What Drives High AI ROI in Pharma Translation? 2026 Benchmark Data

Pharma translation is the continuous, compliance-driven localization of pharmaceutical and life sciences content across clinical, regulatory, and training workflows. It is no longer a one-time project; it is an operational function embedded in the product lifecycle that directly impacts regulatory timelines, product launches, and global revenue.

New data from the State of Global Enterprise Growth Report confirms that enterprise organizations reporting the highest ROI in their AI investments prioritize faster localization workflows and compliance review processes. From a technology perspective, they are also more likely to unify their tech stacks. In life sciences, this means shifting to a process where teams create and translate content in every language they need in parallel, which requires integrating systems to some extent.

• High-ROI teams are 6.5x more likely to report 50%+ faster localization workflows. These high-performing teams are also 1.6x more likely to operate on a unified tech stack that reduces manual bottlenecks.[1]

• 60% of high AI ROI teams automate at the process level, compared to 36% at the task level and 15% with human-driven workflows.[1]

• 48% of teams with unified content and translation stacks report measurable AI ROI, versus 31% of teams without unified systems.[1]

• 38% of enterprises cite governance bottlenecks as a barrier to scaling AI effectively.[1]

• Pharma translation now spans clinical trials, regulatory submissions, product labeling, patient information leaflets, manufacturing documentation, and multilingual training materials.

AI adoption is widespread across life sciences, but ROI outcomes vary significantly. The State of Enterprise Global Growth Report reveals three measurable differences between high-ROI teams and the rest:

• High AI ROI teams are 6.5x more likely to report 50%+ faster localization workflows (13% of high-ROI teams vs. 2% of other teams).[1]

• Teams implementing process-level automation see stronger ROI outcomes (60% of teams) compared to task-level automation (36%) or primarily human-driven workflows (15%).[1]

• Enterprises with a unified content stack correlate with higher AI ROI outcomes (48% with vs. 31% without).[1]

This aligns with broader industry research showing that organizations capturing significant AI value redesign workflows end-to-end rather than layering AI onto existing processes.[4]

For pharma leaders, the implication is clear: depth of automation matters more than surface-level AI adoption.

High AI ROI teams outperform across four measurable indicators in pharmaceutical translation.

Key Insight: AI adoption alone does not determine success. Governance integration, system unification, and process-level automation create the performance gap.

Pharma translation has moved from an operational support role to a strategic driver of global growth and compliance. The pharmaceutical industry is projected to surpass $1.5 trillion globally by 2028[2], with emerging markets driving a significant share of growth. As companies expand into new regions, they must translate pharmaceutical content across clinical trials, regulatory submissions, product labeling, patient information leaflets, manufacturing documentation, and multilingual training materials.

Regulators expect strict documentation and quality controls. The FDA's guidance on computerized systems used in clinical investigations emphasizes validation, audit trails, and data integrity controls.[3] Global content workflows must meet these same standards.

The question for enterprise leaders is no longer whether to invest in pharmaceutical translation services. It is how to structure them for measurable ROI.

Life sciences companies continue expanding language coverage to support commercialization strategies. Industry research shows that emerging markets account for a growing share of pharmaceutical revenue.[2]

• 52% of enterprises are actively expanding into new languages to support global growth.[1]

• 38% report governance bottlenecks slowing AI implementation.[1]

Digital channel expansion has accelerated globally, with omnichannel engagement now becoming a priority for pharmaceutical brands.[5] This increases multilingual content volume across websites, portals, and patient education materials.

For pharmaceutical translation companies and internal teams, the operational challenge is clear: how do you scale language expansion while maintaining regulatory compliance and quality assurance?

21% of enterprises say certain governance requirements often determine whether AI can deploy at global scale.[1] The most productive life sciences teams do not treat governance as a final review checkpoint. They embed it directly into their AI translation workflows.

The benchmark data supports this shift: 38% of enterprises report governance bottlenecks slowing AI implementation.[1]

High AI ROI teams are approximately 30% more likely to avoid these delays through structured governance models.[1]

The difference is operational design. Pharmaceutical translation companies that rely on disconnected systems and manual oversight struggle to deliver this balance. Organizations that integrate AI, workflow orchestration, and governance controls into a unified system achieve measurable time savings.

41% of marketing teams added at least one new language in the past year.[1] These teams benefit from faster regional campaign launches and stronger brand consistency across markets when AI is used within structured workflows.

Unified systems reduce terminology drift and version control issues that commonly slow global launches. Speed improves because approvals are embedded in the workflow rather than managed through parallel processes.

62% of L&D teams added at least one new language in the past year.[1] With structured AI workflows, these teams gain the ability to update compliance content across multiple markets in shorter timeframes without expanding headcount.

• Structured workflows allow training modules to be revised, localized, reviewed, and redeployed with full traceability.

• Audit readiness improves because documentation and approval records are generated automatically.

• Capacity increases without additional hiring.

Life sciences teams are under pressure to launch products and training faster across global markets where accuracy, compliance, and audit readiness are non-negotiable. Medical translation increasingly supports continuous product updates, evolving regulatory requirements, multilingual clinical documentation, clinical research materials, culturally sensitive patient-facing content, ongoing training materials for healthcare professionals, and informed consent forms.

AI is already accelerating content output. 80% of organizations report accelerated content creation, and 68% report more efficient research and summarization.[1]

Workflow integration is the next differentiator. Operational impact is emerging, but it is not yet the default:

• 32% report full workflow orchestration (automation of tasks, handoffs, connectivity).[1]

• 29% report faster personalization, repurposing, and variation of existing content.[1]

Regulators worldwide continue tightening expectations around documentation control and system validation. The FDA emphasizes validated systems and audit trails for computerized clinical systems.[3] The European Medicines Agency requires strict linguistic review processes for approved product information across member states.[6]

For enterprise pharmaceutical translation services teams, this creates a new operational reality: speed cannot come at the expense of compliance or introduce governance risk.

In a rapidly growing market, faster launches and compliant expansion directly influence revenue performance. Pharma translation is no longer a cost center. It is a control center for global growth.

The best teams are fixing it by redesigning how content moves. They automate structured processes, integrate governance within the workflow, unify systems, and increase output without increasing headcount.

The benchmark data confirms that performance gains are not driven by experimentation alone. They are driven by disciplined workflow redesign aligned with growth objectives.

Download the full report: The State of Global Enterprise Growth in 2026

Or Book a Demo to see how Smartcat supports compliant, scalable pharmaceutical translation at enterprise scale.

1. What is pharma translation?

Pharma translation is the localization of pharmaceutical and life sciences content, including clinical trial documents, regulatory submissions, product labeling, patient information leaflets, and training materials. It requires high linguistic accuracy, regulatory compliance, and documented audit trails. Unlike general translation, pharma translation operates within strict validation and governance frameworks.

2. Why is pharmaceutical translation more complex than standard translation services?

Pharmaceutical translations must comply with regulatory guidance from the FDA and EMA. Terminology must be controlled and consistent, systems may require validation, audit trails must be maintained, and linguistic review processes must follow structured workflows. Errors can result in regulatory delays, compliance risks, or patient safety concerns.

3. How do pharmaceutical translation services support global product launches?

Pharmaceutical translation services enable companies to submit multilingual regulatory dossiers, launch product labeling across markets, translate education and training materials, and maintain compliant updates as regulations evolve. High-performing teams integrate translation directly into product and regulatory workflows to reduce turnaround times.

4. What drives high AI ROI for translation in the pharmaceutical industry?

Benchmark data identifies two primary drivers: process-level automation (60% of high-ROI teams automate at the process level vs. 36% at task level) and unified content and translation systems (48% of unified teams see measurable ROI vs. 31% without). Organizations that redesign workflows end-to-end outperform those layering AI onto disconnected systems.[1]

5. How can pharma translation scale without increasing compliance risk?

The most productive life sciences teams embed governance directly into AI translation workflows. This includes automated terminology enforcement, structured review paths, AI deployment within controlled environments, and audit-ready documentation generated within workflows. Embedding governance from the start prevents review bottlenecks while maintaining regulatory standards.

6. How is Smartcat different from a traditional pharmaceutical translation agency?

Traditional pharmaceutical translation agencies typically operate through project-based workflows, manual coordination, and disconnected systems. While they provide linguistic services, they often rely on external tools and fragmented processes that make it difficult to scale, embed governance, or measure AI ROI across the content lifecycle.

Smartcat is an enterprise AI platform designed to orchestrate pharmaceutical translation within a unified system. Instead of managing vendors across siloed tools, life sciences teams can centralize using:

• AI agents aligned to approved terminology and regulatory standards through translation memories and glossaries

• End-to-end workflow orchestration across clinical, regulatory, and training content

• Built-in governance controls, audit trails, and structured review paths

• Vendor collaboration within a single controlled environment

This enables life sciences organizations to operationalize pharma translation as a scalable, compliant, AI-driven workflow — not a one-off outsourcing function.

The result is faster localization, stronger governance, and measurable performance gains across global markets.

1. Smartcat. (2026). The state of global enterprise growth in 2026. Smartcat. https://www.smartcat.com/l/global-growth-report-2026/

2. IQVIA Institute for Human Data Science. (2023). The global use of medicines 2023: Outlook to 2028. IQVIA. https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/the-global-use-of-medicines-2023-outlook-to-2028

3. U.S. Food and Drug Administration. (2023). Computerized systems used in clinical investigations: Guidance for industry. U.S. Department of Health and Human Services. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/computerized-systems-used-clinical-investigations

4. Chui, M., Roberts, R., Yee, L., Hazan, E., Singla, A., Smaje, K., & Zemmel, R. (2023). The state of AI in 2023: Generative AI’s breakout year. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year

5. Accenture. (2023). Next-generation customer engagement in life sciences. Accenture. https://www.accenture.com/us-en/insights/life-sciences/customer-engagement

6. European Medicines Agency. (n.d.). Product information and linguistic review process. European Medicines Agency. https://www.ema.europa.eu/en/human-regulatory/marketing-authorisation/product-information

7. Davenport, T. H., Bean, R., & McElheran, K. (2023). State of AI in the enterprise, 6th edition. Deloitte. https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html