Lancaster University alumnus Jonathan De Vita studied AI and coding as part of his degree. This article will provide an overview of AI and machine learning trends predicted to feature prominently through 2025.
Increased Implementation of Multimodal Generative AI
Generative AI gained huge traction in 2024, marking the start of a trend that is not predicted to abate any time soon. Generative AI is already having a transformational impact in various market sectors, with many more generative AI applications and iterations in the works, including multimodal generative AI.
Rather than focusing on one type of data, multimodal AI processes and translates different data types. Examples include image-to-audio and text-to-image. Multimodal AI is an increasingly important capability in many industries today, prompting significant investment in multimodal AI technology.
Advances in the field enable systems to interpret and generate content across different modalities, facilitating the development of game-changing apps for various industries. In the healthcare industry in particular, multimodal AI is having a revolutionary impact by enhancing clinical diagnosis. Experts predict that the rise of multimodal generative AI will be a catalyst of the continued AI industrial revolution throughout 2025 and beyond.
The Rise of AI Frameworks
Maturation of AI frameworks is predicted to accelerate in 2025, presenting increasingly sophisticated tools for AI app development and deployment. Frameworks will evolve to support seamless integration with existing enterprise systems, facilitating complex agent-based architectures and advanced model optimisation.
Experts predict the emergence of specialised frameworks in the months ahead, created to cater to industry-specific needs, providing pre-built components for common AI patterns. This could culminate in the democratisation of AI development, allowing organisations large and small to develop and deploy sophisticated AI solutions with less overheads and faster time to market.
AI frameworks already in use today include Haystack, which stands out in search-orientated applications, and CrewAI, which facilitates effective task delegation and coordination among AI agents, a particularly useful capability in projects requiring collaboration between specialised agent roles.
Increased Emphasis on Ethical AI
Ethical AI (eAI) is a field that involves developing and deploying AI systems that align with societal values, morals and often legal regulations. eAI’s ultimate goal is to ensure that technology operates in a responsible way without violating people’s rights.
eAI will address topics that are increasingly garnering media attention, such as privacy safeguarding, bias mitigation, security, accountability and transparency. As AI and machine learning models become further entrenched in the corporate world, eAI principles must be upheld.
Moving through 2025, demand for eAI is predicted to intensify. With AI being integrated into critical sectors such as law, finance and healthcare at speed, eAI will become a major concern for regulatory agencies too as governments scramble to establish regulations to govern AI implementation.
The Realities of a Data-Driven Culture
Despite the transformative potential of AI, it has to be said that it cannot solve everything, particularly long-term cultural attributes. In a survey published by MITSloan Management Review, 92% of respondents agreed that cultural and change management challenges were primary barriers to becoming AI and data driven. These findings suggest that technology in isolation is insufficient, underscoring the need for businesses to invest more in creating and polishing their digital strategies. It is worth noting that most survey participants came from legacy organisations founded more than a generation ago, companies with a long history of gradual transformation rather than rapid overhauls.
Data is an invaluable commodity in the modern world. Used correctly, it can transform a company’s fortunes. However, establishing a data-driven culture presents both opportunities and considerable challenges. Forward-looking business leaders must invest in improving their data literacy, enabling them to instil a robust data culture in their organisation to improve decision-making, drive operational efficiency and enhance overall business performance.
