How companies can close the AI execution gap in operations: PwC

Key takeaways:

The gap is real. Companies are investing heavily in AI, but isolated pilots and fragmented initiatives often prevent many from translating that investment into enterprise-wide impact.
Scaling AI requires thinking beyond individual use cases. The bigger opportunity comes from solving business problems that span functions and aligning investments with enterprise priorities.
Data quality should be a competitive advantage. Organizations scaling AI successfully have improved the quality and reliability of the data underpinning their solutions.
Greater value comes from connecting the enterprise. Integrating AI across workflows can improve end-to-end performance, but technology complexity and segmented operating models remain barriers.
AI success should be measured by business outcomes. Leading organizations focus on operational and financial results, not pilot counts or adoption alone.

As AI becomes an essential element in operations, COOs, supply chain officers, and other leaders have high expectations that the technology will improve productivity, strengthen resilience, and accelerate decision-making. But for too many businesses, these expectations remain aspirational.

In fact, PwC’s 2026 Digital Trends in Operations Survey revealed a striking disconnect between digital investments and business results. While 85% of operations and supply chain leaders say their organizations are ahead of most competitors in digital transformation, 89% say their tech investments have fallen short of expectations. Meanwhile, just 27% of respondents say AI is fully embedded across business units. The AI execution gap is real.

Too many companies are struggling to scale isolated AI successes in operations into enterprise-wide impact. With different initiatives and investments in this function and that function, they’re increasing complexity without generating results at scale. And as others—both early adopters and industry leaders—compound their advantages, the cost of fragmented experimentation continues to rise.

For your organization to gain an edge, you should close the gap between AI ambition and measurable impact. Operations can play a critical role in building an intelligent enterprise, turning AI-enabled insights into coordinated action that improves productivity, responsiveness, and business performance. Success starts with understanding how digital operations leaders are adapting—and what they’re doing differently to turn investment into results.

Pilots aren’t driving transformation, but it’s not for lack of trying

Across industries, businesses have been launching pilots, testing generative AI use cases, and embedding intelligent capabilities into individual functions. But even when these investments net solid results, they rarely lead to meaningful transformation. Just 34% of survey respondents say their organizations are currently scaling AI implementation across the enterprise.

Automation tops AI investments priorities, while business model lags

Reimagining the business model

%

Assisting people

%

Creating digital workers

%

Enhancing decisions

%

Automating operations

%

Q: Please rank the top focus areas for your organization’s current AI investments. (Ranked top choice.)
Base: Executives 767
Source: PwC’s 2026 Digital Trends in Operations Survey

Part of the reason is that many business leaders aren’t thinking bigger—where AI can solve real business problems. Current AI investment priorities favor automation over reimagining the business model. AI initiatives often emerge organically as individual business units identify their own unique priorities, oblivious to what other functions are facing.

That approach can help accelerate innovation for those teams, but it also has a way of fragmenting ownership and isolating use cases. And those use cases can pile up. Your company could be sitting on several promising pilot programs and still have no real way of capturing broader operational benefits. Organizations that have made meaningful progress have taken a more enterprise-wide approach.

Turn insight into action: Treat AI as a transformation initiative, align your efforts with enterprise priorities, and establish overarching guidelines for accountability. Identify operational challenges that span functions, then prioritize AI investments that can address them at scale. Instead of tackling forecast accuracy only within supply chain, for instance, recognize that demand volatility also affects production schedules, inventory, and procurement. Frame the opportunity around improving overall demand sensing and response.

Data quality has become a competitive AI advantage

Even when a piloted use case could work across the business, fragmented data often holds companies back from effectively leveraging it. Nearly nine in ten executives report that poor data quality affects their ability to achieve digital initiatives. Large language models and predictive systems are only as effective as the information they draw from, and problems that appear manageable within one business unit—things like inconsistent data definitions or incomplete records—can become magnified when expanded across an enterprise.

Poor data quality is holding digital initiatives back

Significant impact

%

Some impact

%

No impact

%

It’s too early to tell

%

Q: How has poor data quality affected your organization’s ability to achieve value for digital initiatives? (Select one.)
Note: Response to ‘Unsure’ not shown. Percentages may not add up to 100% due to rounding.
Base: Executives 767
Source: PwC’s 2026 Digital Trends in Operations Survey

This is another case where a thoughtful AI strategy can provide an advantage, and it’s no wonder that we see a correlation between data improvement and enterprise-wide AI success. While just 30% of all organizations report significant improvements in data quality and reliability, the number jumps to 51% among those who’ve successfully scaled enterprise-wide AI.

Turn insight into action: Pursue data quality as an ongoing effort rather than a one-off project or problem to solve later. Standardize critical data definitions across functions, address gaps in quality and ownership, and prioritize the data that enables your highest-value use cases. Build ongoing data governance into AI programs so improvements continue as solutions scale.

AI requires a new operating model

To be clear, AI certainly can create value inside isolated functions, and the survey identified substantial use within individual areas of operations. Demand planners might use AI to improve forecasting. Procurement can use it to identify sourcing risks. Manufacturing may use it to optimize production schedules. But greater and more lasting value comes via end-to-end integration, when AI connects those activities, helping improve how work flows from one function to the next.

Planning and sourcing lead AI use across operations

Planning

%

Sourcing

%

Distribution

%

Service & maintenance

%

Research & development

%

Manufacturing

%

Q: In which areas of your operations are AI-native or agentic platforms currently being used? (Select all that apply.)
Note: Response to ‘None of the above’ not shown.
Base: AI-native or agentic platform users in supply chain or health services 671
Source: PwC’s 2026 Digital Trends in Operations Survey

That’s easier said than done. Our survey found that integration complexity is the No. 1 reason technology investments fall short of fully delivering expected results, cited by 52% of respondents. Many organizations recognize the need for a more integrated operating model, but they often struggle to redesign on the fly. Instead, they often just layer new AI capabilities atop existing technology and platforms.

Turn insight into action: Broaden the scope and focus AI investments on improving overall workflows, not just individual tasks. For example, explore how to use AI to detect a potential supplier disruption, assess its impact on production and customer orders, and recommend sourcing or scheduling alternatives. Then address the process, data, and technology integration required to turn those insights into coordinated action across functions.

Aligning AI to business outcomes

When it comes to AI, many organizations wrongly judge success by activity. How many pilots have we launched? How many models have we deployed? How many employees use Copilot, Claude, or other generative AI? Those metrics may signal initial progress, but their actual contributions to improving the business are minimal. Nearly half of survey respondents rank operational efficiency improvements among their top-3 ROI priorities, more than any other outcome.

The digital investment outcomes that matter most

Top 3 priority

End-to-end capabilities

%

Scalability of solutions

%

Total cost of ownership

%

Customer experience or service levels

%

Operational efficiency improvements

%

Q: When evaluating ROI from your organization’s digital investments, which outcomes matter most? (Rank up to 3.)
Note: Showing 5 choices from a list of 10 options.
Base: Executives 767
Source: PwC’s 2026 Digital Trends in Operations Survey

Leading organizations have developed metrics that align with the measurable results they want to achieve—greater productivity, faster cycle times, better service, lower costs, improved resiliency. They work backward to establish KPIs that prove real operational and financial outcomes.

This is rarer than you might think. Just 41% of survey respondents say their companies have measured both the operational and financial impact of their recent digital investments. AI projects should be held to account, just like any other strategic investment.

Turn insight into action: Establish governance structures that span the enterprise, aligning funding models to support true transformation efforts. Make sure that accountability extends beyond the launch stage, holding leaders responsible for integrating AI across functions and improving performance over time. With an AI-enabled procurement tool, for instance, track if it reduces sourcing cycle times, lowers costs, or improves supplier performance—assessing whether results justify continued investment.

The roadmap to AI scale

Closing the AI execution gap requires the discipline to build AI as an enterprise capability rather than a collection of disconnected projects. Four priorities can help you achieve that goal.

Start with enterprise goals, not isolated use cases. Start with the outcomes that matter most and then determine where AI can create measurable business value. Prioritize projects that can lead to company-wide transformation rather than isolated wins.
Strengthen data and technology foundations. Don’t wait for perfect data to start. Take a continuous approach to improving data quality so AI models become more reliable and valuable over time.
Redesign operating models to be more collaborative. One of AI’s greatest strengths is connecting workflows across the business, but getting there requires rethinking the operating model from the ground up to de-silo decision-making.
Create governance and accountability that scales. Establish clear ownership, funding, and accountability for AI initiatives. Governance shouldn’t slow innovation. It should provide the structure needed to turn successful pilots into enterprise-wide capabilities.

The organizations that gain competitive advantage with AI will take a disciplined approach to experimentation and scale. They can close the execution gap, moving beyond pilot projects to building AI into a scalable engine for operational and financial success.

How companies can close the AI execution gap in operations