The Agentic AI Revolution: Reshaping Work and Worrying Workers
Artificial intelligence isn’t just coming for your job; it might be coming as your job. That’s the crux of a significant shift in the AI landscape – the rise of “agentic AI” systems, capable of independent decision-making and action. These aren’t your typical AI tools; they’re autonomous entities that can code, write, manage businesses, and even drive. The implications for data scientists, software developers, and the broader workforce are profound.
Consider the example of Northwest Registered Agent LLC, a company that provides compliance filing and business services. For two years, they’ve used AI-RAH, an AI-powered agent that handles tasks like forming LLCs, managing documents, and resolving minor disputes. Similarly, OtoCo Inc. announced that one of its AI agents independently created an LLC in Delaware. Deepinder Goyal, founder of Zomato, released Nugget, an AI agent that powers millions of support interactions. Addverb, a Reliance Jio-backed robotics company, has launched “Trakr,” an AI-powered robot with autonomous navigation, and plans to create an AI-driven humanoid.
What Distinguishes Agentic AI?
Agentic AI systems possess a high degree of autonomy. Unlike traditional AI that responds to specific commands, agentic systems can initiate actions and make decisions to achieve defined goals. Think of a self-driving car that reacts to traffic or a sophisticated AI that manages stock trades. This independence sets them apart from basic chatbots, which offer predefined answers.
These agentic systems process inputs (text, voice, or images), analyze data to determine actions, and execute tasks by interacting with external programs.
How Agentic AI Works and Why it’s Becoming More Capable
Agentic AI typically uses natural language processing (NLP) or computer vision to interpret information. For example, Skit.ai transcribes and understands customer calls. E42’s HR automation AI decides on leave approvals based on company policy. They then analyze data to decide on action, and execute tasks by interacting with external tools.
Generative AI (GenAI) models are central to the increasing efficiency of these systems. GenAI helps them understand and respond to users in a more natural and human-like way. Rather than relying on fixed rules, GenAI enables agents to learn from data and offer personalized solutions. An agent can recognize that a customer’s order is wrong and help them with an exchange, including initiating a return and communicating in natural language.
Big Tech and Indian Companies are All In
Nvidia is developing agents powered by Nvidia NIM that can summarize content, detect fraud, and automate workflows. Companies like Microsoft and Google have also developed AI tools, such as Microsoft’s Co-pilot and Google’s NotebookLM, that transform PDFs into podcasts or manage video solutions. Many Indian companies, including Tata Consultancy Services, Infosys, Wipro, Tech Mahindra, and Zoho, are developing AI platforms with autonomous decision-making capabilities. UiPath and Automation Anywhere offer AI-powered bots that automate tasks in finance and healthcare. Deeptech startups such as Haptik, Niki.ai, and Gnani AI offer conversational AI solutions with agentic capabilities. Niramai has developed an AI system for breast cancer screening, and Manthan provides AI-powered retail analytics.
The Impact on the Workforce
By 2025, Avasant predicts that 88% of enterprises will incorporate synchronous AI agents in some capacity. Gartner projects that AI agents are projected to save businesses over $80 billion annually by 2026 through automation. These systems offer increased productivity and operational efficiency by handling large volumes of tasks quickly while not tiring. This has major implications for human roles and the overall job market.
Experts predict that AI agents can potentially replace roles performed by humans. OpenAI’s CEO, Sam Altman, envisions AI agents as virtual co-workers, capable of completing tasks with limited human oversight. Nvidia’s CEO, Jensen Huang, suggests that IT departments may become the HR departments for AI.
Reshaping Human Resources
The integration of agentic AI demands a reevaluation of job roles and of how companies organize the workforce. Organizations will have to rethink their onboarding and talent strategies as AI agents work alongside human employees. Companies will potentially shift away from layered structures to flatter, more collaborative ones that enable purpose-led and objective-driven decision-making. This requires HR departments to cultivate human-AI collaboration.
IT and business teams can no longer afford to operate in silos. AI demands a unified approach, making an AI governance board essential, according to Akshay Khanna of Avasant.
Adaptation and Risk
Traditional tech teams, including backend, frontend, and architects, must adapt to the increasing role of AI. AI can draft plans and write code but human oversight remains necessary.
Despite advancements, AI agents still have limitations. They struggle with long-term memory, and their autonomy can come with high computational costs. Ethical and security risks pose further challenges, alongside concerns about trust. AI-driven decisions could result in errors, misinformation, or even cyberattacks. Governments will likely need to step in with regulation.
