For all the hype, many organizations remain stuck in an experimental phase. Although adoption of AI and related technologies is widespread, their impact on the bottom line remains minimal. Companies are tinkering with AI in isolated pockets, often without embedding it into broader digital transformation and IT infrastructure, and failing to achieve enterprise-wide innovation that drives real value. This creates a dangerous gap between testing and true modernization.
While a majority of leaders report that AI is boosting innovation efforts, only 39% see any meaningful impact on earnings. This isn’t a technology problem; it’s a strategy problem. A small cohort of high-performing organizations is breaking away from the pack, not by having better algorithms, but by fundamentally redesigning their workflows around technology platforms. Read on to see how they treat AI and automation systems as growth engines, not just cost-saving tools.
The Great AI Divide: Broad Adoption, Shallow Impact
While 88% of companies report using AI in at least one business function, the majority have not moved beyond small-scale tests. Nearly two-thirds of organizations admit they have not yet started to scale AI across the enterprise. They are running pilots but failing to embed the technology where it matters most: core business workflows supported by cloud platforms, data pipelines, and enterprise software.
This hesitation is most apparent in financial returns. While leaders point to cost savings in specific use cases, such as software engineering or manufacturing, the enterprise-level benefits are elusive. This suggests AI is often used to optimize existing processes rather than to leverage technology to create entirely new sources of value.
The scale of a company often correlates with its AI maturity. Larger enterprises, with revenues over $5 billion, are more likely to have reached the scaling phase than their smaller counterparts. Nearly half of these large companies are actively scaling AI, compared to just 29% of those with revenues under $100 million.
Until companies move beyond isolated pilots and embed AI and enabling technologies deeply into core workflows, its promise will remain aspirational rather than economically transformative.
What High Performers Do Differently: A Focus on Transformation
A small but influential group of organizations demonstrates what is possible when AI is treated as a strategic catalyst rather than a tactical tool.
The 6% of companies qualifying as AI high performers are not just investing more; they are investing differently. Their primary goal is not just efficiency but transformative innovation that drives growth. These organizations report significant earnings impacts of 5% or more, and their approach reveals a clear playbook for success.
The defining characteristic of these leaders is a commitment to redesigning workflows. They are three times as likely as their peers to fundamentally change how work gets done. Instead of simply layering AI onto an old process, they rebuild the process around AI’s capabilities. This unlocks new levels of productivity and creates competitive advantages that are difficult to replicate.
This strategic focus is backed by dedicated leadership and significant investment. Over one-third of high-performing organizations allocate more than 20% of their digital budgets to AI technologies. They also prioritize best practices, such as validating model outputs for accuracy, ensuring that their AI initiatives are both ambitious and reliable.
Their success makes one point clear: sustained value from AI comes not from experimentation alone, but from deliberate reinvention, leadership commitment, and disciplined execution.
The Next Frontier: AI Agents as Autonomous Systems
While many organizations are still grappling with foundational AI, the next wave is already here: AI agents. These systems, built on foundation models, can plan and execute multi-step tasks within a workflow, operating with a degree of autonomy that pushes the boundaries of automation. Currently, 62% of organizations are at least experimenting with this technology, signaling a major shift in the near future.
The most common applications are emerging in IT service desk management and in-depth knowledge management research. For example, an AI agent can do more than answer a support ticket. It can diagnose technical issues, access relevant knowledge bases, execute solutions, and document the resolution without human intervention. This transforms automation from assistance into execution.
Example AI Logistics Transformation
A major logistics provider modernized its operations by moving beyond basic route optimization and deploying a suite of AI-driven autonomous capabilities, including real-time dynamic routing, demand forecasting, and automated carrier decisions. Traditionally, the company relied on static route planning to reduce costs, but this approach couldn’t account for unpredictable factors such as weather or congestion.
After integrating AI into its core operations, the company implemented dynamic rerouting based on real-time weather and traffic data. They also enhanced unified visibility across modes and introduced automated decision support. As a result, the company saw measurable efficiency gains, with operational costs dropping in the mid-teens percentage range and significant improvements in delivery performance.
Operational costs dropped in the mid-teens, with significant improvements in delivery performance.
The Workforce Paradox: Reskilling, Not Just Replacing
The debate over AI’s impact on jobs continues, but the data reveals a nuanced picture. There is no simple story of mass replacement. Instead, organizations are navigating a complex realignment of skills and roles. While 32% of leaders expect AI to decrease their overall workforce size in the coming year, 13% anticipate an increase, and 43% foresee no significant change.
The real story is one of targeted shifts. Functions that rely heavily on AI may see reductions, but most companies also report hiring for new AI-related roles. Software and data engineers remain the most in-demand positions across the board. High-performing companies are more likely to expect significant workforce changes, either through reductions or additions, as they aggressively reconfigure their talent pool to support a transformed operating model. Workforce shifts mirror workflow redesign.
Risk Management Catches Up to Reality
As AI becomes more embedded in operations, its associated risks are becoming more tangible. In response, organizations are finally getting more serious about mitigation. The average company now actively manages four distinct AI-related risks, such as privacy and regulatory compliance, up from just two since 2022 . Interestingly, high-performing AI organizations report experiencing more negative consequences, particularly around intellectual property and compliance. This is not because their systems are less secure, but because their broad and deep implementation of AI exposes them to a wider range of potential issues. Their experience offers a crucial lesson: as AI scales, a proactive and comprehensive risk management strategy is not optional, but foundational.
The organizations that will define the next decade are making a deliberate choice now. They are deciding whether AI will remain a collection of tools scattered across departments or become the backbone of how the enterprise thinks, operates, and competes. As autonomous systems grow more capable, hesitation will compound into structural disadvantage. The strategic divide will not be technological, but organizational. Those who act with conviction will not simply adopt the future. They will design it.
