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Most enterprise AI programmes share the same blind spot. Pilots run well at headquarters, adoption numbers look reasonable in the board update, and then the rollout hits regional teams, and the returns stop making sense.
Language is often the culprit. Google’s April 1 update expanding language availability for Gemini-powered features in Workspace, including AI-assisted form creation, directly targets that problem. It is a small update on paper. In practice, it goes to the heart of why so many Workspace deployments underdeliver.
Why AI adoption stalls before it reaches the whole workforce
In February, UC Today reported on Google adding Gemini usage and threshold reporting to the Workspace Admin console. For the first time, IT teams could see exactly who was using AI features, and who had never opened them.
The picture was uncomfortable. Google’s own research found that only 3% of organisations have meaningfully transformed with AI, with 72% still in early stages. Executives are 15% more likely than employees to report significant AI impact: a gap that suggests the two groups are not experiencing the same rollout.
Language contributes directly to that gap. Research from DeepL found that nearly 70% of US enterprises face daily operational challenges from language barriers, with 96% considering AI tools to address them. A 2026 review of AI adoption patterns, meanwhile, found that countries where lower-resource languages dominate show lower AI uptake even after controlling for economic factors.
Build AI productivity tools around English and a significant share of the global workforce stays in the zero-usage column. Zero usage means zero ROI.
The workflows where language friction costs the most
Form creation sounds minor. But forms start the high-volume internal workflows that drive real operational cost: IT and HR intake, purchase approvals, change requests, project submissions, facilities tickets, compliance sign-offs.
When an employee submits a request in a second language and the intent is unclear, the workflow does not fail, it just slows down. Someone asks a clarifying question. The requester replies a day later. A team assigns the ticket with incomplete information. It comes back. The cycle repeats.
Across thousands of internal requests per month, the cost is cumulative rather than dramatic. Delays, rework, and duplicated effort inflate the operational cost of collaboration without appearing on any single invoice.
If Gemini helps more employees submit clearer, more complete requests in their working language, the value is not better writing. It is fewer back-and-forth exchanges, faster throughput, and less rework — and those are outcomes finance teams can recognise.
Where adoption and ROI connect
Deloitte’s 2026 State of AI in the Enterprise report found that worker access to AI rose 50% in 2025. The number of companies with more than 40% of AI projects in production is set to double this year. Scaling those projects requires consistent adoption across the workforce — not isolated pockets of power users.
Google has been building toward that argument across several recent Workspace updates. Workspace Studio lets any employee build AI agents across Gmail, Drive, and Chat without writing code. Gemini in Calendar targets scheduling friction at scale. Language support follows the same logic — remove a barrier, grow the usage base, make the enterprise ROI case more credible.
What to measure
The relevant metrics already exist in tools most organisations run:
Follow-up messages per request: How many clarification exchanges follow a submission?
Time to action: How long from submission to a request being ready to process?
Completion and rejection rates: How many submissions come back for correction?
Ticket reopen rates: How often does missing information restart a workflow from scratch?
These numbers live in service desk platforms, ITSM tools, and HR systems. They connect directly to staffing overhead, operational delays, and project slippage: cost drivers that hold up in a budget conversation.
The broader picture: Proving AI ROI starts with who can actually use it
April’s UC Today spotlight is Proving AI ROI in UC&C Workflows: and most of that conversation centres on measurement frameworks, business cases, and which metrics to take to a CFO.
Those are the right questions. But they assume AI is already being used consistently across the business. For most global organisations, that assumption does not hold.
As UC Today’s coverage of the Copilot ROI debate has shown, the organisations that produce credible AI returns deploy consistently across the workforce, not just in well-chosen pilots with well-resourced teams. The organisations that struggle tend to have the same problem: a gap between who the tool was designed for and who actually works there.
Language is one of the more stubborn parts of that gap. It does not show up in a product demo. It does not appear in a pilot report. It surfaces months later, in the zero-usage column of an admin dashboard, when the IT team finally asks why adoption in three of their largest regions never took off.
Google’s language expansion does not solve the AI ROI problem. But it does remove one of the quieter reasons it stays unsolved, and in a month where the industry is asking hard questions about where AI investment actually pays back, that is worth more than it looks.
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