8 Emerging Trends in Gen AI To Watch Out For in 2024

In recent years, Generative AI, or Gen AI, has rapidly evolved beyond its foundational principles. Industries spanning technology, media, retail, and financial services are incorporating this technology into their operations. Europe, in particular, with its diverse languages, cultures, and abundant tech talent, stands out as a promising landscape for Gen AI integration. 

As we step into the second part of 2024, we explore cutting-edge trends that are shaping this dynamic field. We delve into the latest developments, adoption rates, and transformative applications of Gen AI globally, and specifically in Europe. 

Tracking Global Adoption of Gen AI Technologies

In the past year, more organisations have started realising Gen AI’s potential to improve efficiency, elevate customer experiences, and drive revenue growth. 

One major benefit reported by companies using Gen AI is substantial cost savings. For example, AI-driven algorithms in supply chain management have optimised logistics processes, leading to significant reductions in operational costs. According to a report by MarketsandMarkets, the Gen AI market in supply chain management is expected to grow at a rate of 45.62%. 

In manufacturing, predictive maintenance models powered by AI have minimised downtime, resulting in a 25% productivity boost and a 70% reduction in breakdowns, as highlighted in industry studies and reports by organisations like the World Economic Forum and McKinsey & Company. 

Moreover, Gen AI is proving to be a powerful tool for revenue generation. Personalised marketing campaigns driven by AI have boosted customer engagement and increased conversion rates. E-commerce platforms are effectively utilising AI recommendation engines to enhance sales by suggesting complementary and higher-value products, resulting in greater average order values. 

8 Trends Defining Gen AI in 2024 

Let’s examine the key trends shaping Gen AI in 2024, from improving natural language processing to addressing ethical concerns in autonomous systems, defining the future of artificial intelligence. 

More realistic expectations: As Gen AI evolves, organisations are shifting from the initial excitement to a pragmatic approach. They’ve realised that Gen AI isn’t a cure-all solution, but rather requires a balanced view between optimism and practicality. Businesses are now prioritising Gen AI’s suitability for specific problems, acknowledging that not every use case benefits equally. Deployments are cautious; while many experiment, official implementations are measured, with only a few moving beyond pilot phases. Real-world success stories emerge, but they typically involve thorough evaluation and manipulation of Gen AI outputs to ensure effective utilisation. 

Multimodal AI: Gen AI is advancing to handle multiple modalities such as text, images, and other data types. This multimodal capability integrates various sensory inputs—like text, images, audio, and video—enabling machines to understand and respond to complex queries more accurately and swiftly. By combining these modalities, AI systems achieve a deeper comprehension of the world, approaching human-like cognition. In practice, multimodal AI models combine individual sensory models (e.g., text, image, audio) to provide comprehensive descriptions of reality, bridging communication across different modes effectively. 

Smaller Language Models (SLMs) and Open Source Advancements: Smaller language models (SLMs) are becoming more popular for their efficiency and accessibility. These models, though smaller in size compared to giants like GPT-3, are proving effective for various tasks due to their reduced computational requirements and faster inference times. Simultaneously, open-source models are advancing quickly, sometimes surpassing proprietary ones in performance. This trend democratises access to advanced AI capabilities, allowing developers and smaller organisations to leverage powerful language processing tools without the hefty costs associated with proprietary solutions. 

GPU shortages and cloud costs: The surge in AI adoption has created a critical challenge with GPU shortages and rising cloud costs impacting organisations. Scarcity of GPUs limits AI model training and inference, slowing down productivity and innovation. High GPU prices strain budgets, compelling businesses to allocate more funds towards hardware acquisition, affecting financial planning. To address these challenges, organisations are reconsidering infrastructure choices, balancing between public clouds, on-premises setups, and emerging GPU/AI cloud solutions. Mitigation strategies include exploring alternative GPU options, optimising existing computing resources, and fine-tuning cloud usage to maximise efficiency and minimise costs, ensuring sustainable AI deployment and development. 

Model optimisation accessibility: Optimising AI models is becoming increasingly user-friendly, empowering developers to fine-tune their models effectively. Automated techniques such as Bayesian optimisation and neural architecture search are simplifying hyperparameter tuning, eliminating the need for manual experimentation and saving time. Pretrained models like BERT and GPT are democratising advanced architectures by allowing developers to adapt them to specific tasks with minimal data, regardless of their expertise level. User-friendly libraries such as TensorFlow and PyTorch are offering high-level APIs that streamline model optimisation, enabling developers to focus on design and experimentation rather than low-level implementation details. Community resources such as blogs, tutorials, and open-source projects are providing practical guidance, fostering shared learning and accelerating developers’ mastery of optimisation techniques. 

Customised local models and data pipelines: Organisations are increasingly prioritising customised AI models and efficient data pipelines tailored to their specific needs. Custom models are crucial, enabling companies to develop domain-specific solutions such as medical diagnosis or financial forecasting. Fine-tuning pretrained models like BERT for tasks such as sentiment analysis further enhances model relevance and accuracy. Hybrid architectures that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are also gaining traction for complex tasks like image-text processing. Efficient data pipelines play a pivotal role by handling data preprocessing tasks like cleaning and normalisation, supporting real-time data ingestion, and enabling parallel processing for scalability. Examples include e-commerce recommender systems, predictive maintenance in manufacturing using sensor data, and customised disease classifiers in healthcare. 

More powerful virtual agents: Generative AI virtual agents are revolutionising customer interactions by bridging the gap between digital and human experiences. These advanced virtual assistants, powered by large language models, computer vision, and personalised data, offer personalised, emotionally intelligent interactions. For customer support, they engage in natural, context-aware conversations, leveraging emotion recognition APIs to respond empathetically. In sales and marketing, they provide personalised recommendations, product demos, and sales support based on real-time reactions and preferences. 

Regulation, copyright and ethical AI concerns: As generative AI becomes more widespread, it brings forth legal and ethical challenges in several key areas. Courts are grappling with issues of copyright infringement and ownership concerning AI-generated works, compelling companies to ensure compliance and establish the origin of content. The data-driven nature of Gen AI raises privacy concerns, with potential biases in training data perpetuating inequalities. Balancing innovation with fairness is essential. Users also demand transparency regarding AI’s role in decision-making, prompting calls for regulations on disclosure and explainability. Policymakers are actively adapting regulatory frameworks to address these complexities, requiring companies to stay informed and align with evolving standards. 

Merit’s Expertise in Data Aggregation & Harvesting Using AI/ML Tools

Merit’s proprietary AI/ML tools and data collection platforms meticulously gather information from thousands of diverse sources to generate valuable datasets. These datasets undergo meticulous augmentation and enrichment by our skilled data engineers to ensure accuracy, consistency, and structure. Our data solutions cater to a wide array of industries, including healthcare, retail, finance, and construction, allowing us to effectively meet the unique requirements of clients across various sectors. 

Our suite of data services covers various areas: Marketing Data expands audience reach using compliant, ethical data; Retail Data provides fast access to large e-commerce datasets with unmatched scalability; Industry Data Intelligence offers tailored business insights for a competitive edge; News Media Monitoring delivers curated news for actionable insights; Compliance Data tracks global sources for regulatory updates; and Document Data streamlines web document collection and data extraction for efficient processing.

Key Takeaways 

Industry Integration: Generative AI is deeply embedded across technology, media, retail, and financial sectors, particularly in Europe. 

Global Adoption: Organisations are increasingly leveraging Gen AI for efficiency gains, enhanced customer experiences, and revenue growth. 

Technological Advancements: Trends include multimodal AI, smaller language models, and accessible model optimisation techniques. 

Challenges: Issues like GPU shortages, rising cloud costs, and ethical considerations around AI deployment are critical concerns. 

Regulatory Landscape: Legal frameworks are evolving to address copyright, privacy, and transparency issues related to AI. 

Customisation and Efficiency: Tailored AI models and optimised data pipelines are essential for addressing specific industry needs effectively. 

Virtual Agents: AI-powered virtual agents are transforming customer interactions with personalised and emotionally intelligent responses. 

Community and Resources: Open-source tools and community-driven resources play a crucial role in advancing AI capabilities and best practices. 

Balanced Approach: Organisations are moving from initial excitement to a pragmatic approach, balancing innovation with practicality in Gen AI deployment. 

Future Outlook: Continued evolution in AI technologies and regulatory frameworks will shape the future landscape of Generative AI.