2026_AI原生劳动力_工程与产品价值链中的工作与技能未来研究报告_32页_12mb

Summary of "AI-native Workforce: Future of work and skills in engineering and product value chain"

Core Content

The document explores the evolving landscape of the technology workforce, emphasizing the transformative impact of Generative AI (GenAI) and hybrid work models. It outlines how the traditional roles of software engineers and product managers are shifting from execution-based tasks to more strategic, oversight-focused responsibilities. The key theme is the transition from "role-based hiring" to "capability-based hiring" and the need for organizations to adapt to AI-driven workflows.

Main Trends and Views

• AI-native Workforce: The workforce is evolving into a collaboration of people, agents, and robots, with AI playing a central role in task execution and system orchestration.
• Productivity Gains: GenAI tools can increase productivity by 30-40% in engineering teams and up to 50% in new product development. These gains come from tasks such as code generation, bug fixes, and UI scaffolding.
• Skills Demand: There is a growing demand for higher education qualifications, with 40-45% of roles in the Americas and Europe requiring a Master's or PhD. Additionally, AI fluency, ethical judgment, and adaptability are becoming more critical than traditional coding skills.
• Role Evolution:
  • Software Engineers: Transitioning from "coders" to "orchestrators" who manage AI agents and focus on system design, risk assessment, and code review.
  • Product Managers: Evolving from coordinators to strategic leaders, using AI to accelerate decision-making and product strategy. They are expected to blend into the "product developer" role, combining product vision with immediate code generation.
• Emerging Roles: New job titles such as AI enablement engineers, agent orchestration engineers, and context (RAG) engineers are becoming more prominent, driven by the need for AI integration and governance.
• Reskilling Over Replacement: Organizations are prioritizing reskilling existing engineers rather than replacing them, especially as AI automates routine tasks.

Key Implications

• Trust Gap: A significant trust deficit exists between developers and AI outputs. 66% of developers cite distrust as a challenge.
• Observability Tools: To bridge the trust gap, organizations must implement observability tools rather than enforce rigid mandates.
• Workforce Redesign: The shift from task automation to workflow redesign is crucial. This includes reimagining teams and roles to focus on high-value tasks such as oversight and strategic planning.
• Leadership Adaptation: Leaders must move from directive supervision to facilitative orchestration, focusing on outcome confidence rather than activity assurance.

R.E.A.D.Y. Framework

To fully leverage the productivity dividend from AI, organizations should adopt the R.E.A.D.Y. framework:

• R: Reimagined organization and AI-human workforce. Focus on workflow redesign and agentic collaboration.
• E: Expansive upskilling and capability building. Address the mentorship gap and prioritize AI fluency.
• A: AI-aligned leadership perspective. Orchestrate trust and expand spans of control.
• D: Dividend of change and adoption readiness. Measure outcomes, not just outputs, and reinvest efficiency gains.
• Y: Yield through transparency and observability. Build trust-based management and support flexible work models.

Organizational Readiness

Organizations must be prepared to:

• Redesign workflows to integrate AI-human collaboration.
• Implement automated governance and observability tools.
• Focus on outcome-based metrics rather than traditional proxies.
• Invest in upskilling and reskilling programs to prepare teams for new AI-driven roles.

Conclusion

The future of work in the engineering and product value chains is being redefined by AI. This shift requires a fundamental change in how organizations approach hiring, training, and leadership. By embracing AI-native workflows and reimagining talent strategies, companies can unlock significant productivity and innovation gains.