AI has taken root, causing markets to grow faster and higher. Competition is fierce, and it’s anyone’s game. Frustratingly, the relentless barrage of AI hype cycle marketing confounds the playing field. Your business needs something new, something innovative to maintain your place in the race, but what’s worth betting on?
As a software consulting and engineering company that has been working with AI since 2018, we see it as our mission to arm you with valuable insights about AI trends that are meaningful and actionable for your business. So, let’s discover the key artificial intelligence trends in 2025.
State of the Artificial Intelligence Market in 2025
There are a few factors playing a key role in the continued growth of spending and investment in AI capabilities:
- Nations now view AI, particularly the prospect of artificial general intelligence (AGI) as a strategic resource and national security risk.
- Increased energy demands are motivating enterprises to seek more powerful and efficient energy solutions to support AI.
According to Statista, AI’s market is projected to reach over $800 billion USD in 2030.
Strategic AI and Government Interest
Where previous viral tech trends like the metaverse have already entered the trough of disillusionment, AI’s path has been different. Instead, an arms race has begun between nations, corporations, and communities to create the most advanced tools. The holy grail of these works is artificial general intelligence (AGI), and later artificial superintelligence (ASI), AI products that can match or exceed human capabilities. Although these concepts are only loosely defined, industry and world leaders believe that whoever can develop these capabilities first will have economic and even strategic advantages over their competitors and adversaries.
When AGI and ASI will disrupt the scene is uncertain. Sam Altman, OpenAI CEO, suggests that AGI will arrive this year in 2025 according to an interview with Y Combinator. However, other experts believe it will be decades or even never. Rupert Macey-Dare predicted in a 2023 paper an average date of 2041.
Increasing Adoption by Industries
Although no one is certain when AGI capabilities will arrive and how they might disrupt industries, businesses are incorporating AI into their products and toolsets right now.
Since AI is being widely adopted by industries everywhere, these capabilities are quickly becoming standard across world markets. Those with AI tools excel, and those who don’t fall behind. If you’re going to keep up with the future of artificial intelligence, we need to identify the most critical AI trends.
AI Trend #1. Multimodal AI Powers Generative AI Capabilities
According to IBM, multimodal AI describes artificial intelligence which can perform several distinct types of tasks in one application, such as text, image, audio, and video processing.
At first, when generative AI tools like ChatGPT and Bing Chat (now Copilot) were more widely released in 2022 and 2023, they were only able to perform text processing as chatbots. However, over time, their sensory capabilities expanded to include images. It’s now possible to input an image into one of these tools and get text descriptions as outputs. These tools can also generate images as outputs and include now voice conversations.
Multimodal AI experiences are some of the most important trends in AI right now because they make interfacing with AI tools much more intuitive. Making more accessible and useful interfaces is a key focus of futurist Jared Ficklin’s vision for an AI-generated future, where eventually it’s possible that every experience a user has with software will be hyper-personalized and generated on the fly by natural language commands, images, gestures, and other multimodal inputs.
For right now though, one of the most promising applications of multimodal generative AI lies in advanced intelligent document processing. With the ability to work with different document formats (PDF files, illustrations, spreadsheets, etc.) systems powered by multimodal AI will be able to automatically convert data into the right digital format, greatly simplifying work processes.
Another use case is leveraging the power of multimodal AI for improved chatbot development. For basic chatbot applications, it means enabling text, vision, and voice input for enhanced human-chatbot communication.
AI Trend #2. Shifting from LLMs to SLMs
Large language models (LLMs) are the workhorse of generative AI tools like OpenAI’s ChatGPT. However, running these models comes with a steep cost. According to SemiAnalysis in 2023, it’s estimated that ChatGPT costs nearly $700,000 to operate per day. This is due to the extreme resource and energy requirements of large language models. The more users and the more powerful the model, the more processing power is needed.
As a result, one of the most important trends in artificial intelligence is the rise of small language models (SLMs), which complete similar tasks to LLMs but with fewer resource requirements.
SLMs are derived from LLMs, but various operations have taken place to reduce their resource requirements — a process called model compression. According to IBM, some methods of model compression include:
- Pruning: trimming unnecessary parameters
- Quantization: lowering the precision of data
- Low-rank factorization: simplifying complex matrices (tables of data) into simpler ones
- Knowledge distillation: teacher model reasoning is passed onto student SLM models
Some of the most well-known SLMs include Phi-3 Mini, Qwen2, Mistral Nemo 12B, and Llama 3.1 8B. One of the most common model sizes in this range is 8B, with over 31,000 8B models on HuggingFace.
Since small language models are easier to run, businesses are starting to find use for them on local user machines or in their private/public clouds. This makes SLMs one of the most useful recent trends in AI.
One of the most promising use cases for SLM is when the data we need to process is private, such as patient data in HIPAA-compliant AI products. You can’t just send patient data to any public APIs, and quite often you can achieve acceptable results with a self-hosted smaller model, as long as it can fit into the local infrastructure.
However, we should remember that SLMs are intrinsically less powerful and accurate compared to their LLM counterparts. Despite these limitations, SLMs are capable and efficient when it comes to simple tasks that don’t need to be outsourced to cloud AI providers.
AI Trend #3. Fueling Virtual Assistants and Chatbots with Agentic AI
It’s one thing to query a chatbot and get a response; it’s another to have an AI autonomously complete a task while you’re away from your keyboard. That’s the premise of agentic AI, or AI agents, one of the latest AI technologies that defines the future of artificial intelligence. The use of multi-agent systems often gives higher quality results because several agents work together at once.
For example, simply giving ChatGPT a prompt to create cold emails might turn out okay. However, having a multi-agent AI system can do it better. A user proxy agent, the agent that operates on behalf of the user, analyzes the initial prompt, and understands what it needs to do.
It then sends its output to assistant agents that have more specialized tasks like writing, understanding the target audience, editing, and ensuring the emails are persuasive. At the end, the user proxy agent returns the result to the user, which will be a much more refined email than one agent acting alone.
This involves multiple actions initiated by a human, with more than one step in the process initiated by AI in the middle. This is the basic concept behind AutoGen Studio, for example.
Another example of this technology at work is an experimental project called AutoGPT, which enables a user to initiate a cascade of actions by AI from a single prompt. Once the user hits enter, the AI goes to work and tries to accomplish the task. It might search the Internet to research information, it might create documents to store knowledge, and it can ask the user for feedback as it works.
How to leverage this trend:
Although current tech limitations prevent Agentic AI from fully realizing its potential, virtual assistant development in business continues to be exemplified by chatbots of varying complexity. The implementation of chatbots to automate workflows and customer support is becoming part of digital strategies even in highly regulated industries. For example, according to Vantage Market Research, the healthcare chatbot development market is expected to $431.47 million by 2028.
AI Trend #4. Open-Source AI Drives Model Optimization
The AI world is divided into two camps: closed source and open source. This debate is the same as with any other type of software but with a few twists. For one, closed-source AI proponents argue that the democratization of AI could be catastrophic for humanity. They say it could significantly increase the chances that a rogue AI could quickly spiral out of control, or that a non-government adversary might use AI as a tool to cheaply create weapons of mass destruction or highly persuasive misinformation campaigns.
However, Mark Zuckerberg, one of the most influential leaders of the open-source AI camp, argues that democratization is the future of AI in business. Open-source models, like Meta’s Llama series, provide a competitive experience and enable developers to create fine-tuned custom models. Zuckerberg says going open-source has a number of advantages, such as
- Independence: businesses can self-host open-source AI to prevent vendor lock-in
- Data Privacy: sensitive data never has to change hands if you self-host an AI model
- Efficiency: self-hosting an open-source model is more affordable
- Customization: fine-tuning models with your business’s own data can give you serious advantages over closed-source models
Although AI safety is still an important topic, it’s important to recognize that many of the same businesses fighting for AI safety stand to benefit the most from controlling closed source AI monopolies. Open-source solutions enable businesses of all sizes and industries to take charge of their data and deliver high quality experiences to their customers without needing to account for expensive licensing fees and data privacy hurdles.
AI Trend #5. Customized Enterprise Generative AI Models Enable Tailored Solutions
Speaking of tailored models, this latest trend in AI deserves a section of its own. Fine-tuning an existing open-source model is possible, but some enterprises have opted to create their own models from scratch. Deciding which of these to choose will depend on your business needs and use cases. For most businesses, fine-tuning a model is more feasible.
Everything starts with datasets. If your business deals with a lot of data already, you’re already on the right track. Fine-tuning then can be done across the entire model, called Full Fine-Tuning or Parameter-Efficient Fine-Tuning (PEFT) where only some of the parameters are changed. Once you’ve evaluated and quantized the model, you’ll have the opportunity to decide if it’s ready for deployment.
Fine-tuning models doesn’t just apply to generative AI. You can also leverage these same strategies for other kinds of models that deal with images, speech and audio, sales, and other kinds of data.
How to leverage this trend:
AI consulting can help you find answers to all your questions about implementing AI in your business. This kind of service is aimed at finding the best possible solution for each specific case by syncing your strategic goals, tech capabilities, and market needs.
AI Trend #6. Using Retrieval-Augmented Generation (RAG) to Reduce AI Hallucinations
LLMs can provide some AI hallucinations when an artificial intelligence model generates incorrect information. One technology that can help to address this is Retrieval-Augmented Generation (RAG). This enables the LLM to reference up-to-date and trusted sources when needed. This is the basic concept behind search-engine powered LLMs like Microsoft Copilot and Google Gemini, which use Internet search results to inform their responses. However, it can also be done in more controlled environments, such as providing models with up-to-date documentation on business policies, prices, and other information.
Although RAG techniques certainly make information more relevant to users, it’s not immune to hallucinations. One issue is that although the information may be more relevant or up to date, it’s not necessarily personalized to the user. For example, you might have the RAG database filled with information about your SaaS business’ pricing structure, but that may not enable a customer service chatbot to provide personalized information about a client’s invoice on its own.
Because of these issues, RAG and other methods of combatting hallucinations are rising trends in AI that shouldn’t be ignored. One option to approach the problem is context. The more that a chatbot understands the context of a session, the more accurate its responses can be.
In the SaaS chatbot example, if the chatbot has access to information about the client’s invoice and subscription model, it can provide more personalized answers. There are other advances being made in this space as well, such as the integration of more powerful reasoning models to help provide more rational responses.
AI Trends #7. The Rise of GPUs Across the AI Industry
The hardware powering the AI revolution, GPUs, have taken on a new importance. Being the most efficient components for hosting AI models, GPU demand has risen significantly, making it one of the most important trends in artificial intelligence. As we explored with self-hosting open-source AI, graphics cards are one of the most important bottlenecks for developing and deploying on-premises or cloud-based AI infrastructure.
According to Mordor Intelligence, the global GPU market size is worth $65.3 billion. Rising at a CAGR of 33.2%, it will climb to a value of $274 billion in 2029.
AI Trend #8. AI Creates Both Opportunities and Threats to Security
Of the rising AI trends, security is the sharpest double-edged sword. One of the greatest lessons we can learn in the AI industry is that data can be used for both good and evil. If you aren’t incorporating AI concepts into your security strategy, you are leaving your business vulnerable to adversaries that are rising in number and capability.
Most of the time when we think of security risks with AI, we think of cybersecurity. However, these risks can extend much further than that. In 2022, researchers inverted the capabilities of MegaSyn, an AI designed to speed up the process of detecting molecule toxicity for medicine development. Instead of helping develop medicines safe for humans, the AI developed as many ideas for toxic drugs as possible. Many of these are potential candidates for harmful bioweapons, and 6,000 possibilities were generated in six hours.
In much the same way, AI can be used to quickly augment and speed up developing malware, phishing, and other harmful software and intrusion techniques. Even AI models themselves can become compromised from prompt injection attacks, and data can be manipulated for malicious ends. As a result, businesses are incorporating AI into their security strategies to fight AI-generated threats. Automating defensive responses, pattern recognition to understand anomalies and alert humans, and detecting fraud are all positive impacts that AI can have on many industries.
Biometrics
With AI becoming a powerful tool for malicious actors, biometrics are becoming more popular for authorization and identification. However, the fight isn’t over yet. With AI’s powerful image generation capabilities, biometric methods like facial recognition are at risk of being spoofed. Thankfully though, techniques exist that make facial recognition spoofing much more difficult to achieve. Here’s an example of anti-spoofing techniques in action:
For more sensitive applications, multi-factor authentication may be preferable to mitigate risk. For example, using facial recognition and fingerprints can reduce the success of spoofing attacks.
How to leverage this trend:
Using AI biometrics, including both physical and unique behavioral characteristics, allows for more effective security systems. Behavioral biometrics can detect inconsistent user behavior and dynamically limit their access.
AI Trend #9. AI Regulations and Ethics Come Under the Spotlight
As we enter the future of artificial intelligence, AI’s global prevalence has captured the attention of governments. Although they are slow to catch up with innovation, special attention has been placed on AI’s ethical and safety issues.
One of the first governments to jump onto the situation has been the European Union with the EU AI Act. This legislation aims to manage the risk of AI systems, high and low impact. It also prohibits some types of AI systems, such as those used deceptively or exploitatively.
Meanwhile, the United States has been slow to adopt AI regulations. In October 2023, President Joe Biden enacted an executive order that pushed for increased oversight and transparency over AI developments and operations. Increased reporting is required of developers to the government to create a better understanding of AI safety and national security. However, the recent election has made the future of this policy uncertain.
Bias in Artificial Intelligence
One major concern of AI’s rise in prevalence has been its ability to reinforce cultural divisions and biases. In 2019, MacMillan & Anderson found that 44 universities used AI-based admission algorithms. Improper datasets can bias AI, especially if applicants from certain backgrounds are favored. This can have profound consequences for applicants, even if these results are unintentional. The most prominent place of focus is data training.
Broadening data across a wide variety of experiences not only can advance the objectives of equity and fairness, but it will also improve an AI’s performance. Some other techniques that she recommends are:
- Audits: auditing the bias and data of AI systems regularly is a great first step to recognizing harmful patterns
- Bias Assessments: specifically auditing an AI system’s capacity for statistical bias is critical
- Retraining: mitigating bias by providing new data and training
AI Trend #10. Narrow-Tailored AI Solutions Promote the Adoption of AI Across Industries
Earlier, I underlined the growing attention toward efforts to develop artificial general intelligence and superintelligence. Most definitions describe the objectives of these systems as being able to manage a very wide range of tasks. Naturally, this has resulted in many AI systems designed to be the “jack of all trades”.
However, this may be a distraction from other opportunities for AI systems. Instead of being generally intelligent, narrow AI can specialize in one field.
Industry-specialized AI already permeates the market, like Amazon’s product recommendation systems or AI demand forecasting systems. They are easier to develop and have higher performance than general models. Let’s briefly explore those opportunities across the industries.
AI Technology Trends in Healthcare
There are several promising AI uses cases in healthcare that are capable of transforming the industry. Natural language processing alone supports medical note extraction, enhancing phenotyping potential, extracting clinical data, identifying patient groups for testing, and administrative support.
Diagnostics stand to benefit from AI as well. This applies to both physical health, like cancer detection, and mental health, like dementia detection. When trained with appropriate data, AI can help streamline the diagnostics process when supervised by experienced doctors. However, to accomplish this goal, large data sets are needed that can be challenging to create and use. Patient privacy takes priority, and that means the development of training data and diagnostic models will take time.
Aside from diagnostics, mobile sports fitness applications are another place of opportunity for AI in sports and fitness. The rich data produced by athletes is a gold mine for insights once analyzed by AI. Human pose estimation is another trend in AI that is assisting athletes and people looking to have more effective workout posture.
In the video below you can see how a human pose estimation app can work using the example of our client BeONE Sports.
How to leverage this trend:
Since healthcare is a highly regulated field and the use of artificial intelligence can be associated with certain biases, the best starting point for implementing AI strategies is to seek AI consulting. This phase will help you assess the feasibility and accuracy of your solution and contribute to making a more informed decision about development. You can start AI healthcare consulting today – book a call to get answers to your key questions.
Artificial Intelligence Trends in Manufacturing
The two most important AI use cases in manufacturing are predictive maintenance and defect detection. With comprehensive IoT sensors, data collected from factory equipment can be useful for AI in predicting when machines will be most likely to fail. The more data you can collect, the more accurate these predictions will be. Predictive maintenance can save businesses money otherwise spent on preventative or reactive maintenance.
Thanks to advancing technologies in computer vision, AI can perform visual inspection for defect detection. It’s entirely feasible for manufacturers to have custom-tailored systems with their own training data to detect defects on assembly lines.
Latest AI Trends in Marketing
AI has unquestionably transformed the marketing industry. The public consciousness about AI’s role in marketing has turned sharply negative over the past few years. There are two critical things we should remember:
- AI is a powerful tool, and it can benefit our marketing campaigns, but it’s not human. There is a price to pay in brand trust and reputation when you cut corners through AI content generation.
- You should carefully choose AI use cases for your marketing activities.
So, what should we use AI for in marketing? Content generation is great for ideation and drafting, but AI has other major strengths in the marketing industry.
For example, recommender systems are one of the trends in AI responsible for Amazon’s domination of the ecommerce marketplace. Predictive analytics help retailers target segmented advertising to consumers.
Finally, sentiment analysis allows businesses to understand how customers are feeling about their products and even automatically respond to Google Maps reviews.
Recent AI Development in Retail
The two most important applications of AI in retail this year are demand forecasting and virtual try-on. Demand forecasting powered by AI is a technique that’s been around for quite some time now. However, it has become even more effective because more data is being collected than ever. This is because of expanding omnichannel trends like connected POS systems both in stores and online. By keeping track of your own sales data and other factors like world events and industry news, you can accurately predict demand and prepare inventories.
Virtual fitting room technology continues to advance, with an emphasis on body measurement features. If cameras and AR frameworks can accurately measure human bodies, then shoppers can have a better chance of purchasing clothes that fit them online.
AI Trends in Fintech
Fintech is an industry rich in not just finance but data as well. Mobile budgeting applications that connect user banks and credit cards have valuable data that AI can use to generate recommendations and budget plans for consumers. Banks can also reap the security benefits of AI for their customers with automatic fraud detection. Since AI is excellent for pattern recognition, potentially fraudulent charges can be detected more easily.
AI’s data-driven insights and forecasting capabilities make it an excellent choice for Predictive market analytics. This can be exceptionally useful for things like investing and personal savings planning.
The Future of Artificial Intelligence: Opportunities and Challenges in 2025 and Beyond
When we look broadly at the latest AI tech trends of 2025, we have a lot of questions to answer:
- How will governments regulate advances in AI?
- How can we use AI in a way that will be genuinely useful for businesses?
The answers to these questions may be just as mysterious as predictions about when AGI will arrive. However, as we saw in Statista’s report, AI’s role in world markets is only going to increase by the order of hundreds of billions of dollars over the next five years.
The time to develop your AI strategy is yesterday. Although the pressure’s on, don’t aimlessly adopt. Consider your specific business needs and how AI can meet those needs. Most importantly, be flexible to change. With how quickly AI is improving and the steady pace at which governments are catching up with oversight and regulations, keeping light on your feet will help you change course when you need to.
As a software consulting & engineering company that has been working with AI for over 6 years, MobiDev has extensive experience to help you start your AI journey. Feel free to check our AI consulting services and contact us to discuss your project.
