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Three Vector AI opportunity: Code, Enterprise & Agents
Three Vector AI opportunity: Code, Enterprise & Agents

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In CY2024, Artificial Intelligence proved to be the undeniable catalyst for the Indian Tech Industry. As AI continues to evolve, three AI Vectors are emerging as the transformative forces for the tech Industry: AI-assisted code development, AI integration into enterprises and the development of AI agents.

  1. AI Assisted Code Development:

Cognizant has launched a platform called “Cognizant Flowsource” for software developer professionals that offers code completions and generation, rich library of prompt templates for development tasks, code explainability, debugging and knowledge search and management etc. Cognizant is training its associates to use Gemini for Software development assistance which is integrated into their Cognizant Flowsource platform. Approximate 35,000 Cognizant developers have been trained on GitHub Copilot, with an additional 40,000 developers slated to receive training. As per TechM, It has partnered with Microsoft for deploying GitHub Copilot for 5000 developers which is expected to increase developer productivity by 35% to 40% within the organization by democratizing access to AI capabilities.

Faster Development cycles (Productivity & Speed):

AI assisted coding tools such as GitHub Copilot, Codeium, Tabnine, and JetBrains AI Assistant auto-complete etc can generate code and can suggest code completion for developers. Developers can focus more on problem-solving and architecture rather than syntax and boilerplate code. It can be integrated with all tasks of software delivery professionals across the software delivery life cycle. This presents an opportunity to rapidly enhance the developer productivity with an improvement in average number of engineering tasks completed, reduction in code review & code documentation time and thereby reducing the overall timing of the project from concept to overall phased expansion of the project.

Code Quality and Security:

AI-powered tools assist in code refactoring, optimization, and bug detection, reducing technical debt. AI models like DeepCode, Amazon CodeWhisperer help identify security vulnerabilities, inefficiencies, and best-practice deviations. These tools can identify vulnerabilities in real time while writing code and provide recommendations for improvement, thereby enhancing code quality and overall efficiency.

Reducing debugging time:

AI-assisted debugging tools, such as Microsoft’s IntelliCode, DeepCode, and Codiga, analyze code to identify potential bugs before execution. AI-driven bug detection reduces time spent on debugging, allowing developers to focus on feature development. Traditional testing methods often require significant manual effort, but AI-powered tools accelerate the process, reduce human errors, and improve overall software reliability.

 

 

  1. AI Integration into Enterprise:

In CY24, Indian tech companies continued working on PoCs, advancing them to production-grade AI use cases. They shifted toward pragmatic applications and vertical AI solutions for enterprise integration. Focus pivoted from raw capabilities to reliability, efficiency, and specificity led by improvements in model reasoning, factuality, and context management. Progress in RAGs and knowledge graphs improved contextual outputs, custom and industry-specific small models became mainstream, and tech providers started integrating AI, cloud, data, and GenAI onto scalable and highly customizable platforms in a strong PoC-to-production push towards enterprises. Strategic partnerships compromised 55% of all AI activity, centered around cocreation, client centricity and rapid go to market activities.

There is a massive opportunity in solving Industry specific challenges by delivering tailored and scalable production ready AI solutions. Developing domain specific AI models across various functions (Healthcare, legal, lifesciences, BFSI etc) can provide a competitive advantage. For Examples, Coforge launched Copilot Innovation Hub in partnership with Microsoft to develop industry specific solutions and introduced Underwriter Copilot for Insurance and Advisor Copilot for Financial Services. Infosys has partnered with Sarvam AI for creating Industry specific applications based on small language models for rapid enterprise adoption.

  1. Development of AI agents:

MIT and Nvidia researchers define agents as AI models and algorithms that can autonomously make decisions in a dynamic world, that are multimodal and can take on complex tasks much like a human assistant. Tech Industry is offering two different formats for consumption of AI agents. They are doing an agentification of their existing platforms. For example: IBM offers AI agents for drug discovery process bundled into the Watsonx platform for healthcare. Cognizant launched multi agent orchestration on its NeuroAI platform. Another way is through, workflow of industry specific AI agents. For example: Infosys is building specific client-usage AI agents for integration across its four industry-specific SLM.

Finally, the three vector AI opportunity will strengthen India in AI adoption and will be clear path to lead AI’s global evolution. The key will be to train the talent effectively, adopt AI proactively, integrate it responsibly, and innovate aggressively.

 

 

 

 


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Madhumay
Deputy Manager - Research

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