The Business Imperative for Agentic AI

According to Deloitte’s latest Point of View on Agentic AI, titled, The Business Imperative for Agentic AI, Generative AI’s limitations, particularly in reasoning, decision-making and autonomous execution, have paved the way for the emergence of Agentic AI systems. Agentic AI enables businesses to operate efficiently by automating the front, middle and back-office functions, including customer outreach and engagement, product innovation and finance operations. In 2025, 25 percent of enterprises that use GenAI will likely launch Agentic AI pilots or proofs of concept, growing to 50 percent in 2027, as per Deloitte’s global 2025 TMT predictions report.

Indian enterprises are actively exploring and implementing Agentic AI. According to Deloitte’s Fourth Wave of the State of Generative AI in Enterprises Report (India insights), more than 80 percent of the surveyed organisations in India are actively looking to develop AI-driven autonomous agents.

“Agentic AI represents a pivotal evolution in enterprise automation, moving from static, rule-based systems to intelligent, goal-driven autonomy. But realising its full potential requires more than just experimentation. For enterprises to effectively embed agentic capabilities into their core workflows, a strategic foundation must be laid. Leaders must evaluate where Agentic AI adds the most value, assess their technology ecosystem’s readiness, and define clear success metrics beyond novelty. This shift also demands new skillsets, redefined roles, and a deep commitment to responsible AI deployment, from mitigating bias to ensuring explainability and governance. In markets like India, where digital infrastructure and AI talent are rapidly maturing, Agentic AI presents a practical path to drive sustained productivity and innovation. But its success hinges on asking and answering the right strategic questions upfront,” said Ashvin Vellody, Partner, Deloitte India.

Key considerations for undertaking a successful Agentic AI journey

As enterprises increasingly embrace the transformative potential of Agentic AI, a strategic and well-structured approach becomes essential to adopt and realise its full outcome value.

• Six critical questions every enterprise must answer to harness the full power of Agentic AI

1. Which business processes best suit Agentic AI implementation, and what criteria should be used to evaluate their sustainability?

• Agentic AI is not a one-size-fits-all solution; only select processes with the right characteristics can truly benefit from its capabilities.
• Organisations must focus on use cases that deliver meaningful influence quickly and sustainably rather than pursuing implementation for novelty or experimentation alone.
• Criteria to evaluate if a use case is Agentic – reasoning, need for autonomy, process with logical end, action and goal-oriented workflows, multi-step process, continuous learning and more.

2. Is your technology ecosystem ready for Agentic AI? What foundational components are essential for enabling agentic workflows?

• Depending on the maturity of their technology ecosystem, organisations can be at varying stages of readiness for Agentic AI adoption.
• Organisations already using GenAI, automation and Large Language Models (LLMs) typically operate in a multisystem environment with robust data pipelines, orchestration layers and integration frameworks.
• In contrast, organisations not yet using GenAI or automation face a steeper path to Agentic AI implementation. Their systems may be siloed, lacking interoperability and real-time data exchange, limiting autonomous agents’ effectiveness.
• For organisations at any stage of readiness, it is critical to take a long-term view when defining their technology roadmaps, especially as Agentic AI begins to reshape enterprise capabilities.

3.How do you define and measure the success of Agentic AI interventions, and what expectations should be set within the organisation?

• The success of Agentic AI implementation hinges on how well it delivers against the organisation’s ultimate business outcomes and strategic goals.
• Agentic AI is not a point solution like many GenAI tools. It is process-oriented, meaning it must be deeply integrated into business workflows and continuously evolve with them.
• Agentic AI value drivers – Faster TAT, Faster TAT, Reduced error rates, Improved efficiency and process accuracy, Optimised future-state process flows, Improved profitability, Enhanced utilisation, Comprehensive risk assessment and mitigation, Feedback loop for continuous improvement, Revenue generation, Cost avoidance, ROI

4. What is the most effective approach to building, scaling and sustaining an Agentic AI journey that aligns with your organisation’s unique goals and capabilities?

• Three key strategic approaches to implement Agentic AI solutions:
  • Build: Developing Agentic AI solutions in-house
  • Partner: Collaborate with Agentic AI specialist partners to build end-to-end solutions
  • Hybrid (Buy + Partner Support): Purchase pre-built generic AI agents or platforms + Use partner for implementation
• Furthermore, organisations must carefully evaluate their processes, choosing between RPA, LLMs, single agents and multi-agent systems that depend on the task’s complexity, adaptability and decision-making requirements. The more effective the choice, the more RoI it generates.

5. Equipping and empowering employees to thrive alongside AI agents, as they become an integral part of the workforce

• To harness the full potential of Agentic AI, businesses must prioritise:

• Upskilling employees with requisite skills for Agentic AI – Key requisite skills for the Agentic AI future – data scientists, machine learning engineers, prompt engineers, AI architects, LLM engineers, AIOps engineers.
• Redefining job roles to emphasise human creativity and strategic thinking – Rise of hybrid roles, focus on creativity and strategy, shift from executing discrete tasks to orchestrating end-to-end outcomes.
• Preparing leadership to orchestrate the future of human – Agentic AI collaboration – culture and change management, prioritise human outcomes beyond metrics and efficiency, ethical oversight, and accountability.

6.Key considerations for ensuring the responsible deployment of Agentic AI

• Agentic AI systems hold immense potential, but inaccuracies or biases in their models can lead to error amplification, posing significant challenges.
• A single error in a multi-step process can propagate and magnify throughout the decision-making chain. chain (e.g. failure to consider real-world complexities, such as cultural or situational nuances, can result in misinterpretations).
• Error amplification in the Agentic AI framework is a complex challenge requiring a multi-disciplinary approach. By addressing the root causes of inaccuracies, employing robust mitigation strategies and aligning AI systems with societal and ethical norms, we can responsibly and effectively harness the full potential of Agentic AI.
• Responsible deployment of the Agentic AI framework must address – bias mitigation, human in the loop, transparency, privacy, risk identification tools

Recommendations for businesses to start Agentic AI journey

• Use multi-agent workflows in core business functions for it to become key drivers of ROI and innovation.
• Build reusable agents and modular agent systems as it reduces development time, enhances maintainability, and accelerates deployment across use cases.
• Integrate seamlessly with your existing tech stack to create a unified, intelligent ecosystem that evolves with business needs.
• Build long-term AI-capable architecture to gain a long-term competitive advantage through AI. This requires a forward-looking approach to infrastructure, data strategy and interoperability.
• Build native Agentic AI function and capabilities.
• Scale frameworks beyond pilots and build a unified lifecycle and ecosystem governance infrastructure across private and public sectors.