Artificial intelligence (AI) has revolutionized many industries and could do the same to many more. And now, thanks to the speed of AI capability advancement, investors, venture capitalists, entrepreneurs and business leaders are also able to evaluate the most promising areas to focus AI investments.
Overview of AI Market Growth Projections
AI has moved beyond the emerging technology phase and entered a rapid growth stage. According to research firm ABI, global spending on AI solutions will reach $98 billion by 2024, more than 2.5 times the $37.5 billion forecast for 2019. The compound annual growth rate for AI between 2020-2024 is expected to be a robust 20.2%.
The more AI capabilities become available, the more real-world business applications will follow. Organizations that want to improve operations, discover data-driven insights, engage customers in personalized ways and use math-solving AI to meet consumers’ digital experience expectations will rely on AI software and services. Below are the areas where AI investment is most promising, and they all have huge growth runways ahead and the potential to generate outsized returns.
AI in Healthcare: Diagnosis, Treatment and Drug Development
AI technologies for augmenting human capabilities and discovering data-based insights that are not possible through normal methods alone have great promise in the healthcare sector.
Precision Medicine and Health Diagnostics
Applying AI algorithms to patient health data, genetics and lifestyle information promises to usher in an era of precision medicine. Machine learning techniques can analyze datasets with hundreds of data points to identify personalized treatments based on a patient’s risk factors, biomarkers and probability of responding to therapies.
There are investment opportunities around startups creating AI diagnostic imaging tools for identifying cancer, eye disorders and other diseases. And AI can power chatbots and telemedicine apps that can do a faster, more accurate initial diagnosis and triage than a patient can see a doctor at their office.
Robotic Surgery and Virtual Nursing Assistants
Surgery relies on ever-advancing technologies to treat patients. Robotic surgery using AI camera guidance and tiny incisions allows more precise, minimally invasive procedures. The global robotic surgery market is forecast to grow at a CAGR of 13% to reach $15 billion by 2027.
For nursing care, AI-powered virtual assistants and chatbots can help hospitals operate more efficiently while providing patients personalized attention. The AI healthcare assistant market alone could be worth $1 billion by 2027.
Drug Discovery and Development
In an effort to dramatically reduce the time and money it takes to discover new medicines, pharmaceutical companies are relying increasingly on AI and machine learning. Molecular interactions are analyzed by AI algorithms based on real-world evidence and lab simulations to predict how drug compounds might impact humans.
According to market reports, the AI drug discovery market segment is going to exceed $4,9 billion by 2028. This segment alone is worth strong consideration around the front-end potential of new life-saving medicines.
Smart Manufacturing Powered by AI and IoT
The manufacturing sector stands poised for radical transformation through integrating smart connected devices, sensors, robots and AI-based analytics. The result will be smart factories that operate at new levels of speed, efficiency, flexibility and quality control.
Predictive Maintenance
Unplanned downtime in factories leads to reduced output and higher costs. AI applications can monitor production line equipment for early warning signs of potential breakdowns. By scheduling predictive maintenance, manufacturers avoid costly outages. The global predictive maintenance market is forecast to reach $10.6 billion by 2024.
Quality Control
Computer vision fueled by machine learning algorithms can automatically scan products on the line for defects and quality issues better than the naked eye. It also helps to reduce response times so that faulty products do not ship. For highly regulated industries such as medical devices and automotive, AI-powered quality control is a compelling value proposition.
Supply Chain Optimization
Today’s global supply chains are complex. The application of AI to analyze data within sourcing, production scheduling, inventory management, logistics and delivery will increase efficiency and resilience against unexpected disruptions. According to projections, the AI supply chain optimization market will be worth $157 billion by 2033.
AI for Next-Gen Cybersecurity Defense
The more organizations depend on data and internet-connected technologies, the more they need advanced, adaptive cybersecurity defence. AI and machine learning have huge potential when applied to cyber threats.
Security Operations Centers (SOCs)
Monitoring today’s dynamic threat landscape stretches the capabilities of security analysts to keep up. AI cybersecurity tools perform triage on security event alerts, identifying the highest risk threats for human analysts to prioritize and investigate. Gartner forecasts AI in SOCs accelerating to become a standard practice within the next 3-5 years.
Anti-Fraud and Anti-Money Laundering
Online payment fraud causes massive losses, reached $343 billion by 2023, according to Juniper Research. AI transaction monitoring solutions can review payment data in real-time and across customer history to identify suspicious patterns, improving fraud detection rates over manual methods.
Autonomous Vehicles and Intelligent Transportation
Self-driving cars may have gotten off to a bumpy start, but autonomous vehicles still have a promising future ahead in specialized applications such as industrial zones, campuses and geo-fenced areas in cities. Additional opportunities exist around AI software for advanced vehicle safety systems in consumer cars and trucks.
Last-Mile Delivery
An area prime for autonomous vehicle disruption is last-mile delivery logistics. Startups have developed self-driving vans and sidewalk robots for small package delivery from warehouses and restaurants, eliminating the need for a human driver. As this technology proves viable, adoption could scale quickly.
Smart Cities and Transportation Infrastructure
AI and big data analytics can help improve urban transportation systems. Dynamic signal and signage applications can be used for traffic monitoring applications in order to reduce congestion and pollution. According to analysts, the smart transportation market globally will be worth around $33.6 billion by 2027, with a CAGR of 20%.
Customer Service and Marketing Enhancement
Brands today need to serve customers in relevant, personalized ways. Customer data and feedback streams can be used to improve experiences and loyalty using AI and machine learning techniques.
Conversational Chatbots
Intelligent chatbots allow customers to interact conversationally with brands 24/7. Virtual assistants equipped with natural language processing (NLP) can understand diverse customer questions and provide helpful answers without human agents. This both cuts service costs and leaves customers happier getting quick resolutions.
Content Recommendations
Online users have come to expect personalized recommendations for relevant content. In apps and websites, machine learning algorithms are used to suggest content based on past user behaviour and preference data. Not only do recommendation engines improve customer experience, but they also present opportunities to show related products and services.
Marketing Campaign Analytics
Testing and optimizing multi-channel digital marketing campaigns requires analyzing volumes of data on ad impressions, clicks, conversions and other metrics. AI tools can surface key performance insights much faster than manual analysis while continually optimizing campaigns toward better results. Marketers can make the most of budgets while showing quantifiable ROAS.
AI Computing Infrastructure and Tools
The common thread across all enterprise AI applications is the need for extensive computing resources to train algorithms and run inferences. The AI hardware and software infrastructure market promises to undergo massive growth in the coming years.
AI Chips and Accelerators
Training sophisticated deep learning algorithms calls for specialized artificial intelligence chips with parallel processing capability above even graphics cards. By 2025, the market for machine learning systems is expected to be $72 billion. Startups creating cutting-edge artificial intelligence chips and computing systems could show great long-term value.
MLOps and Model Governance
As companies deploy more machine learning models to production, tooling needs arise around monitoring models and automation pipelines for drift, compliance and governance. The emerging MLOps category around model management, monitoring and retraining provides crucial capabilities for scaling enterprise AI.
Key Takeaways for Targeting Investments
The most promising areas highlighted in this report share certain commonalities, making them prime considerations for AI investments:
- Applicability across many industry verticals
- Potential for massive data sets to drive insights
- Clear value propositions through automation and augmentation
- Large addressable markets still in the early adoption curve
- Critical business needs unmet by previous technologies
Companies developing innovative applications and infrastructure tools to advance these AI segments broadly could see hockey-stick growth and outsized returns in the coming years. The future continues to look bright for AI.
https://www.ceotodaymagazine.com/2024/11/the-bright-future-of-ai-promising-areas-to-target-for-investment/
