterns and prevent fraudulent activities. This is crucial to maintain user trust and financial security.
  • Personalized security measures: GenAI can analyse individual user behaviour and risk profiles to implement personalized security measures, providing optimal protection without hindering legitimate access.

Data Management & Analytics:

  • Data lakes and intelligent information retrieval: Cloud-based AI can process and analyse massive datasets stored in data lakes, extracting valuable insights, and enabling hi-tech companies to make data-driven decisions across various aspects of their business.
  • Natural language processing for information extraction: AI can understand and extract key information from documents, emails, and unstructured data sources. This allows for insight generation.
  • Generative AI for data augmentation and synthesis: GenAI can generate synthetic data to augment existing datasets and improve the accuracy of machine learning models, particularly beneficial for organizations dealing with sensitive personal data.

Beyond these specific use cases, AI & cloud offer several cross-cutting benefits for hi-tech organizations:

  • Scalability and cost-efficiency
  • Collaboration and knowledge sharing
  • Sustainability and green computing

By harnessing the power of AI & multi-cloud, hi-tech organizations can unlock transformative opportunities. We have done comprehensive analysis exploring specific hi-tech usecases, highlighting the potential for accelerated innovation, optimized operations, and a significant competitive edge. As AI and multi-cloud technologies continue to evolve, we continue to aid our hi-tech customers lead the charge towards a future powered by intelligent and efficient technologies. 

About the Author:

Dr. Anshu Premchand,
Group Function Head – Multi Cloud and Digital Services, Tech Mahindra

Dr. Anshu is a thought leader with 23+ years of experience in digital and cloud services, technical solution architecture, research and innovation, agility, and devSecOps. She heads multi-cloud and digital services for the enterprise technologies unit of TechM. In her last role, she was Global Head of Solutions and Architecture for Google Business Unit at Tata Consultancy Services where she was responsible for programs across the GCP spectrum including data modernization, application and infrastructure modernization, and AI.

She has extensive experience in designing large scale cloud transformation programs and advising customers across domains in areas of breakthrough innovation. Anshu holds a PhD in Computer Science. She has special interest in simplification programs and has published several papers in international journals like IEEE, Springer, and ACM.

Mr. Ajith Pai,
VP and Global Delivery Head - TME, Tech Mahindra

Ajith has 22+ years of experience in leading and driving various customer focused initiatives in business and delivery-based roles. He has worked on multiple programs and projects across customers in the banking, financial services, and hi-tech businesses. He is currently responsible for driving the global delivery and operations for strategic relationships within the hi-tech vertical of Tech Mahindra.

On the delivery front, key experiences include managing delivery for some of the largest financial services customers, building technology capable teams, Agile transformation to being the operations head for BFSI ANZ business and one of the key leaders of the North America BFS Ops team. In addition, he has led various teams focused on enterprise testing, data management, merger and integrations, and application development in his career.