Comprehensive Survey On Generative AI and its Impact on Future of Jobs
By Dr. Sangeetha Viswanathan
Generative AI is the new limelight of Silicon Valley with the biggest advantage of being “ready to consume with a little customization.” Companies can now build their content-specific models upon the foundation language models, breeding innovations. According to a UBS study, “ChatGPT reached 100 million monthly active users in just two months after its launch, making it the fastest-growing consumer application in history.”
Is it AI Utopia or AI dystopia for the future of jobs?
Interestingly, many would have perceived that the onset of Generative AI would primarily replace the blue-collar jobs, such as production line managers, drivers, and assemblers. However, the recent generative AI applications are performing wonderfully for some of the white-collar jobs as well, ranging from strategic thinking to creative problem solving. Right from AI-based systems, such as ChatGPT and DALL-E, to AI-based browser extensions, including Scribe AI, Wiseone, and Voila AI. There are both light weighted and complex tools pursuing new markets every day.
According to a recent survey by Mercer, an American consulting firm, 57% of CEOs and CFOs are planning to increase the use of AI and automation in their organizations; nearly one-third of them are in the plans of redesigning their work nature to reduce the dependency on manpower. Generative AI is transforming organizations and their work in a way which not many may have imagined before.
What areas are currently being impacted by Generative AI?
For example, in the creative content creation sector, which requires human imagination and ingenuity, Generative AI applications, such as DALL-E, Copy AI, Grammarly, are almost obscuring the lines between humans and AI-generated creations. The recent “democratization of creative tools” has made everyone, right from a beginner to a person with no formal training in designing to bring out their ideas to life with the AI powered tools. On the other hand, in the Information management and decision-making scenarios, Generative AI is revolutionizing the way businesses strategize, innovate, and adapt.
Every day, we generate a lot of information; and with the power of Generative AI in handling vast data quickly and deriving insights that are sometimes far beyond the capabilities of even a human expert, there is a change in the trend of some organizations relying on Generative AI. From demand forecasting, lead scoring, market research, automatically assessing customer messages, inventory management, and organizational network analysis to running outreach camps, the use cases of Generative AI In businesses seem to be almost endless.
Several significant challenges with Generative AI still remain, including its reliability
Generative AI is a potent technology that is transforming everyday lives to considerable extent; however, it can come with a cost. There are some underlying challenges in relying on AI, such as Data Bias, cybersecurity threats, maintenance, sustainability, model fairness and interpretability, and ethical considerations.
Having said this, considering some of the significant ways in which Generative AI is transforming our everyday work life, shouldn’t we strive hard to deliver the values of Generative AI, while still addressing its challenges and not-so-desirable impact on everyday jobs?
How can we possibly incorporate Generative AI in our workplace for constructive results?
The efficiency of AI in a workplace comes with its own risks. An AI enabled workplace must strictly abide by the 5 basic AI ethics – Fairness, Trust and Transparency, Accountability, Reliability, Privacy and Security. The U.S Chamber of Commerce released a report “Artificial Intelligence Commission Report” providing a template for organizations to enforce AI regulations. Organizations could enforce such regulations by classifying their AI use cases into low, medium and high risk.
In order to harness the true purpose of Generative AI in a workplace, the usage of AI based solutions could be strategized in several ways, such as:
- Fostering a culture of innovation, research and adaption
- Providing transparency to the employees
- Ensuring legal and ethical compliance
- Upskill and reskill employees
- Partnering with educational institutions to provide for on-the-job learning
Also, as AI is getting more popular in the information management and decision making, the solutions and frameworks used should be made transparent and explainable, bringing Explainable AI into picture. This will make organizations understand the automation, thus aiding them with insights about the consequences of using automation. In industries such as healthcare and law, it is essential for the system to be explainable, where the consequences could be critical.
In the book, “Reinventing Jobs: A 4-Step Approach for Applying Automation to Work”, John Boudreau and Ravin Jesuthasan present us with how optimal human-automation combination increases efficiency and higher turnover. Reinventing jobs under the structured four-step approach – deconstruct, optimize, automate, and reconfigure – could help organizations to align job skills and workload, creating optimal combinations.
Organizations could create an ecosystem of workflow model indicating the working operating model with jobs to be done, tools, and disciplines to execute a work and a skill model with the pipelining of skills that add up progressively with the work done. This could also help out the employees to get insights about the skills that they should concentrate on, for an AI workplace.
What about the loss of some jobs? … How can professionals react to current situation?
With AI being the next big thing in workplace, working professionals might probably have a concern about the jobs being at stake and find it difficult to adapt to change. In order to sustain the change, employees could update their role- and domain-specific knowledge and revise their skills periodically, especially by taking up some specific courses and programmes from leading educational institutions.
Generative AI may curtail some roles in organization, but it will also help create new roles, such as Machine Learning Engineers, Data Scientists, and Maintenance Engineers, to design and build the necessary infrastructure. Though automation is becoming significant in a workplace, there could also be certain partial operations that involve the cognitive and empathetical skills, such as brainstorming and interacting with clients and customers.
To conclude, the challenges of Generative AI on future jobs are two-fold – managing the automation of roles and updating our skills and knowledge time and again. The impact of Generative AI on future jobs is both rewarding and challenging; so, there is a need to make educated, clever, ethical, and responsible decisions to maximize both business success and personal work satisfaction.