Research

Sectors Poised for Change Through Application of AI

BITA

24 Jul 2025 • 8 min read

By Diana Hernandez, Analyst Proprietary Data

Artificial Intelligence systems are in a continuous process of learning, adaptation, and improvement, with the ultimate goal of replicating human cognitive abilities without human intervention. AI models are trained on extensive datasets of wide volume and diversity, utilizing mathematical algorithms and complex statistical modeling techniques, facilitating this continuous adaption. Subfields of AI which are seeing the most advancement currently, are Natural Language Processing (NLP) and the subset large language models (LLM), machine learning, deep learning, computer vision and robotics.

Key Factors Driving the Rapid Recent Advancement of AI

Although AI began developing in the mid-20th century, the most significant advances have occurred in the 21st century, reaching a fulcrum in 2022 with OpenAI’s release of ChatGPT, thanks to the development of three key factors.

Increased Computing Power: The advent of Graphics Processing Units (GPUs) and specialized AI accelerators has significantly reduced AI training times.
Enhanced Algorithms: Continuous improvements in algorithms allow AI models to perform computations more efficiently, enabling the training of larger and more capable models, which in turn enhances overall performance.
Surge of Big Data: the rising volume and continuous flow of data have expanded the datasets available for training AI models, making the careful selection of these datasets crucial for effective training.

Within the AI ecosystem, there is a distinction between traditional AI and generative AI. While traditional AI focuses on analyzing existing data and making predictions or actions based on it, such as Agent AI, generative AI excels at its adaptability to produce different types of content, such as text, images and codes, using deep learning techniques. Large Language Models (LLM) are an important form of generative AI, generating human-like text.

AI applications across sectors

Today, artificial intelligence is gaining prominence and has become an essential technology. Its applications span many sectors, driving revolutionary changes in sectors that have adopted its implementation, enhancing productivity, efficiency, creative processes, and decision-making. Top sectors using and developing AI today are high tech/telecom and financial services, followed by automotive and healthcare/pharma, and retail sector.

Chart — *Source: McKinsey Global Surveys on the state of AI, 2025*

McKinsey’s Global Survey on the state of AI tracks the use of AI within organizations in at least one business function demonstrates adoption rates. Companies responding reported that 20% were using AI in 2017, and jumping to 78% in July 2024, with organizations using it in marketing and sales, product and service development, operations, and software engineering. The survey found that the use of Generative AI skyrocketed during 2024.

Key Sectors are Implementing AI

Finance

AI is transforming financial services by making processes more efficient and cost-effective. In customer support, AI chatbots are available 24/7 to answer questions, significantly improving response times. Robo-advisors provide personalized financial advice and investment management online with little to no human intervention.

AI enhances fraud detection and prevention by utilizing machine learning models. These models analyze millions of transactions to identify subtle patterns that may indicate fraud, allowing for real-time detection that is faster and more accurate than human efforts.

Machine learning models excel at analyzing complex datasets, redefining risk assessment and improving evaluation of risk and financial analyses. AI systems facilitate enhanced predictive analytics and scenario planning, leading to more informed decisions regarding investments, lending, and insurance underwriting. AI tools are employed for know-your-client (KYC) checks and anti-money laundering (AML) monitoring. By analyzing customer data, transaction patterns, and potential connections to high-risk entities, systems can highlight suspicious activities for further investigation.

AI is also used to develop trading algorithms. Through deep analysis of market trends and historical market data, algorithms can create strategies for trading decisions, risk management and executing trades faster than human traders.

The global market for AI in Finance was estimated at USD 38.36 billion in 2024 and is projected to reach USD 190 billion in 2030, growing at a CAGR of 31% during the forecast period 2024-2030, according to Markets and Markets. The report highlights that North America leads the market, and is expected to maintain the largest market share during this period, due to its advanced technological infrastructure, significant investment in AI research and development, and robust fintech ecosystem.

Cybersecurity

As advanced threats increasingly utilize AI in their attacks, cybersecurity must evolve by incorporating AI to enhance threat detection, identify vulnerabilities, and automate security tasks. With the growing adoption of IoT and connected devices—often lacking robust security measures—the need for AI-powered cybersecurity solutions continues to increase significantly. Real-time monitoring and threat detection across multiple endpoints are the focus.

Furthermore, as enterprises increasingly embrace digital transformation and cloud-based infrastructures, the demand for AI-powered network security solutions rises.

The global AI in cybersecurity market was valued at USD 25.4 billion in 2024 (Poliaris Market Research). It is expected to grow from USD 31.38 billion in 2025 to USD 220 billion by 2034, at a CAGR of 24% during the forecast period.

Healthcare and Pharmaceuticals

AI is already being utilized for early diagnosis, clinical trials, task automation, and treatment personalization, yet there is still a vast opportunity for growth in the future. Key areas that can harness the power of AI include diagnosis and treatment, drug discovery, assistance with routine medical tasks, and AI-powered surgical robots.

Recent advancements in diagnosis and treatment are particularly noteworthy. In recent years, AI-powered platforms, including deep learning technologies, have been developed in detection, report generation and differential diagnoses. For example, deep learning has shown promise in screening for diabetic retinopathy, identifying melanoma and nonmelanoma skin cancers, assessing cardiovascular risk, and predicting the progression of Alzheimer's disease. Furthermore, AI can analyze medical images more quickly and accurately than humans.

In drug discovery, AI has proven invaluable. It accelerates the process by identifying novel drug targets and optimizing the design of medications. Utilizing AI technologies, such as machine learning and deep learning, researchers can predict drug absorption, distribution, metabolism, and excretion.

AI can be employed to detect emerging diseases and outbreaks - quickly identifying patterns and strains and is expected to be employed in preparation for major public health crises like pandemics.

According to Precedence Research, the global artificial intelligence (AI) in healthcare market size was estimated at USD 26.69 billion in 2024 and is projected to increase from USD 36.96 billion in 2025 to over USD 600 billion by 2034, growing at a CAGR of 39% from 2025 to 2034.

The same report indicates that North America is the leading region, accounting for 45% of the market share in 2024, followed by Europe at 27%, and Asia Pacific at 22%.

Transportation

AI has numerous applications in the transportation sector, ranging from optimizing traffic management to enhancing vehicle safety and predictive maintenance. These innovations are transforming the sector.

One of the most revolutionary developments in this field is the introduction of autonomous vehicles. Through machine learning, advanced sensors, sophisticated mapping and navigation systems, and computer vision, these vehicles can perceive and respond to their environment with varying levels of automation, from driver assistance features to fully self-driving capabilities. Such vehicles increase safety by reducing human error, alerting drivers to potential dangers, providing transportation options for individuals who cannot drive, and optimizing routes to reduce fuel consumption.

AI also plays a crucial role in predictive maintenance for vehicles and transportation infrastructure by analyzing historical data and real-time information. AI has proven to be an effective tool for addressing traffic congestion. AI algorithms analyze real-time data from various sources — sensors, traffic cameras, GPS — enabling smart traffic management systems to dynamically adjust traffic signals and reroute vehicles to optimize traffic flow.

The global AI in transportation market size is expected to grow from USD 2.12 billion in 2023 to USD 10.25 billion by 2033, at a CAGR of 17.07% during the forecast period of 2023-2033 (Spherical Insights and Consulting).

Manufacturing

AI has significantly enhanced the manufacturing sector by improving production processes, predictive maintenance and quality control. Companies utilize AI to analyze data from sensors, equipment, and production lines, gaining insights that optimize efficiency, enhance product quality, reduce downtime, and lower costs.

From AI systems analyzing data of machinery sensors that forecast potential failures before they occur and AI-powered visual inspection systems to AI-powered robots (cobots) utilized to perform repetitive, hazardous, or strenuous tasks, AI improves productivity and safety in factories.

The global artificial intelligence in manufacturing market size was estimated at USD 5.3 billion in 2024 and is projected to reach USD 47.88 billion by 2030, growing at a CAGR of 46.5% from 2025 to 2030 (Grand View Research).

Similar to the other sectors, the firm found that North America held the largest market share from a regional perspective in 2024, accounting for 33.2% of market revenue. The leading position of North America can be attributed to the presence of leading producers of high-performance hardware components essential for running advanced AI models, alongside the U.S. government's National Strategy for Advanced Manufacturing, which encourages investments in research and development for AI integration within manufacturing processes.

Agriculture

AI applications in agriculture include soil management, crop management, hybrid seed production, efficient irrigation processes, greenhouse management, precision farming, and AI-operated tractors. By leveraging AI, the sector can improve in three fundamental areas: increasing crop yields, reducing water usage in irrigation systems, and enhancing efficiency while minimizing input usage through AI-driven fertilizer and pesticide applications.

Blue River Technology, part of John Deere, developed the See & Spray technology, which uses computer vision and AI algorithms to identify weeds among crops and precisely apply herbicides only to weeds, thus minimizing chemical use. CropX developed an AI-powered platform that uses soil sensors and satellite imagery to optimize irrigation and fertilization. Tarantis Software’s flagship product utilizes AI and high-resolution imagery captured by drones to monitor crop health, and detect pests and diseases, providing actionable insights for early intervention solutions.

The global AI in agriculture market size was valued at USD 1.91 billion in 2023 and is projected to grow at a CAGR of 25.5% from 2024 to 2030 (Grand View Research). The research indicates that the software segment led the AI in the agriculture market, accounting for nearly 54% of the revenue in 2023.

BITA’s Thematic Offering

BITA's Proprietary Data Team monitors companies that provide products, solutions, and services within the artificial intelligence ecosystem - detailing AI software development, AI microprocessors, AI-enabling hardware and infrastructure, and AI training-related services. The team tracks companies on an ongoing basis, compiling into several related indexes.

BITA's AI Leaders Select Index tracks the performance of large publicly traded companies involved at the forefront of AI technologies both from an application and infrastructure perspective.

BITA’s Artificial Intelligence Giants UST Index follows companies with exposure to the AI ecosystem, including areas such as microprocessors, machine learning, and autonomous artificial intelligence development.

BITA’s Global Autonomous Driving Select NTR Index tracks publicly listed companies involved in the development and production of software technology, hardware technology, and supporting systems that enable autonomous driving.