Generative AI – Emerging Use Cases, Challenges & Future Prospects
Gen AI has the potential to significantly impact business operations across almost all industries - right from manufacturing and BPO, to creative sectors such as media & entertainment to generate content. Today, we are already witnessing how Gen AI can assist in generating a variety of content as such as articles, videos, music and many others. Additionally, Gen AI will also help companies to accelerate their production processes and improving creative output as well. Similarly, in terms of design, Gen AI can aid designers, architects and artists by creative novel designs by assisting them right from the ideation process, leading to more innovative and efficient workflows and enabling the designers to explore a wider range of possibilities.
Businesses can facilitate effective collaboration between Gen AI and humans by enhancing humans-AI interaction through training programs for employees on how to effectively work on Gen AI systems
In sectors related to research and scientific discoveries, Gen AI will enable organizations to have improved simulations, better hypothesis generation and many other areas. Additionally, it can also facilitate process optimization and decision making by analyzing large data sets, identifying patterns, provide meaningful insights on various processes, and assisting the business in improving its process. Along with enabling companies to save time and cut-down costs by augmenting speed & efficiency, Gen AI also helps organizations to provide custom- ization of products and services, leading to improved customer experiences. Overall, there are a lot of possibilities with which Gen AI will continue impacting processes across all the industries in the coming days as well. However, there are some ethical considerations such as responsible use of data, potential biases and accountability that organizations need to pay close attention to in order to reap maximum benefits from Gen AI.
Challenges in Gen AI Development & Implementation
While there are several challenges that organizations face while developing and implementing Gen AI, the primary ones are centered around data quality and quantity. Gen AI models require large amount of high quality data to perform efficiently. However, obtaining large quantity of high quality data is a tough nut to crack for most enterprises, especially for niche domains and applications. To address this challenge, companies must create robust data collection pipe- lines, exploring new techniques such as data augmentation, and ensure that the data represents the target domain. Additionally, companies can effectively address and mitigate biases in data by carefully curating the training data, reviewing it and promoting diversity & inclusivity in the data itself. Another major challenge is to measure the performance of the Gen AI models, wherein traditional metrics may fail to capture the desired qualities such as creativity and coherence. Thus, developing suitable evaluation methods combining objective & subjective metrics and involving human reviewers for feedback can help address this challenge effectively.
"Gen AI must be mandatorily designed to collaborate effectively with humans by focusing on a few key principles such as explainability"
Facilitating Effective Humans-Gen AI Collaboration
I personally believe that Gen AI must be mandatorily designed to collaborate effectively with humans by focusing on a few key principles such as explainability, wherein the Gen AI system must be capable of explaining its reasoning and decision-making process to the humans. Thus, the Gen AI model must be designed in such a way that it is easily explainable to human users. This helps the humans to build trust, understand how the model is behaving, and validate the output as well. Secondly, Gen AI must be designed to work alongside humans in order to facilitate a more collaborative process. Lastly, every Gen AI system must have a continuous feedback loop to improve its performance.
Businesses can facilitate effective collaboration between Gen AI and humans by enhancing human-AI interaction through training programs for employees on how to effectively work on Gen AI systems. Also, organizations must encourage a collaborative mindset and creating channels for open communication between humans and AI model. Additionally, the company must ensure to clearly define the work roles separately for humans and AI as well to achieve enhanced process efficiency. Furthermore, organizations must ensure collaboration and trust between humans and AI, encourage teamwork & knowledge sharing, and promote a culture where employees see AI as their teammate and feel empowered to work alongside with it to achieve a common goal. Lastly, companies must continuously reiterate and improve the AI systems based on the feedback of human collaborators.
R&D Areas for Responsible Gen AI Advancement
To ensure responsible advancements of Gen AI, there are many areas that need extra focus in terms of R&D, especially from the implementation perspective. Some of the major ones among them are ethical guidelines & frameworks, bias detection & mitigation, data quality & security, user controls & consent, collaborative development, and many others. However, among these, I strongly feel that organizations must place bias detection & mitigation as the topmost priority in their R&D initiatives. This is because going forward, it is critical to ensure that the models are as fair and ethical as possible since the implications of Gen AI models will not only be far reaching, but its scale will be much higher than ever before.