Generative AI or GenAI was the most viral tech trend of 2024, which is believed to evolve constantly at a rapid pace in the future. More businesses are using generative AI technology to automate creative tasks that require human assistance. The generative AI market which stood at only USD 45 billion at the end of 2023, is estimated to grow by leaps and bounds by 2030, according to Statista. Around 65% of GenAI users are Gen Z or millennials while 72% are employed, reported Salesforce.
What is Generative AI and What It is Used For?
Generative AI is a group of algorithms capable of developing realistic-appearing images, audio, or text from specified training data. Generative AI models can accelerate AI implementation in all organizations, including those with no data science or deep AI expertise. However, customization needs knowledge of data science and AI software solutions for certain tasks.
Models can be trained on massive amounts of online data like copyrighted materials. Therefore, responsible AI practices are significant for organizations. Generative AI is capable of –
- Creating unique content and ideas across several modalities such as generating advertisement videos.
- Customizing information or content according to a specific audience for producing personalized customer experience or targeted ads based on customer behavior patterns.
- Improving efficiency by automating and speeding up repetitive tasks such as summarizing lengthy documents quickly.
Top Generative AI Trends to Look for in the Future
- A Range of GenAI Applications
The advanced Generative AI tool can be used to create audio, images, art, videos, text, and different forms of media. AI companies are developing innovative AI tools that offer incredible functionality for basic and work-associated operations. For example, Harvey is widely used in law to access context out of complicated law terms and generate contracts for several clients involved.
- Rise in Multimodality
Multimodality is the concept that generative AI tools are designed to get inputs and produce outputs in several formats. This concept is nowadays becoming a priority for users and AI companies are realizing its significance. In the coming years, multimodal generative AI tends to become more of a consumer expectation of GenAI models and less of a unique selling point. Multimodal modeling itself is also expected to grow in accuracy and complexity to fulfill consumer demands for an all-in-one tool.
- High-Level Customization
As mentioned above, generative AI is capable of providing customized experiences. Technologies such as advanced machine learning algorithms, GAN (generative adversarial networks), language models, and neural networks can examine personalized choices of a person and produce similar outputs. High-level customization allows businesses to generate high revenue by reaching the right audience and market.
- GAN Advancements
Generative adversarial networks are the main aspect of generative AI without which its future can’t be imagined. GANs can develop new data that looks like the training data. This is one of the top generative AI trends that will continue to grow in the coming years to provide new use cases. GAN features 2 systems – generators and discriminators in its neural network. These systems compete with each other with key deep learning methods to ensure precision in prediction.
- More Focus on Privacy, Security, and Governance
Businesses and AI companies adopting generative AI technologies are expected to emphasize AI governance technologies, policies, and security resources that identify generative AI susceptibilities. More companies will start using innovative security platforms and AI governance to protect against major security damages.
Challenges Involved in Adopting Generative AI
- Privacy and Data Security
Artificial intelligence is designed to manage huge amounts of personal and confidential data, which creates challenges when protecting its confidentiality. So, be sure to implement the best data security practices and privacy measures for data protection.
- Reliance on the Third-Party Platform
Companies adopting generative AI may find it difficult to keep pace with the rapid expansion of this technology over time. It may be challenging when your government suddenly outlaws a specific model that is appropriate for your use case. Thus, be ready to adjust quickly to stay relevant in your business operations.
- AI Training and Adaptation
Using AI software solutions in organizational frameworks and operations requires updating technical networks and adapting organizational work approaches. Effective generative AI adoption includes regular staff training, which can consume time and valuable resources. Alternatively, professionals can enroll for generative AI certifications to learn at their own pace, keeping the perfect balance of their work and study.
- AI Ethical Considerations and Consumer Trust
AI-powered decisions can lead to risks of noncompliance with present regulations. There is a general thoughtfulness about ethics, data security, and trust gap that happens due to a lack of transparency. More ethical AI measures should be practiced at the regulatory and company levels to gain consumer trust.
According to the General Data Protection Regulation (GDPR) of Europe which consists of an individual right of explanation, a person can demand precision for associated automated decisions. The Algorithmic Accountability Act makes it compulsory for companies in the US to perform thorough evaluations of high-risk AI systems.
Conclusion
According to Statista, 37% of the US marketing and advertising sector has adopted generative AI as of 2023. Salesforce’s 2023 State of IT report found that 86% of IT leaders expect generative AI to be an essential aspect of their organizations soon. Businesses can use this technology to create personalized customer experiences, do predictive analysis, create AI-assisted content, and stay competitive. Be sure to leverage emerging trends in generative AI and develop expertise to navigate challenges presented by this technology.
