AI In 2024: Here’s What’s On The Cards

Artificial intelligence, and generative AI in particular, is reshaping industries and revolutionising the way we live and work. In 2024, several key trends are poised to dominate the AI landscape. From generative AI for businesses to its ethical considerations, to AI systems that possess agency, these trends will have a profound impact on businesses and consumers alike.

Generative AI In B2B

Generative AI enables businesses to create new content, designs and even entire products using AI algorithms. With its ability to generate highly realistic and original output, the technology has the potential to streamline creative processes and unlock opportunities in the B2B space. It can help businesses automate tasks like content creation, design prototyping and even product development.

By leveraging generative AI, businesses can save time and resources while bringing new and unique offerings to the market. It can assist in market research and trend analysis by generating data-driven insights. This trend is expected to bring about a shift in marketing strategies and change how businesses interact with customers.

Ethical AI Development

It is imperative that AI algorithms do not perpetuate bias or discriminate against specific individuals or communities. As a result, 2024 will witness a heightened focus on ensuring that AI technology is developed and deployed responsibly.

Companies and governments will prioritise ethical considerations to create AI frameworks that promote fairness, accountability, transparency, privacy and data protection. Businesses will aim to build trust with their customers and stakeholders, and mitigate the potential risks associated with AI.

Custom Generative AI Models For Enterprises

Even as consumer-facing generative AI models like ChatGPT have become popular, custom generative AI models for enterprises are finding their footing too. According to research by TechTarget’s Enterprise Strategy Group, while only 4% of organisations had generative AI in mature enterprise-wide production in 2023, it will gain extensive traction in 2024, with 54% reporting generative AI in early deployment, pilot or planning stages.

Enterprise AI customisation involves developing AI models and systems that are specifically designed for individual businesses. For example, in healthcare, AI models can be customised to analyse patient data and provide personalised treatment recommendations. AI can be tailored to assist with fraud detection and risk assessment in finance too.

Multimodal Generative AI

While text-based generative AI models have been widely explored and applied, 2024 is expected to witness a growing interest in multimodal generative AI. Multimodal AI refers to systems that can generate and understand content in multiple modalities, such as text, images and audio. This technology has the potential to transform various industries, including entertainment, advertising and education, and is expected to play a significant role in shaping the future of content creation and consumption.

In the entertainment industry, multimodal generative AI can be used to create realistic virtual characters and immersive experiences. In advertising, it can assist in developing personalised and visually engaging campaigns. Multimodal AI can also facilitate interactive and adaptive learning experiences in education.

AI: A National Priority

As AI continues to gain momentum, some countries are making it a national priority. As of 2023, more than 60 countries have published their national AI policies. The race to become leaders in the technology is intensifying, with potential implications for economic competitiveness and geopolitical dynamics. By prioritising AI, governments seek to harness the transformative power of this technology and unlock its economic and societal benefits.

Governments are also investing in AI research and development, aiming to position themselves at the forefront of AI innovation. These initiatives include funding research projects, establishing AI research centres and implementing policies to support AI development.

Transforming Software Development

AI is poised to transform software development by automating various aspects of the process. AI-powered tools can automate code generation, bug fixing and suggest improvements, making processes faster and more efficient. With AI, developers can focus on higher-level tasks and accelerate the delivery of high-quality software.

AI can assist in software testing by identifying potential vulnerabilities, potentially improving product reliability and performance, and can analyse data to identify patterns and optimise software performance. By leveraging AI in software development, businesses will aim to optimise processes, reduce development costs and deliver better products.

Agentic AI

Agentic AI refers to AI systems that possess agency and decision-making capabilities. These systems can act autonomously, making decisions and taking actions based on their programming and past experiences. Agentic AI has potential in industries such as healthcare, finance and transportation.

For example, the technology can assist in medical diagnosis and treatment recommendation, augmenting the capabilities of healthcare professionals. In finance, agentic AI can autonomously execute trades and make investment decisions based on market trends and data analysis.

Open-Source AI Models

Open-source AI models are democratising AI development, fostering collaboration and knowledge sharing among AI practitioners. They are publicly available, allowing developers to access pre-trained models and datasets, build on existing work and accelerate projects.

Additionally, they promote transparency and peer review, allowing for identification and mitigation of potential biases and limitations. By merging open-source pre-trained AI models with private or real-time data, enterprises can increase productivity and reduce costs.

While AI promises to accelerate advancements across a diverse section of industries and benefit consumers as well, AI systems have thrown up challenges too.

Challenge To Cybersecurity

A 2023 report showed that 85% of security professionals attributed the rise in cyberattacks over the past 12 months to bad actors using generative AI. Cybercriminals can also exploit vulnerabilities in AI algorithms to manipulate systems, steal sensitive data or launch destructive attacks. Adversarial attacks, where malicious actors manipulate AI systems through carefully crafted inputs, pose a significant risk.

To combat these risks, robust cybersecurity measures must be implemented to protect AI-powered systems. This includes techniques such as secure AI model training, anomaly detection and continuous monitoring.

Rising Threat From Deepfakes

AI-powered deepfake technology is expected to be one of the most significant cybersecurity threats in 2024. The number of deepfakes detected globally rose 10x in 2023, compared to a year earlier, and the threat is only set to increase, with around 60 countries poised for elections this year.

Cybercriminals can modify audio or video footage to generate extremely convincing false media by using deepfakes. They can be employed to disseminate false information, trick people and extort victims. A major potential threat is vishing attacks, where deepfake audio and large language models allow AI systems to engage in phone conversations with people, extracting personal details and sensitive information that can be misused further.

Shadow AI

Shadow AI refers to AI systems that operate in the background without user awareness. These systems analyse vast amounts of data, make decisions and take actions without explicit human interaction. While shadow AI can offer convenience and efficiency, it also raises concerns about privacy, control and transparency. Users may not be aware of the extent to which AI systems are collecting their data.

Additionally, when AI systems operate without transparency, it becomes challenging to understand how decisions are made, potentially resulting in discriminatory or biased outcomes. As AI becomes more pervasive, it is crucial to strike a balance between the benefits of shadow AI and the need for transparency and user control.