The 2-Z of 2025 predictions: V is for verticals, part 1

V is for Verticals

Automotive: AI adoption

Source: NVIDIA. Wu.

Xinzhou Wu, VP of Automotive, NVIDIA, said that autonomous vehicles (AVs) will become more performant as developers tap into advancements in generative AI. "For example, harnessing foundation models, such as vision language models, provides an opportunity to use Internet-scale knowledge to solve one of the hardest problems in the AV field, namely that of efficiently and safely reasoning through rare corner cases," he explained.

Corner cases are situations where parameters are at their most extreme, or when extremely unusual conditions coincide.

"Effectively, new advances in AI will catalyse AV software development across the three key computers underpinning AV development — one for training the AI-based stack in the data centre, another for simulation and validation, and a third in-vehicle computer to process real-time sensor data for safe driving. Together, these systems will enable continuous improvement of AV software for enhanced safety and performance of cars, trucks, robotaxis and beyond," he said.

"More broadly, new AI-based tools will enable breakthroughs in how AV development is carried out. For example, advances in generative simulation will enable the scalable creation of complex scenarios aimed at stress-testing vehicles for safety purposes. Aside from allowing for testing unusual or dangerous conditions, simulation is also essential for generating synthetic data to enable end-to-end model training."

Construction, engineering and design: AI adoption

Source: NVIDIA. Pette.

Bob Pette, VP of Enterprise Platforms, NVIDIA, said that there will be a rise in generative AI models tailored to the construction, engineering and design industries that will boost efficiency and accelerate innovation.

"In construction, agentic AI will extract meaning from massive volumes of construction data collected from onsite sensors and cameras, offering insights that lead to more efficient project timelines and budget management. AI will evaluate reality capture data (lidar, photogrammetry and radiance fields) 24x7 and derive mission-critical insights on quality, safety and compliance — resulting in reduced errors and worksite injuries," he said.

"For engineers, predictive physics based on physics-informed neural networks will accelerate flood prediction, structural engineering and computational fluid dynamics for airflow solutions tailored to individual rooms or floors of a building — allowing for faster design iteration."

When it comes to design, retrieval-augmented generation (RAG) will enable compliance early in the design phase by ensuring that information modelling for designing and constructing buildings complies with local building codes, Pette added. 

"Diffusion AI models will accelerate conceptual design and site planning by enabling architects and designers to combine keyword prompts and rough sketches to generate richly detailed conceptual images for client presentations. That will free up time to focus on research and design."

Source: Couchbase. Duddi.

Gopi Duddi, SVP, Engineering, Couchbase said: "As engineering teams rapidly adopt AI technologies, enterprises will face a growing risk of AI silos, with different departments implementing varied AI solutions and creating convoluted data architectures across organisations.

"However, this challenge is driving the emergence of unified AI platforms that will fundamentally reshape how engineering teams operate. Just as database management systems arose to address data silos, these new platforms will serve as abstraction layers across an organisation's AI initiatives, providing a single interface for managing AI resources."

According to Duddi, the most profound change will be in how engineers interact with these systems. "Natural language interfaces will increasingly replace specialised query languages, democratising access to technical systems. Organisations that move quickly to establish unified AI architectures will gain significant advantages, but success will require viewing AI not as just another tool, but as a fundamental layer of the technical stack that requires enterprise-wide coordination and governance," he explained.

"As AI assumes a larger role in code development, generating and evaluating code, commits, design documents and other development artifacts, organisational hierarchies will likely flatten. This transformation will reduce the need for multiple layers of pure people management, as AI systems take on more of the technical oversight and quality control functions traditionally managed by middle management layers." 

Engineering: data management

Duddi added that the rapid adoption of AI tools is forcing engineering teams to rethink their approach to data management. "Within the next few years, the integration of AI across enterprise applications will generate unprecedented volumes of data – not just from AI processing, but from the cascade of automated workflows, interconnected systems and intelligent features that AI enables," he predicted.

"To handle this data explosion, engineering teams must abandon traditional monolithic architectures in favor of distributed systems that can scale dynamically. Success will require implementing hybrid solutions that balance data across on-premises and multiple cloud regions, while maintaining strict data sovereignty and privacy controls. 

"Teams must also develop expertise in automated data management and cross-application data flow, as the future enterprise ecosystem will demand seamless data sharing between AI-enhanced applications."

When it comes to the role of engineering teams, Duddi said they will no longer build standalone applications, instead orchestrating complex data networks "that can handle exponential growth in data volume while maintaining performance, security and compliance across the entire organisational workflow."

"Enterprise catalogues will evolve into dynamic agent directories, enabling applications to self-register as AI agents for autonomous discovery and collaboration. This transformation will create a fluid enterprise fabric where AI agents can independently locate and work with one another, fundamentally changing how applications interact and share capabilities across the organisation," he elaborated.

Energy: AI adoption

Source: Hitachi Vantara.
Hardman.

"In energy management, AI is revolutionising smart grid technologies across the region. By integrating machine learning models into grid operations, governments and utility providers can optimise energy distribution, predict demand fluctuations, and seamlessly incorporate renewable sources like solar and wind," said Matthew Hardman, CTO, APAC, Hitachi Vantara.

"Delivering the optimal power stack has always been mission-critical for the energy industry. In the era of generative AI, utilities will address this issue in ways that reduce environmental impact," agreed Marc Spieler, Senior MD of Global Energy Industry, NVIDIA.

"As the smart grid takes shape, smart meters — once deemed too expensive to be installed in millions of homes — that combine software, sensors and accelerated computing will alert utilities when trees in a backyard brush up against power lines or when to offer big rebates to buy back the excess power stored through rooftop solar installations," he suggested.

Source: NVIDIA. Spieler.

"Expect in 2025 to see a broader embrace of nuclear power as one clean-energy path the industry will take. Demand for natural gas also will grow as it replaces coal and other forms of energy. These resurgent forms of energy are being helped by the increased use of accelerated computing, simulation technology and AI and 3D visualisation, which helps optimise design, pipeline flows and storage. We’ll see the same happening at oil and gas companies, which are looking to reduce the impact of energy exploration and production."

Finance: AI adoption 

Broader regulation will mark a turning point for financial institutions in Singapore, accelerating AI adoption across the industry, noted Andy Ng, VP and MD for Asia South and Pacific Region, Veritas Technologies. 

"The Monetary Authority of Singapore (MAS) is actively fostering AI adoption through initiatives such as the S$100 M Financial Sector Technology and Innovation Grant Scheme (FSTI 3.0), which funds AI innovation centres, industry-wide AI platforms, and enhanced risk management frameworks. However, as AI deployments scale across the financial sector, challenges remain – particularly in aligning regulatory standards, strengthening workforce training, and addressing compliance risks," he said. 

"The turning point will be governed gen AI deployments safeguarded against compliance risks. Clear and robust regulations will be essential to safeguard against compliance risks and foster growth, encouraging hesitant adopters to embrace AI. These frameworks will unlock high-impact innovations, ensuring trust, governance, and the full realisation of AI’s potential in the financial sector."

Source: NVIDIA. Levitt.

Kevin Levitt, NVIDIA's Global Director of Financial Services, said that AI-powered agents will be deeply integrated into the financial services ecosystem, improving customer experiences, driving productivity and reducing operational costs.

"AI agents will take every form based on each financial services firm’s needs. Human-like 3D avatars will take requests and interact directly with clients, while text-based chatbots will summarise thousands of pages of data and documents in seconds to deliver accurate, tailored insights to employees across all business functions," he said.

AI use cases are growing, from improving identity verification for anti-money laundering and know-your-customer regulations, to reducing false positives for transaction fraud and generating more productive trading strategies, Levitt said, predicting that financial institutions will set themselves apart from the competition with AI factories that maximise the performance of AI-enabled applications.

"Due to the sensitive nature of financial data and stringent regulatory requirements, governance will be a priority for firms as they use data to create reliable and legal AI applications, including for fraud detection, predictions and forecasting, real-time calculations and customer service," Levitt added.

"Firms will use AI models to assist in the structure, control, orchestration, processing and utilisation of financial data, making the process of complying with regulations and safeguarding customer privacy smoother and less labour-intensive. AI will be the key to making sense of and deriving actionable insights from the industry’s stockpile of underutilised, unstructured data."

Finance: security

A deepfake of a Fortune 500 CEO will cause significant disruption in the financial markets, catalysing interest and adoption in advanced identity verification in the financial industry, iProov predicted. "The fabricated video, announcing a false merger, will trigger a temporary market dip and erode investor confidence before being exposed. This incident will highlight the growing need for robust identity verification solutions to ensure the authenticity of information and maintain trust in an increasingly digital world," the company said.  

"Companies and investors will respond by prioritising biometric authentication, investing in deepfake digital injection protection tools, enhancing communication protocols, and putting a strong focus on digital identity verification in all online interactions to prevent impersonation and fraud. This incident will serve as a catalyst, accelerating the adoption of advanced identity verification solutions in the financial industry."

 

Healthcare: AI adoption

Source: Temus. Wu.

Wu Chun Wei, MD, Technology, Temus said: "AI continues to transform every aspect of our lives, from health to our homes and the way we work. In parallel, digital healthcare solutions, among other innovations, have come to the forefront, accelerated by COVID and ageing population challenges.

"Combining these trends, we can expect to see AI and people integrate more closely to address these challenges - especially in preventive care and fostering collaboration. These include interpreting sensor data for health rate variability, providing tailored therapeutic analysis, and measuring health and wellbeing through digital platforms." 

"Healthcare organisations and pharmaceutical companies will explore new ways to implement AI-driven insights at every level, from patient care personalisation to faster drug development cycles, with a focus on expanded use of AI in targeted areas of the ecosystem. As AI proves its own value in a variety of ecosystem-specific settings, we expect to see increased governance and directives for the use of AI from CIOs, CTOs, regulators and industry leaders in the form of company-specific AI playbooks," said Alyssa Farrell, Director, Global Health Care & Life Sciences Industry Marketing, SAS.

Source: NVIDIA. Powell.

The dawn of agentic AI and multi-agent systems will address the twin challenges of workforce shortages and the rising cost of care, said Kimberly Powell, VP of Healthcare, NVIDIA. "Administrative health services will become digital humans taking notes for you or making your next appointment — introducing an era of services delivered by software and birthing a service-as-a-software industry," she suggested.

"Patient experience will be transformed with always-on, personalised care services while healthcare staff will collaborate with agents that help them reduce clerical work, retrieve and summarise patient histories, and recommend clinical trials and state-of-the-art treatments for their patients."

Powell also said robots will play a larger role in healthcare. "New virtual worlds for training robots to perform complex tasks will make autonomous surgical robots a reality. These surgical robots will perform complex surgical tasks with precision, reducing patient recovery times and decreasing the cognitive workload for surgeons," she said. 

Drug design will also benefit from AI, Powell added. "Techbio and biopharma companies have begun combining models that generate, predict and optimise molecules to explore the near-infinite possible target drug combinations before going into time-consuming and expensive wet lab experiments.

"The drug discovery and design AI factories will consume all wet lab data, refine AI models and redeploy those models — improving each experiment by learning from the previous one. These AI factories will shift the industry from a discovery process to a design and engineering one," she added.

Healthcare: data management

"Pharma and healthcare are working more closely than ever, using data and shared insights to drive innovation in patient care and treatment development. In 2025, this convergence is no longer experimental – it will be foundational to how these industries operate. However, data interoperability will remain the primary challenge for these traditionally siloed industries," said Gail Stephens, VP, Health and Life Sciences, SAS.

"Ensuring that data flows freely and securely across systems will be a critical focus in the year ahead to move toward tangible convergence. For patients, this means more cohesive health experiences where care delivery and medical advancements are intertwined," added Brittany Shriver, Head of Life Sciences Industry Consulting, SAS.

"Robust data management will once again emerge as a critical priority, driven by the increasing complexity of data, regulatory requirements and the growing recognition of not only the value of data assets but also the value of clean, quality data. Organisations will look to enhance data management practices through cloud-based data and AI platforms that seamlessly connect data across the enterprise (e.g., clinical data, real-world data, commercial customer data) and boost productivity through automation in the cleansing, quality, and mastering steps. This will lead to accelerated timelines and processes, while increasing patient-centricity throughout the pharmaceutical life cycle." 

Healthcare: modernisation

"Despite extraordinary advances, many parts of the healthcare technology stack remain fragmented and outdated and the need for a digital overhaul is unavoidable. Healthcare organisations, from hospitals to research labs, must reimagine their infrastructure, embracing solutions that modernise and integrate the various systems on which they rely. But even with the right tools, financial investment will be paramount. 

"For the industry to benefit from the promise of AI, substantial resources must be directed toward infrastructure, prioritising data integrity, security and usability," stated Steve Kearney, Global Medical Director, SAS.

Logistics: AI adoption

Source: Aicadium. Poon.

"2025 will likely see significant advancements in the use of predictive AI within supply chains. The combination of data analytics and AI will empower organisations to anticipate disruptions and respond with agility. By implementing real-time tracking and predictive models, businesses can optimise their supply chain operations, improving resilience and sustainability," said Phoebe Poon, VP of Product Management at Aicadium.

Azita Martin, VP of Retail, Consumer-Packaged Goods and Quick-Service Restaurants, NVIDIA, said AI would benefit many aspects of the supply chain.

"Intelligent supply chains created using digital twins, generative AI, machine learning and AI-based solvers will drive billions of dollars in labour productivity and operational efficiencies," she said. 

Digital twin simulations could help to optimise store layouts that increase in-store sales, and accelerate throughput in distribution centres, she added.

Source: NVIDIA. Martin.

"Agentic robots working alongside associates will load and unload trucks, stock shelves and pack customer orders. Also, last-mile delivery will be enhanced with AI-based routing optimisation solvers, allowing products to reach customers faster while reducing vehicle fuel costs," she said.

Explore

More industry-related predictions are in V is for verticals, part 2.

Read the rest of the 2-Z of 2025 predictions at https://www.techtradeasia.com/2025/01/the-techtrade-asia-2024-roundup-2025.html

Hashtag: #2025Predictions