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There’s a gap between optimism and execution when it comes to technology investments, AI achievements, and measurable innovation in company operations. In our 2026 Digital Trends in Operations Survey of 767 operations and supply chain leaders at US companies, 85% say they’re ahead of most competitors in digital transformation, yet 89% say their tech investments haven’t fully delivered the expected results.
Other key issues are posing challenges to reinventing operations.
- More than four-fifths (83%) of respondents say AI agents and automation will accelerate the breakdown of traditional functional silos. But only 27% have fully embedded an AI strategy across business units, and just 37% are comfortable assigning AI agents to execute full end-to-end processes in operations.
- 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.
- Nearly all intend to reorganize their operations, but while 94% of those with siloed or partially integrated operating structures expect to shift toward a more horizontal, networked model, only 41% of companies operate that way today.
While these challenges are real and widespread, a few companies seem to be cracking the code. A deeper dive into survey data reveals a handful of leaders who say their companies have fully embedded AI, modernized data foundations, and redesigned operating models in tandem—and are outperforming peers, sometimes dramatically.
This reinforces a mandate for organizations that are falling short. Competitive advantage won’t come from isolated pilots or incremental upgrades but by moving from fragmented progress to bold, integrated reinvention.
Develop integrated digital muscle to deliver more value
At many companies, attention to AI remains short-sighted. While digitizing operations should be done hand in hand with business strategy, many survey respondents report an approach of addressing issues in isolation instead of working across the enterprise.
Not surprisingly, 72% rank automating operations as a top 3 AI investment focus. Other critical actions aren’t nearly as much a priority. Only 30% rank scalability of solutions and end-to-end capabilities among the top 3 outcomes when evaluating ROI from digital investments, and just 24% rank reducing enterprise complexity as a top 3 factor influencing their build versus buy decisions when investing in digital operations or supply chain technologies.
These numbers contribute to a concerning finding that continues to come up in our annual surveys. Similar to last year, 89% of respondents give at least one reason why tech investments haven’t fully delivered the expected results, and a substantial majority cite two or three reasons. Integration complexity tops the list, followed by data issues and user adoption challenges. This confirms what we see among many clients: Connecting systems, platforms, and data remains a big obstacle to realizing digital value in operations.
Done right, however, AI could—and should—be an equalizer. An overwhelming majority agree or strongly agree that emerging AI and cloud technologies allow organizations at any level of digital maturity to leapfrog industry leaders (91%), and that affordable cloud- and AI-enabled data tools help smaller firms reach parity with digital leaders (93%). This should set off alarms at larger, established companies. Traditional advantages such as scale or infrastructure may no longer safeguard them from newer, nimbler digitally enabled competitors, as just throwing money at technology without committing to innovation won’t cut it.
Make your move: Shift the narrative from technology and capabilities in isolation to integration, and prioritize scalable capabilities over point solutions. Instead of a “nice to have,” make integration simplicity a board-level KPI. If you can’t connect AI across workflows, you're just accumulating complexity, not innovating or transforming.
Building operational value without perfect data
The foundation for leveraging data in operations and supply chains is improving but remains a work in progress for many. Only about half (51%) of all respondents say their companies establish a clean, structured data foundation before scaling digital initiatives, while 60% tell us that poor data quality has had some impact on achieving value for those initiatives.
These issues come as most say the quality and reliability of their data has improved over the past two to three years. But 58% say improvement is only slight, and only 30% say it’s significant. The latter number was higher for certain respondents, however, including more than half of those reporting no significant adoption barriers to adopting or scaling autonomous agents, as well as those who say AI is fully embedded across business units.
The survey also raises questions about the need for pristine data. Most respondents agree that actionable data is more important than comprehensive data (89%) and that they’ve become comfortable making decisions even when data isn’t perfect (84%). In addition, 73% agree that data doesn’t need to be perfect to drive value.
These numbers highlight a common paradox. Many leaders say data doesn’t have to be perfect to launch digital initiatives, yet many also acknowledge that poor data can undermine outcomes. Some are threading that needle and finding success by improving data quality in a targeted manner, iteratively alongside transformation.
Make your move: Bad data is often used as an excuse, but AI can help bridge the gap, especially through agents that can reason like humans and make faster decisions using available data. Determine an approach that enables transformation while encouraging experimentation, pairing AI with disciplined governance, and iterative cleansing. Competitive advantage comes from working with data you have today and improving it continuously, not waiting for perfection.
Different chains, different digital playbooks
Our survey also illuminates something we see often. Digital initiatives—and their impact—vary by industry, sometimes greatly. That makes sense considering the fundamental differences in operations. Product- and asset-intensive businesses typically compete through supply chains that move materials and goods. Service-intensive businesses compete through service chains that orchestrate people, processes, and digital delivery across the front, middle, and back office.
Each demands a different playbook that addresses nuances in risk patterns, transformation blockers, and levers for performance. Service chain respondents, for instance, are more likely to report that no significant factors are slowing adoption or scaling of autonomous agents, while a higher percentage of supply chain respondents say they lack the necessary skills or talent. In addition, service chain respondents are more likely to say their companies have integrated digital capabilities end to end.
These differences illustrate how pairing sector‑specific knowledge with digital transformation efforts is critical. In manufacturing, operational automation may refer to physical processes, while in service industries it may refer to digital workflows such as claims processing or risk assessment.
Rethinking operating models in the AI era
“Silo” has become a four-letter word for operations at many companies, and dismantling them is a driver for many digital initiatives. More than four-fifths (83%) of survey respondents say AI and automation will accelerate the breakdown of traditional functional silos. But our survey also found multiple barriers to achieving a horizontal, networked organizational model.
These hurdles may explain why only 41% of all respondents say their companies operate with collaborative, horizontal structures today. That can make a big difference in the success of digital investments. Among those companies with horizontal models, performance gains in speed, accuracy, visibility, and collaboration are significantly higher.
Make your move: AI can drive new operating structures whether your organization intentionally designs them or not. Reassess and reinvent your operating model in parallel with AI deployment, and incentivize cross-functional outcomes, not silo performance. Upskilling for tech-enabled, higher-value roles is no longer optional, as collaboration between humans and machines won’t emerge organically.
Leading the pack in redefining operational performance
Among the 767 operations and supply chain leaders we surveyed, a rare cohort may be rewriting the performance curve. Just 4% report success in four key areas: AI fully embedded enterprise-wide, no significant barriers to scaling autonomous agents, a collaborative and horizontal operating structure, and technology investments that are fully delivering expected results.
What’s their secret? Among the many ways these companies leading others:
They connect tech across segments: 87% of leaders say their companies have integrated digital capabilities end to end, enabling technologies to operate across workflows of internal teams, suppliers, and customers rather than inside functional silos.
They’re seeing enterprise impact from tech initiatives: 73% have achieved broad organizational impact from digital investments.
They dig in on innovation: 74% deploy AI-native or agentic platforms in R&D.
They don’t shortchange metrics: 83% measure both operations and financial impact of recent digital investments.
They’ve boosted data hygiene: 63% say the overall quality and reliability of their data has significantly improved in the past two to three years.
Unlike many organizations that are still early in their AI maturity journey, these companies aren’t experimenting at the edges. They’re integrating at the core. By aligning AI, data, and operating model transformation into an enterprise-wide mandate, they convert digital ambition into measurable, scalable advantage while others endure fragmented progress.
Survey insights by industry
The time to act was yesterday
… but the next best time is now. While only a small group of companies are currently achieving significant enterprise impact, nearly all are in some stage of their digital operations journey. Here’s how you can start moving from experimentation to scaled transformation.
Rethink how you measure value to reflect enterprise impact. Using only cost or only operational metrics understates AI’s true value. Connect both financial and operational metrics to strategic priorities such as growth, resilience, and customer experience—moving toward integrated performance management and more disciplined capital allocation.
Design an operating model that enables experimentation with control. Establish clear guardrails for model usage, data access, and risk management while enabling cross-functional teams to test and scale AI use cases quickly. Define decision rights, accountability, and oversight upfront so experimentation can drive value without creating fragmentation.
Unite segments by combining industry expertise and customer insight. Unlocking AI value requires connecting knowledge across the enterprise. Bring together domain experts from supply chain, operations, customer functions, and data teams to help solve specific problems end to end processes. This helps ground AI solutions in real operational context and customer outcomes, not just technical capability.
Orchestrate systems, data, and AI as one. Stop treating integration as a backend IT problem and elevate it to a core business priority. Join leading companies by redesigning your architecture around end-to-end workflows and connecting AI, data, and core systems through integration layers and platforms.
Shift from AI pilots to enterprise activation. Pilot programs and point solutions rarely scale impact. Identify a few enterprise-critical processes where AI can be embedded across decision points and commit to scaling with clear ownership and funding—turning AI from experimentation into a performance driver.
Practice progress-over-perfection with data discipline. You can move forward without perfect data, but you should have a structured approach to improving it. Launch priority AI use cases using available data while deploying AI-enabled data governance, cleansing, and enrichment in parallel. Treat data improvement as an ongoing capability embedded in transformation, not a prerequisite that delays it.
Pressure-test your ecosystem for disruption. Constantly assess the impacts—and opportunities—from tech, economic, geopolitical and other disruption, including your potential to build capabilities internally that reduce reliance on external platforms. Evolve your scenario planning to identify where to build, collaborate, or invest to take advantage of emerging opportunities and stay ahead of emerging threats.
The gap between ambition and execution in operations remains wide and can be difficult for many companies to bridge. Those businesses that integrate AI, data, and operating model transformation—simultaneously and decisively—won't just improve operational performance. They can redefine it.
Explore the 2026 survey data
Access our interactive dashboard, filter by industry and more.
About the survey
PwC’s 2026 Digital Trends in Operations Survey surveyed 767 operations executives and supply chain officers in January and February 2026. Respondents in the online survey included C-suite executives, upper management, directors, and managers of organizations based in the US with annual revenues of $100 million or more. Respondents either have sole responsibility for business decisions on operations and supply chain or procurement operations or share influence with others regarding those decisions. Sectors surveyed include 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%).
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