AI 主權為人力策略

• Legacy data and infrastructure architectures cannot support real-time, autonomous AI deployments; modernization requires building a 'living' AI backbone—an organization-wide, real-time system that adapts dynamically to business and regulatory change[2] • Physical AI deployments extending beyond software into devices, machinery, and edge locations demand evaluation of technology foundations' readiness[2] • Data quality and governance are foundational: 43% of leaders cite data readiness as the most significant barrier to AI alignment with business objectives, with data quality as the most common data integrity priority[3] • Organizations with data strategy and governance already in place expect positive ROI from AI in 6-11 months, compared to those without such foundations[3] • Agentic AI adoption has reached 85% among surveyed organizations, but agentic-ready data infrastructure lags significantly behind adoption rates[3]

The enterprise AI landscape in 2026 is defined by a critical inflection point: the shift from ambition to execution. Organizations face a decisive moment where success depends on closing the 'readiness paradox' between rapid technology deployment and workforce capability. Those that invest strategically in change management, governance, and talent development will establish sustainable competitive advantages, while those that prioritize technology speed over organizational readiness risk operational failures and missed ROI. The convergence of regulatory pressure, board scrutiny for AI ROI proof, and skill shortages will force enterprises to fundamentally rethink their approach to AI as a people-centric transformation rather than a technology implementation. By 2026, readiness—measured by infrastructure, governance, and people alignment—will become the primary differentiator between enterprises that merely use AI and those that truly capture its transformative potential.