In 2022, ChatGPT took the internet by storm, showcasing the transformative potential of Generative Artificial Intelligence.
Fast-forward to today, nearly two years after launch. Business leaders have been actively exploring GenAI use cases and integrating this advanced technology across their operations. Large organizations are even moving past general-purpose generative artificial intelligence applications to develop custom solutions tailored to their specific needs.
A new era of Generative AI technology is unfolding. According to Gartner, adopting tailored generative AI models in large enterprises is projected to rise from just 1% in 2023 to 50% by 2027. What’s more, by 2026, 75% of companies are expected to utilize Generative AI tools for synthetic data generation, up from under 5% just a few years prior. 30% of Generative AI implementations will rely on energy-efficient computational methods, reflecting a growing emphasis on sustainability by 2030.
Once we understand the future of Generative AI, we can better prepare for the advancements ahead, ensuring we harness its capabilities in ethical and productive ways.
So, what does the future hold for this remarkable technology? Read on for our top 11 forecasts.
The concept of multimodality, where Gen AI tools can process and produce outputs in various formats, is gaining traction among consumers. OpenAI pioneered in this area, introducing multimodal capabilities with GPT-4. Following this, Google’s Gemini and Anthropic’s Claude 3 have also made strides. However, many artificial intelligence or tech companies have yet to make these multimodal models widely accessible. Even those that do have significant restrictions on input and output options.
In years to come, multimodal generative AI may shift from being a unique feature to becoming a standard expectation for users, particularly in paid subscriptions for large language models (LLMs).
Moreover, the complexity and accuracy of multimodal models are anticipated to increase to satisfy consumer demand for comprehensive tools. This means the quality of AI-generated images and non-text outputs is enhanced a lot. Also, new features for videos, file attachments, and internet search capabilities are being added—similar to the advancements seen with Claude and Gemini.
Currently, ChatGPT allows interaction through text, voice, and image inputs and outputs but lacks video capabilities. This might soon change, as OpenAI is testing Sora, a text-to-video generation tool, which may integrate its features into ChatGPT, similar to the incorporation of DALL-E.
Google’s Gemini also supports multiple formats—text, code, images, and voice—but has limitations, particularly with image generation involving people.
As DeepMind co-founder Mustafa Suleyman pointed out, the future of Generative AI may include “interactive AI.” In other words, some systems can perform various tasks by coordinating with other software or even people to accomplish goals.
With their potential to transform healthcare, finance, and customer service, multimodal AI systems might see massive growth. Grand View predicts the multimodal AI market will expand by over 30% annually through 2030, emphasizing the high demand for these increasingly versatile models.
The LLM market is projected to grow from $6.4 billion in 2024 to $36.1 billion by 2030, according to MarketstoMarkets. Amidst this rapid expansion, a notable shift is taking place: AI companies are now turning their attention to smaller language models (SLMs). While today’s large language model, which powers a Gen AI chatbot, boasts billions of parameters, it’s often too complex and expensive for many companies to create and maintain. In contrast, smaller models are more cost-effective and easier to manage, making them an appealing alternative. These compact models offer similar capabilities to larger ones but with far fewer computational demands. From just a few million to several billion parameters, SLMs are ideal for running on local devices like mobile phones, IoT devices, and low-resource environments.
On top of that, unlike massive LLMs that require extensive training data, SLMs can be trained on smaller, more specialized datasets, reducing both training time and cost. This makes them particularly well-suited for applications that focus on specific domains or tasks.
Some examples of SLMs include Microsoft’s Phi-3, Google’s Gemma, Meta’s Llama 3, and Apple’s OpenELM. In a recent interview with TechRound, Sebastien Bubeck, Microsoft’s Vice President of Generative AI Research, highlighted the significance of this trend:
These models are not only about size but about making powerful AI functionalities accessible at a lower cost and with greater ease.
As more companies adopt SLMs, we can foresee genAI becoming increasingly integrated into everyday devices and applications, all while reducing the resource demands typically associated with large-scale AI apps.
Another significant trend is the development of domain-specific generative AI models. These models can cater specifically to the needs of particular industries or sectors, moving away from the broad, general-purpose capabilities we currently see. By focusing on a specific field, these models offer a deeper understanding of the context, language, and challenges unique to that domain.
The result? More accurate, relevant, and customized content generation. Whether it’s creating specialized medical reports, legal documents, or industry-specific marketing materials, domain-specific models can produce content fine-tuned to meet the particular demands of the sector they serve. This level of precision marks a significant step toward making genAI tools more relevant and practical in professional environments where context and detail matter greatly.
Generative AI tools perform really well with real-time applications for more interactive and immediate use cases. In the future, genAI will be able to generate content dynamically during live conversations, create personalized visuals on the spot, and respond to changing situations in real-time.
This shift from static, pre-programmed outputs to responsive, real-time interactions will elevate user experiences across industries. Imagine having genAI chatbots create content instantly during live interactions, customize visuals dynamically, and generate real-time responses based on changing contexts. This ability to adapt on the fly will enhance everything from customer service interactions to live entertainment and education, making genAI tools more immersive and flexible for users than ever before.
The trend of AI as a Service (AIaaS), already popular in AI and machine learning (ML), is beginning to take off in the Generative AI revolution. As businesses are increasingly adopting GenAI technologies, companies that hesitate to invest in building their own infrastructure will look for external help. Generative AI consulting firms will become essential for those needing expert support without the heavy upfront investment. They can help identify the right opportunities for AI solutions for your business before investing in them.
In particular, AI Modeling as a Service (AIMaaS) is expected to thrive, with more AI providers offering customizable, open-source models that cater to various business needs. These services will likely expand their focus to include essential elements like AI governance, security, and integration support, helping businesses implement generative AI more effectively and safely. This trend will open the door for more companies to leverage AI without needing to develop the expertise in-house.
Also read: 12 Steps to Choose the Right Generative AI Development Company
Artificial General Intelligence (AGI) is currently a hot topic in the tech industry. It’s predicted to match human capabilities in most tasks and critical thinking. Google’s DeepMind, along with OpenAI, Meta, and Adept AI are at the forefront of research in this area. However, there is still considerable debate about what AGI truly means and what it will look like.
Currently, research in AGI has largely taken place in isolation. Moving forward, this field will become more collaborative as companies aim to establish a shared understanding and framework for AGI. While true AGI may still be years away, advancements in generative AI will continue to bring us closer to that goal, fostering clearer definitions and objectives along the way.
The impact of generative AI on the workplace is undeniable, but experts are wondering whether it will ultimately benefit or harm employees. In its current phase, generative AI mostly assists office workers in automating routine tasks, generating content, offering insights, and helping with analytics. As a result, they can offload mundane responsibilities and focus on more strategic and creative tasks.
Despite some doubt among both companies and employees, more users are discovering how genAI can streamline their daily tasks. It can support replying to emails, making reports, and creating social media content. These small applications are already proving their potential to significantly alter the way people work across industries, departments, and roles. Initially, AI would mainly affect manufacturing and physical labor, but its most immediate impact has been on creative, administrative, and customer service jobs. People in marketing, sales, design, customer service, and administrative positions have felt the effects of generative AI. Many are even concerned about job security and whether their roles will disappear entirely.
Amid these uncertainties, workplaces and educational institutions are responding by providing courses, certifications, and training programs dedicated to the effective use of AI and Generative AI. New undergraduate and graduate programs in AI are emerging, and soon, pursuing a degree in AI could become as prevalent as studying data science or computer science. This educational shift aims to prepare the workforce for a future where generative AI is integral, equipping individuals with the necessary skills to thrive in a rapidly changing job market.
In discussing the impact of generative AI on the workforce, we must also emphasize the importance of certain human skills such as emotional intelligence, critical thinking, leadership, and complex problem-solving – uniquely human traits that are just too difficult for machines to replicate for the time being.
In March 2024, the EU Parliament passed the landmark EU AI Act, marking a significant step in regulating AI, particularly generative models. Companies operating within the EU or handling EU citizen data will soon need to comply with this new set of rules. While this is the first regulation specifically targeting Generative AI and its impact on data privacy, it certainly won’t be the last, as public and consumer concerns about AI continue to rise.
On a smaller scale, U.S. states including California, Virginia, and Colorado have also introduced their own AI regulations. Many industries are also developing internal frameworks for responsible AI use. Meanwhile, the United Nations has initiated global discussions on the governance of AI, promoting international cooperation and ethical standards. While a unified, enforceable global regulation may not be on the horizon, these conversations are expected to shape the way countries and regions approach AI ethics and legal frameworks.
As AI regulations increase and public scrutiny intensifies, businesses and AI companies should invest more in AI governance, security, and privacy. Currently, only a handful of organizations prioritize AI governance, but this will change as fears surrounding AI misuse and vulnerabilities increase.
In the near future, companies will adopt dedicated AI governance and security tools to manage the risks associated with Generative AI. Human-in-the-loop processes – where humans oversee and review AI-generated content and decisions – are becoming standard practice to guarantee ethical and responsible use. Additionally, businesses across all sectors will establish clear AI policies to protect against potential liabilities and reputational damage. As generative AI solutions get more deeply embedded in daily operations, safeguarding its implementation will surely be a top priority for both firms and regulators.
As more users, businesses, and governors discover inaccuracies, false information, or unethical content generated by AI, pressure will mount on AI companies to prioritize quality control and hallucination management. While many enterprises already focus on improving data sourcing and training methods, this commitment to delivering high-quality results will only rise as transparency becomes indispensable for maintaining both public trust and market position.
What will this focus on quality look like? OpenAI is among the top companies leading the way, demonstrating improvements in accuracy and reducing hallucinations with each model release. OpenAI also provides detailed research and documentation to showcase its advancements, raising public confidence in its models.
Google’s Gemini, on the other hand, has created a robust feedback system where users can easily rate responses, report issues, and even compare generated content with real-time internet sources. This user feedback loop not only enhances product quality but also gives users the reassurance that their input is valued. As a result, more AI companies are expected to adopt similar quality assurance approaches driven by community feedback and enhanced transparency.
Many organizations are increasingly embedding generative AI into both internal operations and customer-facing tools to elevate workflows and user experiences. This trend is particularly evident with established models like GPT-3.5 and GPT-4, which are often integrated into existing applications, websites, and genAI chatbots.
Embedding generative AI will soon become standard practice for managing online customer experiences. Users will come to expect AI-powered tools that offer tailored answers, recommendations, and seamless interactions as part of their shopping, research, and planning activities. Thus, businesses that fail to integrate generative AI into their platforms may find themselves at a disadvantage as customers gravitate toward tools that offer more intuitive, AI-enhanced experiences.
Generative AI is here to stay, and navigating it requires clear strategies that align with your business goals. To ensure genAI enhances your operations without overwhelming your priorities, consider adopting the following approaches:
Generative AI has already demonstrated its benefits across domains. As we look to the future, advancements and trends, such as multimodality, smaller language models, domain-specific generative models, and real-time applications, promise even greater opportunities. However, with such rapid technological growth comes the need for careful planning and a cautious approach.
At Neurond, we provide innovative generative AI consulting services that address uncertainties and future-proof your business against emerging trends. Our team of generative AI consulting experts will work with you to create a customized genAI roadmap tailored to your specific needs. We focus on identifying the most impactful applications of genAI for your objectives while prioritizing data privacy and security at every step. Our AI engineers also ensure seamless integration of genAI solutions into your existing systems, facilitating a smooth transition and providing ongoing support for optimal performance and return on investment.
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