By Mukund Kalmanker
As AI adoption accelerates, a new wave of innovation is reshaping the technological landscape: agentic AI. While generative AI has dominated conversations with its transformative applications in business operations, agentic AI systems are poised to redefine automation, intelligence, and decision-making in enterprises.
Generative AI relies on human prompts to perform tasks. On the other hand, agentic AI systems possess autonomy – making decisions and taking actions without direct human intervention. Gartner predicts 33% of enterprise software will feature agentic AI by 2028, up from under 1% in 2024. This evolution underscores urgency for business leaders to understand and integrate these systems to remain competitive.
Agentic AI are autonomous agents — independent entities that make decisions and act on real-time inputs. From virtual assistants in customer service to robots optimising supply chains, these agents help enterprises streamline operations and improve decision-making.
Agent-based models, integral to agentic AI systems, simulate interactions analyse complex behaviours. These simulations provide valuable insights into market fluctuations, supply chain bottlenecks, and other challenges, equipping businesses with actionable strategies.
Unlike traditional AI systems, agentic AI operates independently. These systems continuously learn and adapt to new scenarios. As these systems are task-specific—they function within clearly defined boundaries. For instance, an agent designed to manage inventory can independently analyse supply levels, predict shortages, and initiate restocking processes, all while adhering to its primary goal.
Agentic AI systems combine agent-based models, autonomous agents, and intelligent systems. Autonomous agents handle tasks independently, such as automating customer support, boosting efficiency and scalability. Intelligent systems enable learning and adaptation, offering tailored solutions like precise diagnostics in healthcare helping businesses tackle diverse challenges.
Integrating agentic AI into daily operations presents challenges for business leaders like chief data officers (CDOs), business consultants, and IT managers. CDOs must ensure agentic AI aligns with organisational goals by creating roadmaps and providing quality data. Business consultants can focus on applying AI for customer experience and supply chain optimisation, while IT managers must stay updated on AI trends to simplify concepts for non-technical stakeholders.
Organisations must establish ethical frameworks to guide AI deployment, address potential biases, ensure transparency, and safeguard data security. In an era of rapid technological change, agentic AI is more than a passing trend – it is the foundation for a smarter, more adaptive future. Visionary leadership is crucial for embracing agentic AI as a transformative asset. Empowering employees through technology and leveraging agentic AI to augment human decision-making will create value across ecosystems.
The writer is global head – Data, Analytics & AI Practice, Apexon.
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