Operating models, outdated systems block companies from AI success

2026-06-22 · Source: Information and Enterprise Technology News | CIO Dive - Www.ciodive.com · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Intermediate, quick

Summary

A Publicis Sapient report, based on a survey of 1,550 enterprise technology decision-makers and published on June 22, 2026, reveals that businesses are failing to achieve AI success despite significant spending. The study found that over 70% of U.S. respondents anticipate substantial AI scaling within two years, yet only 20% feel their organizations are prepared. Nearly one-quarter of respondents identified organizational structure as the primary obstacle, noting that while AI is used across most teams, companies have not modernized legacy systems, workflows, or operating models to fully benefit. Shubhradeep Guha of Publicis Sapient emphasized that barriers are often outdated systems, fragmented data, siloed teams, and slow governance, rather than the AI models themselves. The report concludes that AI investment must be coupled with systems modernization, workforce reorganization, and operational adoption to achieve enterprise-scale impact.

Key takeaway

For CTOs and Directors of AI/ML planning significant AI investments, recognize that simply deploying AI platforms is insufficient. Your strategy must prioritize concurrent modernization of legacy systems, data foundations, and operating models. Reorganize teams and invest in workforce training to ensure operational readiness. Without these foundational changes, your organization risks limited measurable impact and failure to scale AI effectively, turning substantial spending into minimal business value.

Key insights

AI success requires holistic transformation of systems, operations, and workforce, not just technology adoption.

Principles

• AI value is blocked by legacy systems and fragmented data.
• Organizational structure and slow governance hinder AI impact.
• Workforce reorganization and training are crucial for AI adoption.

Method

Modernize data foundations, restructure roles, deploy human-to-agent teams, and implement new incentive structures to align operations with AI capabilities.

In practice

• Overhaul systems and workflows for AI integration.
• Break down data silos for better AI preparation.
• Invest in workforce training for AI implementation.

Topics

• Enterprise AI Strategy
• Digital Transformation
• Legacy System Modernization
• AI Adoption Barriers
• Workforce Reorganization
• Data Governance

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Information and Enterprise Technology News | CIO Dive - Www.ciodive.com.