The Future of Generative AI

With the launch of ChatGPT last year, the innovative and transformative power of generative AI almost instantaneously became known to the world at large. A Deep Learning subset of Machine Learning, generative AI uses neural networks to register patterns from existing information to generate unique new artifacts that display the training data characteristics without replicating it.

With nearly $15bn invested [1] in artificial intelligence in the first six months of this year alone, the global generative AI market is expected to hit $1tn within a decade [2]. Let's take a look at what the future of generative AI holds, especially for the business enterprises.

Trends that will shape the future of Generative AI

• Generative AI’s ability to create unique content using natural language processing will dramatically change the process of text-based content creation, impacting everything from creative writing and entertainment, to marketing and customer service.

• As organizations gather tremendous amounts of data, there will be an increasing need for domain-specific and self-hosted Large Language Models or LLMs, leading to specialized generative AI models for different business needs.

• Customer experience, revenue growth and productivity as the key focus of business’ AI-related investments will lead to the niche implementation of generative AI.

• ‘Prompt Engineering’ will become an essential skill in driving optimal results from the generative AI solutions, likely becoming a key job role.

• Generative AI will transform critical industry processes, from new drug discovery by creating novel molecular structures after recognizing patterns in existing drug compositions, to financial risk mitigation by detecting anomalies in transactional data.

• Increasing concerns about ethical considerations relating to intellectual property rights, information accuracy and privacy will lead to policy developments for better controls and monitoring.

Industry-specific future use cases of AI Generative Models

As deep learning in artificial intelligence leads to niche specialization, we will see focused applications of generative AI tools for different business enterprises.

Healthcare

By analyzing vast amounts of case histories and medical data, recurrent neural networks in generative AI can identify patterns to aid disease prediction and diagnosis for early, accurate and effective patient treatment.

Significant investments are being made in AI-based drug discovery applications with generative models expected to lead over 30% of new drug discovery by 2025 [3].

Marketing & Advertising

Current natural language processing AI tools are already augmenting marketing processes like conducting research, creating customer-facing content & outbound messaging, and campaign planning. However, future developments in artificial intelligence will have deep learning language models better understand human psychology to produce emotionally engaging and relevant content for targeted consumers.

Design

Generative design will augment the design process across multiple disciplines, accelerating prototyping with optimal material consumption by generating waste-reducing patterns and suggesting lighter, cheaper or more durable materials. With effective application in automotive, manufacturing and aerospace industries, generative design AI will automate a significant proportion of the design effort.

Future generative AI tools will require designers to simply input material and product feature requirements, delivering increasing complex design outputs and engineering details.

Finance

Perfectly suited for analyzing financial data, generative models using recurrent neural networks will transform into personal wealth managers, collecting customers’ scattered financial information and portfolios, and consolidating it into a single customer profile with a tailored financial objective. With the opportunity to streamline and integrate areas like taxation, financial institutions will be able to provide value-added services to their customers.

Deep learning architecture adept at identifying anomalies will aid fraud detection, while synthetic data generation will provide generative models with ample training data to build robust fraud models.

Software Development

Artificial intelligence will augment programming in the future as machine learning algorithms in generative models help automate time-consuming tasks like code generation, translation, optimization and debugging.

In the immediate future generative AI will help with legacy code modernization, while nearly 15% of new applications will be generated by artificial intelligence by 2027 [3].

Media & Entertainment

As generative adversarial networks in generative AI models create realistic images and natural language processing aids content creation, AI will become invaluable in the entertainment industry.

The creation of unique and novel compositions coupled with AI generated songwriting will augment the creative process for musicians, while AI voice synthesis and speech recognition will make automated real-time dubbing, translations and voiceovers possible.

Combining a transformer neural network and generative adversarial network, the generative AI tool Dall-E can generate images creating highly imaginative AI art. By some estimates, the majority of the content in mainstream movies will be AI-generated by the end of this decade.

VR/AR & Gaming

Generative AI will speed up the development of immersive 3D environments in video games and virtual reality spaces like the Metaverse. Such AI systems will be able to generate high resolution images of 3D worlds, and life-like dynamic avatars using computer vision, as well as real-time interactions with adaptive non-player characters no longer restricted by scripts.

Retail

The retail sector can avail multiple benefits from the inclusion of generative AI in its operational processes, as AI models will provide engaging customer support, increasingly personalized offerings and improved demand planning, forecasting and inventory management.

Generative AI benefits for enterprises

Generative AI trained on enterprise data will change how businesses access and utilize information internally. Data extracted and structured to generate responses upon prompts will enhance decision making and empowerment of the workforce in nearly all business functions.

Business Strategy & Knowledge Management

Conversational AI embedded into domain-specific LLMs, fine-tuned to manage unstructured data will create highly efficient retrieval systems. Nearly 30% of enterprises [3] will implement AI-augmented strategy within 2 years, removing information biases, increasing data privacy and simulating future business scenarios.

Customer Experience

Generative AI will increasingly be used to enhance customer experience, provide personalized recommendation and initiate engaging interactions with customers, especially with sentiment analysis and natural language AI chatbot integrations.

Brand Marketing and Content

Besides customer data management, a marketing-specific machine learning algorithm can help organizations conduct better marketing research and offer insights that can shape entire marketing strategies.

Tools like Dall-E and Jasper will impact brand development and content planning, with text or image based AI generated content delivered as per brand guidelines and personalized.

NLP may end up providing the first draft of all content including blogs, social media posts, reports and emails in the near future, giving content writers and marketers more time to work on strategy.

Productivity

Incorporating generative AI into workflows has drastically shortened task durations while automating repetitive tasks, allowing individuals to focus on their core activities. Within 5 years, more than 100 million workers [3] will employ AI to contribute to their work.

Human Resources

Even as generative AI alters workers’ skillset to manage information, employees’ tasks will be executed in partnership with AI. Talent acquisition tasks will become automated as AI aids the search for suitably matched talent to fill positions, and AI enabled models will provide instant performance analyses of employees from past data and current KPIs.

Compliance

Generative AI will assist enterprises in complying with regulations relating to risk mitigation and sustainability. Computer vision, a deep learning technique can visually analyze information and behaviors to detect fraud.

Jobs expected to be transformed by Generative AI

Despite the expected widespread adoption of AI, human intelligence will still be required at both the start and end of the process, from prompt-creation to content evaluation.

Writers

Large language model tools like ChatGPT will generate text in any form prompted by user queries, inspiring writers with original content ideas and improving their work in terms of time saving and content quality as AI generates anything from blogs to articles and full-length novels in any human language.

Musicians

Input based on a concept, theme or thought will generate unique compositions or songs as generative AI employs a neural network language model to produce new data in the form of an original piece of music or songwriting.

Artists

As with other creative forms, art will see a renaissance in the form of image generation. Midjourney, a generative model, is capable of creating photorealistic images and even surreal AI generated art based on both human language and image prompts. These tools support creativity and expression unrestricted by logic and free from licensing requirements associated with stock image usage.

Customer Service Agents

With AI algorithms based chatbot plugins and integrations, repetitive and simple queries will be automated, leaving agents to provide better, focused and personalized service to their clients.

Programmers

Contrary to the belief that AI will take human jobs, code generation tools like Codex and GitHub CoPilot are created with the purpose of pairing with human programmers for greater efficiency and speed.

With better bug identification and code fixing capabilities, programmers will be able to increase their productivity as well as skills by co-learning with AI.

New Skills for the future

Prompt writing or engineering is one of the skills that will become critical in using generative AI models. As the quality of prompts directly impacts the result generated, prompt design would become one of the top skills in demand for operating specialized AI.

As domain-specific LLMs will be employed, user experience will change with workers becoming editors rather than creators of information and content.

Generative AI challenges and controls

As with any new technology, there are concerns and challenges associated with generative AI, especially in terms of security and ethical usage.

Concerns

• Generative AI does not take consent from authors of works used as input, leading to new data that is often similar to original copyrighted works.

• Identity misappropriation for individuals and brands can lead to fraud or misinformation damaging to concerned parties.

• Lack of legal frameworks concerning intellectual property makes ownership rights questionable and uncertain.

• GDPR does not cover AI tools trained on public data, hence confidential enterprise data entered into tools like ChatGPT risk becoming public information.

• As the tools do not provide references or credits to the original work, the sources cannot be verified or approached for consent.

• Deepfakes, realistic media created by AI, make it difficult to separate AI generated content from what is real.

• As LLMs train on input data, any biases or discriminatory themes in that data will be replicated in the output.

• ‘Hallucination’ is a concern in generative AI where lack of relevant information leads the system to produce inaccurate or fabricated data asserted as being correct.

Controls

• To prevent cybersecurity fraud, cyber insurance providers will need to provide adequate coverage under their policies.

• Self-hosted LLMs will eliminate privacy concerns by restricting confidential information to the company’s internal systems.

• Watermarking AI generated artifacts is one way to control fake or counterfeit imagery, but stricter controls will be needed.

• Continuous testing of models by validating correct results and rejecting errors will train the model to deliver accurate results.

• Frameworks on ethical use of generative models and increased accountability will need to be developed.

Conclusion

Despite the relatively short history of generative AI, the technology has a far-reaching impact on the future. Where generative AI is transforming enterprises and creative expression, the threat from lack of oversight and legal frameworks puts both organizations and individuals at risk of fraud, misinformation, identity as well as AI generated property theft.

Understanding the potential value-addition that generative AI brings to business will allow enterprises and people to adapt to challenges and explore its multiple benefits, some yet to be discovered.

References

1. Pitchbook data, 2023

2. Bloomberg, 2023

3. Gartner, 2023