Agentic AI Primer (PowerPoint PPTX Slide Deck)

This PPT slide outlines the evolution of agentic AI systems from basic task support to enterprise-scale autonomy. It presents a four-level maturity model, each stage expanding in scope, capability, and strategic impact. The progression begins with simple agent systems that automate basic tasks and advances toward cross-functional, agent-driven systems capable of managing complex workflows across multiple functions.

The first level, labeled "Agentic system," focuses on agents that help automate and improve straightforward tasks. The second stage, "Individual Augmentation," involves agents that assist individual users, increasing efficiency, but still limited in scope. The third level, "Task and Workflow Automation," introduces automation of existing, low-complexity workflows, enabling more streamlined operations. The final stage, "Cross-Functional Agent Systems," features agent-driven systems working on complex workflows across functions, with high-level decision-making capabilities.

A yellow sidebar on the right explains the significance of this continuum. It notes that as systems mature, they shift from technical support to fully autonomous entities managing enterprise-wide processes. The evolution reflects a move from improving productivity to reshaping work, coordination, and value creation at scale. The slide emphasizes that this progression is driven by increasing task complexity and strategic impact, highlighting the importance for organizations to understand where they are on this continuum and how to advance.

The bottom blue banner references a resource called "Agentic AI Primer," crafted by McKinsey and Big 4 consultants, designed to help organizations understand and implement these AI systems. Overall, the slide offers a clear framework for assessing AI maturity and planning strategic investments in automation and autonomy at an enterprise level.

This PPT slide explains how AI systems, specifically functional agent workflows, are evolving to improve operational efficiency and decision-making. It starts with a clear definition of the system, emphasizing teams of AI agents working together to optimize various aspects of business functions. The overview highlights the transition from manual task automation to interconnected AI agents that coordinate across different steps like planning, analysis, and validation, aiming for seamless outcomes. The focus is on the shift toward intelligent, self-monitoring ecosystems that can adapt and improve continuously.

The primary benefits section underscores the gains from this approach. It states that these workflows deliver faster cycle times and significant efficiency improvements within core functional domains. Decision quality is also enhanced through better data integration, contextual understanding, and reasoning at each process step. The reduction of handoffs and rework is a key point, as seamless coordination among agents and humans minimizes delays and errors. Customer satisfaction and revenue growth are expected to improve due to increased responsiveness and reliability, which are critical in customer-facing or revenue-generating functions.

Key constraints reveal the challenges involved in redesigning these workflows. Deep process redesign is necessary, and existing workflows must be deconstructed and rebuilt based on agent logic. Advanced engineering is required to coordinate large teams of AI agents while maintaining transparency and control. Governance gaps may arise as agent interactions increase, introducing new operational and ethical risks. Human teams might resist adopting these agent-driven processes because of perceived loss of control or unclear accountability. The slide emphasizes that overcoming these constraints is essential for scaling AI workflows effectively, with technical expertise and governance frameworks being central to success.

Overall, the slide presents a comprehensive view of how functional AI workflows are maturing, highlighting both the benefits and the hurdles to wider adoption in enterprise settings.

This PPT slide compares 2 forms of enterprise automation: Agentic Labor and Agentic Engine. It emphasizes how Agentic AI is bridging the gap between current human-assisted productivity and fully autonomous systems. The core idea is that organizations are shifting from augmenting human workflows to deploying autonomous systems that can execute end-to-end operations.

The left side describes Agentic Labor as focusing on enhancing existing workflows through digital assistants that handle routine tasks like summarizing, drafting, or approving. It highlights efficiency gains of 20-40 percent,, but notes that this approach still requires structured change management and cultural adaptation. The benefits are primarily in productivity lift,, but strategic differentiation remains limited because these systems operate within functional silos.

The right side introduces the Agentic Engine as a more transformative approach. It re-architects operations into autonomous, agent-first systems capable of learning and acting independently. This enables enterprise-wide transformation across functions such as finance, supply chain, and customer care. The engine achieves significantly higher automation levels—up to 80 percent—and accelerates cycle times by up to 70 percent. It shifts the CEO agenda from efficiency to broader organizational reinvention and value creation, signaling a move toward self-evolving enterprise systems. The slide underscores the evolution from incremental productivity improvements to fundamental operational reengineering driven by AI.