> **来源：[研报客](https://pc.yanbaoke.cn)** # Summary of Lenovo CIO Playbook 2026: The Race for Enterprise AI ## Core Content The Lenovo CIO Playbook 2026 focuses on the rapid advancement of artificial intelligence (AI) and its growing role as a core driver of business transformation. It outlines the strategic imperatives for CIOs to lead their organizations in the adoption and scaling of AI technologies, emphasizing the need for robust infrastructure, governance, and cross-functional collaboration. The document highlights the increasing urgency for enterprises to integrate AI across all functions, not just IT, and outlines key insights and considerations for 2026. ## Main Points ### AI as the Engine for Business Reinvention - In 2026, "Enhance/innovate/reinvent our business with AI" is the top business priority, surpassing productivity and profit growth. - AI is transitioning from an efficiency tool to a strategic engine for transforming business models, operations, and customer value. - CIOs are expected to lead this transformation by aligning AI initiatives with business goals and fostering a culture of experimentation and innovation. ### AI Adoption and Implementation - AI is now a business-wide imperative, with over half of organizations moving beyond early stages of implementation. - Non-IT functions such as finance, marketing, and sales are increasingly adopting AI, with 47% of AI projects funded by departmental budgets outside IT. - 93% of organizations expect a positive ROI from AI, with an average return of $2.79 per dollar invested. ### AI Investment Trends - 96% of organizations plan to increase AI investment over the next 12 months, with an average growth of 13%. - The primary AI investment priority is deploying and supporting AI infrastructure, followed by AI devices, agentic AI, and generative AI. - Agentic AI and generative AI are gaining attention, but spending is expected to be similar across AI types. ### AI Returns and Benefits - AI delivers both financial returns and non-financial benefits such as improved customer experience, increased employee engagement, and faster decision-making. - Top areas where AI has shown positive returns include IT operations, data and analytics, cybersecurity, customer service, and software development. ## Key AI Types and Their Roles ### Predictive AI - Analyzes large datasets to uncover long-term behavioral patterns for forecasting. - Example: Retail inventory forecasting engines. ### Interpretive AI - Analyzes unstructured data (language, images, event streams) to uncover patterns and derive insights. - Example: Manufacturing quality control systems. ### Generative AI (GenAI) - Creates new content or code using existing content or code as a foundation, leveraging Large Language Models (LLMs). - Example: Software development assistants that generate code, documentation, and tests. ### Agentic AI - Uses machine learning and deep learning to exhibit agency—setting goals, making decisions, and taking actions through the perception-reasoning-action loop. - Example: Cybersecurity defense agents. - Only 21% of organizations have deployed agentic AI at scale, with most still in pilot or testing phases. ## Strategic Imperatives for CIOs 1. **Champion AI as a core driver of business innovation** - Work closely with business leaders to identify and prioritize high-impact use cases. - Focus on areas with proven value to maximize impact. - Foster a culture of experimentation and regularly communicate business outcomes. 2. **Enable AI adoption across all business units** - Provide shared platforms, tools, and best practices. - Facilitate cross-functional collaboration and invest in training to build AI literacy and confidence. 3. **Invest in modern, scalable AI infrastructure** - Ensure infrastructure supports both current and future AI workloads. - Evaluate platforms for performance, security, and flexibility. - Partner with vendors offering integrated infrastructure and solution stacks. 4. **Build trust through governance and data security** - Develop and enforce comprehensive AI governance policies. - Address security, privacy, and data sovereignty concerns. - Implement responsible AI practices and ensure transparency in decision-making. 5. **Prepare for agentic AI implementation** - Develop a structured approach to agentic AI use cases. - Invest in platforms that enable effective monitoring, control, and optimization. - Focus on measurable business outcomes and prepare for cultural and process changes. ## AI Infrastructure and Deployment Models - Hybrid AI deployment models (on-premises, private, and public cloud) are preferred by 84% of organizations, driven by security, control, and mission-critical data needs. - 50% of enterprise PC purchases by 2027 are expected to include on-device AI agents. - 80% of enterprises will deploy distributed edge infrastructure to improve latency and responsiveness of AI applications. ## AI Trust Concerns - Top concerns include lack of responsible AI, poor data security, and shadow AI risks. - Only 27% of organizations have comprehensive AI governance frameworks, with 56% still in development. - CIOs must ensure that AI systems are secure, transparent, and compliant with ethical and regulatory standards. ## Industry Insights: BFSI Sector - The BFSI sector is leading in AI adoption, with 69% of organizations having implemented AI, compared to the overall average of 60%. - AI is transforming fraud detection, risk management, and customer personalization. - 96% of BFSI firms expect positive ROI from AI investments, with a projected 12% increase in AI budgets next year. - Hybrid deployment models are still a top priority for BFSI organizations, driven by data privacy, regulatory compliance, and security needs. ## Conclusion The Race for Enterprise AI is reshaping the CIO agenda, requiring a strategic and holistic approach to AI adoption, governance, and infrastructure. CIOs must act swiftly to build trust, invest in scalable and secure platforms, and foster collaboration across the organization to ensure AI delivers both financial and non-financial value. The future of enterprise AI depends on the ability to balance innovation with responsibility, control with flexibility, and speed with security.