Top 12 AI Future Trends to Watch Out for

Are you as excited as we are to uncover the top AI future trends? At McKenna Consultants, our team is enthralled by how artificial intelligence continues to evolve, introducing innovations we could never have imagined. The evolution of AI is shaping our future in transformative ways, from streamlining mundane tasks to revolutionising entire industries.

With so much promise for progress, we’re taking a look at the top 12 AI future trends so you can get ahead of the game and start thinking about future implementations in your business. Let’s get right to it!

Contents of AI Future Trends

1. Generative AI
2. Human-AI Collaboration
3. Stronger Ethical Standards
4. Advancements in Language Models
5. AI-Powered Cybersecurity
6. Quantum AI
7. Advanced Robotics
8. Shadow AI
9. Innovative Government and Law Enforcement
10. Multimodal AI
11. Personalisation
12. Edge AI

12 AI Future Trends to Look Out For

So, what is the next big trend in AI? Here’s our list so far!

1.      Generative AI

When it comes to generative AI future trends, this type of intelligence is shaking up and improving how we approach creative processes. Unlike traditional AI models that mainly classify or analyse data, generative AI models can develop novel outputs from images to music based on patterns learned from existing datasets.

For example, models like GPT-4 can develop text, images, and other forms of content with remarkable speed and levels of personalisation. Text generation is particularly useful to aid writers with articles, stories, and reports. On the other hand, tools like DeepArt can use generative techniques to generate artwork and visual images based on textual descriptions, which can be valuable for saving time and costs in business.

Furthermore, in industries like healthcare, generative AI can develop potential drug candidates and simulate their effects, reducing the time and cost of introducing new treatments to the market.

AI also has the ability to improve medical imaging by developing synthetic images. Generating synthetic images can offer a larger dataset for training diagnostic models, which can develop AI systems capable of detecting various conditions and address the limitation of data scarcity. Plus, by enhancing image resolution, AI can increase the accuracy of diagnosis. Win-win!

We can’t wait to see how generative AI trends evolve in the future and how this incredible advancement will continue to revolutionise businesses in almost every sector.

2.      Human-AI Collaboration

While many employees fear that artificial intelligence will put them out of a job, the trend of human-AI collaboration is growing ever more prominent. By enabling AI systems to enhance human capabilities while still preserving a personalised human touch, businesses could celebrate a winning approach.

For example, in customer service, AI systems can improve customer experience with personalised recommendations, while humans can manage more complicated and emotionally sensitive communications with consumers.

The benefits of human-AI collaboration have already been clear to see in some industries. With AI automating repetitive tasks, humans are allowed to focus on more complex and creative aspects of their work, enhancing speed and productivity. AI’s ability to process large amounts of data at rapid speeds has also helped humans make faster and more informed decisions. For example, in the finance sector, AI can scrutinise market trends and create reports, which human employees can then handily utilise to make strategic decisions.

However, there are a few things to keep in mind here. Users must be trained to utilise AI tools effectively and interpret their outputs for a successful collaboration, and businesses must put guidelines in place to prevent misuse. They must understand how the systems work and address any bias in AI systems to ensure fair and equitable outcomes.

3.      Stronger Ethical Standards

There’s just no avoiding it – businesses need to be cautious about generative AI future trends. The potential consequences of misusing AI technologies could not only damage their brand, but could jeopardise legal compliance.

Factors like algorithmic bias have been called into question. AI technologies may present biased outputs based on the data they are trained on rather than recognising discrimination.

Furthermore, protecting privacy is also an important matter. AI systems typically require large amounts of data, which can include sensitive information. Therefore, there are concerns about protecting data privacy and preventing unauthorised access.

There are already a few initiatives advocating for more stringent standards where AI ethics is concerned. With this in mind, we can expect ethical standards and considerations to grow stricter, which will put increasing pressure on companies to govern the use of AI. Such dedication can help businesses mitigate risks concerning AI.

To learn more about the ethics of AI in business, please see our blog.

4.      Advancements in Small Language Models

In future trends of AI, we can expect to come across more sophisticated language models that have the ability to enhance human-computer interactions and translations. Putting Large Language Models aside, Small Language Models are an upcoming trend in AI that focuses on generating efficient yet lightweight models that have fewer parameters. These models are optimised to balance performance, efficiency, and resource utilisation.

This means that businesses can enjoy scalable and accessible AI solutions that can work on edge devices without having to depend on large cloud computing resources.

Other benefits include reduced costs (as smaller models are less expensive to train and deploy) and faster response times. The latter is critical for language processing tasks and applications demanding real-time interactions like customer support assistants.

Despite this, there are still challenges surrounding Small Language Models, which we can expect to be addressed in the future. For example, knowledge limitations due to their reduced training data and parameters are a problem. However, with continued optimisation, we can only expect Small Language Models to become increasingly sophisticated and efficient.

5.      AI-Powered Cybersecurity

Some companies have been using AI in cybersecurity solutions for a few years now. However, as artificial intelligence advances, it is likely that more organisations will utilise AI for real-time threat detection and automated responses in cybersecurity.

After all, cyber threats are becoming more sophisticated, which means that more adaptive and efficient security measures are needed to address them.

By enhancing overall security protocols in a streamlined way, organisations can greatly reduce their response times to potential threats and boost accuracy in identifying and mitigating risks. Businesses can protect sensitive data and systems with this AI future trend, although they will be required to continuously update and review AI systems to guard against new and emerging threats.

6.      Quantum AI

Quantum AI is an emerging field that blends the principles of quantum computing with AI, involving machine learning and data analysis. In doing so, this approach is able to tackle complicated problems and boost computational capabilities compared to regular computers.

For example, quantum AI may increasingly tackle simulation problems, which can aid applications in drug discovery and material science. Furthermore, quantum AI can improve machine learning algorithms by offering new ways to perform tasks like classification, leading to better performance and efficiency. Not only this, but by performing enhanced data analysis of complex datasets, quantum computing can offer deep insights and, thus, more accurate predictions.

As this AI future trend continues to evolve, we can expect to see further advancements in quantum hardware and a broader adoption. For example, expanding access to quantum through cloud-based services could facilitate the development and deployment of quantum AI applications across a wide variety of industries.

7.      Advanced Robotics

Advanced robotics driven by AI solutions are becoming more and more popular, and now it’s no unrealistic feat to think about having little robot companions working alongside us. This AI future trend integrates AI with robotics to generate systems capable of performing complex tasks both independently and efficiently.

Furthermore, if the robot is integrated with natural language technology, it will be able to communicate with us and learn about us, taking our interactions with technology to the next level.

This is designed to streamline various sectors, such as manufacturing and logistics. For example, in manufacturing, AI-enabled robots are utilised for tasks like assembly, painting, and welding, increasing productivity and precision in the process. In logistics, robots can optimise inventory management, automate picking and packing chores, and even handle warehouse materials to boost efficiency while slashing operational costs.

8.      Shadow AI

As another AI future trend building a lot of excitement, Shadow AI refers to AI technologies that are employed within a company without formal approval or integration into the company’s official IT systems.

Shadow AI comes about when employees or departments want to streamline their processes and tasks quickly without going through IT for oversight or approval.

As employees may not have all the required information regarding security and compliance, this approach may lead to potential legal, economic, or reputational impacts. With this in mind, it’s not something to overlook.

Therefore, organisations need to stay agile and adapt their practices to address these emerging challenges and opportunities. To reduce the risks associated with Shadow AI, companies could provide training and resources and generate a streamlined process for integrating new AI tools so employees can always leverage approved solutions.

9.      Innovative Government and Law Enforcement

Since AI has already weaved itself into industries across a wide range of sectors, there is no doubt that it will become more firmly implemented into our government infrastructure. While this doesn’t necessarily mean that we’ll have a robot prime minister, you may expect to see a deeper integration of smart governance technology, especially into tasks such as administration.

As in other sectors, AI may be used as virtual assistants to streamline citizen interactions with the government by answering simple inquiries and offering relevant information. It may also be used to automate document processing to free up burdens on current employees.

Furthermore, by using AI to streamline processes, policymakers may be able to detect what changes the public wants via data insights and patterns to take proactive and informed steps. To take things further, AI models could then simulate the consequence of implementing a policy decision to help policymakers review the likely outcome.

Additionally, predictive AI technology could be leveraged in law enforcement. By using models to analyse crime data, we may be able to predict where and when future crimes might happen. Forecasting in this way may also aid law enforcement by helping allocate resources more effectively. By being more prepared for crime, AI can help to mitigate it and improve overall public safety.

10.  Multimodal AI

The future AI trend refers to integrating and processing multiple types of data within one AI system. Data types can include anything from images and text to audio and video. While AI models have traditionally focused on a single modality, multimodal AI strives to blend these various modalities to create an enhanced understanding of the data and, thus, accurate outcomes.

There’s just no avoiding it – multimodal AI can be instrumental in generating more engaging content experiences. For example, AI systems can develop videos from text descriptions or even create interactive experiences that offer a seamless combination of images, text, and audio.

Moreover, multimodal natural language processing models improve AI’s ability to understand and generate human language through the incorporation of additional modalities. For example, integrating audio with text can enhance speech recognition systems or even allow for more natural interactions in virtual assistants, which can be valuable to a host of companies requiring better customer service.

Not only this, but AI can also personalise content to individual preferences, integrating a variety of data types, which could result in more tailored recommendations. This tool is particularly valuable in retail and eCommerce, where companies want to entice consumers with relevant product recommendations.

11.  Personalisation

As you may already be aware, personalisation refers to customising services, products, or services to meet individual customers’ specific needs and preferences. Personalisation has become the key to conversation. The world has fallen in love with its power to enhance users’ experience, engagement, and relationship with brands. By fostering stronger connections and relevance, personalisation has the power to increase customer loyalty and satisfaction.

Companies like Spotify and Netflix are already leveraging AI to take personalisation to the next level. For example, Spotify’s AI tools utilise your listening history and prompts to create playlists that you’ll likely fall in love with. At the same time, it’s normal to see relevant recommended shows in your favourite genre listed on Netflix.

As AI tools continue to evolve, we can expect personalisation to become even more sophisticated as time goes on, making interactions feel tailored to each unique user. We predict AI-driven personalisation to become the standard and for generic, mass communication to fade away into the past.

12.  Edge AI

Edge AI refers to deploying AI algorithms directly on – you guessed it – edge devices. This can be anything from smartphones to IoT sensors, meaning there is no need to rely on centralised cloud computing. In essence, this approach brings AI processing closer to the source of data generation. And, by processing data locally, edge AI limits the risk of data exposure during cloud transmission. This can improve privacy and security, which is important across industries.

Edge devices’ capability to run AI models and perform data analysis on-site can boost data processing efficiency and decrease latency by minimising data transmission delays. This is particularly valuable for applications that demand immediate responses, like autonomous driving. Edge AI enables autonomous vehicles to process sensor data, like that from cameras, to facilitate navigation and decision-making without having to depend on cloud-based data analysis.

Furthermore, rapid response times can boost user experience in applications such as virtual reality. By leveraging local processing power, edge AI can also enhance performance and responsiveness when it comes to VR. Furthermore, edge AI is able to analyse user behaviour and preferences in real-time, adapting and personalising the VR content as necessary. For example, this could include recommending new experiences or adjusting the game level difficulty.

What Will AI Become in the Future?

It’s fair to say that AI has already transformed our personal and professional lives significantly, but, as you can see from our grand list of AI future trends, it’s showing no signs of slowing down!

There’s no certainty of what AI will become in the future, but we can expect to see more innovative approaches that can streamline how we approach tasks. We can expect AI to become:

• More integrated
• More personalised
• More responsible
• More collaborative

Advancements in AI hold enormous potential for advancing technology and improving our society, although they must be guided carefully with ethical, societal, and economic impacts in mind. Balancing these factors will be crucial in leveraging the benefits that AI solutions have to offer.

Get Ahead of Future AI Trends with McKenna Consultants

Here at McKenna Consultants, we’re all about innovative digital solutions and want to help your business thrive as the future becomes the present. With our team’s forward-thinking approach and ability to integrate AI future trends and technologies, we can assist companies in staying ahead of the competition.

For example, our experts have created AI assistants and can assist with optimising an LLM’s output through Retrieval-Augmented Generation. If you’re interested in learning more about our services, please contact us today for further details.

Additionally, for more AI insights, be sure to check out our blog.