By: Jitendra Putcha (Jit), Senior Vice President & Global Head - Data & AI, LTIMindtree

As we step into 2025, the data and AI landscape is not just evolving but experiencing a complete shift in direction. The coming year is not about incremental improvements; it’s about redefining how technology augments and amplifies human potential.

Enterprises must move beyond experimenting with AI applications and focus on strategic implementation that impacts every aspect of their operations. Several forces drive this transformation: the exponential growth of computational power, advancements in machine learning models, improved data processing capabilities, and a growing appreciation for AI’s role in decision-making. In this blog, you will learn about the six key trends that will shape the trajectory of data and AI in 2025, ushering new possibilities and challenges.

Trend 1: A new purpose for Data & AI—from insights to impact

2025 will focus on creating tangible value with AI rather than merely delivering insights. AI is no longer a topic in the server room, it is the front and center of the board agenda. According to a McKinsey study, gen AI has the potential to generate value equivalent to US $2.6 trillion to US $4.4 trillion in global corporate profits annually.^[1]

• AI will drive business value: The goalpost of data and AI has shifted from “knowing more” to “doing more”. Organizations will increasingly focus on leveraging AI to drive business value rather than just delivering information or insights. This means creating new revenue streams through innovative applications of AI. It also means use cases related to business optimization and operational excellence will gain traction. 2025 will mark the definitive shift away from the legacy purpose of Data and AI, which focused solely on delivering the right information to the right people at the right time.
• AI will become a strategic partner for decision making: Rather than being viewed as a tool to generate information, AI will be seen as a strategic partner in decision-making processes. It will enhance human capabilities and foster collaboration between humans and machines. The transition from decision support to augmentation and automation will gain traction. This marks a shift towards AI-enhanced processes that empower humans to make faster, smarter choices.
•  AI will create value for everyone: AI will empower non-technical stakeholders with accessible data tools and AI-driven platforms. AI will become a creative partner, generating novel ideas, designing innovative products, and crafting compelling narratives.

Trend 2: Infrastructure 2.0—from generic to special purpose compute

Data and AI Infrastructure is undergoing a fundamental redesign to support complex, distributed, and intelligent AI workloads. The infrastructure will enable new kinds of workloads and applications.

• Use of polymorphic compute: The demand for AI processing power will fuel the development of infrastructure tailored for specific tasks. Graphics processing units (GPUs), with their parallel processing capabilities, excel in tasks like training deep neural networks and are supported by frameworks like TensorFlow and PyTorch. Tensor processing units (TPUs) specialize in tensor operations, offering high performance and energy efficiency for large-scale AI workloads, particularly within the TensorFlow ecosystem. Neural processing units (NPUs) focus on low-power AI processing for edge devices, enabling on-device applications such as image recognition and voice processing. Together, these processors cater to diverse AI requirements, from cloud-based training to real-time edge deployments.
• Use of distinct compute for AI training vs. inference: AI training and inference require distinct infrastructure due to their differing computational demands. AI training requires high-performance GPUs or TPUs and large-scale data centers, while inference demands latency-sensitive edge computing for real-time results. These distinct requirements highlight the growing need for specialized setups.
• Hybrid and multicloud environments: Businesses will adopt hybrid cloud environments that combine on-premises data centers with public cloud resources, allowing for greater flexibility and scalability in managing data workloads. Also, due to the varying capabilities of public cloud, organizations will use multiple cloud providers based on workload and cost needs. According to a Cloudzero study, 81% of enterprises have adopted or are planning to adopt a multicloud strategy to leverage the unique strengths of different cloud providers for specific workloads, ensuring cost optimization and workload distribution ^[2].

Trend 3: Next-gen architectures—beyond ETL, data lakes, and BI

The traditional three-tier architecture of extract, transform, and load (ETL) tools, MPP databases and business intelligence (BI) tools will be replaced by specialized, composable systems. The architecture will evolve from data lakehouse to data mesh style.

• Adoption of data mesh style architecture: Data mesh will decentralize data ownership to domain-specific teams promoting scalability and agility in data management, therefore aligning data architecture with business needs. Data mesh will empower data teams to own and manage their data products, fostering agility and innovation. It will change the mindset from collecting data (data lake mentality) to leveraging data (data product mentality).
• From SQL engines to reasoning engines: The traditional Structured Query Language (SQL) engine, while powerful, is limited to structured queries. In 2025, we’ll see a shift towards reasoning engines that can handle more complex, unstructured, and contextual queries. These engines will augment SQL by enabling deeper analysis, understanding, and inference from data. Thus, by combining the power of SQL with the flexibility of reasoning, businesses will unlock new insights and drive innovation in fields like healthcare, finance, and scientific research. Reasoning engines will emulate human problem-solving through deductive, inductive, and abductive reasoning, enabling swift and complex decision-making.
• From catalogs to knowledge fabric: The transition from traditional data catalogs to a comprehensive knowledge fabric represents a significant advancement in data management and utilization. Unlike conventional data catalogs that primarily index and organize data assets, a knowledge fabric integrates structured, semi-structured, and unstructured data into a unified framework.

Trend 4: A new toolkit for AI creators and consumers

Data engineers, data scientists and analysts will be powered by AI improving experience and accelerating productivity.

• No-code/low-code platforms for creators: These platforms will democratize and accelerate access to AI development, allowing non-technical users to build applications and automate processes without extensive coding knowledge.
• AI-powered data analytics for analysts: Advanced analytics platforms that utilize AI for conversational analytics, predictive modeling, and insights generation will become standard in organizations seeking to leverage their data effectively. This will make everyone an analyst in the organization.

Trend 5: New form factors and experiences

AI will reshape applications into multimodal, collaborative tools that elevate user experiences.

• Virtual assistants and copilots: Voice-activated assistants and intuitive chatbots will allow users to engage with systems more naturally, improving productivity.
• Personalized experiences: AI will continue tailoring content and interactions to individual needs, redefining how users interact with platforms.
• Empathetic AI interfaces: Interfaces capable of understanding emotional nuances will enhance engagement, going beyond basic transactional interactions.

Trend 6: Trust and sustainability take center stage

Applications will be designed with trust and sustainability as the first principles. The success of failure of strategic AI initiatives will depend on trust and sustainability.

• Transparent algorithms: According to a study by IBM, more than half (57%) of CEOs surveyed are concerned about data security and 48% worry about bias or data accuracy ^[3]. More organizations will prioritize transparency in AI decision-making, ensuring users understand how outcomes are derived.
• Ethical AI practices: Organizations will adopt ethical frameworks guiding the development and deployment of AI technologies, ensuring fairness and accountability in their applications.
• Enterprises will monitor carbon footprint of AI workloads: Enterprises will adopt tools to measure and optimize the energy consumption of AI training and inference processes.

A pivotal year for Data and AI

2025 promises to be a landmark year for data and AI, redefining how businesses operate and thrive in a digital-first world. By integrating responsible and sustainable frameworks, organizations can unlock AI’s vast potential while keeping risks in check.

As the Greek mathematician Archimedes once said, “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.” Today, AI is that lever—let’s embrace it to reshape the future responsibly.

Citations

¹AI could increase corporate profits by $4.4 trillion a year, according to new research, Michael Chui and Lareina Yee, McKinsey Global Institute, July 7, 2023

²The Cloud Cost Playbook, CloudZero, July 18, 2024

³IBM Study: CEOs Embrace Generative AI as Productivity Jumps to the Top of their Agendas, IBM, Jun 27, 2023