PUBLISHED DATE: 2026-06-17 04:00:41

THE ENTERPRISE WASN’T BUILT FOR AI

The Enterprise Wasn’t Built for AI

Introduction

AI changed how work gets done, but most enterprises still run like nothing has changed.

Inside most large enterprises, two things are now true at once: AI has changed how work actually gets done, and almost nothing about how the enterprise operates has changed to match.

Teams ship code faster, draft reports in minutes and run customer workflows that used to take weeks. Meanwhile, budgeting cycles, governance reviews and the systems underneath them still move at the pace they did 10 years ago.

This is AI theater, where leaders can point to adoption but not outcomes. AI investments spread across the business, but growth, speed and productivity remain frustratingly difficult to find at enterprise scale. And AI theater is expensive: Companies risk pouring millions into AI while existing operational bottlenecks remain untouched. AI may be changing how work gets done, but enterprises aren’t making the strategic shifts they need to get the most value out of it. To do that, they need to dismantle the structural bottlenecks that prevent AI from having a real, business-wide impact.

When enterprises don’t fix these structural issues, leaders are left to explain why the technology is everywhere, but the business still feels largely the same, all while watching more agile competitors speed ahead.

To understand this new dynamic, we conducted a global survey of 1,550 enterprise AI decision-makers. Our respondents are business leaders responsible for AI strategy at companies with at least 500 employees and $100 million in annual revenue.

What emerged was a story about a technology moving faster than the enterprise can keep up. The organizations making progress aren’t necessarily the ones adopting more AI. They’re the ones learning how to operate their workflows and infrastructure differently because of it.

Table of contents

“In order to actually scale AI, you have to think about how AI is going to fundamentally redesign how people and platforms in an organization interact to create value, not just very simply apply AI to either an existing workflow or an existing area of the business.”
– Nigel Vaz, CEO, Publicis Sapient
  1. 02 Introduction
  2. 04 AI is showing up everywhere except the operating model
  3. 07 The enterprise has become the bottleneck
  4. 13 The next enterprise divide will be about adaptation
  5. 16 Conclusion
  6. 18 About the research

SECTION 01

AI is showing up everywhere except the operating model

AI adoption inside large enterprises no longer looks experimental. Across industries, teams already use AI inside everyday work. They generate code, automate reporting, personalize customer experiences and speed up delivery cycles that used to take months.

In many organizations, AI changed the pace of execution before leadership fully realized what was happening. Employees now expect work to move faster because, in many parts of the business, it already does.

Teams have incorporated AI into their daily work

More than 73 percent of respondents say AI is used regularly or in most processes. Yet, widespread usage doesn’t necessarily translate into enterprise-wide impact: Only 10 percent report AI is core to how their business operates [Figure 1]. Despite AI’s growing presence inside daily workflows, the vast majority of leaders do not believe AI is fundamentally changing how the business operates. The size of that gap varies significantly by region [Figure 2].
Figure 1: AI is part of everyday work
The majority of employees are using AI regularly if it isn’t already core to how they operate.

Leaders believe the technology works, but they don’t believe their organizations do

One of the clearest shifts in the survey appears when leaders assess what limits their ability to realize AI value today. They don’t have an issue with the technology itself, since nearly half (47 percent) say AI is already fully capable of fulfilling current business needs. Instead, the bottleneck is structural. Forty-two percent say their organizations are not structured to capture the value AI can already create [Figure 3].

That distinction reflects organizational complexity rather than technological maturity. In highly regulated, operationally dense environments, legacy systems and siloed structures drag down teams and become harder to work around, turning what should be a seamless deployment into a coordination crisis.
Figure 2: Business-wide transformation is uneven
Across regions, the majority of respondents don’t believe AI is fundamentally changing how the business operates.

AI changed the pace of business. Enterprise systems did not.

The data is clear: Teams are already using AI in their day-to-day work. But changing teams’ tools is very different from changing how enterprises operate, especially when most organizations still rely on systems, models and structures built before AI. And many companies adopted the technology much faster than they could coordinate around it.
Figure 3: AI’s capabilities aren’t the issue
Enterprise leaders believe in their organization’s AI capabilities.
That’s where the real friction begins: figuring out whether the enterprise itself can keep up with the speed of AI.

SECTION 02

The enterprise has become the bottleneck

AI adoption spread through enterprises faster than most organizations could manage. Teams adopted them in their individual functions quickly, but coordinating and scaling those changes consistently across the enterprise turned out to be far more difficult.

Now, organizations are confronting a sobering, uncomfortable reality: The enterprise itself has become the hurdle. Across regions, respondents increasingly describe friction tied to coordination, governance, workflow integration and organizational readiness rather than AI capability itself.

This bottleneck shows up in the day-to-day experiences of trying to scale AI across a large organization:
These hurdles may be invisible during early adoption, but they become much harder to ignore once organizations try to scale.

AI workflows scaled faster than enterprises can integrate them

Even though AI usage is now widespread, most enterprises haven’t been able to operationalize AI consistently across the organization. Fewer than one in five respondents say AI is fully integrated across the enterprise [Figure 4].
Figure 4: What AI usage looks like in the business today
Organizations are starting to embed AI into workflows, but most enterprises still lack the systems to scale it coherently.

Fragmentation became the default operating model

The problems slowing AI scaling are operational: Systems don’t connect and data remains fragmented [Figure 5]. Without integrated systems or shared data, AI will always be limited in scope and unable to deliver enterprise-wide value.
Figure 5: What limits AI scaling most
Respondents point to a number of issues that block their ability to scale AI across the enterprise.
“So when you think about AI more broadly at an enterprise level, you have to think about the systems and the operating models that it is going to interact with. And that interaction is typically where these points of failure occur.”
– Nigel Vaz, CEO, Publicis Sapient

Executive ambition keeps rising while operational readiness lags behind

Globally, respondents have high hopes for AI. They expect significant progress in scaling AI across the business over the next 12–24 months. Yet fewer than one in five say their organizations are fully equipped to support those expectations today. And just 30 percent say executive expectations are fully aligned with operational reality [Figure 6]. That combination suggests many enterprises are accelerating AI expectations faster than organizations can adapt.
Figure 6: Scaling vs. readiness
Enterprises expect massive AI scaling without operational readiness.
Regional comparison

The organization itself has become the constraint

One of the clearest findings in the survey is how often respondents point back toward the enterprise operating model itself as a hurdle. More than one-in-five respondents say “the way our organization runs” is now the primary blocker to AI success [Figure 7].

That finding changes the conversation. Most enterprises already have access to the models, and many already run pilots across the business. But the harder problem is managing how the business actually runs, both in terms of building the right infrastructure and equipping teams with the right workflows. Organizations now have to coordinate around a technology that speeds up processes faster than they were built to handle.
Figure 7: Technology isn’t the issue anymore
Enterprise leaders are twice as likely to blame how their organization operates as they are to blame AI itself.

Perceived constraints of AI success

Enterprises can’t keep operating like they always have

As AI spreads across teams and functions, enterprises started running into the limits of their systems and structures. What began as a technology rollout has quickly snowballed into organizational quagmire.

Enterprises can no longer organize and behave the way they did before AI arrived. Instead, if they want to translate their AI investments into enterprise-wide impact, they’re going to need to make broader operational changes around it.
“You have to be really intentional about redesigning your operating model around AI rather than simply deploying it. In the context of how things work today, you need to be thinking about this whole notion of how a digital workforce and a human workforce can start to work together.”
– Nigel Vaz, CEO, Publicis Sapient

SECTION 03

The next enterprise divide will be about adaptation

Most large enterprises now have access to the same AI models, cloud providers and copilots. The bigger difference increasingly comes down to how organizations adapt around them.

Some enterprises are redesigning workflows, modernizing infrastructure and coordinating AI across the business in more systematic ways. Others are layering AI onto fragmented operating environments that still rely on slower governance models, disconnected systems and legacy delivery structures. That gap is starting to shape the next phase of enterprise competition.

Many enterprises still mistake AI adoption for transformation

The survey shows a growing gap between AI usage at the team level and actual enterprise transformation. Though teams may be using AI in various functions, that usage isn’t having a significant, enterprise-wide impact yet. Nearly half (49 percent) say that AI is just starting to change parts of the business, while only 38 percent indicate that it’s fundamentally changed how their business operates [Figure 8].

That gap increasingly reflects operational fragility underneath the enterprise itself. Respondents consistently point to integration, governance and silos as major barriers—these are all organizational issues, and they all stem from the enterprise’s inability to adapt [Figure 5]. Disconnected systems and slow workflows have become increasingly difficult to scale around, and that pressure has made it clear that enterprises need to make operational shifts.
Figure 8: AI adoption is outpacing enterprise transformation
AI is already embedded into how work gets done but most enterprises still haven’t redesigned the business around it.

The enterprises that are pulling ahead are adapting their operating model to AI

Some organizations are beginning to move beyond AI deployment and into operational adaptation. That distinction matters because many individual functions are no longer the main enterprise bottlenecks—it’s the lack of coordination between them. Product, risk and compliance may all use AI tools independently, but the business still slows down as decisions stall every time work crosses a functional boundary. This is something that AI can’t fix on its own.

The enterprises that are able to adapt in the AI era are changing how their business operates. Instead of deploying AI team by team, they are rebuilding how work moves through the business itself.

How do they do that? They’re increasingly focusing on a few specific shifts:

Modernization

What slows down the enterprise
What adapted enterprises do differently

Coordination

What slows down the enterprise
What adapted enterprises do differently

Resilience

What slows down the enterprise
What adapted enterprises do differently
These shifts may sound operational on the surface, but they determine whether AI creates business-wide outcomes or simply adds more chaos to already fragmented enterprises.

Organizations must adapt to succeed

Many organizations already embedded AI into workflows and daily operations. The next challenge involves redesigning the enterprise itself around AI.

Some enterprises are adapting around those conditions faster than others. More and more, the difference comes down to modernization, operational coordination and organizational adaptability rather than technology access alone.

The enterprises that reorganize around AI-era speed fastest may shape the next era of enterprise competition.
“I think when you start to think about any major technology shift, you have to start to get the foundation right. You have to recognize that the enterprise today was not built for a fully autonomous agentic architecture. That journey essentially means that you can start to lay out a very clear path that goes from ‘how do we modernize legacy,’ to ‘how do we move into the cloud,’ to ‘how do we actually establish a new architectural stack across the technology layer,’ and then start to move to the human aspects.”
– Nigel Vaz, CEO, Publicis Sapient
“I think you have to get really focused on value pools in order to think about how you can reimagine them in the context of real goals, like improving margins or speed, or resilience, or customer outcomes. Because, ultimately, the scale and strategic choices that you make follow economics, not novelty or technology.”
– Nigel Vaz, CEO, Publicis Sapient

SECTION 04

Conclusion

The dominant enterprise AI story assumes organizations naturally adapt once AI enters the business. But organizations are finding the opposite is happening. AI is already changing how work gets done, but it’s not fundamentally shifting organizations to create value. These shifts simply can’t happen when the systems surrounding AI operate the same way they did before.

That gap increasingly defines enterprise AI execution. Many organizations now run 21st-century technology inside 20th-century operating models. Teams work at the speed of AI while tripping over governance systems, budgeting cycles, delivery structures and legacy infrastructure built for another era.

As AI becomes more deeply embedded into operations, enterprises face growing pressure to modernize their infrastructure, coordinate workflows, integrate systems and operate with far greater organizational flexibility than most legacy environments were designed to support. The enterprises adapting fastest are focused on three operational shifts:
  1. 01 They modernize the systems slowing the business down.
  2. 02 They create ways to coordinate AI activity across the enterprise itself.
  3. 03 They rethink operational resilience for environments where AI speeds up execution.
Those shifts mark the difference between real operational change and AI theater. AI needs to be more than simply visible across the business; it needs to be integrated into the enterprise’s very core.

AI has a way of revealing how slow, fragmented and operationally inflexible enterprises already were before it arrived. But it also gives enterprises permission to reorganize systems, workflows and ways of operating that have needed to change for years. For many organizations, this may be the first real opportunity in decades to stop working around enterprise complexity and start removing it.

How Publicis Sapient can help

Publicis Sapient’s platforms are built to run AI at enterprise-scale and integrate it into the business. They equip enterprises to make the three operational shifts they need to see a return on their AI investments:

Modernization

Sapient Slingshot helps enterprises modernize software delivery and legacy infrastructure so teams can build, ship and scale faster without dragging decades of technical debt behind them.

Coordination

Sapient Bodhi helps organizations coordinate AI agents, workflows and enterprise systems with the assistance of operational and industry context that’s embedded directly into the platform.

Resilience

Sapient Sustain helps businesses stay resilient as AI accelerates operational complexity, using agent-driven operations to automate incident response and reduce overhead.
“Publicis Sapient, as an enterprise technology company, is built around a series of platforms: Slingshot, Bodhi and Sustain. Each of these individually solves part of the problem. But together, they provide an incredible AI platform capability and organization where they can really enable the shift of significant workflows around the business to accelerate how intelligence is brought to the organization.”
– Nigel Vaz, CEO, Publicis Sapient

About the research

To better understand how enterprises are moving from AI experimentation to operational scale, Publicis Sapient surveyed 1,550 AI decision-makers across the United States, United Kingdom, France, Germany, Australia and the United Arab Emirates between April 29 and May 14, 2026.

Respondents work at organizations with at least 500 employees and $100 million in annual revenue and hold responsibility for evaluating, influencing or selecting enterprise AI technologies and platforms. Fieldwork was conducted by Protege on behalf of Publicis Sapient. The findings reflect the perspectives of leaders navigating AI adoption inside large, operationally complex enterprises where integration, governance and organizational coordination increasingly shape the ability to scale AI successfully.

Publicis Sapient is a technology company that provides enterprise AI platforms and services. With over 30 years of digital business transformation experience, we enable enterprise clients to transform how they operate and serve their customers, unlocking new value and enabling them to thrive in an AI-driven world.

Our platforms Sapient Slingshot, Sapient Bodhi and Sapient Sustain use AI built off this deep enterprise context to help organizations modernize their legacy tech systems, build agentic solutions, and automate their IT operations. The combination of our AI platforms and the expertise of our people enables us to deliver faster and more effective outcomes through solutions that are specific to the unique needs of our clients’ businesses, their industries and their customers. Publicis Sapient is the technology hub of Publicis Groupe, uniting 20,000 people worldwide across 28 countries.

For more information, visit publicissapient.com.