eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
Artificial Intelligence trends continue to redefine the technological landscape, introducing innovations that enormously enhance software capabilities and greatly influence human activities across various sectors.
As we power through 2024, staying ahead of today’s key AI trends is essential for IT professionals and businesses aiming to leverage cutting-edge technologies to drive growth and innovation. Here’s a quick glance at the top AI trends of the year and their potential impacts:
- Generative AI grows its already-popular presence: More accurate and automated content creation and more efficient implementation in sectors like the medical field is rapidly developing.
- Multimodal AI further bridges data types for richer interactions: AI’s enhanced ability to understand and interact using multiple forms of data is improving user experiences and decision-making processes.
- AI-driven cybersecurity enhances digital security: AI offers more accurate threat detection and more autonomous cybersecurity solutions to help respond to cyber threats more efficiently.
- Embedded AI and UX-focused AI expand: Direct integration of AI into user interfaces and operational processes significantly boosts efficiency and user experience.
- AI democratization and widespread AI access develops: Greater accessibility of powerful AI tools beyond big tech companies fosters innovation across all business sizes.
- Computer vision and hyperautomation in manufacturing merges: AI is transforming traditional manufacturing with sophisticated automation and enhanced precision.
- Shadow AI becomes more prevalent: More robust policies to address the challenges posed by the use of unsanctioned AI technologies within organizations are needed.
- Open source’s role in AI expands: Greater innovation and collaboration is occurring due to freely available open source AI resources and tools.
- Compliance and ethics face higher expectations: Companies are developing responsible and more ethical AI tools to align with global standards and regulations.
- Language models shrink in size yet grow stronger: AI models are gaining greater function even as they grow smaller, offering higher performance with fewer resources.
TABLE OF CONTENTS
1. Generative AI Grows Its Already-Popular Presence
Generative AI has driven major change across industry sectors, especially sectors like healthcare and creative industries.
In healthcare, it’s aiding in diagnosing diseases and speeding up drug discovery by simulating complex biological systems, thereby identifying potential drug candidates more quickly. In the creative sectors, it enhances content creation across various mediums such as digital art, music, and video production, enabling high-level personalization and content generation that aligns closely with user or consumer preferences.
The surge in user interest around the generative AI landscape continues, primarily due to its ability to automate and enhance creative processes, which means significant time and cost savings and new possibilities for customization. However, gen AI’s widespread adoption raises ethical concerns, particularly regarding the accuracy of its output, authenticity and the potential displacement of jobs in creative fields. The technology also poses risks of misuse, such as in creating deepfake content, which can have serious societal implications.
For businesses, generative AI promises to continue revolutionizing product design, marketing, and customer engagement by enabling the creation of more adaptable and innovative user experiences. However, companies need to be careful with the use of generative AI. They must navigate the ethical considerations and potential backlash from the misuse of artificial intelligence technologies, and ensure they use generative AI in a manner that enhances brand integrity and customer trust.
To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024
2. Multimodal AI Further Bridges Data Types for Richer Interactions
Multimodal AI processes and integrates multiple types of data inputs, such as text, images, and audio. This AI trend has gained traction as businesses are realizing the value of creating more nuanced and contextually aware AI systems that can understand and interact in ways that mirror human sensory and cognitive capabilities.
The integration of various data types allows for a more comprehensive understanding and responsiveness from AI systems. The benefit? Improved user experiences and decision-making support. However, the complexity of designing these systems introduces challenges in terms of data integration, processing power, and maintaining privacy across different data streams.
With the growth of multimodal AI, we can expect to see significantly enhanced customer service interfaces and product interaction capabilities. Multimodal AI will make them more intuitive and responsive, as it can better take advantage of multiple human senses simultaneously. We can also expect to see technologies such as creativity applications and search tools become more seamless and precise. Additionally, multimodal AI can help businesses have more robust security postures, as combining data types can lead to more comprehensive threat detection.
3. AI-Driven Cybersecurity Enhances Digital Security
AI has been incorporated into some cybersecurity solutions for at least a few years now, but AI-powered cybersecurity tools are now more quickly becoming more popular as they expand their capabilities.
It’s projected that the AI cybersecurity market will grow from about $24 billion in 2023 to approximately $134 billion by 2030. AI-driven cybersecurity uses machine learning to predict, detect, and respond to cyber threats with greater speed and efficiency than traditional methods. Cyber threats aren’t slowing down, especially with the intersection of generative AI and cybersecurity, which introduces a whole new set of AI-based challenges. These threats become more sophisticated by the day, which requires more dynamic and adaptive security measures.
AI enhances threat detection capabilities and can autonomously respond to threats in real time. This significantly reduces the window for attackers to cause harm. In some cases, relying on AI for security may pose risks related to potential AI vulnerabilities and biases, as attackers can exploit these gaps in AI systems, convert them into threats, and use biases for maximum damage.
Implementing AI-driven cybersecurity solutions can help businesses protect critical data and systems more effectively and maintain compliance with evolving regulatory requirements. However, organizations need to continuously update and monitor AI systems to guard against emerging threats and ensure that security measures do not end up inadvertently compromising user privacy or system integrity.
4. Embedded AI and UX-Focused AI Expand
Embedded AI integrates AI directly into user interfaces and operational processes, improving efficiency and user experience. The embedded AI platform market is expected to grow by 5.4 percent per year as it’s increasingly used in applications ranging from predictive text to complex industrial automation systems.
A number of AI companies and startups offer AI models that can be fine-tuned and embedded into third-party systems. These models make it possible for businesses to create AI-powered search, assistance, and other UX-focused experiences in everything from internal employee databases to external-facing website search bars and knowledge bases.
Embedded AI is capable of providing real-time analytics and decision-making without needing constant connectivity to cloud services. This reduces latency and can greatly enhance user privacy and data security. On the downside, the deployment of embedded AI requires substantial initial investment in hardware and software development. Plus, there are ongoing challenges related to maintaining and updating AI models directly on devices, issues around dependency on technology, potential privacy breaches, and the amplification of existing biases in AI algorithms.
As UX-driven AI continues to grow, AI companies are likely to focus more heavily on their global footprint and multilingual capabilities. Some AI tools currently don’t work well beyond English-language queries. However, a number of companies are currently building out their AI model training processes and global datasets to make natural language processing and understanding possible for dozens of languages.
For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders
5. AI Democratization Develops
AI isn’t just for tech giants anymore. Thanks to AI democratization, even startups and mid-sized companies can harness powerful AI tools. This trend is fueled by the development of user-friendly AI platforms, cloud-based services, and open-source frameworks that simplify the creation and deployment of AI models. Now, everyone can tap into AI’s potential.
Innovation in AI is no longer siloed. This democratization encourages a broader base of users to innovate and apply AI to diverse problems, which in turn can speed up digital transformation and foster inclusivity in technology use.
The downside? This accessibility can lead to issues with misuse of AI technology, challenges in quality control, and the risk of oversimplification, where critical nuances of AI applications may be overlooked by non-experts.
Wider access to AI tools enables businesses of all sizes to have a slice of advanced analytics and machine learning to improve decision-making and operational efficiency. It also opens up new markets for AI tool providers. Nevertheless, enterprises must provide adequate training and guardrails to ensure AI is used ethically and AI resources are managed correctly to strike a balance between accessibility and responsible AI usage.
6. Computer Vision and Hyperautomation in Manufacturing Merges
Hyperautomation in manufacturing combines advanced technologies such as AI, robotics, and notably, computer vision, to revolutionize traditional production processes. Computer vision has fundamentally transformed manufacturing by enabling machines to visually interpret their environments. It has evolved from basic image processing to complex systems capable of real-time decision-making and has become integral in automating intricate manufacturing tasks. This has been key to automating complex tasks that require high precision, like assembly and quality inspection.
More integration between computer vision and hyperautomation has benefits like increased precision and faster production rates, which greatly raise product quality and reduce waste. It also improves safety by continuously monitoring the manufacturing environment to detect potential hazards. For instance, computer vision systems can flag defects that the human eye can’t identify. Unfortunately, the initial investment for setting up state-of-the-art computer vision systems can be substantial, involving not just financial outlays but also substantial training and development for staff. Additionally, heavy reliance on such sophisticated automation can render manufacturing processes vulnerable to technical failures or cybersecurity threats.
Merging computer vision and hyperautomation allows businesses to significantly streamline their manufacturing processes, enhance product quality, and reduce operational costs. It also facilitates more agile responses to market changes and customer demands by enabling quicker adjustments in production processes based on real-time data from computer vision systems. Looking ahead, the potential for growth in the application of computer vision and hyperautomation in manufacturing is immense. We shouldn’t be surprised to see more technologies, such as 3D vision and augmented reality (AR), further enhancing the capabilities of computer vision and hyperautomation.
7. Shadow AI Becomes More Prevalent
Shadow AI emerges when employees use AI technologies without explicit approval from IT departments, often leading to a lack of oversight in how AI tools are deployed within organizations. For instance, a survey by Salesforce says that 49% of people have used generative AI, with 52% of the respondents declaring that they use generative AI more than when they first started with the technology. This trend has grown as AI tools have become more accessible and user-friendly, allowing departments or individuals to implement solutions independently. This can lead to a lack of control and governance over AI-related activities within a company.
It can be frustrating to wait for approvals before tackling problems in an enterprise workplace. Shadow AI can drive innovation and allow departments to quickly solve problems and improve efficiency without waiting for central approval, which presents a culture of agility and proactive problem-solving.
This approach, however, introduces a few concerns, including security risks, as unmonitored AI applications might not comply with corporate data policies, leading to potential data breaches. Additionally, inconsistency in AI applications can result in inefficiencies and fragmented data insights, making it challenging to scale solutions effectively.
The rise of shadow AI compels organizations to set up robust corporate AI policies to manage and mitigate risks. Clear guidelines and frameworks for AI deployment are needed to balance the need for innovation with governance and security. This can help ensure that AI tools are used responsibly and support business objectives while protecting the organization’s integrity and data.
8. Open Source’s Role in AI Expands
Open source AI consists of AI technologies that are developed on publicly accessible platforms that allow developers to modify and distribute their code. The movement towards open-source AI has democratized AI development, meaning that a broader range of developers contribute to and innovate with AI technologies. A great example is the geospatial AI foundation model by NASA and IBM, which could help users track and adapt to landscape changes.
Open source AI accelerates innovation, reduces the costs of developing AI software and solutions, and involves the community in development. Such collaborations enable AI to develop rapidly and grow in effectiveness. But it’s not without its challenges, as open source AI deals with varying levels of support and maintenance, potential security vulnerabilities, and the complexity of integrating open source AI into proprietary systems.
Businesses leveraging open source AI can drastically reduce costs and become more agile in deploying AI solutions. Even so, they must carefully manage these tools to ensure they meet enterprise standards for security and reliability. To leverage open source AI in a compliance-focused corporate environment, organizations should establish rigorous evaluation and integration processes.
9. Compliance and Ethics Face Higher Expectations
Artificial intelligence tools continue to mature and reach into new areas of our lives, relying on massive amounts of personal and sensitive data to run effectively. But businesses and individuals alike are growing concerned about what data is collected, how it’s used, and whether or not it is appropriately secured during use and disposed of after use.
As a result, there’s currently a push for AI companies to make their data collection and model training processes more transparent so users know how their data is being used. Many customers are also pushing for explainable AI. On a related note, there’s also an increasing focus on developing stronger compliance and ethical frameworks to govern AI’s use. Strong compliance and ethical standards help build trust and reliability in AI systems, ensuring that they are used responsibly. On the flip side, implementing these frameworks can be costly and complex, possibly slowing down the deployment of new AI technologies.
Adhering to stringent compliance and ethics guidelines cannot be overlooked if businesses want to maintain their reputational integrity and adhere to regulatory requirements. Such commitment not only mitigates risks but also enhances consumer and stakeholder trust in the company’s AI applications.
10. Language Models Shrink in Size Yet Grow Stronger
Small Language Models (SLMs) represent a significant shift in AI technology as these models look to achieve the high performance of larger models but with far fewer parameters. Why? There’s a need for more efficient, scalable, and accessible AI solutions that can operate on edge devices without relying on extensive cloud computing resources. Innovations from major tech companies like Microsoft have highlighted the potential of SLMs to match or even surpass the capabilities of much larger models in specific tasks.
SLMs have several benefits, with the most straightforward one being reduced computational demands, which lower the barriers to AI implementation, particularly in mobile devices and other hardware with limited processing capabilities. They are also more sustainable, requiring less energy consumption. However, while SLMs are becoming more capable, they generally do not yet match the broad capabilities of large models, particularly in tasks requiring extensive knowledge or complex reasoning. Their performance is greatly dependent on the quality of the training data, and achieving high performance typically requires more curated and carefully selected datasets.
The development of SLMs is particularly relevant for businesses looking to integrate AI into consumer devices or operate in environments with strict data privacy regulations or limited connectivity. SLMs enable businesses to deploy powerful AI tools directly where they are needed, minimizing latency and enhancing user privacy. Additionally, the lower operational costs associated with SLMs make AI accessible to a wider range of businesses. We can expect to see more involvement of SLMs in driving innovation in sectors like healthcare, finance, and manufacturing that benefit most from bespoke AI solutions.
The Future of AI: Quantum AI to Filmmaking to Robotics
Based on what we see today, we can conclude that AI isn’t showing any signs of slowing down. We expect to see even more impactful trends in the coming years. First among these additional trends is the rise of quantum AI. The fusion of quantum computing with AI is set to open up new frontiers in processing power and problem-solving capabilities. We anticipate that this will revolutionize areas that need immense computational resources, like complex simulations in drug discovery and financial modeling, by performing tasks at speeds unattainable by legacy computers.
Advancements in AI will have even more influence over creative industries. For instance, we can expect to see AI playing a pivotal role in film production, from scriptwriting assisted by AI to special effects and even post-production processes. At the moment, with developing tools like OpenAI’s Sora, which will allow generation of high-quality video content, we see AI tools poised to further disrupt creative industries.
We also expect healthcare to be heavily impacted, as AI’s role in healthcare is expected to expand significantly, particularly in diagnostics and personalized medicine. Advances in AI algorithms will enhance the precision of medical imaging and diagnostics, enabling earlier and more accurate disease detection and tailored treatment plans.
We’re also set to see robotics become more autonomous and versatile, which will allow the field to finally move beyond specialized tasks to general-purpose applications. This shift will be driven by AI models that enable robots to learn and adapt to a variety of tasks through advanced machine learning techniques.
Finally, we’ll see more AI involvement in global challenges such as climate change. AI will be central to developing climate solutions. Its applications in energy optimization, smart grids, and environmental monitoring will help in more efficient management of resources and disaster response, significantly contributing to sustainability efforts.
Bottom Line: AI Trends are Rapidly Reshaping Business
AI is set to deeply influence operational efficiencies, innovation, and decision-making processes across all business sectors. The convergence of AI advancements such as generative AI, ethical AI frameworks, and automation in various sectors like healthcare and creative industries offers a dual-edged sword.
On one hand, these technologies promise unprecedented scalability, cost reduction, and the ability to solve complex challenges that can transform industries. On the other hand, they present new governance challenges and ethical considerations that businesses must navigate carefully. Companies that adapt quickly, implementing AI responsibly while fostering innovation, will likely thrive and gain a competitive edge. As we advance, staying current with AI trends will require a balanced approach to harnessing technological potential while complying with rigorous standards of transparency and accountability to stakeholders.
For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies 2024