Introduction
Envision your organization a few years into the future – where the lines between physical and digital environments have blurred. A world where all essential operations take place in the digital realm –
- An automation-driven processes to track, monitor and connect every asset, process and person across the organization
- An environment where employees work and train within a simulated but real-time setting with the help of augmented reality (AR) and virtual reality (VR).
This immersive world might seem like science fiction today, but the journey toward making it a reality has already begun. And it is generative AI that is making it possible - by changing the ways in which enterprises use AI across their operational ecosystems. ChatGPT, GitHub Copilot, DALL-E and Stable Diffusion are recent examples of AI-empowered tools that have captured the world's attention and opened new pathways to improving business processes and outcomes. In fact, with generative AI, organizations are well-positioned to take assistive technology to the next level and bring powerful technical capabilities to non-technical users.
Use cases abound
A generative AI tool such as ChatGPT attracted over a million users within five days of its launch in November 2022, overwhelming its servers and creating an explosion of AI-made solutions. With a clear indication of the high demand for such a tool from both the consumers and industry verticals, major players in the IT space have already fast-tracked their commercial offerings for enterprise use. From the IT sector to the entertainment sector and from education to healthcare, every industry stands to benefit from generative AI. However, generative AI has also attracted its fair share of criticism and challenges, as is the case with any new technology.
Over the years, the world has experienced the power and potential of AI and ML in various industries, such as providing medical imaging analysis, improving business operations and providing high-resolution weather forecasts. Based on this experience, AI adoption has reached unprecedented levels, having doubled in a few short years and we are still only scratching the surface. Similarly, commercial 5G deployment has boosted AR and VR as well. Faster edge computing is empowering unique use cases and unlocking new growth opportunities for businesses. This is the same path that generative AI is poised to follow as it unleashes the new potential for not only organizations but also society at large.
For enterprises, this means real-time collaboration, remote experts, remote training and guided maintenance, repair and operations. The ultra-low latency and high bandwidth that private 5G offers will enable these innovations and more. Today’s systems explore only a fraction of the design space. But with the use of edge computing, image rendering is far closer to the end-user, enhancing usability and efficiency even further.
With generative AI, the evolving possibilities are boundless. Enterprises of the future will have digital twins of the end-to-end supply chain, which provide a real-time and accurate replication of raw materials and deliveries for enhanced operations planning and management. In case of any disruption, the processes necessary to source and onboard new components will become seamless, avoiding delays, mitigating obstacles, saving time and optimizing production.
In sales and marketing, these new technological innovations are continuing to take on critical roles. One of the most common use cases is for vendors to virtually walk prospective customers through real-life versions of their shops and factory floors. IoT has allowed businesses to reach customers directly on their smart devices, enabling them to experience their products through AR.
With tools such as DALL-E and D-ID AI, content creators in the media and entertainment are experimenting and creating animated content to generate ideas that will further enhance customers' webAR experiences to drive businesses. Generative AI can also be used to generate dialogue for video games. Training the model on a dataset of existing game scripts can teach the conventions of game dialogue and use that knowledge to create new, branching conversations for NPCs (non-player characters) in the game. This allows game developers to create more dynamic and immersive worlds for players to explore.
Similarly in healthcare, generative AI is expected to revolutionize everything – by reducing process friction, easing data access and helping automate time-consuming manual tasks such as drafting reports and responding to insurance claims. Until recently, AR and VR were already changing the healthcare game with niche use cases, viz. digitally mapping a patient’s vascular system, improving efficiency in locating veins to place IVs successfully, practicing complex procedures and enhancing the patient experience.
But with generative AI-enabled tools, we can do so much more, such as rapidly identifying abnormalities on medical scans and X-rays to alert doctors of any underlying issues. Generative AI can be used as a supporting diagnostic tool since it can also factor in crucial indicators from a patient’s medical history, risk markers and symptoms. This approach provides physicians with valuable data-driven insights in real-time that helps them identify and explore treatment plans more effectively than ever before while also democratizing access to high-quality healthcare at reduced costs.
With the timely amalgamation of 5G with edge computing technologies and generative AI, the life sciences industry can develop solutions faster with real-time monitoring and control of new drug discovery and precision medicine. With this new range of capabilities, generative AI will be a critical enabler of new telehealth services and directly improve patient outcomes by transforming life sciences innovation and providing better patient care models.
In the field of education, we have seen the deployment of AR and VR solutions to help teach students in new and enhanced ways. And with 5G technologies, edge computing, IoT and generative AI coming together - we can enrich these experiences further by enabling immersive learning built-in virtual environments, powered by vast amounts of data to create student-specific educational journeys, whilst nurturing student creativity.
The use of generative AI tools is not new in the field of academia. Since the launch of GPT3 by Open AI in 2020, some scientists have acknowledged the use of chatbots as research assistants. However, the easy accessibility of ChatGPT, followed by an explosion of fun and sometimes alarming writing experiments, has led to growing concerns about its impact on education.
This is particularly a challenge in cases where people use it in exam and text settings by leveraging these large language models (LLMs). Education centers have reacted differently to these issues. New York City has banned the use of ChatGPT in public school devices, while for others, it is about using LLMs ethically by taking a less conservative route that allows students to cite LLMs in their assignments as research references. How all these changes will evolve and crystalize in the coming years is yet to be seen, but there is every reason to be positive.
Value and possibilities galore
Enhancing the value generative AI calls for focus on several areas, the most significant of which are:
Uplifting the diversity of training data: Currently, generative AI models are trained on a narrow data set, leading to biased and limited results. Increasing the diversity of training data, including underrepresented groups, can help produce more accurate and representative outputs.
Improving the interpretability of models: Generative AI models are often opaque and complex, which can limit expanding their usefulness in certain applications. Improving the interpretability of models can help them in becoming more transparent and trustworthy.
Developing more advanced algorithms: As generative AI becomes more widespread, there is a need for more advanced algorithms that can manage complex and diverse data. This includes developing algorithms that can handle multiple modalities (e.g., text, images, video) and generate more complex outputs.
Integrating Generative AI with other next-generation technologies: Generative AI use cases can be combined with other technologies, viz. Computer vision, Natural Language Processing (NLP) and Robotics, to name a few, cater to more sophisticated applications. Integrating generative AI with these can lead to unique possibilities in the future.
Addressing ethical and societal concerns: As with the introduction of any modern technology, there are ethical and societal concerns around issues such as privacy, bias and the impact on the workforce. Developing guidelines and regulations can help ensure that generative AI is used ethically and responsibly.
The road ahead: Given the revolutionary rise and acceptance of AI tools, one cannot help but imagine boundless possibilities for the future. If an AI tool can transcribe Zoom calls in real-time, it may soon enable real-time translations as well. People will not need to learn multiple languages because conversations will be translated as subtitles on our screens or as AI-generated voices during a meeting. Like all breakthrough technologies, it is only natural for our excitement with generative AI to ignite our imaginations about what may be coming.
And while these technologies are only in their nascency, we can expect their capabilities to develop further over time with greater efficiency and productivity. However, their full potential in real-world applications can only be realized with large-scale adoption by organizations. So as generative AI continues to improve and in combination with other recent next-generation technologies, we can expect to see ground-breaking use cases across industries that not only boost innovation and business growth but also offer surprising and unexpected changes to how we live and work.
