• Nearly three-quarters of organizations that have implemented AI report measurable top-line or bottom-line impact.
• Only 13% have scaled their AI initiatives completely in line with the original business case.
• More than 6 in 10 organizations report AI-induced workforce overcapacity of at least 10% today.
• 9 in 10 organizations would continue investing in AI despite limited expected ROI.

Management and technology consultancy BearingPoint has released a new study, Scaling AI for measurable impact, showing that artificial intelligence is increasingly delivering measurable business value. But most organizations still struggle to turn successful AI initiatives into organization-wide impact.

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“AI has crossed an important threshold. Organizations have shown that AI can create real business value, but proving value and scaling value are two very different things,” said Frédéric Gigant, Global Leader Customer & Growth at BearingPoint. “We found that the organizations pulling ahead are not simply investing more. They are connecting AI to financial accountability, trusted data, governance, architecture, and workforce decisions from the start. Management discipline is what turns isolated success into organizational impact.”

Based on a global survey of 1,050 C-suite executives and senior leaders in 13 countries across Europe, the US and China, the study finds that nearly three-quarters of organizations that have implemented AI already report measurable top-line or bottom-line impact. Around four in ten report both revenue growth and cost reduction. Yet only 13% have scaled their AI initiatives fully in line with the original business case, while almost three-quarters have either adjusted the original scope or achieved less scale than anticipated.

For the analysis, BearingPoint groups organizations into four AI maturity stages: Explorers, Experimenters, Implementers, and Leaders.

• Explorers (15%) are assessing where AI could create value but have not yet initiated active projects.
• Experimenters (20%) have active AI projects or pilots underway but have not yet implemented them in operations.
• Implementers (54%) have deployed AI solutions and are generating value, with some initiatives expanded across business units or geographies.
• Leaders (11%) have deeply integrated AI into most operations, measure its impact, and have a clear transformation roadmap.

Agentic ambition is outpacing readiness: More than three-quarters of organizations are still learning about agentic enterprise architecture, identifying it as a priority, or exploring pilots. Only 13% have a defined strategy with active initiatives, while 10% are scaling across the enterprise.

As organizations deploy more autonomous AI agents, they must manage new risks arising from agents’ access to data and systems, their ability to initiate actions, and their interaction with employees and other agents. Clear permissions, decision rights, monitoring, and human accountability must therefore develop alongside greater autonomy.

“Responsible AI does not mean slowing down innovation. It is about capturing value today while ensuring that AI autonomy never advances faster than governance and human oversight,” said Frédéric Gigant.

AI is expected to deliver broader revenue gains and deeper cost reductions by 2030

AI maturity is improving. The share of organizations reporting that AI is deeply integrated across operations increased from 7% in 2025 to 11% in 2026. More than half of organizations have now reached the implementation stage. However, more than one-third remain in exploration or experimentation, and full institutionalization remains rare.

The financial impact tells a similar story. AI is already creating measurable value, particularly through cost reduction, but the biggest gains have yet to materialize. Revenue or service-delivery impact is currently concentrated below 10%. Among the 685 organizations that have implemented AI, 4% report current revenue or service-delivery gains of at least 10%. By 2030, 22% expect gains of at least 10%.

The impact is even more visible in cost reduction. Among organizations with implemented AI, 24% currently report cost reductions of at least 10%. Looking ahead to 2030, 35% of respondents expect cost reductions of at least 10%.

The challenge is therefore to reproduce proven value across the organization, rather than leave it concentrated in individual use cases.

Scaling AI is increasingly an organizational challenge

The study finds that the biggest obstacles to scaling AI extend well beyond the technology itself. Complex regulatory frameworks are the leading barrier, followed by integration with legacy systems and processes. At the same time, 54% of executives identify high-quality, trusted data as critical to scaling AI, followed by connected data, governance and ownership, and data accessibility.

Cross-functional collaboration is already widespread among more mature organizations. Yet fewer than one-third formally assess scalability before launching an AI initiative. As a result, organizations often address architecture, governance, ownership, data integration, and operating model requirements only after a use case has already demonstrated value.

The gap between Leaders and Implementers shows how sharply scaling outcomes diverge with maturity. Almost half of Leaders scale fully as planned, compared with only 6% of Implementers. Seventy percent of Leaders also link most of their AI projects to measurable financial KPIs, compared with 34% of Implementers.

AI-induced overcapacity is creating a new workforce challenge

As AI becomes embedded in business processes, organizations must decide how to use the capacity released by automation and augmentation. 62% estimate that AI has already created workforce overcapacity of at least 10%. By 2030, 94% expect overcapacity at that level.

At the same time, organizations require new capabilities in areas such as AI governance, agent orchestration, data science, and human–AI workflow design. The challenge is therefore not simply to reduce capacity, but to redeploy it while closing new capability gaps.

“AI adoption releases excess capacity, but management decisions determine whether that capacity becomes growth, or simply unused effort,” said Frédéric Gigant. “Organizations need to redesign roles, redeploy resources, build new skills, and align workforce planning with their AI roadmaps. Otherwise, productivity can improve without producing the financial impact executives expect.”

Managing AI as an enterprise value portfolio

The study concludes that organizations need to move beyond managing AI as a collection of individual use cases and start managing it as an enterprise value portfolio. The most mature organizations already show signs of this broader approach. 59% of Leaders have either defined an agentic architecture strategy or are scaling one across the enterprise, compared with 26% of Implementers. Leaders are also further ahead in integrating sustainability into their AI portfolios, with 40% reporting that more than one-quarter of their AI projects target a net-positive environmental impact, compared with 27% of Implementers.

BearingPoint’s recommendations converge around a common principle: AI needs to be managed as an organizational transformation.

BearingPoint identifies five priorities for executives:

1. Manage AI as an enterprise value portfolio.
2. Connect operational metrics to revenue, cost, margin, and sustainability outcomes.
3. Assess scalability before approving major AI investments.
4. Align AI deployment with workforce planning and role redesign.
5. Establish architecture and governance for human-agent operating models.

The goal is not simply to deploy more AI, but to create sustained value for customers, shareholders, and the planet.

About the study

Scaling AI for measurable impact is based on a global survey of 1,050 C-suite executives and senior leaders from public and private sector organizations in 13 countries across Europe, the United States, and China. The research was conducted through online interviews in August 2026.

The full study is available for download at https://www.bearingpoint.com/en/insights-events/insights/scaling-ai-for-measurable-impact/.

About BearingPoint

BearingPoint is an independent management and technology consultancy with European roots and a global reach. We help businesses transform by combining deep industry expertise with strong capabilities in strategy, operations, and technology. Dedicated SAP and Microsoft transformation units, a strong focus on AI, and outcome-based products enable us to provide tailored, innovative solutions that create measurable and sustainable value.

In addition to our core consulting operations, we run two joint ventures. Arcwide, our joint venture with IFS, specializes in business transformation enabled by IFS technology. BearingPoint North America, our joint venture with ABeam Consulting, focuses on consulting excellence and business transformation built on SAP.

BearingPoint works with many of the world’s leading companies and public-sector organizations. Together with its strategic alliance partner ABeam Consulting, the firm brings together more than 15,000 professionals and serves clients in over 70 countries, delivering seamless business transformation, strengthening performance, and driving sustainable impact.

BearingPoint is recognized among TIME World’s Best Companies and Forbes World’s Best Employers. The firm is also a certified B Corporation, committed to responsible business and creating long-term value for organizations, people, and society.

For more information, please visit:
Homepage: www.bearingpoint.com
LinkedIn: www.linkedin.com/company/bearingpoint

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Responsible AI does not mean slowing down innovation. It is about capturing value today while ensuring that AI autonomy never advances faster than governance and human oversight.