Artificial intelligence (AI) continues to advance at a breakneck pace, shaping industries, redefining workflows and transforming everyday life. As we progress into 2025, several key trends are set to define AI’s trajectory, bringing forth innovative capabilities and new challenges for a dynamic and diverse country like India. On the development side of things, numerous capabilities or solutions will increasingly gain popularity this year.
A Year of Evolution
Modular AI systems will continue to evolve. For instance, customer service-AI modules will be tailored for specific sectors such as finance, retail or healthcare, offering bespoke solutions that address unique industry requirements. This will allow businesses to deploy task-focused AI components rather than rely on monolithic models. Additionally, plug-and-play AI modules will significantly reduce deployment timelines, making AI integration seamless for enterprises.
Small and medium businesses (SMBs), which often face budgetary and technological constraints, will especially benefit from these affordable, adaptable solutions. For example, a plug-and-play fraud detection module can be integrated into banking systems, while an inventory forecasting module can be used in retail, providing scalable yet niche applications. Further, standardised application programming interfaces (APIs) and communication protocols between AI modules will enhance interoperability, enabling seamless collaboration between multiple AI systems in dynamic environments. Businesses will have the flexibility to combine various AI capabilities to create robust and adaptable solutions for complex challenges.
Applications where instant decision-making is required will benefit from edge AI devices, such as autonomous drones, predictive maintenance and medical imaging. By processing data locally, edge AI can deliver faster insights while minimising latency. Not to mention, edge AI has the potential to power advanced hardware systems like self-driving cars, autonomous robots and internet of things (IoT) devices. The development of AI-specific hardware, such as neuromorphic chips and TPUs, will bring about energy-efficient on-device intelligence. This will allow AI to be deployed in constrained environments like satellites, wearables and remote sensors, expanding its reach to areas previously considered unfeasible.
Organisations will increasingly adopt methods like parameter-efficient tuning and Low-Rank Adaptation (LoRA) to fine-tune large foundation models for specific tasks. This approach will allow businesses to leverage advanced AI capabilities without incurring the high costs of training models from scratch. Efforts to distil large models into smaller, task-oriented systems will make AI more efficient while retaining high performance, reducing the computational and environmental costs associated with large-scale AI deployments.
Foundation models optimised for regional use cases, especially in non-English languages, will become more prevalent, enabling AI to address diverse needs and engage broader audiences.
In 2025, AI performance optimisation will also see a deeper focus on energy-efficient AI models, helping address environmental concerns related to high-energy training processes. As LLMs mature, their potential plateaus without fresh training data. This is where AI agents become invaluable, filling this gap by integrating with external sources like databases, APIs, and real-time feeds to ensure relevance.
This shift is worldwide driving businesses to prioritise AI agents as the natural next step after robust LLMs. AI agents will become increasingly adept at understanding nuanced contexts, enabling adaptive and personalised interactions in areas such as customer service, healthcare and education. For example, a context-aware healthcare agent could provide tailored advice based on a patient’s medical history and real-time health metrics. Agents equipped with feedback loops will learn and improve from interactions, enhancing their effectiveness over time. These self-evolving agents will deliver better long-term performance and adaptability across various applications.
The Way Ahead
For starters, AI's superpower to facilitate hyper-personalisation will allow enterprises to deliver unique customer experiences across sectors like retail, travel and finance. Predictive maintenance powered by AI will help identify equipment issues before they escalate, saving costs and reducing downtime. Enhanced decision-making through AI-powered analytics will improve business agility and performance. What AI can offer to the business landscape is essentially a laundry list.
However, challenges remain. Privacy concerns are significant, with increased data usage posing risks of breaches and misuse. The unregulated use of AI, particularly generative AI, could amplify misinformation and deepfakes while extensive AI-driven surveillance can infringe on personal privacy. Regulatory complexity adds another layer of difficulty, as fragmented global rules complicate AI deployment and are also often trailing behind AI’s rapid advancement.
While the potential benefits of AI are highly promising, addressing the challenges will be equally critical. How this plays out in India will also be interesting to observe in 2025—because given its unique diversity, emerging digital ecosystem, AI-niche start-ups and proximity to talent pools, India can position itself as a global AI powerhouse, creating solutions that benefit both its population and the world at large.
The author is director, AI research, Zoho Corporation. Views are personal.
