In 2024, a wave of AI hype swept across all industries. While the initial buzz around AI may be gone, AI's impact on various sectors is here to stay.
Curious about the current state of AI in core industries and what the future holds? Keep reading to find out!
Healthcare: the current state of AI in the sector
With thousands of articles on how AI can improve clinical treatment and interaction between physicians and patients, the industry players are still very cautiously approaching the AI topic. At the beginning of 2024, 43% of healthcare organizations stated they were still in the proof-of-concept with AI tools, slowly implementing them.
The reason for that is the number of regulations controlling the healthcare sector. As the industry deals with sensitive patient data, regulations such as HIPPA (US) and GDPR (EU) ensure patient safety and data privacy to provide quality healthcare. Regulations such as the FDA (US), MDR (EU), and ISO control medical equipment and software production according to all quality standards.
Therefore, AI medical devices must undergo lengthy and costly clinical trials to ensure they are safe for patient care. This is something that healthcare companies rarely want to deal with due to a lack of resources.
Despite the cautious attitude to AI, industry players show interest in AI, especially in streamlining administrative work or driving medical research. Around 60% of investments in healthcare are spent on administrative and assistive AI implementation as it faces fewer regulatory hurdles than clinical AI.
Right now, AI is used in the healthcare industry for processes like:
- Drug development. Some big pharma titans have already incorporated AI to accelerate drug development and clinical trials processes. For example, Pfizer revealed they used AI to streamline the development of their oral COVID-19 treatment PAXLOVID™. AstraZeneca will pay $18 million to a biotechnology company to use its AI model of the immune system to conduct its cancer drug trials.
- Remote patient monitoring. AI is used for remote patient monitoring to assist workers in intensive patient care. AI analyzes data from wearable devices and sensors to track vital signs and alert healthcare providers of any changes in health for efficient and timely treatment.
- Image analysis in dermatology. AI-empowered image analysis helps speed up the detection of skin conditions. Advanced algorithms scan the image, catching the signs of disease and assisting dermatologists in making highly accurate diagnoses.
- Doctor appointment scheduling. Large medical facilities are struggling with booking management, but AI-powered technologies efficiently match patients with physicians, reducing the chances of scheduling clashes.
What’s next for AI in healthcare?
We can expect AI to create a solid foundation for advances in medical research and drug development, with medtech companies leading the way. In addition, as AI develops, more medical institutions will work to modernize their infrastructure and train employees to move from administrative AI to clinical applications, focusing on treatment personalization. In the long run, we can expect AI to increase human life expectancy and even find treatment for incurable diseases, such as cancer.
Energy: the current state of AI in the sector
Similarly to the healthcare sector, the energy industry is not rushing to implement AI advancements. However, a new IBM study revealed that 74% of energy and utility companies already use or plan to implement AI for talent acquisition and AI governance. This data reveals a similar pattern as in the healthcare industry – energy companies invest more in administrative AI than operational.
Even though most energy sector leaders acknowledge AI’s capabilities to transform sector productivity, there are still only a few small-scale uses of AI in some narrow parts of the business.
The significant obstacle to AI initiatives for some energy companies is their physical assets. Organizations rely heavily on data collected from physical assets that are geographically scattered and not connected to digital networks, so it is hard to collect and structure this data quickly and efficiently.
Some industry leaders have expressed concern that poor quality or unavailability of data will undermine the effectiveness of AI applications and lead to operational disruptions. To deal with this issue, energy and utilities companies have started incorporating IoT technologies to fill the data gap, collecting data from grids, meters, and other assets.
Right now, the energy sector has several focal points where they actively integrate AI capabilities, namely:
Predictive asset maintenance. AI analyzes IoT sensor data from grids in real time and gives insights into potential parts of the system that are likely to fail. Now, grid operators can proactively fix the issue to prevent downtime in the system.
Forecasting supply and demand. AI tools are actively used to optimize energy distribution by predicting energy loads and demand peaks. For example, using its artificial intelligence-based technology, Google can predict wind power output up to 36 hours in advance, increasing the financial value of its wind power by 20%.
Outage prediction. AI predicts potential energy outages before they happen by analyzing vast amounts of data from smart grids, weather patterns, and historical data. Considering alerts from AI systems, energy companies communicate the outage to their customers or take preventive actions to avoid it.
What’s next for AI in energy?
In the near future, AI will be used to modernize the energy infrastructure, fostering efficient renewable sources implementation. Also, we can expect the development of AI-driven robotics to perform high-risk operations on power plants and wind turbines, which can be hazardous for human workers.
In the long run, AI will be a big step for humanity in resolving the energy crisis and driving the development of renewable energy solutions. AI will help to optimize energy consumption, minimize carbon emissions, and strengthen energy system efficiency.
Automotive: the current state of AI in the sector
Automotive is one of the leading industries significantly interested in adopting AI to compete in the market. For example, US car manufacturers are the leaders in AI adoption, with 25% fully integrating AI into their operations. Meanwhile, for the UK, this figure stands at 14%, and then goes Germany, with 12% of vehicle manufacturers actively deploying AI solutions.
As one of the most competitive industries, even the most traditionalist vehicle manufacturers are shifting away from their usual approaches. With Tesla leading the rest of the market in implementing AI innovations, other key players in the industry are also following suit and investing in technological innovation. For example, Toyota is investing $13bn in AI initiatives, and Mercedes-Benz is spending €2 billion to upskill its workforce to become data and AI specialists.
The main focus area for AI adoption in the automotive industry is R&D, as around 75% of sector leaders are experimenting with at least one GenAI tool, mainly for product design and optimization, software testing, and requirements engineering.
However, apart from that, the most common AI use cases in the automotive industry are:
Vehicle personalization. Car manufacturers widely adopt AI-powered recommendation engines to give their customers a more personalized experience. Such recommendation engines assist customers in suggesting customized car packages based on their tastes.
Supply chain management. AI enables more accurate demand forecasting and optimizes production planning. With advanced AI algorithms, automotive supply chain management becomes more agile, and companies can better predict and respond to market changes.
Accident prevention. Car manufacturers use AI technologies to make their vehicles safer and avoid potential collisions. For example, Tesla incorporated an AI-powered interior camera to monitor the driver’s facial features, detect signs of drowsiness, and alert the driver to take a break.
What’s next for AI in automotive?
We can expect the rise of AI in-car assistance, which is currently applied on a limited scale. In the next few years, it will become the norm for most car manufacturers to include it in their vehicles.
In the long run, artificial intelligence will accelerate the testing of autonomous cars, leading to their faster deployment on the market. This means we can expect to see more self-driving cars on the streets of our cities in the coming years.
Agriculture: the current state of AI in the sector
Surprisingly, AI adoption is taking place at an unprecedented pace in the agriculture sector, with 87% of AI adoption. AI is not entirely new for the sector, as many farmers have been using automatic management systems for row crops, drones, GPS tools, and field monitoring systems for almost two decades.
This interest in AI tools stems from the need to reduce labor-intensive and time-consuming work that affects employees' health and productivity. Human resources are the industry's backbone, and unfortunately, many employees cannot cope with such intensive manual work.
Initiatives from non-profit organizations, in cooperation with local governments, are facilitating AI incorporation in the sector to establish food security and streamline food production to fulfill the population's demands. For instance, the United States Department of Agriculture and the National Science Foundation are funding the AI Institute for Resilient Agriculture to drive the development of AI technologies for the sector.
GSM Association report also highlighted that developing countries are now joining the AI adoption trend. Among the most widespread AI use cases are chatbots, which provide recommendations for small-scale farmers on optimizing fertilizer use and detecting pests. For example, the non-profit Digital Green launched their chatbot to handle the shortage of agricultural extension agents in India, Kenya, and Ethiopia. Now, farmers can get timely and accurate advice with just several clicks.
Apart from the chatbots, AI technologies also made it possible for farmers of different scale to implement such solutions as:
Livestock management. Using cameras and sensors, AI helps farmers monitor animals in real time, detecting any changes in their behavior or physical qualities. For example, one European agricultural company implemented an embedded AI system based on image recognition to streamline the process of weighing pigs without stressing the animals.
Weather forecasting. Farmers rely on weather forecasting to optimize their crop yield, so they use AI models to plan planting and harvesting schedules more efficiently. AI predicts weather fluctuations and provides highly accurate forecasts to minimize the risks of crop loss.
Pests identification. Big agriculture producers use AI-driven pest detection systems to monitor crop health. Using drones and sensors to scan the fields, AI algorithms with image recognition can detect problem areas with early signs of pest activity.
Product grading. Using computer vision, machine learning, and sensors, AI algorithms analyze visual characteristics of agricultural products to identify, detect, and enhance quality control.
What’s next for AI in agriculture?
Soon, we can expect more automation of human labor, with workers relying on AI-powered equipment to do all the time-consuming and dangerous work, such as handling chemicals. The human workers will oversee the process to ensure everything is in order.
AI can make agriculture more sustainable in the long run by helping to adapt to climate change. In addition, AI solutions can help farmers optimize resource management, minimizing water use, fertilization, and carbon dioxide emissions from agricultural activities.
Final thoughts
AI has become a regular part of the routine across various sectors – some use it more cautiously, while others experiment to the fullest. With AI becoming a norm, the question of regulations will rise with more significant power. For example, the EU AI Act, rolled out in 2024, will control AI development across businesses and has already imposed bans on certain AI-based products.
Despite the challenges with AI integration, industry leaders still should prioritize data quality, as the success of AI solutions relies on it. Data also must be regularly monitored and updated for continuous AI system improvement. Apart from that, business owners need to invest in robust data governance and strong cybersecurity strategies to safeguard data against breaches.
To successfully integrate AI solutions and effectively manage data, we recommend partnering with a trusted AI vendor adhering to industry regulations and compliance standards. With over 15 years of experience developing complex AI solutions, the Lemberg Solutions team is ready to drive your business forward with our AI development services. Contact us today and discuss your needs with our experts to build a resilient AI adoption strategy.
