> **来源：[研报客](https://pc.yanbaoke.cn)** # Summary of "Driving Adoption of the 'Human with Agentic AI' Era: The CXO Playbook" ## Core Content This whitepaper explores the transformative potential of **agentic AI** in the modern business landscape and outlines a **human-centric change management strategy** for CXOs aiming to harness its power. Agentic AI, as opposed to traditional AI, is characterized by its **autonomous nature**, enabling systems to perceive, reason, plan, and act independently to achieve specific goals. It is seen as a critical tool for **enterprise automation**, **innovation**, and **operational efficiency**, with significant implications for the future of work. ## Main Points ### Agentic AI: A New Frontier in AI - Agentic AI is an **autonomous system** capable of independent decision-making and task execution. - It is **more advanced** than generative AI (GenAI) and multi-agent systems, offering **proactive execution** rather than passive responses. - Agentic AI is **driving efficiency**, **cost reduction**, and **strategic value** across industries, including IT, customer support, HR, finance, sales, and logistics. ### The Human-AI Synergy - The **true competitive advantage** of agentic AI lies in the **synergy between human intelligence and AI capabilities**. - Human oversight, creativity, and judgment are **essential** for ensuring ethical, fair, and effective AI outcomes. - Ignoring the human element can lead to **biases, hallucinations**, and **suboptimal performance**, which can harm **ROI** and **organizational trust**. ### The CXO AI Conundrum - Agentic AI adoption is not just about **technology**; it's about **organizational transformation**. - **Workforce resistance** is a major barrier to AI adoption, leading to **underutilisation**, **higher costs**, **delayed integration**, **missed innovation**, **declining morale**, and **escalating enablement costs**. - A **strategic, human-centred approach** is needed to overcome these challenges and unlock the full potential of agentic AI. ## Key Information ### Agentic AI Use Cases - **IT management**: Resolves issues autonomously, improving resolution speed and accuracy. - **Customer support**: Provides **personalised responses** to queries. - **HR and onboarding**: Automates **account creation** and **access provisioning**. - **Operations and logistics**: Optimises **supply chain operations** through coordination. - **Sales and marketing**: Analyses **customer data** for tailored strategies. - **Finance and accounting**: Automates **data collection**, **document generation**, and **report creation**. ### The Agentic AI Boom in India - India is **leading the agentic AI adoption** globally, with a projected **CAGR of 44%** by 2031. - **80% of enterprises** are actively developing autonomous AI agents. - **50% of organisations** are focusing on **multi-agent workflows**. - **70% of companies** prioritise **automation** as a core outcome of their GenAI investments. ### The Human-AI Operating Model - **95% of routine tasks** are handled by AI agents. - **5% of exceptions** require **Human-in-the-Loop (HITL)** judgment. - The model is designed to ensure **trust**, **ethical AI**, and **sustainable transformation**. ### The AgenticAdopt Kompass™ Framework - A **six-pillar framework** for guiding organizations through agentic AI adoption. - The pillars include **Leadership**, **Local microculture**, **Layered communication**, **Learning**, **Listening loops**, and **Legacy of innovation**. ## Strategic Recommendations ### Leadership - **Vision and strategy**: Define a clear Agentic AI vision and align it with organizational goals. - **Human-centred design**: Ensure AI is used as a **tool to augment**, not replace, human capabilities. - **Emotional intelligence**: Communicate with **empathy**, build **psychological safety**, and **reduce resistance**. - **Role modeling**: Leaders should actively **use agentic AI** in their decision-making processes. ### Local Microculture - **Microculture mapping**: Identify and engage key microcultures (Champions, Enthusiasts, Sceptics, Laggards) to drive **buy-in** and **adoption**. - **Targeted engagement**: Tailor communication and support based on **trust levels** and **employee mindset**. - **Community building**: Create **communities of practice** where employees can share experiences and insights. ### Layered Communication - **Compelling vision**: Clearly articulate the **"why"** behind agentic AI adoption. - **Transparent communication**: Share regular updates and use **multiple channels** for consistent messaging. - **Two-way dialogue**: Create **safe spaces** for employees to voice concerns and provide feedback. - **Segmented messaging**: Adjust communication based on **microculture preferences** and **trust data**. ### Learning - **Role evolution**: Anticipate changes in roles and develop **differentiated learning strategies**. - **Skill gap analysis**: Identify **collaboration-critical skills** and assess **current readiness**. - **Training programs**: Develop **structured learning modules** on agentic AI tools and human-AI collaboration. - **Mentorship and peer learning**: Support employees through **hands-on guidance** and **internal champions**. - **Gamified learning**: Use **quizzes, leaderboards**, and **badges** to make learning **interactive and motivating**. - **Learning platforms**: Adopt scalable models like **Deloitte AI Academy™** to accelerate **capability building**. ### Listening Loops - **Feedback collection**: Use **multi-channel methods** to gather insights and understand employee **emotional journeys**. - **Trust quotient tracking**: Monitor **trust levels** across microcultures and adjust strategies accordingly. - **Dual-dimension feedback analysis**: Evaluate **AI system performance** and **human-centered experiences**. - **Actionable insights**: Translate feedback into **system improvements** and **employee engagement**. - **Recognition and reinforcement**: Acknowledge **insights and contributions** to build **trust and momentum**. ### Innovation Labs - Establish **dedicated innovation labs** to experiment with AI technologies and **prototype solutions**. - These labs should be **safe spaces** for **cross-functional collaboration** and **digital experimentation**. - Encourage **ongoing creativity** through **hackathons**, **innovation challenges**, and **gamification**. - Use labs to **train change agents** and **build internal AI capabilities**. ## Conclusion The **agentic AI era** presents both **opportunities and challenges** for organizations. While it offers the potential for **exponential growth** and **operational excellence**, it also demands a **human-centric approach** to ensure **trust**, **engagement**, and **sustainable transformation**. The **Deloitte India AgenticAdopt Kompass™ framework** provides a **comprehensive roadmap** for CXOs to navigate this shift effectively, ensuring that **AI adoption** is not only **technologically sound** but also **emotionally and culturally sustainable**. This paper highlights the **importance of leadership**, **microculture engagement**, **communication**, **learning**, and **feedback mechanisms** in successfully transitioning to a **human-AI collaborative model**. It serves as a **strategic guide** for CXOs to **lead with empathy**, **drive innovation**, and **ensure long-term success** in the agentic AI era.