Autonomous agents can solve complex problems: making their arrival an enterprise game changer.
Beyond reactive assistance
Recent advances in agentic AI bring autonomous systems closer to reality for workplaces that seek to be both intelligent and human centered. Unlike traditional AI assistants waiting for commands, agentic systems use sophisticated reasoning to identify opportunities and execute complex tasks independently.
While agentic AI systems can respond to prompts, at their most useful to their human collaborators they can act independently, pursue goals, and learn from outcomes.
What makes AI “agentic”?
Agentic AI uses iterative planning to solve complex, multi-step problems autonomously: understanding targets, planning actions, interacting with systems or humans, and adapting based on feedback. For instance, a digital procurement agency can monitor inventory, contact vendors, execute purchase orders, and document everything for compliance ¾ without human supervision.
TCS emphasizes integrating such AI agents into digital workplaces to promote collaboration between AI and human workers for greater productivity and strategic growth.
Why it matters now
Autonomous systems are revolutionizing enterprises, with digital workplaces becoming key entry points for these innovations.
Employees are accessing AI through APIs, data lakes, low-code platforms, and genAI models, which allows CIOs to automate more business operations, achieving scalability to manage thousands of micro-decisions.
Efficiency can thus be gained by reducing the need for manual intervention in IT, finance, supply chain, and HR; and innovation can result, freeing up human capital for creative tasks.
The evolution path: from Microsoft Copilots to autonomous agents
The transition to agentic systems happens in stages. Most enterprises are currently in the copilot phase: task-specific AI assistants that augment workflows (approximately 80% of the companies we have observed are currently evaluating or embracing genAI technology like Microsoft Copilot). Following that, the maturity journey typically follows this progression:
Along the way, agentic AI can deliver specific improvements across business functions.
For example:
- Employee experience: Onboarding agents can personalize training paths; meeting agents can transcribe discussions, identify action items, and follow up automatically; knowledge agents can consolidate information across siloed systems.
- Revenue enablement: Sales intelligence agents can analyze customer interactions for upsell opportunities; market analysis agents can monitor competitor activities; customer retention agents can detect early warning signs of churn.
- Operational efficiency: Self-healing infrastructure agents can predict potential failures; regulatory compliance agents can monitor changing laws; document processing agents can extract and route information from unstructured documents.
The CIO’s role
This transformation requires rethinking governance frameworks, data architecture, and workforce strategies. As AI systems become more compatible, businesses can swap, upgrade, and integrate models with less friction, while federated governance models can balance team autonomy with centralized risk control.
A strategic imperative
Within the next 12 to 24 months, agentic AI is expected to revolutionize business operations. Organizations that successfully integrate these technologies can create workplaces capable of responding to market changes with unprecedented speed.
The future isn’t about replacing humans but elevating their potential. By delegating routine decisions to intelligent systems, companies will free their people to focus on innovation and strategic thinking that will define competitive advantage in the coming decade.
To learn more visit TCS and Microsoft Cloud: Driving Businsess Transformation
