Watch Out for the Generative AI Trends of 2024

Generative AI (Gen AI) continues to push the boundaries of machine capabilities, driving
innovations across industries and reshaping business operations globally. As a form of
artificial intelligence, Gen AI models generate unique outputs—ranging from text and code to
images, music, and even 3D models—by processing vast amounts of unstructured data. In
2024, the development and adoption of Gen AI have surged, marking it as one of the most
impactful technologies of the year. Let’s explore the global trends of this rapidly evolving
technology.

Why Generative AI Matters?

Generative AI has made significant strides over the past year. Tools like OpenAI’s GPT-4,
Anthropic’s Claude, and Google’s Gemini have demonstrated AI’s ability to generate human-
like responses in text, produce photorealistic images through platforms like DALL-E 3 and
MidJourney, and even compose music using models such as Suno. With the recent
introduction of OpenAI’s Sora, a text-to-video generator, Gen AI is poised to revolutionize
content creation.
According to a McKinsey Global Survey on the state of AI, 65% of organizations now use
Gen AI in at least one business function, up from 33% last year. The report further says that
the economic potential is immense, with Gen AI use cases forecasted to generate annual
values ranging from $2.6 trillion to $4.4 trillion.
However, the technology also presents risks such as bias, misinformation, and the
proliferation of deepfakes. As organizations scale Gen AI, investments in risk mitigation,
talent development, and technological capabilities will be crucial to harness its full potential
without unintended consequences.

Latest Developments in Generative AI

2024 has been a transformative year for GenAI, with several developments propelling the technology forward:

1. Multimodal Generative Models: Multimodal AI, which integrates text, images, audio, and video, is gaining prominence. Google’s Gemini, for instance, processes information in various formats, allowing for a wider range of applications across industries such as media, education, and healthcare.
2. Open-Source Models: Open-source GenAI models like Llama 3 are challenging proprietary models like GPT-4. Developers and users embrace open-source solutions for their flexibility and innovation, creating a robust community of contributors and accelerating advancements​.
3. Expanding Natural Language Processing (NLP): The context window in NLP is expanding, with tools like Google’s Gemini 1.5 Pro allowing for prompts that contain up to two million tokens. This enables more coherent and contextually accurate AI responses, although larger prompts sometimes cause the model to lose focus on key details.
4. Enterprise Integration: Large language models (LLMs) are increasingly being integrated into enterprise software tools. Platforms like Salesforce Einstein and ServiceNow leverage LLMs to automate processes, enhance personalization, and provide actionable insights. This trend is especially notable in marketing and customer service.
5. Multiagent Systems: A growing trend in GenAI is the use of multiple AI agents working collaboratively. This approach enhances problem-solving by leveraging specialized models to tackle different aspects of a challenge, delivering more efficient solutions​.

The Talent Landscape and Economic Impact

The demand for talent in GenAI has seen a dramatic increase. Job postings for roles related to GenAI rose by 111% between 2019 and 2023, highlighting a sharp rise in interest and investment across industries. Positions like data scientists, software engineers, and machine learning engineers are particularly in demand. Employers are looking for individuals with proficiency in AI, machine learning, Python, and software engineering​.

The high demand for GenAI talent underscores the critical need for organizations to build internal capabilities to stay competitive. However, the gap between available talent and demand remains a challenge, particularly for specialized roles. Businesses are prioritizing upskilling and reskilling programs to meet this need.

Adoption Across Industries

Generative AI is transforming industries across the globe. Sectors such as technology, media, telecommunications, and financial services have been leaders in Gen AI adoption. Companies are now focusing on scaling GenAI across their operations to unlock value in areas like sales, marketing, and product development.

In regions like India and Japan, efforts are underway to develop local language models, addressing the lack of language support for non-English speakers. Africa, too, is advancing by prioritizing data locality and the proximity of large language models.

Real-World Applications of Generative AI

Several real-world examples demonstrate how companies are using GenAI to revolutionize their business operations:

• ING developed a bespoke GenAI chatbot to enhance customer service in the Netherlands. This chatbot reduced wait times by 20% and is expected to reach 37 million customers across ten markets.
• Recursion, a biotech company, used Gen AI to accelerate drug discovery by processing large datasets, significantly reducing the time it takes to develop new drugs.
• Itaú Unibanco, a leading bank in Latin America, leveraged GenAI to create a campaign dedicated to women football athletes, demonstrating AI’s creative potential in media and advertising.
• Nubank piloted a Gen AI virtual assistant to offer personalized financial services to its customers, improving credit-related solutions and enhancing the overall customer experience.

Challenges and Key Uncertainties

Despite its rapid growth, Gen AI faces several uncertainties:

1. Cybersecurity and Privacy Concerns: As Gen AI handles increasingly sensitive data, the risk of data leakage and cybersecurity breaches is growing. Companies must invest in robust security measures to protect user data.
2. Ethical Considerations: Issues surrounding data governance, fairness, accountability, and explainability remain a significant concern. Gen AI’s ability to generate biased or inaccurate outputs can have far-reaching consequences if not addressed properly.
3. Regulation and Compliance: Regulatory frameworks for Gen AI are still evolving. Questions about the ownership and copyright of AI-generated content are becoming more pressing as open-source models become more popular.
4. Environmental Impact: The energy consumption required to train large AI models is substantial. As more organizations adopt Gen AI, concerns about its environmental footprint will grow.

Big Questions for the Future

As Gen AI continues to advance, several key questions will shape its future:

• How will the cost of creating AI models evolve, and how will it impact market competition?
• Will the exponential growth seen in consumer adoption of Gen AI translate to enterprise-level adoption?
• How will open-source solutions compete with proprietary models, and what strategies will companies need to mitigate the risks associated with data privacy and security?
• How can companies address GenAI’s accuracy issues and ensure error-free large-scale implementations?

These questions will shape the trajectory of GenAI as businesses and policymakers navigate the complexities of this transformative technology.

Conclusion

2024 has been a pivotal year for Generative AI. With its growing adoption across industries, the technology holds the promise of revolutionizing everything from customer service to drug discovery. As businesses continue to scale Gen AI, the focus must remain on addressing the challenges related to security, ethics, and environmental impact. The future of GenAI is indeed bright, but it will require careful management to ensure its long-term success and positive impact on the global economy.