How AI Agents are Changing the Workforce
The Ethical Implications of AI and How to Stay Relevant in the Age of AI Agents
This article is part of my Viewpoints collection, which focuses on current events in the world of technology, data, and AI. It is a space where I can share my viewpoints on a specific topic of interest.
The Impact of AI on the global workforce is a complex and evolving topic.
Not too long ago, ChatGPT and Generative AI (GenAI) revolutionized the technology space.
Since then, GenAI has evolved from a nice-to-have to a must-have capability.
GenAI projects are maturing and transforming organizations by turning enterprise data into actionable knowledge and helping employees work more efficiently.
GenAI applications that respond to prompts using natural language can solve problems with minimal human intervention. The interpretation of prompts and responses is built using large language models (LLM).
The pre-trained scaling law for LLM was the original scaling law for building the larger scale of training data.
The post-trained with reinforced learning and human feedback helped with problems like hallucinations in the responses generated by pre-trained LLMs.
Both pre-trained and post-trained models have reached their saturation and diminishing returns.
As the next phase in the evolution of LLMs, with test-time scaling law (a.k.a. reasoning), it is now seen as the next big opportunity.
Agentic AI and reasoning LLMs utilize the test-time scaling.
It represents an evolution beyond the traditional single, less complex interaction of AI systems like chatbot applications.
Agentic AI is designed to act independently, making decisions and taking actions to achieve specific goals without constant human intervention.
There are definitive advantages of Agentic AI -
Increase productivity and efficiency.
Improved planning, reasoning and decision-making.
New job-creation like AI Engineering and advancements into DSML.
Improved Safety.
However, it still raises the ethical concerns, in the near future, like-
Bias and Discrimination.
Job Displacements.
Wages Stagnation.
The impact of AI is visible in various streams of the corporate world -
Customer Services: Chatbots and virtual assistants are used to handle customer service inquiries.
Manufacturing: Robotic and automation systems are used to perform various manufacturing tasks.
Transportation: Advancement into Robotic-taxis, self-driving cars, and trucks will impact taxi and truck drivers.
Finance: AI-powered systems are used to perform advanced anomaly and fraud detection, investment analysis, and low-risk portfolio management with Robotic advisors.
Agentic AI is a broader paradigm focused on creating AI agents.
The AI Agents, built to accomplish predefined multi-step and complex tasks, can analyze their performance, learn from their experiences, and improve their future actions.
In some cases, multiple AI agents can collaborate to achieve complex objectives. AI agents working towards the same goals are orchestrated in the broader paradigm of Agentic AI.
One such example of a platform for creating AI Agents for various industries is Agentforce.
Salesforce, a leading provider of Cloud-Based Enterprise Software Solutions, heavily emphasizes "Agentforce - A Digital Labor Platform" as a key part of its AI strategy.
Salesforce views Agentforce as representing the "third wave of AI," moving beyond predictive and generative AI to autonomous, action-oriented agents that take advantage of test-time scale and reasoning LLMs.
Agentforce is designed to create AI agents that can take independent actions, provide information or responses to a prompt, and accomplish a larger objective by automating complex and multi-step tasks, making decisions, and adapting to changing conditions.
Salesforce integrates Agentforce deeply within its platform, allowing seamless integration with other products and services.
This allows the AI agents to have access to the full spectrum of enterprise data and actionable knowledge to make autonomous decisions.
Salesforce is prioritizing the development of Agentforce with a strong focus on trust and reliability, addressing concerns about AI accuracy and potential risks.
In January, at CES 2025, Jensen Huang, Nvidia's President, co-founder, and CEO, delivered the keynote announcing the next wave of AI trends.
He stated that Agentic AI would be "A next Trillion Dollar Opportunity."
Here is the link for more details.
The agentic AI market is projected for rapid growth. It is driven by the increasing demand for automation, improved decision-making, and enhanced efficiency across industries.
Viewpoint
So, with the advancement into the third wave of AI and the ability to create AI agents, it is inevitable that AI will impact how the corporate world functions in the near future.
There is no crystal ball to see clearly into the future and know what exactly will happen -
how the next-gen AI-powered economy will shape up,
where will the next Trillion Dollar Opportunities emerge,
what will be the next ChatGPT moment in the technology space,
In the same way, there is no clear foresight on the impact of AI on the job market.
Some experts believe that AI will create more jobs than it destroys, while others believe that AI will lead to widespread job losses.
This is an evolving topic and the most debated one.
One thing is highly predictable: the nature of newly created jobs and skills required to fuel the next-gen AI-powered economy are also evolving.
Key Takeaways
It is no surprise that the nature of some job functions may look different in the future.
Some of the skills that will be in continuous demand:
AI literacy: Understanding how to work alongside and operate Agentic AI technologies will become increasingly essential.
Technical skills: The ability to design, implement AI-development frameworks, and deploy AI products, as well as the ability to maintain and troubleshoot them.
Strategic-Thinking: As AI takes over more routine tasks, human workers will need to focus on creativity for higher-level problem-solving and strategic thinking to effectively leverage AI's capabilities. Ethical AI practices will be in high demand.
Domain expertise: A deep understanding of the specific industry vertical or domain like healthcare, finance, manufacturing, or even cybersecurity and governance-regulation compliance (GRC).
Communication and collaboration skills: The ability to communicate and collaborate effectively with other team members and AI-powered systems.
Lifelong learning: To mitigate the potential negative impacts of job displacement, investing in robust re-skilling and upskilling includes learning new skills and technologies as they become available. Since GenAI's evolution, there has been a wave of new technologies to learn. GenAI prompts are a good way to get started on knowing this technology.
By developing these skills, we can ensure that we are prepared for the challenges and opportunities of the AI-powered economy.