A PwC 2026 survey of 767 U.S. operations and supply chain leaders reveals a widening gap between ambition and execution in digital transformation. While 85% claim to be ahead of competitors in digital initiatives, 89% say technology investments haven't delivered expected outcomes. Only 27% have fully embedded AI strategies across business units, and just 37% are comfortable assigning AI agents to manage full end-to-end processes. Poor data quality—cited by 87% of respondents—remains a key obstacle. Despite 83% belief that AI will break down functional silos, only 41% of companies currently operate with a horizontal, networked model. The findings highlight persistent challenges in scaling AI and automation across supply chain operations.
AI Ambition vs. Execution: A Growing Chasm
According to www.dcvelocity.com, 85% of U.S. operations and supply chain leaders claim to be ahead of most competitors in digital transformation, yet 89% report that technology investments have not delivered expected results. The findings come from PwC’s 2026 Digital Trends in Operations survey, which analyzed responses from 767 executives across industries including energy, industrial products, and health services.
Embedded AI Strategy Remains Limited
Despite widespread belief in AI’s transformative potential, only 27% of respondents said their organizations have fully embedded an AI strategy across business units. Just 37% feel comfortable assigning AI agents to execute full end-to-end operational processes. This gap between aspiration and implementation is particularly pronounced in organizations with siloed structures: while 94% of such companies plan to adopt a more horizontal, networked operating model, only 41% currently operate this way.
“While data foundations are stronger, only 30% report significant improvement in data quality and reliability, and 87% say poor data quality has hampered their progress in achieving value for digital initiatives.” — PwC 2026 Digital Trends in Operations Report
Data Quality: A Persistent Bottleneck
Even as companies invest in digital infrastructure, data quality remains a critical hurdle. The survey found that only 30% of respondents reported a significant improvement in data quality and reliability. Meanwhile, 87% cited poor data quality as a barrier to realizing value from digital transformation initiatives. This suggests that investments in AI and automation are being undermined by foundational weaknesses in data governance.
Industry Representation and Executive Involvement
The survey included C-suite executives, upper management, directors, and managers from U.S.-based organizations with annual revenues of $100 million or more. Industry representation was as follows: consumer markets (13%), energy, utilities, and resources (16%), financial services (5%), health industries (11%), industrial products (18%), insurance (5%), pharmaceuticals and life sciences (11%), and technology and telecommunications (16%).
- 83% of respondents believe AI agents and automation will accelerate the breakdown of traditional functional silos.
- Only 37% say they are comfortable using AI agents for full end-to-end operational execution.
- 41% of companies currently operate with a horizontal, networked structure, despite 94% of siloed firms planning to transition.
- 87% of respondents identify poor data quality as a key obstacle to digital initiative success.
- 85% believe their companies are ahead of most competitors in digital transformation.
Challenges in Scaling AI Across Functions
The disconnect between strategic intent and operational execution is evident in how AI is deployed. While 83% anticipate that AI will break down silos, the lack of cross-functional integration limits actual impact. Only 27% of companies have fully embedded AI strategies, and only 37% are confident in deploying AI agents to manage complete processes. This suggests that AI adoption is still largely confined to pilot projects or isolated use cases rather than systemic transformation.
Source: DC Velocity
Compiled from international media by the SCI.AI editorial team.
