By: Archana Joshi, Head - Strategy, Enterprise AI & Banking and Financial Services, LTIMindtree

Introduction

Artificial Intelligence (AI) is transforming industries and redefining possibilities at an unprecedented pace. As we look toward the future, several trends are poised to shape its future in remarkable ways.One of the most promising advancements in AI during the past few years is Generative AI, which uses large language models (LLMs) to create digital content like text, images, and code. Unlike traditional AI, which performs predefined tasks, generative AI takes prompts as inputs and independently learns patterns from sample data to produce unique, realistic outputs. This makes it a powerful tool for driving innovation across industries by automating creative and complex tasks. From ethical innovation to generative breakthroughs, here are the top 10 AI trends in 2025 that will influence the AI’s trajectory.

1. Agentic AI

   Agentic AI refers to adaptive AI systems capable of performing complex and repetitive tasks using natural language inputs with minimal human intervention. These intelligent agents interact with their surroundings to achieve goals through decision-making. They can be AI systems, collaborative robots, or any computational entities capable of sensing and acting on their environment.

   According to Gartner, Agentic AI is expected to be integrated into 33%¹ of enterprise software applications by 2028, compared to just 1% today. As one of the Top AI trends in 2025, it has the potential to transform industries by streamlining workflows and enhancing operational efficiency.

   Applications and Impact :

   • Multi-robot Factories : Coordinates robots to optimize production lines and reduce downtime.
   • Automated Trading Systems : Analyzes market data in real-time, improving efficiency and profitability.
   • Personalized Treatment Planning : Tailors treatment plans based on extensive medical data analysis.

   Agentic AI streamlines workflows by reducing reliance on multiple applications and products, allowing tasks to be completed seamlessly through multimodal interfaces. This technology is crucial for integrating legacy systems and providing a natural framework for task allocation and planning.

2. GraphRAG

   GraphRAG (Retrieval Augmented Generation) combines large language models (LLMs) with knowledge graphs to automate the creation of semantic data structures from text documents. These knowledge graphs represent relationships and context within data without requiring extensive manual intervention.

   Applications and Impact :

   – Healthcare : Supports universal patient records for efficient, data-driven decision-making.

   – Retail & Consumer Packaged Goods : Optimizes operations by analyzing customer feedback and product ratings.

   – Media & Entertainment : Tracks social media trends to personalize content recommendations.

   – Hi-Tech : Creates enterprise data fabrics for centralized data management and boosts search engine optimization through intelligent data linking.

   GraphRAG’s ability to create automated knowledge graphs fosters deeper insights and paves the way for intelligent decision-making across industries.

3. Fractional GPU (FGPU)

   FGPU is a software-driven mechanism that partitions a GPU’s computing and memory resources. This approach allows multiple applications to operate in parallel with strong performance isolation, ensuring optimal resource utilization and cost-efficiency. By enabling a single GPU to be split into smaller, independent parts, FGPUs are transforming the way enterprises are managing workloads.

   Why It Matters?

   As AI workloads become more diverse and resource-intensive, effective GPU resource management becomes critical. FGPUs are a powerful solution that address key challenges in this space such as:

   • Optimal Resource Allocation : Tailors GPU resources to application needs, preventing overuse.
   • Performance Isolation : Ensures tasks don’t interfere with one another.
   • Cost Efficiency : Maximizes GPU utilization to reduce expenses.

   Real-World Applications

   FGPUs are versatile and domain-agnostic, making them applicable across a wide range of industries and use cases:

   • Dynamic GPU Partitioning for Scalable Access: Seamlessly allocates GPU resources to match fluctuating workloads.
   • Workload Isolation: Runs multiple AI tasks or applications without cross-interference, ensuring reliable results.
   • Usage-based Billing Models: Implements fair and transparent pricing structures for GPU usage.
   • Resource Scheduling: Optimizes GPU resource distribution across teams, projects, or tenants.

4. Quantum AI

   Quantum AI is transforming problem-solving by leveraging quantum mechanics principles like superposition and entanglement.

   Transformative Applications of Quantum AI

   Quantum AI’s potential spans multiple sectors, unlocking efficiency, innovation, and problem-solving capabilities:

   • Banking and Financial Services (BFS)
     • Real-Time Derivatives Pricing: Quantum AI provides lightning-fast computations for derivatives pricing, enabling financial institutions to make data-driven decisions in real-time.
     • Portfolio Optimization: Optimizes asset allocation and risk management strategies with unparalleled accuracy and efficiency.
   • Manufacturing
     • New Material Development: Accelerates the discovery of advanced materials by accurately predicting their properties, driving innovation in product design.
     • Manufacturing Process Optimization: Enhances operational efficiency and reduces waste by leveraging quantum-driven optimization models.
   • Public Sector
     • Weather Forecasting: Quantum AI powers highly accurate weather models, improving disaster preparedness and agricultural planning.
     • Environmental Planning: Supports sustainable development by analyzing complex environmental datasets and predicting long-term outcomes.

5. AI Governance

   As AI adoption accelerates, governance frameworks ensure its safe, ethical, and fair use.

   Applications and Impact:

   • Compliance Management: Aligns with regulations like GDPR to protect data privacy.
   • Risk Management: Identifies and addresses governance risks.
   • Transparency: Simplifies auditing and testing processes for AI systems.

   Implementing AI governance brings many benefits, including fostering trust, ensuring compliance, and promoting responsible AI development and use.

6. AI as a Service (AIaaS)

   AIaaS provides businesses with scalable AI capabilities through external providers, reducing upfront investments. This trend supports automation, decision-making, and customer engagement.

   Applications and Impact:

   • Communication & Media: Automated content creation and audience analytics.
   • Retail: Real-time inventory management and recommendations.
   • Manufacturing: Quality control and rapid prototyping
   • Insurance: Streamlines claims processing and premium calculations.

   AIaaS drives innovation while addressing privacy and security concerns, ensuring accessible and affordable AI-powered solutions.

7. Explainable AI (XAI)

   Explainable AI (XAI) is an approach to developing AI systems designed to be transparent and understandable to humans. XAI aims to create AI systems that can clarify their decision-making processes and provide transparent, easily understandable rationales for the decisions they generate.

   Applications and Impact:

   • Finance: Explains credit score calculations to ensure fairness.
   • Healthcare: Provides insights into clinical trial analyses.
   • Customer Support: Enhances trust with transparent chatbot responses.

   XAI enhances the precision and efficiency of AI systems and contributes to their improvement by facilitating error identification and correction.

8. AI-powered Hyper Automation

   Hyper automation combines AI and human intelligence to automate complex workflows. It is reshaping how organizations extract insights and innovate.

   Applications and Impact:

   • Cybersecurity Operations: AI can automate threat detection and response, significantly enhancing an organization’s security posture.
   • R&D Automation: In research and development, AI can automate data analysis and experimental procedures, accelerating innovation.
   • Customer Service Automation: AI-powered chatbots and virtual assistants can handle customer inquiries, improving service efficiency and customer satisfaction.

   Hyper automation integrates AI across all organizational layers without needing technical expertise, dynamically discovering business processes and creating bots to automate them.

9. Edge AI

   Edge AI processes data locally, enabling real-time data processing and decision-making. Its low latency and high reliability make it a standout among the Top AI trends in 2025.

   Applications and Impact:

   Autonomous Driving: Edge AI processes data from vehicle sensors in real-time, enabling safe and efficient autonomous driving.

   Healthcare: Supports real-time monitoring of patient vitals and provides immediate responses to critical conditions.

   Smart Cities: Powers smart city applications, such as traffic management and environmental monitoring, thereby enhancing urban living.

   All in all, Edge AI reduces the need for constant connectivity, making it ideal for applications in remote or mobile environments.

10. Compact LLM

    Compact LLMs are AI models with fewer than 100 million parameters, designed to perform tasks such as text generation, summarization, language translation, and question answering. Compared to larger models, they offer advantages like improved efficiency, lower costs, and customization flexibility.

    Applications and Impact:

    • Banking & Financial Services (BFS):
      • Prevents sensitive data leaks by processing data locally.
      • Powers customer service bots for real-time assistance.
    • Communication, Media & Entertainment:
      • Generates natural language for animated content.
      • Builds dialogue models for immersive gaming experiences.
    • Hi-Tech:
      • Supports secure collaboration without data sharing.
      • Enhances data security through localized data processing.
    • Retail & Consumer Packaged Goods:
      • Delivers personalized advertisements in real time.
      • Enables dynamic product pricing based on market trends.

Compact LLMs democratize AI by making powerful language capabilities accessible even to resource-constrained organizations. Their versatility and efficiency position them as critical enablers of digital transformation across industries.

Conclusion

These Top 10 AI trends in 2025 highlight the diverse and transformative potential of AI across various industries and applications. From enhancing operational efficiency and security to fostering creativity and innovation, these trends are set to shape the future of technology and society. As AI continues to evolve, it is crucial to address the ethical, governance, and transparency challenges to ensure its responsible and beneficial use. For more insights into the trends shaping 2025, please refer to the LTIMindtree AI Technology Trends Radar Report.

Reference:

¹Agentic AI Set to Rise, With New Cybersecurity Risks: Gartner, AI Business, Heidi Vella, December 3, 2024: https://aibusiness.com/automation/agentic-ai-set-to-rise-with-new-cybersecurity-risks-gartner