The Future of Generative AI (2024): 8 Predictions to Watch

Generative AI began as a niche project for a few tech companies but quickly became a global phenomenon and one of the hottest technology initiatives of the moment. It’s unlikely to surrender the spotlight anytime soon. So what does the future of generative AI look like?

As it continues to evolve at a stunningly rapid pace, it’s being used to address a wide range of business use cases with increasing power and accuracy and restructuring the way organizations do and view their work. Here’s what you need to know about the applications, tools, and challenges defining the future of generative AI.

KEY TAKEAWAYS

•Generative AI is rapidly advancing to handle multiple input and output formats, including text, images, voice, and video, making AI tools more versatile and integral to various applications. (Jump to Section)
•Businesses are increasingly adopting AI-as-a-service models to stay competitive. This shift allows companies to leverage advanced AI capabilities without significant infrastructure investment. (Jump to Section)
•
Generative AI is transforming the workforce by automating routine tasks. While this enhances productivity, it also raises concerns about job displacement and the need for upskilling and reskilling. (Jump to Section)

TABLE OF CONTENTS
ToggleThe Future of Generative AI: 8 PredictionsConsumer Trust and Ethical ConsiderationsStrategies for Navigating the Future of Generative AI3 Generative AI Tools To Explore3 Beginner-Friendly Courses in Learning Generative AIFAQBottom Line: Preparing for the Future of Generative AI

The Future of Generative AI: 8 Predictions

Looking ahead, expect to see generative AI trends focused on three main pools: quick and sweeping technological advances, faster-than-expected digital transformations, and increasing emphasis on the societal and global impact of artificial intelligence.

Growth in Multimodality

Multimodality—the idea that a generative AI tool is designed to accept inputs and generate outputs in multiple formats—is starting to become a top priority for consumers, and AI vendors are taking notice. OpenAI was one of the first vendors to provide multimodal model access to users through GPT-4, and Google’s Gemini and Anthropic’s Claude 3 followed suit. So far though, most AI companies have not made multimodal models publicly available; even many who now offer multimodal models have significant limitations on possible inputs and outputs.

In the near future, multimodal generative AI is likely to become less of a unique selling point and more of a consumer expectation of generative AI models, at least in all paid LLM subscriptions.

Additionally, expect multimodal modeling itself to grow in complexity and accuracy to meet consumer demands for an all-in-one tool. This may look like improving the quality of image and non-text outputs or adding better capabilities and features for things like videos, file attachments (as Claude has already done), and internet search widgets (as Gemini has already done).

ChatGPT currently enables users to work with text (including code), voice, and image inputs and outputs, but there are no video input or output capabilities built into ChatGPT. This may change soon, as OpenAI is experimenting with Sora, its text-to-video generation tool, and will likely embed some of its capabilities into ChatGPT as it has done with DALL-E.

Similarly, while Google’s Gemini currently supports text, code, image, and voice inputs and outputs, there are major limitations on image possibilities, as the tool is currently unable to generate images with people.

A growing number of generative AI models are offering multimodal content generation capabilities to customers.

Wider Adoption of AI as a Service

AI as a service is already growing in popularity across artificial intelligence and machine learning business use cases, but it is only just beginning to take off for generative AI. However, as the adoption rate of generative AI technology continues to increase, many more businesses are going to start feeling the pain of falling behind their competitors. When this happens, the companies that are unable or unwilling to invest in the infrastructure to build their own AI models and internal AI teams will likely turn to consultants and managed services firms that specialize in generative AI and have experience with their industry or project type.

Specifically, watch as AI modeling as a service (AIMaaS) grows its market share. More AI companies are going to work toward public offerings of customizable, lightweight, and/or open-source models to extend their reach to new audiences. Generative AI-as-a-service initiatives may also focus heavily on the support framework businesses need to do generative AI well. This will naturally lead to more companies specializing and other companies investing in AI governance and AI security management services, for example.

Movement Toward AGI and Related Research

Artificial general intelligence (AGI)—the concept of AI reaching the point where it can outperform humans in most taskwork and critical thinking assignments—is a major buzzword among AI companies today. So far, however, it’s little more than that. Google’s Deepmind is one of the leaders in defining and innovating in this area, along with OpenAI, Meta, Adept AI, and others. At this point, there’s not much agreement on what AGI is, what it will look like, and how AI leaders will know if they’ve reached the point of AGI or not.

So far, most of the research and work on AGI has happened in silos. In the future, AGI will continue to be an R&D priority, but much like other important tech and AI initiatives of the past, it will likely become more collaborative, if for no other reason than to develop a consistent definition and framework for the concept. While AI leaders may not achieve true AGI or anything close to it in the coming years, generative AI will continue to creep closer to this goal while AI companies work to more clearly define it.

Learn more about the top generative AI tools and apps available in 2024.

Significant Workforce Disruption and Reformation

Most experts and tech leaders agree that generative AI is going to significantly change what the workforce and workplace look like, but they’re torn on whether this will be a net positive or net negative for the employees themselves. In this early stage of workforce impact, generative AI is primarily supporting office workers with automation, AI-powered content and recommendations, analytics, and other resources to help them get through their more mundane and routine tasks.

Though there is some skepticism both at the organizational and employee levels, new users continue to discover generative AI’s ability to help them with work like drafting and sending emails, preparing reports, and creating interesting content for social media, all of which saves them time for higher-level strategic work. Even with these more simplistic use cases, generative AI has already shown its nascent potential to completely change the way we work across industries, sectors, departments, and roles. Early predictions expected generative AI would mostly handle assembly lines, manufacturing, and other physical labor work, but to this point, generative AI has made its most immediate and far-reaching impacts on creative, clerical, and customer service tasks and roles.

Workers such as marketers, salespeople, designers, developers, customer service agents, office managers, and assistants are already feeling the effects of this technological innovation and fear that they will eventually lose their jobs to generative AI. Indeed, most experts agree that these jobs and others will not look the same as they do now in just a couple of years. But there are mixed opinions about what the “refactored” workforce will look like for these people—will their job simply change or will it be eliminated entirely?

With all of these unknowns and fears hanging in the air, workplaces and universities are currently working on offering coursework, generative AI certifications, and training programs for professional usage of AI and generative AI. Undergraduate and graduate programs of AI study are beginning to pop up, and in the coming months and years, this degree path may become as common as those in data science or computer science.

Increasing Regulatory, Ethical, and Societal Pressures

In March 2024, the EU Parliament officially approved the much discussed EU AI Act. Over the coming months and years, organizations that use AI in the EU or in connection with EU citizen data will be held to this new regulation and its stipulations. This is the first major regulation to focus on generative AI and its impact on data privacy, but as consumer and societal concerns grow, don’t expect it to be the last.

There are already state regulations in California, Virginia, and Colorado, and several industries have their own frameworks and rules for how generative AI can be used. On a global scale, the United Nations has begun to discuss the importance of AI governance, international collaboration and cooperation, and responsible AI development and deployment through established global frameworks. While it’s unlikely that this will turn into an enforceable global regulation, it is a significant conversation that will likely frame different countries’ and regions’ approaches to ethical AI and regulation.

Bigger Emphasis on Security, Privacy, and Governance

With the regulations already in place and more expected, not to mention public demand, AI companies and the businesses that use this technology will soon invest more heavily in AI governance technologies, services, and policies, as well as security resources that directly address generative AI vulnerabilities. A small number of companies are focused on improving their AI governance posture, but as AI usage and fears grow, this will become a greater necessity.

Companies will begin to use dedicated AI governance and security platforms on a greater scale, human-in-the-loop AI model and content review will become the standard, and all companies that use generative AI in any capacity will operate with some kind of AI policy to protect against major liabilities and damage.

Tools like Watson’s OpenScale are likely to pop up as users demand improved AI explainability, transparency, privacy, and overall governance.

Greater Focus on Quality and Hallucination Management

As governments, regulatory bodies, businesses, and users uncover dangerous, stolen, inaccurate, or otherwise poor results in the content created through generative AI, they’ll continue to put pressure on AI companies to improve their data sourcing and training processes, output quality, and hallucination management strategies. While an emphasis on quality outcomes is part of many AI companies’ current strategies, this approach and transparency with the public will only expand to help AI leaders maintain reputations and market share.

So what will generative AI quality management look like? Some of today’s leaders are providing hints for the future. For example, with each generation of its models, OpenAI has improved its accuracy and reduced the frequency of AI hallucinations. In addition to actually doing this work, it has also provided detailed documentation and research data to show how its models are working and improving over time.

On a different note, Google’s Gemini already has a fairly comprehensive feedback management system where users can easily give a thumbs-up or thumbs-down with additional feedback sent to Google. They can also modify responses, report legal issues, and double-check generated content against internet sources with a simple click. These features provide users with the assurance that their feedback matters, which is a win on all sides: Users feel good about the product and Google gets regular user-generated feedback about how their tool is performing. Expect to see more generative AI companies adopt this kind of approach for better community-driven quality assurance in generative AI.

Widespread Embedded AI for Better Customer Experiences

Many companies are already embedding generative AI into their enterprise and customer-facing tools to improve internal workflows and external user experiences. This is most commonly happening with established generative AI models like GPT-3.5 and GPT-4, which are frequently getting embedded as-is or are being incorporated into users’ pre-existing apps, websites, and chatbots.

Expect to see this embedded generative AI approach as an almost universal part of online experience management in the coming years. Customers will come to expect that generative AI is a core part of their search experiences and will deprioritize the tools that cannot provide tailored answers and recommendations as they research, shop, and plan experiences for themselves.

Consumer Trust and Ethical Considerations

Public sentiment on generative AI is currently mixed. In North America in particular, there’s excitement and interest in the technology, with more users experimenting with generative AI tools than in most other parts of the globe. However, even among those with enthusiasm for generative AI, there is a general caution about data security, ethics, and the general trust gap that comes with a lack of transparency, misuse and abuse possibilities like deep fakes, and fears about future job security.

According to Forrester’s December 2023 Consumer Pulse Survey results, only 29 percent of respondents agreed that they would trust information from GenAI, while 45 percent of online adults said it poses a serious threat to society. However, 50 percent of respondents said they believed the technology could help them to find the information they need more effectively.

To earn consumer trust, more ethical AI measures must be taken at the regulatory and company levels. The EU AI Act is a great step in this direction, as it specifies banned apps and use cases, obligations for high-risk systems, transparency obligations, and more to ensure private data is protected. However, it is also the responsibility of AI companies and businesses that use AI to be transparent, ethical, and responsible beyond what this regulation requires. Taking steps toward more ethical AI will not only bolster their reputation and customer base but also put in place safeguards to prevent harmful AI from taking over in the future.

Learn more about the issues and challenges of generative AI ethics.

Strategies for Navigating the Future of Generative AI

Generative AI is here to stay. The key to working with this technology without letting it overrun your business priorities is to go in with well-defined effective AI strategies and clear-cut goals for using AI in a beneficial way. The following strategies will help:

Create an AI Strategy Specific to Your Business: Explain what technologies can be used, who can use them, how they can be used, and more. Keep strategies and policies both flexible and iterative as technologies, priorities, and regulations change.

Support Employees Through Role and Workplace Transitions: At the rate generative AI innovation is moving, there’s little doubt that existing jobs will be uprooted or transformed entirely. To support your workforce and ease some of this stress, be the type of employer that offers upskilling and training resources that will help staffers—and your company—in the long run.

Think Globally and Collaboratively: If you’re in a position of power or influence, consider doing work to mitigate the increasing global inequities that are likely to come from widespread generative AI adoption. Partner with firms in developing countries, work toward generative AI innovations that benefit people and the planet, and support multilingual solutions and data training that are globally unbiased.

Embrace AI Innovations With Caution: Especially in the pursuit of AGI, be cautious about how you use generative AI and how these tools interact with your data and intellectual property. While generative AI has massive positive potential, its potential to do harm can be said to be the same—pay attention to innovations and don’t be afraid to hold AI companies accountable for a more responsible AI approach.

3 Generative AI Tools To Explore

Generative AI tools such as Jasper, Midjourney, and AlphaCode are transforming the way organizations work by automating content production, improving visual design, and addressing complex programming challenges. These tools use advanced AI to improve workflows, cut costs, and promote innovation across different industries.

Jasper

Jasper is designed to create high-quality content such as articles, product descriptions, blog posts, emails, and other marketing copies. It can speed up the content production processes, allowing marketers to focus more on analytical and complex tasks. Jasper can produce text-based content that matches the company’s tone of voice and branding as long as it is prompted with the right instructions.

Jasper offers a free trial on all of its plans, allowing you to test which plan is best for you. The Creator Plan costs $39 per month and gives you one brand voice for AI-generated content, SEO mode, and access to Jasper Chat. The Pro Plan allows five users and includes everything in the Creator Plan plus three brand voices, 10 knowledge assets, and three instant campaigns.

Visit Jasper

Midjourney

Midjourney is an AI image generator accessed in a Discord chatbot, making it accessible and easy to use for creating custom images. Users can create high-quality images without having professional graphic design skills. By entering detailed prompts, the Midjourney Discord chatbot will interpret these prompts to produce unique artwork, illustrations, or designs.

Midjourney recently announced that new users can create 25 free AI-generated images before signing up for a paid subscription. Its Basic Plan costs $10 per month, Standard Plan costs $30 per month, Pro Plan costs $60 per month, and its Mega Plan costs $120 per month. Subscriptions allow users to have faster GPU time and will be prioritized in the job queue.

Visit Midjourney

AlphaCode

AlphaCode by DeepMind is a free AI coding tool that solves complex coding problems and generates code from natural language descriptions. It is useful especially for businesses in the tech and software industries where it may help with algorithm development, automate repetitive coding activities, and even participate in competitive programming. AlphaCode improves productivity since it can solve complex coding problems faster, which allows developers to focus more on more complex tasks.

Visit Alpha Code

3 Beginner-Friendly Courses in Learning Generative AI

Taking a certification or training course from an online education provider can provide you with fundamental knowledge of generative AI, boost your existing skills, or give you the hands-on experience you need to succeed in the field or just learn more about this exciting technology.

Generative AI for Everyone

This DeepLearning.AI course offered on Coursera provides fundamental information to give a deeper understanding on how generative AI works, different LLMs, as well as the day-to-day usage of web user interface LLMs. This course costs $49 per month via Coursera’s monthly subscription and offers self-paced reading materials, quizzes, and hands-on activities to expose you to practical activities that help strengthen your knowledge about generative AI.

Visit Coursera

Generative AI for Beginners

Udemy’s course helps you get a deeper understanding of generative AI while teaching you about LLMs, prompt engineering, and more. It offers hands-on experience creating a chatbot and exposes you to recent and future trends in generative AI. This course costs $10 and includes a certificate of completion.

Visit Udemy

Generative AI for Beginners

Microsoft is offering courses for students with a basic knowledge or understanding of Python or TypeScript. Each lesson covers different topics, such as setting up a development environment, understanding how generative AI and LLMs work, creating advanced prompts, and building texts for text generation applications. This course is created and regularly updated by Microsoft and is free for students to take.

Visit Microsoft

FAQ

Will Generative AI Replace Human Jobs?

Generative AI is unlikely to replace humans. While it is intended to automate certain processes, it still requires significant human intervention for its operation. Humans are required to guide, train, and fine-tune generative AI models so that they provide correct and relevant outputs. Also, many jobs require creative and strategic skills that AI cannot provide. Generative AI is primarily an augmentation tool, assisting in the speeding of different job processes and allowing the workforce to focus more on complex, higher-level tasks that require critical thinking, creativity, and emotional intelligence.

What Will be the Most Significant Impact of GenAI on Jobs?

The integration of generative AI into the workplace is expected to establish new professions focused on AI development, maintenance, and oversight. As more businesses use generative AI software, there will be a greater demand for experts in AI-related domains including AI ethics, machine learning engineering, and data science. While certain regular operations may be automated, freeing up workers’ time to engage in more strategic activities. This transition will also contribute to the upskilling and reskilling of the workforce to efficiently handle new AI-powered software and platforms.

What is the Main Goal of Generative AI?

Generative AI’s goal is to produce content as fast and efficiently as possible that is similar to its training data. This is best used for predictive analytics and forecasting future events as well as content creation. Since the launch of ChatGPT in 2022, there are a lot of generative AI platforms emerging that can help different industries, especially those who work in marketing. A lot of marketing materials can be produced using generative AI tools to speed up the production process. This allows marketers to focus more on strategy creation and let the content production be handled by these generative AI tools.

Bottom Line: Preparing for the Future of Generative AI

Generative AI has already proven its remarkable potential to reshape industries, economies, and societies even more than initially thought. But with this incredible technological development should come a heavy dose of caution and careful planning. Developers and users alike must consider the ethical implications of this technology and continue to do the work to keep it transparent, explainable, and aligned with public preferences and opinions regarding how this technology should be used.

They must also consider some of the more far-reaching consequences—such as greater global disparities between the rich and the poor and more damage to the environment—and look for creative ways to create generative AI that truly does more good than harm. The best way forward toward a hopeful future for GenAI is collaboration. Leaders, users, and skeptics from all over the globe must collaboratively navigate the challenges and opportunities presented by generative AI to ensure a future that benefits all.

Browse our reviews of the best generative AI tools available in 2024 to find the right software for your particular interests, needs, or budget.
The post The Future of Generative AI (2024): 8 Predictions to Watch appeared first on eWEEK.