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TOP 10 Artificial Intelligence Trends That Will Make a Big Difference in Business in 2024

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As a company working with AI since 2018 with an eye on the trends in AI technology, we’ve gathered general AI trends and AI implementation trends that will transform business in 2024. 

From this article, you will learn how to use tools and solutions developed in subfields of AI such as Neural Networks, Machine Learning, Computer Vision, Natural Language Processing, and Speech Processing to achieve your business goals. The overviewed trends are rather practical than futuristic and can be leveraged by small and medium businesses. If you are looking for a development team to implement AI into your product or enhance your company processes, consider MobiDev. Our extensive in-house expertise in software engineering and AI allows us to find effective solutions for specific business needs and implement AI technology to best match the specifics of the product. 

Trend №1. Generative AI Dominates the Market 

In 2024, generative artificial intelligence is predicted to continue its expansion, triggering a game-changing transformation across the global economic landscape as businesses recognize its expansive potential. According to research by Bloomberg Intelligence (BI), the generative AI market is poised to explode, growing to $1.3 trillion over the next 10 years from a market size of just $40 billion in 2022. Generative AI employs diverse techniques and models, including diffusion models for image generation and transformer-based models for text generation. LLMs are also based on transformer architectures. These methods enable the system to learn from existing data and produce novel data that closely resembles the input information. Advanced generative algorithms will be able to achieve unprecedented levels of capability, accessibility, and scalability in various domains, making more and more organizations adopt them.

Generative AI has demonstrated its advantages in various domains, including demand forecasting, supply chain optimization, and product development. Its capacity to analyze vast datasets, discern patterns, and offer valuable insights has led to more efficient processes and improved decision-making. For example, Generative Adversarial Networks (GANs) are winning the market, primarily serving as entertainment tools. Two well-known examples are FaceApp, designed for the Western market, and ZAO, tailored for the Eastern market.  

Moreover, the implementation of generative AI in the healthcare sector led to significant improvements in patient care and operational efficiency, particularly in areas such as medical inventory management, and the tracking of medical equipment. The same can be said about customer care. With the help of Natural Language Processing technology (NLP), it’s possible to improve client satisfaction with online searches, spell-checkers, and voice assistants.

Generative AI for Software Development

Generative AI plays a key role in creating code solutions for site builders and no-code AI platforms. Automated code generation streamlines the development workflow, expanding the accessibility of sophisticated AI solution creation to a wider audience without extensive coding expertise. Generative AI solutions contain pre-built algorithms and AI site builders, facilitating a more inclusive and user-friendly AI development experience. The complexity of site builders may vary widely from simple ones with automatically generated pictures/texts and customized color schemes to advanced, for example, AI web crawler solutions trained for specific goals. 

No-code AI platforms are in demand in cases where customization of the developed products is not so critical. Companies often use these options for computer programs to identify and classify images, objects, poses, sounds, and more. Google Cloud Auto ML, Google ML Kit, Runaway AI, CreateML, and MakeML are among the most popular environments. The scheme below will help you prepare to use no-code AI platforms for your business.

How no-code platforms work

Generative AI is also important in driving the integration of application programming interface (API) endpoints, facilitating the development of sophisticated applications for developers. In 2024, there are forecasted advancements in software development kits and APIs, empowering developers to enhance off-the-shelf AI models through the utilization of AI microservices like RAG as a service. This customization will allow organizations to fully leverage the productivity of AI, incorporating intelligent assistants and summarization tools that provide access to current business information. 

Trend №2. LLMs Power Up Virtual Assistants and Chatbots 

ChatGPT has elevated both LLMs and AI in the media and everyday discourse. Many of us are already incorporating ChatGPT into our daily lives as needed. Implementing Large Language Models (LLMs) in a systematic within corporate settings presents a wide array of benefits in addressing customer queries, performing sentiment analysis, helping human agents, and identifying customer patterns

Digital assistants based on LLMs are widespread and often serve as people’s initial experience with AI. The distinctive feature of such AI solutions lies in their ability to interact with users on a personal level. For instance, an AI-driven chatbot goes beyond simple predefined commands. It focuses on grasping customer intent and behaviors. These tools facilitate communication at a level that closely resembles human-to-human interaction, ensuring that essential information is delivered in a manner comfortable for the customer.

The use of chatbots is actively spreading in industries such as healthcare, finance, marketing and sales, travel and hospitality, etc., significantly reducing the need for human labor.  For example, a medical chatbot can easily help schedule a patient’s doctor’s appointment, provide answers to frequently asked questions, and remind patients when it’s time to take their medicine and get some exercise.

In other sectors, chatbots play a crucial role in categorizing and directing customer inquiries for processing, delivering tailored messages, and offering users personalized assistance. In the realm of education, chatbots have evolved into indispensable aids, readily available to reinforce knowledge at the learner’s convenience. It is no surprise that, as per Business Insider, the chatbot market is anticipated to achieve a value of USD $9.4 billion by 2024.

The popularity of AI-powered virtual assistants is quite understandable due to the pace of modern life. Interaction with conversational AI assistants helps a person to get the necessary information without breaking away from other everyday activities. Ultimately, advances in Natural Language Processing (NLP) and speech recognition have greatly increased the capabilities of customized automated solutions. For example, the NLP-based Question Generation system demonstrated in the following video prevents errors in the secure authentication process.

Trend №3. Narrow-Tailored AI Solutions Promote the Adoption of Artificial Intelligence

Narrow AI refers to artificial intelligence systems designed for particular, well-defined tasks or applications. These systems excel in performing specific functions within a confined scope, promoting the adoption of AI on different levels in business. While AI in general is versatile by default, Narrow AI solutions are more adjusted for specific business goals and easier to develop, especially with a limited budget. 

In 2024, we anticipate the rise of more specialized AI solutions. While ChatGPT serves as an excellent general AI assistant, it’s unlikely that it will be the best choice for every task. Thus, 2024 is expected to be time for AI platforms tailored for researchers, writing generation tools designed for journalists, and rendering platforms specifically crafted for designers, along with other specialized applications.

Looking ahead, AI solutions will be upgraded to resolve specific use cases, whether with a proprietary underlying model or a dedicated workflow built around it. Companies will have the opportunity to establish leadership for the next technological era by excelling in one category and then expanding their offerings. In this context, a more focused and specialized initial product is likely to be more successful.

Trend №4. AI Enhances Security and Surveillance

The development of new artificial intelligence technologies has made it possible to achieve an elevated standard of quality in security systems. Video surveillance can now be combined with biometric authentication using face and voice recognition, human pose estimation, and automated image analysis. AI-based security and monitoring systems allow businesses and customers to work with more precise settings and more accurately identify objects that should be responded to when they appear. In crowded areas, video capture and analysis software helps secure large public and private spaces by detecting potential threats. Automated detection of violent behavior and theft can promptly notify authorities, potentially saving lives. 

Voice recognition capabilities in AI-powered applications have advanced to include the identification of a person’s age, gender, and emotional state. Additionally, biometric facial recognition plays a key role in maintaining overall security.

It is important to mention that potential attackers and fraudulent users possess numerous technologies at their disposal. Instances of spoofing attacks, where an individual pretends to be someone else to gain unlawful advantages, are not uncommon. They may employ malicious programs, fake photos, or stolen personal data for such purposes. It’s crucial to consider that many Internet protocols lack mechanisms for authenticating the source of a request. That’s why software should incorporate robust and secure identity verification functionality to ensure the legitimacy and safety of user interactions. Thus, the development of various advanced anti-spoofing techniques will remain in high demand in 2024.

Leveraging biometrics for “what-you-are” authentication enables the verification of an individual’s identity based on distinctive characteristics like fingerprints, iris patterns, voice, or facial features. The Edge AI approach involves implementing and running biometric artificial intelligence programs directly on the user’s peripheral device, eliminating the necessity for a continuous connection to the Internet and a cloud service. The data is autonomously processed locally on the user’s portable, and potentially wearable, device. A common case is the use of edge biometrics for office security. Such solutions have become a promising area of AI-based development for improving security and surveillance.

Trend №5. AI Boosts Real-Time Video Processing 

The live-streaming market has experienced significant expansion in recent years, driven by factors such as the widespread availability of the internet, advancements in video-streaming technologies, the ubiquity of smartphones and mobile devices, and the surge of social media platforms. According to market research from the CMI Team, the global Live Streaming Market is expected to record a CAGR of 28% from 2023 to 2032. By 2032, the valuation is expected to reach USD $256.56 billion.

Ensuring accurate data transmission and minimizing video processing latency is crucial for the efficient handling of real-time video streams. Artificial intelligence plays a pivotal role in the fundamental components of this process, namely data pipeline processing. The contemporary real-time video processing system integrates a pre-trained neural network model, user scenario implementation algorithms, and cloud infrastructure. This integration is key to achieving the swiftness of real-time streaming. 

Video processing acceleration can be realized through algorithm enhancements and process parallelization. The optimal choice for rapid and high-quality video processing is the pipeline architecture, offering the additional benefit of incorporating effects like face recognition and blurring. You can take a detailed look at this use case in our article on AI in real-time video processing.

AI background removal and blurring in real-time video are based on creating a model that separates the person in the frame from the background. This task relies on a neural network. For its operation, you can choose one of the existing models, such as BodyPix, MediaPipe, or PixelLib. Next, you must integrate the chosen model with the relevant framework and organize the optimal execution process using WebAssembly, WebGL, or WebGPU. 

We’ll support you throughout the AI ​​journey

From idea crystallization to its tech implementation


TOP 5 AI Trends Winning the Market Across Industries

In the upcoming chapter, we will delve into the evolving landscape of artificial intelligence within distinct industries. Let’s examine how AI trends are shaping and influencing sectors, exploring the advancements, applications, and implications within each industry’s unique context.


The latest AI technologies have been fundamentally changing healthcare app development. In the healthcare domain, recent trends highlight the following promising areas of AI development:

  • Individual wearable and non-wearable health devices that monitor essential health indicators in real-time and offer personalized feedback, effectively serving as personal health advisors.
  • Integration of AI in the automation and customization of research and trials for drug and vaccine development, including the utilization of digital twins.
  • Advancements in AI-based solutions for IoMT, elevating medical equipment capabilities, and the development of “Software as a Medical Device” (SaMD) to maximize the potential of medical devices.
  • Application of AI in the segmentation of medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT), to enhance the efficiency of anatomical data analysis.

Learn more about how image segmentation works in MobiDev’s video below.

Another promising use case for AI in healthcare is connected with diagnostics. Researchers and healthcare specialists utilized AI technology in many disease states, such as detecting cancer, diabetic retinopathy, and EKG abnormality and predicting risk factors for cardiovascular diseases. For example, take a look at the study conducted in South Korea, where diagnoses of breast cancer made by radiologists and AI were compared. The AI-utilized diagnosis was more sensitive to diagnose breast cancer masses compared to radiologists, 90% vs. 78%, respectively. Also, AI was better at detecting early breast cancer (91%) than radiologists 74%.

Dementia is another healthcare challenge characterized by the irregularity of its manifestations and the difficulty in pinpointing symptoms. Artificial intelligence comes to some results in addressing this issue through the development of speech processing models. These models enable the identification of communicative and logical issues that serve as indicators of the risk of developing this disease. 

Using AI for dementia diagnosis is associated with three subfields of artificial intelligence – Natural Language Processing (NLP), Machine Learning (ML), and Neutron Networks – and can be used both for early diagnosis of dementia and for monitoring the progression of the disease. Neuropsychological testing for early diagnosis can be performed even on telephone recordings, and classification models help monitor changes in the patient’s condition.

The adoption of the latest AI technologies has significantly contributed to the emergence of new methods of diagnosing oncological diseases with AI, which provide doctors with multi-point and complete information. As specialists use whole slide imaging (WSI), which contains tons of unstructured data, a lot of time and effort are needed during the manual examination. The problem with manual examination can be effectively addressed through AI. 

Let’s also talk about the application of AI in fitness and rehabilitation. In these areas, it is related to human pose estimation (HPE). HPE is a computer vision task aimed at identifying and precisely tracking key points on the human body. These points play a crucial role in recognizing motion patterns, determining specific joint positions, or analyzing the overall pose. The applications of HPE extend to various tasks, including body motion detection, posture correction tools, exercise supervision, and AI-powered fitness coaching. Human pose estimation and natural language processing algorithms allow users to go through a number of workouts, and give and get real-time feedback. 


AI-driven visual inspection in manufacturing is a transformative trend that leverages artificial intelligence technologies to enhance the quality control and inspection processes within manufacturing environments. This approach involves the integration of computer vision, machine learning, and other AI techniques to automate and optimize the inspection of products, components, or processes. 

AI-powered visual inspection is used to control the suitability of components for assembling cars, as well as for defect detection on the conveyor. Further improvement in the detection of defects in manufacturing enterprises will focus on the automation of analysis and decision-making.

Use cases of AI inspection

AI software can determine the nature of defects in parts or finished products based on data from cameras and IoT sensors.

Leveraging AI-driven computer vision allows for the smooth identification and retraction of defective products, meeting production requirements, and upholding strict quality standards. This guarantees the seamless operation of manufacturing processes with minimal disruptions.


AI data analytics makes decision-making much more effective, smooth, and less time-consuming for business owners.  AI-powered demand forecasting empowers businesses with more accurate predictions, faster decision-making, better marketing strategizing, improved supply chain efficiency, and enhanced customer satisfaction, ultimately contributing to overall operational excellence and competitiveness in the market.

According to McKinsey research, businesses that adopt artificial intelligence to manage their supply chains ahead of others have seen a 15 percent improvement in logistics costs, a 35 percent reduction in inventory levels, and a 65 percent increase in service levels compared to those that implemented AI later.

Recent years have witnessed numerous instances of AI-driven transformation in the retail sector. Traditional bottlenecks in supermarkets can be alleviated, and the implementation of self-checkout through the automation of cash registers using computer vision not only saves customers’ time but also enhances their satisfaction. The use of the latest artificial intelligence technologies makes it possible to improve retail with a full range of automation solutions from partial modular automation, such as vending machines, to a fully automated “grab-and-go” store.

Another trend that is worth mentioning here is the development and popularization of virtual fitting room technology in retail. Harnessing artificial intelligence, image processing, and machine learning, this cutting-edge virtual fitting solution accurately mirrors the fit and appearance of clothing on users with diverse body types. This innovation enables customers to virtually try on clothing in the convenience of their homes, allowing them to visualize how the items will look before committing to a purchase.

Approaches used in retail are also successfully transferred to catering. Innovations focus on the main stages of digital purchase, which are identification of the buyer’s identity and tracking of their actions, product recognition, purchase verification, and payments.


The advancement in the quality of content generated by Artificial Intelligence serves as a notable illustration of progress within the field, a topic explored in this article. Research and development in this domain fall under the umbrella of Generative AI. Specifically, Natural Language Processing (NLP) is dedicated to text generation algorithms. This subset of artificial intelligence focuses on creating models that enhance search engine capabilities, generate text in business applications, and contribute to the development of chatbots.

Another trend is connected with AI product recommendations and AI smart search. By having AI analyze historical data, it is possible to predict how performance will look in the future based on a variety of factors. More importantly, analyzing what users like most can be useful when looking to suggest products to them. Moreover, AI can help businesses tailor site search results for a particular customer based on their past search history, in-platform behaviors, and product features. 

AI is also improving automation in marketing. It includes almost everything from automated email deliveries to social media posts. This advancement may empower quicker pivots, the execution of more micro-campaigns, the feasible targeting of smaller segments, and more.

The heightened adoption of AI-powered marketing automation has the potential to reshape audience expectations. Individuals might come to anticipate more timely campaigns, finer segmentation, and content that is even more hyper-personalized.

You will find even more AI use cases in marketing in our specialized article. 


The adoption of AI in the Fintech sector has given rise to several noteworthy trends, shaping the industry’s landscape.

AI in Fintech allows financial organizations to achieve better security and prevent scams. AI-powered algorithms enhance fraud detection capabilities, providing real-time analysis of transactions to identify and prevent suspicious activities. This strengthens security measures in the financial sector overall and eases decision-making on the different levels of management. Biometric authentication, backed by AI, is becoming more prevalent in Fintech for secure user verification. Facial recognition, fingerprint scanning, and voice authentication enhance the security of financial transactions.

Intelligent document processing powered by AI algorithms is transforming credit scoring and underwriting processes. By analyzing a broader set of data, including non-traditional sources, AI helps in more accurate risk assessment, enabling better-informed lending decisions. AI helps to handle voluminous documentation more effectively. To implement regulatory changes, financial regulators need to manually manage tons of compliance documents, which also require qualified employees from different business departments. The OCR technology can make these processes much easier by automating document management for client onboarding, payments, and receipt recognition. Plus, AI is utilized to streamline regulatory compliance processes and reporting in the Fintech sector. Automated systems ensure adherence to complex financial regulations, reducing errors and enhancing efficiency.

Fintech companies are increasingly utilizing AI-driven chatbots and virtual assistants to enhance customer interactions. These tools provide personalized assistance, streamline customer support, and offer insights into financial queries. Moreover, AI can help in making personal financial decisions. AI-powered financial applications offer financial planning solutions. By analyzing users’ financial behaviors and goals, these tools provide tailored advice, budgeting assistance, and investment recommendations. AI algorithms are extensively used in algorithmic trading for making rapid and data-driven investment decisions. These algorithms analyze market trends, news, and various data points to execute trades at optimal times. Smart advisors, powered by AI, offer automated and algorithm-driven investment advice. These tools analyze market trends, investor preferences, and risk profiles to provide personalized and cost-effective investment strategies.

We’ll support you throughout the AI ​​journey

From idea crystallization to its tech implementation


The Future of Artificial Intelligence: Opportunities and Challenges of the Year 2024

Bearing in mind the use cases outlined earlier, we anticipate ongoing dynamic advancements in the field of artificial intelligence. Solutions like chatbots, assistants, NLP tools, robots, and sensors for analysis and predictions have already become mainstream, and this trend is expected to intensify. It’s also expected that two topics — AI explainability and AI ethics will be in the spotlight in the near future.

AI Explainability

In recent years, AI has predominantly focused on scaling — exploring the possibilities by applying extensive computing power and data to train these models. However, as these models transition into real-world applications, the crucial question arises: why? Why do these models generate the outputs they do?

Explainable AI (XAI) seeks to enhance trust, assurance, and acceptance of AI technologies by making them more understandable for users, regulators, and stakeholders. Its applications extend to various fields where decision-making carries substantial consequences. For instance, in finance, XAI can elucidate how credit scores are determined for loan applicants. In healthcare, it can clarify the judgments made by AI-powered diagnostic tools to both doctors and patients.

Ethics and AI Regulations 

The increasing regulatory and compliance demands in diverse sectors, including finance, healthcare, and automotive, pose a significant challenge to the utilization of AI. These requirements aim to establish fairness, accountability, and ethical use. Recognizing the significance of transparency and accountability in AI systems, governments, and regulatory bodies are taking steps to prevent biases and discriminatory outcomes, ensuring ethical deployment.

As the utilization of AI increases, more and more regulations in this field will appear. For example, generative AI, such as Chat-GPT, comes with specific prerequisites. These include the obligation to disclose that the content was generated by AI, implementing measures within the model to prevent the generation of illegal content, and providing summaries of copyrighted data used for training when publishing. Therefore, businesses should be ready to adapt quickly in this regard.

While AI continues to be perceived as an innovative “magic pill” for startups, established companies are progressively incorporating this technology to stay competitive and address evolving customer demands. However, many businesses, both new and existing, often lack a defined strategy for the successful implementation of artificial intelligence. With AI product consulting services, there is the potential to bridge the gap between business and technology, facilitating the truly effective implementation of AI solutions.

With its versatile industry experience in delivering high-quality AI solutions in various domains since 2018, MobiDev ensures that organizations can apply AI to achieve their business goals with the best results. 

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