New Research Unveils the Operating Models of High-ROI AI Enterprise Teams Navigating Global Growth

Market expansion still matters. But for many enterprises, global growth is now defined by the ability to reach customers and employees with speed, cultural relevance, and governance in larger volumes and faster cycles of change.

Smartcat’s flagship research report, The 2026 State of Global Enterprise Growth, indicates that while market expansion remains a priority, the primary pressure point for global leaders has shifted. In 2026, the defining challenge is sustaining speed and control as operational complexity rises across markets and channels.

The participants included teams accountable for developing and enabling global workforces, building brands, and generating revenue. According to the research, 98% of surveyed enterprises report a significant increase in content demands over the last year. The drivers of this strain are increasingly nuanced:

• The depth of personalization: Moving beyond generic translation to deliver culturally adapted content that meets specific local expectations.

• Omnichannel density: Managing exponential increases in content volume across fragmented channels—cited as the top complexity driver for marketing teams.

• Regulatory velocity: Navigating frequent compliance shifts that require immediate, accurate, and governed content updates, particularly in high-stakes industries.

Traditional localization workflows are built around manual handoffs, disconnected tools, and sequential review cycles. But the pressures teams face today are exposing the limits of what traditional approaches can sustain. Content demands are accelerating while team budgets remain largely static, and the research reveals a widening divide: a smaller group of high-performing teams is achieving better AI ROI by changing how their operating models support speed, consistency, and governance at a global scale.

The research highlights a significant performance gap between teams reporting the strongest AI outcomes and the broader market. Content teams with the highest AI ROI are nearly seven times more likely to have achieved significantly faster localization workflows compared to their peers.

These teams do not simply use AI to complete isolated tasks. Instead, they use AI inside the workflow, not as a standalone tool. AI becomes part of how content is created, reviewed, localized, and maintained across markets. This is where capacity becomes repeatable across teams and no longer depends on a few individuals.

According to the data, what separates these teams from the rest is typically defined by three operating transformations:

• From fragmentation to orchestration: High performers are more likely to use unified tech stacks. Rather than using AI for one-off tasks, they orchestrate entire workflows that connect content creation, linguistic review, and regional distribution to eliminate manual handoffs.

• Structured human upskilling: While most enterprises currently rely on informal or no AI training, leading teams tend to come from organizations that implement structured AI curricula. These teams are about twice as likely to achieve deep, process-level automation rather than simple task-level assistance.

• Proactive governance: With more than one-third of teams facing recurring bottlenecks in AI deployment reviews, high-ROI teams have typically integrated security and regulatory checks directly into their workflows. This allows them to maintain velocity and compliance, without the review friction reported by the majority of their peers.

To help organizations respond to rising content demand and operational complexity in 2026, the report introduces an operational framework for evaluating and scaling the impact of AI across global operations. It is designed to be useful across all levels of AI maturity.

The framework provides a stage-based approach that identifies the steps needed to move from experimental AI pilots to unified orchestration. This transition is critical for organizations looking to overcome common implementation barriers, such as the inherent risk of uncoordinated AI usage: when AI usage is not standardized, outputs vary across teams and regions, increasing rework and slowing approvals.

The 2026 State of Global Enterprise Growth report reveals a widening gap in workforce readiness to operationalize AI beyond individual productivity, especially in highly regulated industries, where compliance requirements and frequent updates can slow global rollouts and raise the cost of inconsistency.

Of the industries included in the study, Life Sciences leads in AI readiness, with nearly half of enterprises adopting structured training to support complex regulatory needs. Manufacturing teams, facing overlapping update cycles and high channel complexity, move faster by orchestrating parallel workflows across content creation, localization, and distribution. On the other hand, retail and CPG currently lag in adoption, with nearly three-quarters of enterprise organizations relying on informal or no training despite high pressure for omnichannel consistency.

By identifying their team’s current AI maturity levels, leaders can prioritize the specific investments in technology, training, and governance that have the greatest impact on AI ROI.

The report’s survey findings are complemented by examples from enterprise organizations applying AI to global content and to enablement in production environments. 

Huel, a global direct-to-consumer nutrition company, has described consolidating localization across web and performance marketing to reduce handoffs and support consistent messaging across markets. Cummins, a global manufacturer supporting distributed frontline teams, has emphasized delivering enablement in employees’ primary languages so training remains accessible and effective for a global workforce. These examples are illustrative: stronger AI ROI tends to show up when AI is embedded in workflow design and review processes, rather than used only for isolated tasks.

The findings in this report serve as both a benchmark for current performance and a strategic guide for future growth. As enterprises navigate the complexities of 2026, those that achieve higher AI ROI by amplifying output will remain the primary differentiator for global success.

The full report is available for download below.