Enterprises are scaling AI while their systems and workforce lag behind - Business Standard

2026-07-08 · Source: artifical intelligence via Google News · Field: Business & Management — Corporate Strategy & Leadership, Human Resources & Workforce Development, Operations & Process Management · Depth: Intermediate, medium

Summary

Kyndryl's 2025 Readiness Report, surveying 3,700 business and technology leaders across 21 countries, and a separate 2026 People Readiness Report, covering 1,100 leaders in eight markets including India, reveal that enterprises face significant hurdles in scaling AI. The primary bottlenecks are ageing IT infrastructure and an unprepared workforce, not the AI technology itself. A quarter of mission-critical IT systems are nearing end-of-service, with 57% of leaders reporting innovation delays due to foundational technology issues. Concurrently, while 57% of leaders indicate broad AI deployment, only 23% believe their workforce is ready, a six-point drop from the prior year. The reports highlight that 79% of leaders expect AI adoption to outpace organizational adaptation, with technical debt and IT complexity further hindering progress. A small group of "pacesetter" organizations, 13% in 2025 and 9% in 2026, achieve greater AI success by prioritizing underlying infrastructure and workforce readiness.

Key takeaway

For Directors of AI/ML overseeing enterprise AI scaling, your focus must shift from AI capabilities to foundational readiness. Prioritize upgrading ageing IT infrastructure and actively developing your workforce's AI skills. Without robust systems and a prepared team, your AI investments risk stalling at the proof-of-concept stage, as seen in nearly three in four Indian organizations. Implement clear AI governance and workforce reskilling pathways to ensure successful, trusted AI integration across your organization.

Key insights

Enterprise AI scaling is hindered by outdated IT infrastructure and workforce unpreparedness, not AI capabilities.

Principles

• Foundational IT readiness precedes AI success.
• Workforce adaptation is crucial for AI integration.
• AI governance must match increasing autonomy.

In practice

• Prioritize upgrading end-of-service IT.
• Develop comprehensive employee skill inventories.
• Define clear AI decision-making policies.

Topics

• AI Adoption
• Enterprise AI
• IT Infrastructure Modernization
• Workforce Readiness
• AI Governance
• Technical Debt

Best for: CTO, Executive, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by artifical intelligence via Google News.