Only 13% of Companies Are on Track With AI Plans as Regulation and Legacy...

Only 13% of companies are on track with their artificial intelligence initiatives, highlighting the widening gap between corporate enthusiasm for AI and the ability of businesses to integrate the technology into their operations at scale.

A BearingPoint study published Thursday found that nearly three-quarters of companies surveyed had already achieved positive financial results from AI, but fewer than one-third had managed to move beyond pilot projects.

The findings indicate that the central challenge for businesses is shifting from proving that AI can deliver value to embedding it deeply enough into existing operations to generate that value consistently.

"AI has crossed an important threshold," said BearingPoint expert Frederic Gigant, adding that proving value and scaling it were two different things.

Companies are moving past the initial phase of experimenting with generative AI. Businesses have spent the past several years testing chatbots, coding assistants, automated customer-service tools and other AI applications. The next stage requires connecting those systems to core business processes, legacy software and corporate data.

For many companies, that is proving considerably harder.

Regulation and Legacy Technology are Slowing The AI Transition

Around 40% of companies surveyed identified legal and regulatory requirements as their main obstacle to scaling AI, while 34% cited difficulties integrating new AI systems with existing IT infrastructure.

The figures point to a problem that is less about the availability of AI technology and more about the environment into which companies are attempting to introduce it.

Large businesses often operate complex technology stacks accumulated over decades. AI applications may need to interact with enterprise resource planning systems, customer databases, internal communications platforms, and proprietary software that were not designed to accommodate autonomous or machine-learning systems.

That can make deployment slower and more expensive than a successful pilot suggests.

Regulation adds another layer of complexity. Companies deploying AI in areas such as finance, healthcare, employment and customer services must now consider how systems handle personal data, make or support decisions, explain outputs and comply with sector-specific requirements.

As a result, an AI system that performs well in a controlled experiment can encounter substantial barriers when a company attempts to deploy it across thousands of employees or customers.

The BearingPoint findings show that this implementation gap remains substantial. Although the proportion of companies with AI deeply integrated into their operations increased to 11% in 2026 from 7% in 2025, the majority remain some distance from full-scale adoption.

AI is Producing More Cost Savings than Revenue Growth

The financial results in the survey also provide an indication of where companies are currently finding the most tangible value from AI. About 24% of respondents reported AI-driven cost savings of at least 10%, compared with only 4% reporting revenue growth of at least 10%.

The difference suggests that businesses are currently extracting more measurable value from AI by making existing operations cheaper or more efficient than by creating substantial new revenue streams. That can include automating repetitive tasks, improving employee productivity, reducing processing costs, and handling larger workloads without proportionately increasing headcount.

The finding also complicates some of the more aggressive expectations surrounding AI's ability to generate entirely new business models. For many companies, the immediate economic case appears to be operational efficiency rather than dramatic top-line expansion.

That is expected to provide some value as corporations determine how much additional capital to allocate to AI.

If AI is primarily producing cost savings, companies may prioritize automation and workforce productivity projects with relatively clear returns. Revenue-generating applications may require longer development cycles and greater integration with products, customers, and distribution channels.

The employment implications are already becoming visible in the survey.

Nearly two-thirds of companies estimated that they have excess staffing levels of at least 10%. That does not establish that AI is responsible for those excess positions, but it points to a labor market in which companies increasingly see opportunities to handle more work with fewer employees or to reorganize existing roles around AI-enabled systems.

The eventual impact will depend on whether AI eliminates tasks, augments employees, or creates sufficient new activities to offset displaced work.

China and US Move Faster than Germany

The geographic differences in AI deployment are also notable. China and the United States led the survey, with 20% and 18% of companies respectively reporting comprehensive AI implementation. In Germany, the figure was only 8%.

The gap suggests that AI adoption has become more than just companies having access to the technology. Corporate investment priorities, regulatory environments, digital infrastructure and the availability of AI talent can all influence how quickly businesses move from experimentation to deployment.

The United States has a large concentration of AI developers, cloud providers and technology companies, giving domestic businesses access to mature AI infrastructure and a broad ecosystem of tools.

China has similarly been pushing aggressive adoption of AI across manufacturing, technology and other industries while supporting domestic AI development.

Germany's lower level of comprehensive implementation is more significant given the country's industrial base. Its manufacturers could potentially use AI across production, logistics, engineering and industrial automation, but integration with established industrial systems can be complex.

The broader numbers show that progress is being made. The share of companies with AI deeply embedded in operations increased by four percentage points in a year, from 7% to 11%. But the fact that only 13% of companies were considered on track with their AI initiatives indicates that adoption is still far from becoming a routine enterprise capability.

Analysts thus see the emerging corporate AI story as more about execution rather than experimentation. Companies have demonstrated that AI can generate financial benefits, particularly through cost reduction. The harder task is redesigning processes, modernizing legacy systems, navigating regulation, and integrating AI into the parts of the business where it can operate continuously.

That situation is expected to determine the next phase of the AI investment cycle. The first wave was dominated by companies buying access to models and running pilots. The next will depend on whether those experiments can be converted into durable productivity gains and new sources of revenue.